Integrative analysis of drug-gene signatures in human pluripotent stem cells reveals prazosin as a novel SQSTM1 regulator for ALS therapeutics.

Code ↔ Paper

2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 2 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted

[1] § STAR★Methods › Method details › Generation of heterozygous and homozygous SQSTM1 knockout PSC lines ↔ crispor.py, lines 2721–2779 · score 0.70 · spCas9, length polymorphism, mutation induced, enzyme, RFLP, CRISPOR
[2] § STAR★Methods › Method details › Prazosin treatment of zebrafish with sqstm1 knockdown ↔ js/jquery-ui.min.js, the whole file · a weak match · score 0.52 · duration, Touch, escape, submitted, LI, fast

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The paper is loaded by your browser, with JavaScript: read it at doi.org or on Europe PMC.

The authors' code

Python · 4,761 lines · 195 KB · other · 1 match

#!/data/www/crispor/venv/bin/python3
# if you do not want the hardcoded PATH above, delete this line and the one above to use the default Python3 interpreter
#!/usr/bin/env python3
# I know that this line looks unprofessional to you, but modifying the PATH on a shared Apache webserver is not obvious.
# the tefor crispr tool
# can be run as a CGI or from the command line
# python std library
import subprocess, tempfile, optparse, logging, atexit, glob, shutil, signal, pdb
import http.cookies, time, sys, cgi, re, random, platform, os, pipes
import hashlib, base64, string, logging, operator, urllib.request, urllib.parse, urllib.error, time
import traceback, json, pwd, gzip, zlib
from io import StringIO
from collections import defaultdict, namedtuple
from datetime import datetime
from itertools import product
from os.path import abspath, basename, dirname, isdir, isfile, join, relpath
try:
# prefer the pip package, it's more up-to-date than the native package
import pysqlite3 as sqlite3
SQLITEERROR=pysqlite3.dbapi2.OperationalError
except:
import sqlite3
SQLITEERROR=sqlite3.OperationalError
try:
from collections import OrderedDict
except ImportError:
from ordereddict import OrderedDict # python2.6 users: run 'sudo pip install ordereddict'
# for matplotlib, improves "import" performance
os.environ["MPLCONFIGDIR"] = "/tmp/matplotlib-cache"
# try to load external dependencies
# we're going into great lengths to create a readable error message
needModules = set(["pytabix", "twobitreader", "pandas", "matplotlib", "scipy"])
try:
import tabix # if not found, install with 'pip install pytabix'
needModules.remove("pytabix")
except:
pass
try:
import twobitreader # if not found, install with 'pip install twobitreader'
needModules.remove("twobitreader")
except:
pass
try:
import pandas # required by doench2016 score. install with 'pip install pandas'
needModules.remove("pandas")
import scipy # required by doench2016 score. install with 'pip install scipy'
needModules.remove("scipy")
import matplotlib # required by doench2016 score. install with 'pip install matplotlib'
needModules.remove("matplotlib")
import numpy # required by doench2016 score. install with 'pip install numpy'
needModules.remove("numpy")
except:
pass
if len(needModules)!=0:
print("Content-type: text/html\n")
print(("Python interpreter path: %s<p>" % sys.executable))
print(("These python modules were not found: %s<p>" % ",".join(needModules)))
print("To install all requirements in one line, run: sudo pip install biopython numpy scikit-learn==0.16.1 pandas twobitreader<p>")
sys.exit(0)
# our own eff scoring library
import crisporEffScores
# don't report print as an error
# pylint: disable=E1601
# optional module for Excel export as native .xls files
# install with 'apt-get install python-xlwt' or 'pip install xlwt'
xlwtLoaded = True
try:
import xlwt
except:
sys.stderr.write("crispor.py - warning - the python xlwt module is not available\n")
xlwtLoaded = False
# optional module for mysql support
#try:
#import MySQLdb
#mysqldbLoaded = True
#except:
#mysqldbLoaded = False
# version of crispor
versionStr = "5.2"
# contact email
contactEmail='[email hidden]'
# url to this server
ctBaseUrl = "http://crispor-max.tefor.net/temp/customTracks"
# write debug output to stdout
DEBUG = False
#DEBUG = True
# use bowtie for off-target search?
useBowtie = False
# calculate the efficienc scores?
doEffScoring = True
# system-wide temporary directory
#TEMPDIR = os.environ.get("TMPDIR", "/var/tmp")
TEMPDIR = "/var/tmp"
# a hack for cluster jobs at UCSC:
# - default to ramdisk
if isdir("/scratch/tmp"):
TEMPDIR = "/dev/shm/"
# skipAlign is useful if your input sequence is not in the genome at all
# - don't do bwasw
# - this will trigger auto-ontarget: any perfect match is the on-target
# - do not calculate efficiency scores
skipAlign = False
# prefix in html statements before the directories "image/", "style/" and "js/"
HTMLPREFIX = ""
# alternative directory on local disk where image/, style/ and js/ are located
HTMLDIR = "/usr/local/apache/htdocs/crispor/"
# directory of crispor.py
baseDir = dirname(__file__)
# filename of this script, usually crispor.py
myName = basename(__file__)
# the segments.bed files use abbreviated genomic region names
segTypeConv = {"ex":"exon", "in":"intron", "ig":"intergenic"}
# directory for processed batches of offtargets ("cache" of bwa results)
batchDir = join(baseDir,"temp")
# sqlite3 db with gzipped old json batch files, to avoid hitting the ext4 inode limits
batchArchive = "/data/crisporJobArchive.db"
# the file where the sqlite job queue is stored
#JOBQUEUEDB = join(TEMPDIR, "crisporJobs.db") # TEMPDIR is mapped away for security reasons under Redhat/Centos for CGIs
JOBQUEUEDB = "/data/www/temp/crisporJobs.db"
# alternatively: connection info for mysql
jobQueueMysqlConn = {"socket":None, "host":None, "user": None, "password" : None}
# directory for platform-independent scripts (e.g. Heng Li's perl SAM parser)
scriptDir = join(baseDir, "scripts")
# directory for helper binaries (e.g. BWA)
# system() is one of 'Linux', 'Darwin', 'Windows', machine() is one of 'x86_64', 'arm64', 'aarch64'
os_name = platform.system() # 'Linux', 'Darwin', 'Windows'
arch = platform.machine() # 'x86_64', 'arm64', 'aarch64'
binDir = abspath(join(baseDir, "bin", platform.system()+"-"+platform.machine()))
# directory for genomes
genomesDir = join(baseDir, "genomes")
DEFAULTORG = 'hg19'
DEFAULTSEQ = 'cttcctttgtccccaatctgggcgcgcgccggcgccccctggcggcctaaggactcggcgcgccggaagtggccagggcgggggcgacctcggctcacagcgcgcccggctattctcgcagctcaccatgGATGATGATATCGCCGCGCTCGTCGTCGACAACGGCTCCGGCATGTGCAAGGCCGGCTTCGCGGGCGACGATGCCCCCCGGGCCGTCTTCCCCTCCATCGTGGGGCGCC'
# used if hg19 is not available
ALTORG = 'sacCer3'
ALTSEQ = 'ATTCTACTTTTCAACAATAATACATAAACatattggcttgtggtagCAACACTATCATGGTATCACTAACGTAAAAGTTCCTCAATATTGCAATTTGCTTGAACGGATGCTATTTCAGAATATTTCGTACTTACACAGGCCATACATTAGAATAATATGTCACATCACTGTCGTAACACTCT'
pamDesc = [ ('NGG','20bp-NGG - Sp Cas9, SpCas9-HF1, eSpCas9 1.1'),
('NNG','20bp-NNG - Cas9 S. canis'),
('NGN','20bp-NGN - SpG'),
('NNGT','20bp-NNGT - Cas9 S. canis - high efficiency PAM, recommended'),
('NAA','20bp-NAA - iSpyMacCas9'),
('TTN', 'TTN-23bp - hfCas12Max - as recommended by Synthego'), # Casey Jowdy by email
('TNN','TNN-23bp - hfCas12Max, broader PAM, as recommended by Synthego'), #
('NGG-22', 'NGG-22bp - eSpOT-ON (ePsCas9), as recommended by Synthego'),
('NNGRRT','21bp-NNG(A/G)(A/G)T - Cas9 S. Aureus'),
('NNGRRT-20','20bp-NNG(A/G)(A/G)T - Cas9 S. Aureus with 20bp-guides'),
('NGK','20bp-NG(G/T) - xCas9, recommended PAM, see notes'),
#('NGN','20bp-NGN or GA(A/T) - xCas9 (low efficiency, not recommended)'),
#('NGG-BE1','20bp-NGG - BaseEditor1, modifies C->T'),
('NNNRRT','21bp-NNN(A/G)(A/G)T - KKH SaCas9'),
('NNNRRT-20','20bp-NNN(A/G)(A/G)T - KKH SaCas9 with 20bp-guides'),
('NGA','20bp-NGA - Cas9 S. Pyogenes mutant VQR'),
('NNNNCC','24bp-NNNNCC - Nme2Cas9'),
('NGCG','20bp-NGCG - Cas9 S. Pyogenes mutant VRER'),
('NNAGAA','20bp-NNAGAA - Cas9 S. Thermophilus'),
('NGGNG','20bp-NGGNG - Cas9 S. Thermophilus'),
('NNNNGMTT','20bp-NNNNG(A/C)TT - Cas9 N. Meningitidis'),
('NNNNACA','20bp-NNNNACA - Cas9 Campylobacter jejuni, original PAM'),
('NNNNRYAC','22bp-NNNNRYAC - Cas9 Campylobacter jejuni, revised PAM'),
('NNNVRYAC','22bp-NNNVRYAC - Cas9 Campylobacter jejuni, opt. efficiency'),
('TTCN','TTCN-20bp - CasX'),
('TTTV','TTT(A/C/G)-23bp - Cas12a (Cpf1) - recommended, 23bp guides'),
('TTTV-21','TTT(A/C/G)-21bp - Cas12a (Cpf1) - 21bp guides recommended by IDT'),
('TTTN','TTTN-23bp - Cas12a (Cpf1) - low efficiency'),
('ATTN','ATTN-23bp - BhCas12b v4'),
('NGTN','NGTN-23bp - ShCAST/AcCAST, Strecker et al, Science 2019'),
('TYCV','T(C/T)C(A/C/G)-23bp - TYCV As-Cpf1 K607R'),
('TATV','TAT(A/C/G)-23bp - TATV As-Cpf1 K548V'),
('TTTA','TTTA-23bp - TTTA LbCpf1'),
('TCTA','TCTA-23bp - TCTA LbCpf1'),
('TCCA','TCCA-23bp - TCCA LbCpf1'),
('CCCA','CCCA-23bp - CCCA LbCpf1'),
('GGTT','GGTT-23bp - CCCA LbCpf1'),
('YTTV','YTTV-20bp - MAD7 Nuclease, Lui, Schiel, Maksimova et al, CRISPR J 2020'),
('TTYN','TTYN- or VTTV- or TRTV-23bp - enCas12a E174R/S542R/K548R - Kleinstiver et al Nat Biot 2019'),
('NNNNCNAA','20bp-NNNNCNAA - Thermo Cas9 - Walker et al, Metab Eng Comm 2020'),
('NNN','20bp-NNN - SpRY, Walton et al Science 2020'), # https://science.sciencemag.org/content/368/6488/290.abstract
('NRN','20bp-NRN - SpRY (high efficiency PAM)'),
('NYN','20bp-NYN - SpRY (low efficiency PAM)'),
#('VTTV','(A/C)TT(A/C)-23bp - enCas12a S542R - Kleinstiver et al Nat Biot 2019'),
#('TRTV','T(A/G)T(A/C)-23bp - enCas12a K548R - Kleinstiver et al Nat Biot 2019'),
]
DEFAULTPAM = 'NGG'
# the default base editor modification window
DEFAULTBEWIN = "1-7"
# for some PAMs, there are alternative main PAMs. These are also shown on the main sequence panel
multiPams = {
#"NGN" : ["GAW"],
"TTYN" : ["VTTV", "TRTV"]
}
# these PAMs are not specific. Allow only short sequences for them.
slowPams = ["TTYN", "NNG"]
# allow only very short sequences for these
verySlowPams = ["NNN", "NRN", "NYN"]
# for some PAMs, we allow other alternative motifs when searching for offtargets
# MIT and eCrisp do that, they use the motif NGG + NAG, we add one more, based on the
# on the guideSeq results in Tsai et al, Nat Biot 2014
# The NGA -> NGG rule was described by Kleinstiver...Young 2015 "Improved Cas9 Specificity..."
# NNGTRRT rule for S. aureus is in the new protocol "SaCas9 User manual"
# ! the length of the alternate PAM has to be the same as the original PAM!
offtargetPams = {
"NGG" : ["NAG","NGA"],
#"NGN" : ["GAW"],
"NGK" : ["GAW"],
"NGA" : ["NGG"],
"NNGRRT" : ["NNGRRN"],
"TTTV" : ["TTTN"],
'ATTN' : ["TTTN", "GTTN"],
"TTYN" : ["VTTV", "TRTV"]
}
# maximum size of an input sequence
MAXSEQLEN = 2300
# maximum input size when specifying "no genome"
MAXSEQLEN_NOGENOME = 25000
# maximum input size when using xCas9 or sCanis
MAXSEQLEN2 = 600
# maximum input size for NNN SpRY or similar PAMs
MAXSEQLEN3 = 150
# BWA: allow up to X mismatches
maxMMs=4
# maximum number of occurences in the genome to get flagged as repeats.
# This is used in bwa samse, when converting the same file
# and for warnings in the table output.
MAXOCC = 60000
# the BWA queue size is 2M by default. We derive the queue size from MAXOCC
MFAC = 2000000/MAXOCC
# the length of the guide sequence, set by setupPamInfo
GUIDELEN=None
# length of the PAM sequence
PAMLEN=None
# the name of the base editor, if any. This is the flag to activate
# baseEditor mode in the UI
baseEditor = None
# input sequences are extended by X basepairs so we can calculate the efficiency scores
# and can better design primers
FLANKLEN=100
# the name of the currently processed batch, assigned only once
# in readBatchParams and only for json-type batches
batchName = ""
# are we doing a Cpf1 run?
# this variable changes almost all processing and
# has to be set on program start, as soon as we know
# the PAM we're running on
pamIsFirst=None
saCas9Mode=False
# Highly-sensitive mode (not for CLI mode):
# MAXOCC is increased in processSubmission() and in the html UI if only one
# guide seq is run
# Also, the number of allowed mismatches is increased to 5 instead of 4
#HIGH_MAXOCC=600000
#HIGH_maxMMs=5
# minimum off-target score of standard off-targets (those that end with NGG)
# This should probably be based on the CFD score these days
# But for now, I'll let the user do the filtering
MINSCORE = 0.0
# minimum off-target score for alternative PAM off-targets
# There is not a lot of data to support this cutoff, but it seems
# reasonable to have at least some cutoff, as otherwise we would show
# NAG and NGA like NGG and the data shows clearly that the alternative
# PAMs are not recognized as well as the main NGG PAM.
# so for now, I just filter out very degenerative ones. the best solution
# would be to have a special penalty on the CFD score, but CFS does not
# support non-NGG PAMs (is this actually true?)
ALTPAMMINSCORE = 1.0
# how much shall we extend the guide after the PAM to match restriction enzymes?
pamPlusLen = 5
# global flag to indicate if we're run from command line or as a CGI
commandLineMode = False
# names/order of efficiency scores to show in UI
cas9ScoreNames = ["fusi", "crisprScan", "rs3"]
allScoreNames = ["fusi", "chariRank", "ssc", "wuCrispr", "doench", "wang", "crisprScan", "ccTop", "rs3"]
mutScoreNames = []
spCas9MutScoreNames = ["oof", 'lindel'] # lindel is only added for spCas9
otherMutScoreNames = ["oof"] # lindel is only added for spCas9
cpf1ScoreNames = ["seqDeepCpf1"]
saCas9ScoreNames = ["najm"]
# to make the CFD more comparable to the MIT score, Nicholas Parkinson suggests to multiply it with 100.
# can be switched on with the URL argument fixCfd=1
doCfdFix=False
# how many digits shall we show for each score? default is 0
scoreDigits = {
"ssc" : 1,
}
# List of AddGene plasmids, their long and short names:
addGenePlasmids = [
("43860", ("MLM3636 (Joung lab)", "MLM3636")),
("49330", ("pAc-sgRNA-Cas9 (Liu lab)", "pAcsgRnaCas9")),
("42230", ("pX330-U6-Chimeric_BB-CBh-hSpCas9 (Zhang lab) + derivatives", "pX330")),
("52961", ("lentiCRISPR v2 (Zhang lab)", "lentiCrispr")),
("52963", ("lentiGuide-Puro (Zhang lab)", "lentiGuide-Puro")),
]
addGenePlasmidsAureus = [
("61591", ("pX601-AAV-CMV::NLS-SaCas9-NLS-3xHA-bGHpA;U6::BsaI-sgRNA (Zhang lab)", "pX601")),
("61592", ("pX600-AAV-CMV::NLS-SaCas9-NLS-3xHA-bGHpA (Zhang lab)", "pX600")),
("61593", ("pX602-AAV-TBG::NLS-SaCas9-NLS-HA-OLLAS-bGHpA;U6::BsaI-sgRNA (Zhang lab)", "pX602")),
("65779", ("VVT1 (Joung lab)", "VVT1"))
]
# list of AddGene primer 5' and 3' extensions, one for each AddGene plasmid
# format: prefixFw, prefixRw, u6-G-suffix, restriction enzyme, link to protocol
addGenePlasmidInfo = {
"43860" : ("ACACC", "AAAAC", "G", "BsmBI", "https://www.addgene.org/static/data/plasmids/43/43860/43860-attachment_T35tt6ebKxov.pdf"),
"49330" : ("TTC", "AAC", "", "Bsp QI", "http://bio.biologists.org/content/3/1/42#sec-9"),
"42230" : ("CACC", "AAAC", "", "Bbs1", "https://www.addgene.org/static/data/plasmids/52/52961/52961-attachment_B3xTwla0bkYD.pdf"),
"52961" : ("CACC", "AAAC", "", "BsmBI", "https://www.addgene.org/static/data/plasmids/52/52961/52961-attachment_B3xTwla0bkYD.pdf"),
"61591" : ("CACC", "AAAC", "", "BsaI", "https://www.addgene.org/static/data/plasmids/61/61591/61591-attachment_it03kn5x5O6E.pdf"),
"61592" : ("CACC", "AAAC", "", "BsaI", "https://www.addgene.org/static/data/plasmids/61/61592/61592-attachment_iAbvIKnbqNRO.pdf"),
"61593" : ("CACC", "AAAC", "", "BsaI", "https://www.addgene.org/static/data/plasmids/61/61592/61592-attachment_iAbvIKnbqNRO.pdf"),
"65779": ("CACC", "AAAC", "", "BsmBI (aka Esp3l)", "https://www.addgene.org/static/data/plasmids/65/65779/65779-attachment_G8oNyvV6pA78.pdf"),
"52963": ("CACC", "AAAC", "", "BsmBI (aka Esp3l)", "https://www.addgene.org/static/data/plasmids/52/52963/52963-attachment_IPB7ZL_hJcbm.pdf")
}
# the barcodes for subpool tagging for oligo pool tables
satMutBarcodes = [
(0, "No Subpool barcode"),
(1, "Subpool 1: CGGGTTCCGT/GCTTAGAATAGAA"),
(2, "Subpool 2: GTTTATCGGGC/ACTTACTGTACC"),
(3, "Subpool 3: ACCGATGTTGAC/CTCGTAATAGC"),
(4, "Subpool 4: GAGGTCTTTCATGC/CACAACATA"),
(5, "Subpool 5: TATCCCGTGAAGCT/TTCGGTTAA"),
(6, "Subpool 6: TAGTAGTTCAGACGC/ATGTACCC"),
(7, "Subpool 7: GGATGCATGATCTAG/CATCAAGC"),
(8, "Subpool 8: ATGAGGACGAATCT/CACCTAAAG"),
(9, "Subpool 9: GGTAGGCACG/TAAACTTAGAACC"),
(10, "Subpool 10: AGTCATGATTCAG/GTTGCAAGTCTAG"),
]
# Restriction enzyme supplier codes
rebaseSuppliers = {
"B":"Life Technologies",
"C":"Minotech",
"E":"Agilent",
"I":"SibEnzyme",
"J":"Nippon Gene",
"K":"Takara",
"M":"Roche",
"N":"NEB",
"O":"Toyobo",
"Q":"Molecular Biology Resources",
"R":"Promega",
"S":"Sigma",
"V":"Vivantis",
"X":"EURx",
"Y":"SinaClon BioScience"
}
# labels and descriptions of eff. scores
scoreDescs = {
"doench" : ("Doench '14", "Range: 0-100. Linear regression model trained on 880 guides transfected into human MOLM13/NB4/TF1 cells (three genes) and mouse cells (six genes). Delivery: lentivirus. The Fusi score can be considered an updated version this score, as their training data overlaps a lot. See <a target='_blank' href='http://www.nature.com/nbt/journal/v32/n12/full/nbt.3026.html'>Doench et al.</a>"),
"wuCrispr" : ("Wu-Crispr", "Range 0-100. Aka 'Wong score'. SVM model trained on previously published data. The aim is to identify only a subset of efficient guides, many guides will have a score of 0. Takes into account RNA structure. See <a target='_blank' href='https://genomebiology.biomedcentral.com/articles/10.1186/s13059-015-0784-0'>Wong et al., Gen Biol 2015</a>"),
"ssc" : ("Xu", "Range ~ -2 - +2. Aka 'SSC score'. Linear regression model trained on data from >1000 genes in human KBM7/HL60 cells (Wang et al) and mouse (Koike-Yusa et al.). Delivery: lentivirus. Ranges mostly -2 to +2. See <a target='_blank' href='http://genome.cshlp.org/content/early/2015/06/10/gr.191452.115'>Xu et al.</a>"),
"crisprScan" : ["Moreno-Mateos", "Also called 'CrisprScan'. Range: mostly 0-100. Linear regression model, trained on data from 1000 guides on >100 genes, from zebrafish 1-cell stage embryos injected with mRNA. See <a target=_blank href='http://www.nature.com/nmeth/journal/v12/n10/full/nmeth.3543.html'>Moreno-Mateos et al.</a>. Recommended for guides transcribed <i>in-vitro</i> (T7 promoter). Click to sort by this score. Note that under 'Show all scores', you can find a Doench2016 model trained on Zebrafish scores, Azimuth in-vitro, which should be slightly better than this model for zebrafish."],
"wang" : ("Wang", "Range: 0-100. SVM model trained on human cell culture data on guides from >1000 genes. The Xu score can be considered an updated version of this score, as the training data overlaps a lot. Delivery: lentivirus. See <a target='_blank' href='http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3972032/'>Wang et al.</a>"),
"chariRank" : ("Chari", "Range: 0-100. Support Vector Machine, converted to rank-percent, trained on data from 1235 guides targeting sequences that were also transfected with a lentivirus into human 293T cells. See <a target='_blank' href='http://www.nature.com/nmeth/journal/v12/n9/abs/nmeth.3473.html'>Chari et al.</a>"),
"fusi" : ("Doench '16", "Aka the 'Fusi-Score', since V4.4 using the version 'Azimuth', scores are slightly different than before April 2018 but very similar (click 'show all' to see the old scores). Range: 0-100. Boosted Regression Tree model, trained on data produced by Doench et al (881 guides, MOLM13/NB4/TF1 cells + unpublished additional data). Delivery: lentivirus. See <a target='_blank' href='http://biorxiv.org/content/early/2015/06/26/021568'>Fusi et al. 2015</a> and <a target='_blank' href='http://www.nature.com/nbt/journal/v34/n2/full/nbt.3437.html'>Doench et al. 2016</a> and <a target=_blank href='https://crispr.ml/'>crispr.ml</a>. Recommended for guides expressed in cells (U6 promoter). Click to sort the table by this score."),
"fusiOld" : ("OldDoench '16", "The original implementation of the Doench 2016 score, as received from John Doench. The scores are similar, but not exactly identical to the 'Azimuth' version of the Doench 2016 model that is currently the default on this site, since Apr 2018."),
"rs3" : ("Doench-RuleSet3", "The Doench Rule Set 3 (RS3) score (-200-+200). Similar to the Doench 2014 and Doench 2016/Fusi/Azimuth score, but updated and more accurate. See <a href='https://www.nature.com/articles/s41467-022-33024-2' target=_blank>. Scores shown are multiplied with 100 for easier display. RS3 is configured here to use the Hsu-TRACR sequence."),
"najm" : ("Najm 2018", "A modified version of the Doench 2016 score ('Azimuth'), by Mudra Hegde for S. aureus Cas9. Range 0-100. See <a target=_blank href='https://www.nature.com/articles/nbt.4048'>Najm et al 2018</a>."),
"ccTop" : ("CCTop", "The efficiency score used by CCTop, called 'crisprRank'."),
"aziInVitro" : ("Azimuth in-vitro", "The Doench 2016 model trained on the Moreno-Mateos zebrafish data. Unpublished model, gratefully provided by J. Listgarden. This should be better than Moreno-Mateos, but we have not found the time to evaluate it yet."),
"housden" : ("Housden", "Range: ~ 1-10. Weight matrix model trained on data from Drosophila mRNA injections. See <a target='_blank' href='http://stke.sciencemag.org/content/8/393/rs9.long'>Housden et al.</a>"),
"proxGc" : ("ProxGCCount", "Number of GCs in the last 4pb before the PAM"),
"seqDeepCpf1" : ("DeepCpf1", "Range: ~ 0-100. Convolutional Neural Network trained on ~20k Cpf1 lentiviral guide results. This is the score without DNAse information, 'Seq-DeepCpf1' in the paper. See <a target='_blank' href='https://www.nature.com/articles/nbt.4061'>Kim et al. 2018</a>"),
"oof" : ("Out-of-Frame", "Range: 0-100. Out-of-Frame score, only for deletions. Predicts the percentage of clones that will carry out-of-frame deletions, based on the micro-homology in the sequence flanking the target site. See <a target='_blank' href='http://www.nature.com/nmeth/journal/v11/n7/full/nmeth.3015.html'>Bae et al. 2014</a>. Click the score to show the predicted deletions."),
"lindel": ("Lindel", "Wei Chen Frameshift ratio (0-100). Predicts probability of a frameshift caused by any type of insertion or deletion. See <a href='https://academic.oup.com/nar/article/47/15/7989/5511473'>Wei Chen et al, Bioinf 2018</a>. Click the score to see the most likely deletions and insertions."),
}
# the headers for the guide and offtarget output files
guideHeaders = ["guideId", "targetSeq", "mitSpecScore", "cfdSpecScore", "offtargetCount", "targetGenomeGeneLocus"]
offtargetHeaders = ["guideId", "guideSeq", "offtargetSeq", "mismatchPos", "mismatchCount", "mitOfftargetScore", "cfdOfftargetScore", "chrom", "start", "end", "strand", "locusDesc"]
# library descriptions
libLabels = [
# https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4486245/
("human_brunello" , "Human, Brunello, Doench Nat Bio 2016 (recommended)"),
("human_avana" , "Human, Avana, Doench Nat Bio 2016"),
("human_geckov2" , "Human, GeCKO V2, Sanjana Nat Meth 2014"),
("mouse_brie" , "Mouse, Brie, Doench Nat Bio 2016 (recommended)"),
("mouse_geckov2" , "Mouse, GeCKO V2, Sanjana Nat Meth 2014"),
("mouse_asiago" , "Mouse, Asiago, Doench Nat Bio 2016"),
]
# a file crispor.conf in the directory of the script allows to override any global variable
myDir = dirname(__file__)
confPath =join(myDir, "crispor.conf")
if isfile(confPath):
exec(open(confPath).read())
#execfile(confPath)
cgiParams = None
# ====== END GLOBALS ============
def setupPamInfo(pam):
" modify a few globals based on the current pam "
global GUIDELEN
global pamIsFirst
global addGenePlasmids
global PAMLEN
global scoreNames
global baseEditor
global saCas9Mode
global mutScoreNames
global isSpg
PAMLEN = len(pam)
pamIsFirst = False
scoreNames = cas9ScoreNames
pamOpt = None
if "-" in pam:
pam, pamOpt = pam.split("-")
if pamOpt=="BE1":
baseEditor = "BE1"
elif pamOpt=="spg":
isSpg = True
if pamIsCasX(pam):
logging.debug("switching on CasX mode, guide length is 20bp")
GUIDELEN = 20
pamIsFirst = True
scoreNames = cpf1ScoreNames
if pamIsCas12max(pam):
logging.debug("switching on hfCas12max mode, guide length is 20bp")
GUIDELEN = 20
pamIsFirst = True
scoreNames = cpf1ScoreNames
elif pamIsCpf1(pam):
logging.debug("switching on Cpf1 mode, guide length is 23bp")
GUIDELEN = 23
pamIsFirst = True
scoreNames = cpf1ScoreNames
#if pamOpt:
#GUIDELEN=int(pamOpt)
elif pam=="NGTN":
logging.debug("switching on Cpf1 mode for ShCAST, guide length is 23bp")
GUIDELEN = 23
pamIsFirst = True
elif pam=="NNNNRYAC" or pam=="NNNVRYAC":
GUIDELEN = 22
elif pam=="NNGRRT" or pam=="NNNRRT":
logging.debug("switching on S. aureus mode, guide length is 21bp")
addGenePlasmids = addGenePlasmidsAureus
GUIDELEN = 21
#if pamOpt=="20":
#GUIDELEN=20
saCas9Mode = True
scoreNames = saCas9ScoreNames
elif pam=="NNNNCC":
GUIDELEN = 24
else:
GUIDELEN = 20
if pamOpt and pamOpt.isnumeric():
GUIDELEN = int(pamOpt)
if (GUIDELEN==20 or GUIDELEN==22) and pam=="NGG":
mutScoreNames = spCas9MutScoreNames
else:
mutScoreNames = otherMutScoreNames
logging.debug("Enzyme info: pam=%s, guideLen=%d, pamIsFirst=%s, saCas9Mode=%s" %
(pam, GUIDELEN, pamIsFirst, saCas9Mode))
return pam
# ==== CLASSES =====
class JobQueue:
"""
simple job queue, using a db table as a backend
jobs have different types and status. status can be updated while they run
job running times are kept and old job info is kept in a separate table
>>> q = JobQueue()
>>> q.openSqlite()
>>> q.clearJobs()
>>> q.waitCount()
0
>>> q.addJob("search", "abc123", "myParams")
True
only one job per jobId
>>> q.addJob("search", "abc123", "myParams")
False
>>> q.waitCount()
1
>>> q.getStatus("abc123")
'Waiting'
>>> q.startStep("abc123", "bwa", "Alignment with BWA")
>>> q.getStatus("abc123")
'Alignment with BWA'
>>> jobType, jobId, paramStr = q.popJob()
>>> q.waitCount()
0
>>> q.jobDone("abc123")
>>> q.waitCount()
0
can't pop from an empty queue
#>>> q.popJob()
#(None, None, None)
#>>> os.system("rm /tmp/tempCrisporTest.db")
#0
"""
_queueDef = (
'CREATE TABLE IF NOT EXISTS %s '
'('
' jobType text,' # either "index" or "search"
' jobId text %s,' # unique identifier
' paramStr text,' # parameters for jobs, like db, options, etc.
' isRunning int DEFAULT 0,' # indicates steps have started, done jobs are moved to doneJobs table
' stepName text,' # currently step, internal step name for timings
' stepLabel text,' # current step, human-readable status of job, for UI
' lastUpdate float,' # time of last update
' stepTimes text,' # comma-sep list of whole msecs, one per step
' startTime text ' # date+time when job was put into queue
')')
#def __init__(self):
#" no inheritance needed here "
#self.openSqlite(JOBQUEUEDB)
def openSqlite(self, dbName=JOBQUEUEDB):
self.dbName = dbName
# isolation_level=None = autocommit mode: we manage transactions explicitly
# timeout=30: wait up to 30s for locks (multiple daemons + CGI share this DB)
self.conn = sqlite3.connect(dbName, timeout=30, isolation_level=None)
# WAL mode allows concurrent readers + writer, essential for CGI + daemon(s)
result = self.conn.execute("PRAGMA journal_mode=WAL;").fetchone()
if result[0] != "wal":
logging.warn("Could not set WAL mode, journal_mode is: %s (is another connection open with the old mode?)" % result[0])
#self.conn.set_trace_callback(print) # for debugging: print all sql statements
self._chmodJobDb()
try:
self.conn.execute(self._queueDef % ("queue", "PRIMARY KEY"))
except SQLITEERROR as ex:
errAbort("cannot open the sqlite jobs file %s: %s" % (JOBQUEUEDB, ex))
def _chmodJobDb(self):
# umask is not respected by sqlite, bug http://www.mail-archive.com/[email hidden]/msg59080.html
try:
os.chmod(JOBQUEUEDB, 0o666)
except OSError:
# if the file was created by other job, we can't chmod, as we're the CGI. Just silently ignore this
pass
def addJob(self, jobType, jobId, paramStr):
" create a new job, returns False if not successful "
self._chmodJobDb()
sql = 'INSERT INTO queue (jobType, jobId, isRunning, lastUpdate, ' \
'stepTimes, paramStr, stepName, stepLabel, startTime) VALUES (:jobType, :jobId, :isRunning, :lastUpdate, ' \
':stepTimes, :paramStr, :stepName, :stepLabel, :startTime)'
now = "%.3f" % time.time()
values = {'jobType' : jobType, 'jobId' : jobId, 'isRunning' : 0, 'lastUpdate' : now, 'stepTimes':"", 'paramStr':paramStr, 'stepName':"wait",
"stepLabel":"Waiting", "startTime":now}
try:
# in autocommit mode, this INSERT commits immediately
self.conn.execute(sql, values)
return True
except sqlite3.IntegrityError:
# job already in queue (e.g. resubmit, or daemon restart) - that's fine
return True
except SQLITEERROR:
errAbort("Cannot open DB file %s. Please contact %s" % (self.dbName, contactEmail))
def getStatus(self, jobId):
" return current job status label or None if job is not in queue"
sql = 'SELECT stepLabel FROM queue WHERE jobId=?'
try:
rows = self.conn.execute(sql, (jobId,)).fetchmany(1)
if len(rows) == 0:
status = None
else:
status = rows[0][0]
except (StopIteration, IndexError):
logging.debug("getStatus: job %s not found" % jobId)
status = None
return status
def dump(self):
" for debugging, write the whole queue table to stdout "
sql = 'SELECT * FROM queue'
for row in self.conn.execute(sql):
print("\t".join([str(x) for x in row]))
def jobInfo(self, jobId, isDone=False):
" for debugging, return all job info as a tuple "
print("job info<br>")
if isDone:
sql = 'SELECT * FROM doneJobs WHERE jobId=?'
else:
sql = 'SELECT * FROM queue WHERE jobId=?'
try:
row = next(self.conn.execute(sql, (jobId,)))
except StopIteration:
return []
return row
def startStep(self, jobId, newName, newLabel):
" start a new step. Update lastUpdate, status and stepTime "
try:
self.conn.execute('BEGIN IMMEDIATE')
sql = 'SELECT lastUpdate, stepTimes, stepName FROM queue WHERE jobId=?'
logging.debug(sql)
rows = self.conn.execute(sql, (jobId,)).fetchmany(1)
if len(rows)==0:
logging.error("startStep: no row for jobId %s" % jobId)
self.conn.commit()
return
lastTime, timeStr, lastStep = rows[0]
lastTime = float(lastTime)
# append a string in format "stepName:milliSecs" to the timeStr
now = time.time()
timeDiff = "%d" % int((1000.0*(now - lastTime)))
newTimeStr = timeStr+"%s=%s" % (lastStep, timeDiff)+","
sql = 'UPDATE queue SET lastUpdate=?, stepName=?, stepLabel=?, stepTimes=?, isRunning=? WHERE jobId=?'
self.conn.execute(sql, (now, newName, newLabel, newTimeStr, 1, jobId))
self.conn.commit()
except:
self.conn.rollback()
raise
def jobDone(self, jobId):
" remove the job from the queue and add it to the queue log"
print("job done<br>")
try:
self.conn.execute('BEGIN IMMEDIATE')
sql = 'SELECT * FROM queue WHERE jobId=?'
row = self.conn.execute(sql, (jobId,)).fetchone()
if row is None:
logging.warn("jobDone - job %s has been removed already" % jobId)
self.conn.commit()
return
sql = 'DELETE FROM queue WHERE jobId=?'
self.conn.execute(sql, (jobId,))
self.conn.commit()
except:
self.conn.rollback()
raise
# good to have a log file of the old jobs
with open("doneJobs.tsv", "a") as ofh: # if this triggers an error: run 'touch doneJobs.tsv && chmod a+rw doneJobs.tsv' in the crispor dir.
row = [str(x) for x in row]
line = "\t".join(row)
ofh.write(line)
ofh.write("\n")
def waitCount(self):
" return number of waiting jobs "
sql = 'SELECT count(*) FROM queue WHERE isRunning=0'
return self.conn.execute(sql).fetchone()[0]
def popJob(self):
" return (jobType, jobId, params) of first waiting job and set it to running state "
print('pop job<br>')
try:
self.conn.execute('BEGIN IMMEDIATE')
sql = 'SELECT jobType, jobId, paramStr FROM queue WHERE isRunning=0 ORDER BY lastUpdate LIMIT 1'
row = self.conn.execute(sql).fetchone()
if row is None:
logging.debug("popJob: no waiting jobs")
self.conn.commit()
return None, None, None
jobType, jobId, paramStr = row
sql = 'UPDATE queue SET isRunning=1 where jobId=?'
self.conn.execute(sql, (jobId,))
self.conn.commit()
except:
self.conn.rollback()
raise
return jobType, jobId, paramStr
def clearJobs(self):
" clear the job table, removing running jobs, too "
self.conn.execute("DELETE from queue")
def close(self):
" "
self.conn.close()
# ====== FUNCTIONS =====
contentLineDone = False
# the queue workers should be able to never abort
doAbort = True
def getTwoBitFname(db):
" return the name of the twoBit file for a genome "
# at UCSC, try to use local disk, if possible
locPath = join("/scratch", "data", db, db+".2bit")
if isfile(locPath):
return locPath
path = join(genomesDir, db, db+".2bit")
return path
def errAbort(msg, isWarn=False):
" print err msg and exit "
if commandLineMode:
raise Exception(msg)
if not contentLineDone:
print("Content-type: text/html\n")
print('<div style="position: absolute; padding: 10px; left: 100; top: 100; border: 10px solid black; background-color: white; text-align:left; width: 800px; font-size: 18px">')
if isWarn:
print("<strong>Warning:</strong><p> ")
else:
print("<strong>Error:</strong><p> ")
print((msg+"<p>"))
print(("If you think this is a bug or you have any other suggestions, please do not hesitate to contact us %s<p>" % contactEmail))
if isWarn:
print("In the email, please also send us the full URL of the page.")
else:
print("Please also send us the full URL of the page where you see the error. Thanks!")
print('</div>')
if doAbort:
sys.exit(0) # cgi must not exit with 1
# allow only dashes, digits, characters, underscores and colons in the CGI parameters
# and +
notOkChars = re.compile(r'[^+a-zA-Z0-9/:\n\r_. -]')
def checkVal(key, inStr):
""" remove special characters from input string, to protect against injection attacks """
if key!="geneIds":
if len(inStr) > 10000:
errAbort("input parameter %s is too long" % key)
else:
if len(inStr) > 100000:
errAbort("Pasting more than tens of thousands of gene IDs makes little sense. Copy/paste error?")
matchObj =notOkChars.search(inStr)
if matchObj!=None:
errAbort("input parameter %s contains an invalid character %s (ASCII %d)" % (key, repr(matchObj.group()), ord(matchObj.group())))
return inStr
def cgiGetParams():
" get CGI parameters and return as dict "
form = cgi.FieldStorage()
global cgiParams
cgiParams = {}
# parameters are:
#"pamId", "batchId", "pam", "seq", "org", "download", "sortBy", "format", "ajax
for key in list(form.keys()):
val = form.getfirst(key)
if val!=None:
# "seq" is cleaned by cleanSeq later
val = urllib.parse.unquote(val)
if key not in ["seq", "name"]:
checkVal(key, val)
cgiParams[key] = val
if "pam" in cgiParams:
legalChars = set("ACTGNMKRYVBE120345/-")
illegalChars = set(cgiParams["pam"])-legalChars
if len(illegalChars)!=0:
errAbort("Illegal character in PAM-sequence. Only %s are allowed."+"".join(legalChars))
if "batchId" in cgiParams:
batchId = cgiParams["batchId"]
if not batchId.isalnum() or len(batchId) > 30:
errAbort("Invalid batchId")
return cgiParams
def cgiGetStr(params, argName, default=None):
val = params.get(argName, None)
if val==None and default==None:
errAbort("'%s' parameter must be specified" % argName)
if val==None:
return default
return val
def cgiGetNum(params, argName, default):
" get CGI parameter which must be a number "
val = params.get(argName, None)
if val==None:
return default
if not val.isdigit():
errAbort("'%s' parameter must be a number" % argName)
val = int(val)
return val
transTab = str.maketrans("-=/+_", "abcde")
def makeTempBase(seq, org, pam, batchName):
"create the base name of temp files using a hash function and some prettyfication "
hasher = hashlib.sha1(seq.encode("latin1")+org.encode("latin1")+pam.encode("latin1")+batchName.encode("latin1"))
shortHash = hasher.digest()[0:20]
batchId = base64.urlsafe_b64encode(shortHash).decode('latin1').translate(transTab)[:20]
return batchId
def makeTempFile(prefix, suffix):
" return a temporary file that is deleted upon exit, unless DEBUG is set "
if DEBUG:
fname = join("/tmp", prefix+suffix)
fh = open(fname, "wt")
else:
fh = tempfile.NamedTemporaryFile(mode="wt", dir=TEMPDIR, prefix="primer3In", suffix=".txt")
return fh
def pamIsCpf1(pam):
" if you change this, also change bin/filterFaToBed and bin/samToBed!!! "
return (pam in ["TNN", "TTN", "TTTN", "TYCV", "TATV", "TTTV", "TTTR", "ATTN", "TTTA", "TCTA", "TCCA", "CCCA", "YTTV", "TTYN"])
# test : modify cleavage sites for hfCas12max according to Synthego specifications
def pamIsCas12max(pam):
return (pam in ["TNN", "TTN"])
def pamIsCasX(pam):
" if you change this, also change bin/filterFaToBed and bin/samToBed!!! "
return (pam in ["TTCN"])
def pamIsSaCas9(pam):
" only used for notes and efficiency scores, unlike its Cpf1 cousin function "
return (pam.split("-")[0] in ["NNGRRT", "NNNRRT"])
def isSlowPam(pam):
" do not allow input sequences > 500 bp "
if pamIsXCas9(pam) or pam=="TTYN" or pam=="NNG" or pam=="TNN":
return True
else:
return False
def pamIsXCas9(pam):
" "
return (pam in ["NGK", "NGN"])
def pamIsSpCas9(pam):
" only used for notes and efficiency scores, unlike its Cpf1 cousin function "
return (pam in ["NGG", "NGA", "NGCG"])
def saveSeqOrgPamToCookies(seq, org, pam):
" create a cookie with seq, org and pam and print it"
cookies=http.cookies.SimpleCookie()
expires = 365 * 24 * 60 * 60
if len(seq)<3000:
cookies['lastseq'] = seq
else:
cookies['lastseq'] = "(last sequence was too long, could not be saved in Internet Browser cookie)"
cookies['lastseq']['expires'] = expires
cookies['lastorg'] = org
cookies['lastorg']['expires'] = expires
cookies['lastpam'] = pam
cookies['lastpam']['expires'] = expires
print(cookies)
def debug(msg):
if commandLineMode:
logging.debug(msg)
elif DEBUG:
print(msg)
print("<br>")
def gcContent(seq):
" return GC content as a float "
c = 0
for x in seq:
if x in ["G","C"]:
c+= 1
return (float(c)/len(seq))
def findPat(seq, pat):
""" yield positions where pat matches seq, stupid brute force search
"""
seq = seq.upper()
pat = pat.upper()
patLen = len(pat)
for i in range(0, len(seq)-patLen+1):
subseq = seq[i:i+patLen]
if patMatch(subseq, pat):
yield i
def rndSeq(seqLen):
" return random seq "
seq = []
alf = "ACTG"
for i in range(0, seqLen):
seq.append(alf[random.randint(0,3)])
return "".join(seq)
def cleanSeq(seq, db):
""" remove fasta header, check seq for illegal chars and return (filtered
seq, user message) special value "random" returns a random sequence.
"""
#print repr(seq)
if seq.startswith("random"):
seq = rndSeq(800)
lines = seq.strip().splitlines()
#print "<br>"
#print "before fasta cleaning", "|".join(lines)
if len(lines)>0 and lines[0].startswith(">"):
line1 = lines.pop(0)
#print "<br>"
#print "after fasta cleaning", "|".join(lines)
#print "<br>"
newSeq = []
nCount = 0
for l in lines:
if len(l)==0:
continue
for c in l:
if c not in "actgACTGNn":
nCount +=1
else:
newSeq.append(c)
seq = "".join(newSeq)
msgs = []
tooLongHint = """
Please split your input sequence into shorter sequences or use
the <a href='downloads/'>stand-alone version</a> on your own Linux or Mac server to process longer sequences in batch.<br>
"""
if len(seq)>MAXSEQLEN and db!="noGenome":
errMsg = "<strong>Sorry, this tool cannot handle sequences longer than %d bp</strong><br>" % (MAXSEQLEN)
errAbort(errMsg+tooLongHint)
if len(seq)>MAXSEQLEN_NOGENOME and db=="noGenome":
errMsg = "<strong>Sorry, this tool cannot handle sequences longer than %d bp when using the 'No Genome' option.</strong><br>" % (MAXSEQLEN_NOGENOME)
errAbort(errMsg+tooLongHint)
if nCount!=0:
msgs.append("Sequence contained %d non-ACTGN letters. They were removed." % nCount)
return seq, "<br>".join(msgs)
revTbl = {'A' : 'T', 'C' : 'G', 'G' : 'C', 'T' : 'A', 'N' : 'N' , 'M' : 'K', 'K' : 'M',
"R" : "Y" , "Y":"R" , "g":"c", "a":"t", "c":"g","t":"a", "n":"n", "V" : "B", "v":"b",
"B" : "V", "b": "v", "W" : "W", "w" : "w"}
def revComp(seq):
" rev-comp a dna sequence with UIPAC characters "
newSeq = []
for c in reversed(seq):
newSeq.append(revTbl[c])
return "".join(newSeq)
def docTestInit(isCpf1, guideLen):
global pamIsFirst
global GUIDELEN
pamIsFirst=isCpf1
GUIDELEN=guideLen
def findPams (seq, pam, strand, startDict, endSet):
""" return two values: dict with pos -> strand of PAM and set of end positions of PAMs
Makes sure to return only values with at least GUIDELEN bp left (if strand "+") or to the
right of the match (if strand "-")
If the PAM is cpf1, then this is inversed: pos-strand matches must have at least GUIDELEN
basepairs to the right, neg-strand matches must have at least GUIDELEN bp on their left
>>> docTestInit(False, 20)
>>> findPams("GGGGGGGGGGGGGGGGGGGGGGG", "NGG", "+", {}, set())
({20: '+'}, {23})
>>> findPams("CCAGCCCCCCCCCCCCCCCCCCC", "CCA", "-", {}, set())
({0: '-'}, {3})
>>> docTestInit(True, 20)
>>> findPams("TTTNCCCCCCCCCCCCCCCCCTTTN", "TTTN", "+", {}, set())
({0: '+'}, {4})
>>> docTestInit(False, 20)
>>> findPams("CCCCCCCCCCCCCCCCCCCCCAAAA", "NAA", "-", {}, set())
({}, set())
>>> findPams("AAACCCCCCCCCCCCCCCCCCCCC", "NAA", "-", {}, set())
({0: '-'}, {3})
>>> findPams("CCCCCCCCCCCCCCCCCCCCCCCCCAA", "NAA", "-", {}, set())
({}, set())
>>> findPams("GTTGTGTTTTACAATGCAGAGAGTGGAGGATGCTTTTTATACATTGGTGAGAGAGATCCGACAGTACAGATTGAAAAAAATCAGCAAAGAAGAAAAGACTCCTGGCTGTGTGAAAATTAAAAAATGCGTTATAATGTAATCTGGTAAGTTGAGCATATTCATTCTGGTACAAAGCAGATGTCTTCAGAGGTAACA", "TATV", "-", {}, set())
({37: '-', 129: '-'}, {41, 133})
>>> findPams("GTTGTGTTTTACAATGCAGAGAGTGGAGGATGCTTTTTATACATTGGTGAGAGAGATCCGACAGTACAGATTGAAAAAAATCAGCAAAGAAGAAAAGACTCCTGGCTGTGTGAAAATTAAAAAATGCGTTATAATGTAATCTGGTAAGTTGAGCATATTCATTCTGGTACAAAGCAGATGTCTTCAGAGGTAACA", "TATV", "+", {}, set())
({37: '+', 129: '+'}, {41, 133})
"""
assert(pamIsFirst is not None)
if pamIsFirst:
maxPosPlus = len(seq)-(GUIDELEN+len(pam))
minPosMinus = GUIDELEN
else:
# -------------------
# OKOKOKOKOK
minPosPlus = GUIDELEN
# -------------------
# OKOKOKOKOK
maxPosMinus = len(seq)-(GUIDELEN+len(pam))
#print "new search", seq, pam, "minPosPlus=",minPosPlus, "guideLen=", GUIDELEN, "<br>"
for start in findPat(seq, pam):
if pamIsFirst:
# need enough flanking seq on one side
#return("Cpf1 mode found", start,"<br>")
if strand == "+" and start > maxPosPlus:
continue
if strand == "-" and start < minPosMinus:
continue
else:
# return("non-Cpf1 mode found", start,"<br>")
if strand=="+" and start < minPosPlus:
continue
if strand=="-" and start > maxPosMinus:
continue
#print "match", strand, start, end, "<br>"
startDict[start] = strand
end = start+len(pam)
endSet.add(end)
return startDict, endSet
def rulerString(maxLen):
" return line with positions every 10 chars "
texts = []
for i in range(0, maxLen, 10):
numStr = str(i)
texts.append(numStr)
spacer = "".join([" "]*(10-len(numStr)))
texts.append(spacer)
return "".join(texts)
def varDictToHtml(varDict, seq, varShortLabel):
" make a list of one html string per position in the sequence "
if varDict is None:
return None
varHtmls = []
for i in range(0, len(seq)):
if not i in varDict:
varHtmls.append(".")
else:
varHooverLines = []
showStar = False # show a star if change is non-simple SNP
varInfos = varDict[i]
for chrom, pos, refAll, altAll, infoDict in varInfos:
varHooverLines.append("%s: %s → %s<br>" % (varShortLabel, refAll, altAll))
if "freq" in infoDict:
varHooverLines.append(" <b>Freq:</b> %s<br>" % infoDict["freq"])
#if "dbg" in infoDict:
#varHooverLines.append("%s<br>" % infoDict["dbg"])
if "varId" in infoDict:
varHooverLines.append(" <b>ID:</b> %s<br>" % infoDict["varId"])
if "ExAC" in varDict["label"]:
endPos = int(pos)+len(refAll)
varHooverLines.append(' <a target=_blank href="http://exac.broadinstitute.org/region/%s-%s-%d">ExAC Browser</a><br>' % (chrom, pos, endPos))
if len(refAll)!=1 or len(altAll)!=1:
showStar = True
if len(varInfos)!=1:
showStar = True
varDesc = "".join(varHooverLines)
if showStar:
dispChar = "*"
else:
dispChar = altAll
varHtmls.append("<u class='tooltipsterInteract' title='%s'>%s</u>" % (varDesc, dispChar))
return varHtmls
def cssClassesFromSeq(guideSeq, suffix=""):
" The CSS class of guide row and links in seq viewer depend on the first nucl of guide "
classNames = ["guideRow"]
if guideSeq[0].upper()!="G":
classNames.append("guideRowNoPrefixG"+suffix)
if not guideSeq.startswith("GG"):
classNames.append("guideRowNoPrefixGG"+suffix)
if guideSeq[0].upper()!="A":
classNames.append("guideRowNoPrefixA"+suffix)
classStr = " ".join(classNames)
return classStr
def buildCodonTable():
" from http://www.petercollingridge.co.uk/tutorials/bioinformatics/codon-table/ "
bases = "TCAG"
codons = [a + b + c for a in bases for b in bases for c in bases]
amino_acids = 'FFLLSSSSYY**CC*WLLLLPPPPHHQQRRRRIIIMTTTTNNKKSSRRVVVVAAAADDEEGGGG'
codon_table = dict(list(zip(codons, amino_acids)))
return codon_table
def buildOneToThree():
" return one-letter -> three-letter conversion table for amino acids "
oneToThree = \
{'C':'Cys', 'D':'Asp', 'S':'Ser', 'Q':'Gln', 'K':'Lys',
'I':'Ile', 'P':'Pro', 'T':'Thr', 'F':'Phe', 'N':'Asn',
'G':'Gly', 'H':'His', 'L':'Leu', 'R':'Arg', 'W':'Trp',
'A':'Ala', 'V':'Val', 'E':'Glu', 'Y':'Tyr', 'M':'Met',
'U':'Sec', '*':'Stop',
'X':'Stop', # is this really used like that?
'Z':'Glx', # special case: asparagine or aspartic acid
'B':'Asx' # special case: glutamine or glutamic acid
}
return oneToThree
def makeExonLines(exonInfo, seq, selTransId):
""" create text that draws exons, input is transId -> (exonNumber, exStart, exEnd, exFrame).
returns a list of (transId (=label), symbol (=mouseover), ASCII-line) """
lines = []
#maxLabelLen = 0
codonTable = buildCodonTable()
#oneToThree = buildOneToThree()
seqLen = len(seq)
seq = seq.upper()
for (transId, symbol), exRows in exonInfo.items():
if selTransId!="allTrans" and transId!=selTransId:
continue
line = [" "]*seqLen
mouseOvers = {} # position -> mouseOver-text or null, for end of mouse over
for exIdx, (exNum, exStart, exEnd, exFrame, nextFrame, exStrand) in enumerate(exRows):
if exFrame==-1:
for i in range(exStart, exEnd):
line[i]="="
exonLabel = "noncoding"
if (exEnd-exStart)>len(exonLabel)+4:
# center the exon label on the exon
mid = exStart+int((exEnd-exStart)*0.5)
halfLen = int(len(exonLabel)*0.5)
labStart = mid-halfLen
for i in range(0, len(exonLabel)):
line[labStart+i] = exonLabel[i]
line[labStart-1] = " "
line[labStart+len(exonLabel)] = " "
else:
exonDesc = "gene %s<br>transcript %s<br>exon %d<br>start phase %s" % (symbol, transId, exNum+1, exFrame)
if nextFrame is not None:
exonDesc += "<br>end phase %s" % nextFrame
if (exFrame+nextFrame) % 3 == 0:
exonDesc += "<br>Removing the exon retains the reading frame"
else:
exonDesc += "<br>Removing the exon will destroy the reading frame"
mouseOvers[exStart] = exonDesc
mouseOvers[exEnd] = None
for i in range(exStart, exStart+exFrame):
line[i] = "-"
for i in range(exStart+exFrame, exEnd, 3):
codon = seq[i:i+3]
if len(codon)==3:
shortAa = codonTable[codon]
if exStrand=="+":
longAa = shortAa+"]]"
else:
longAa = "[["+shortAa # highlighting rev. dir. more
else:
# codon is split by splice site
longAa = "-"
for j in range(0, len(longAa)):
line[i+j] = longAa[j]
# now merge the mouse overs as span tags into the ASCII line
newLine = []
for pos, char in enumerate(line):
if pos in mouseOvers:
overString = mouseOvers[pos]
if overString is not None:
newLine.append("<span class='tooltipsterInteract' title='%s'>" % overString)
if char=="-":
newLine.append("<span class='tooltipsterInteract' title='This codon goes over a splice site. The nucleotides of the split codon are not translated to amino acids but shown as dashes.'>-</span>")
else:
newLine.append(char)
if overString is None:
newLine.append("</span>")
else:
newLine.append(char)
# and fix up the < signs
newLineStr = "".join(newLine)
newLineStr = newLineStr.replace("[[", "<lc><<</lc>")
newLineStr = newLineStr.replace("]]", "<lc>>></lc>")
lines.append((symbol, transId, newLineStr))
#maxLabelLen = max(maxLabelLen, len(transId))
return lines
def getGeneModels(org):
" read possible gene models for org and return as list (name, desc) or None if no gene models "
mask = join(genomesDir, org, "*.bb")
fnames = glob.glob(mask)
descFname = join(genomesDir, org, "genes.tsv")
if not isfile(descFname):
return None
geneDescs = {}
for line in open(descFname):
fname, desc = line.split(maxsplit=1)
geneDescs[fname] = desc
ret = []
for fname in fnames:
baseName = basename(fname)
name = baseName.split('.')[0]
desc = geneDescs.get(baseName, name)
ret.append((name, desc))
return ret
def getSelGeneModel(org):
" return (list of (name, desc) of models, selected gene model name) "
geneModels = getGeneModels(org)
selGeneModel = None
selTransId = None
if geneModels:
#selGeneModel = cgiParams.get("geneModel", geneModels[0][0])
selGeneModel = cgiParams.get("geneModel", "noGenes")
geneModels.insert(0, ("noGenes", "Do not show"))
possNames = [x for x,y in geneModels]
if not selGeneModel in possNames:
errAbort("The gene model name specified with the argument geneModel is invalid")
selTransId = cgiParams.get("transId", "allTrans")
return geneModels, selGeneModel, selTransId
def printSeqForCopy(seq):
" print a hidden text area so we can copy the sequence to the clipboard "
print('<input id="seqAsText" type="text" style="display:none">')
print(seq)
print("</input>")
def calcKomorScore(guideSeq, pos):
" return base editing score given the guide sequence and the position "
return pos/7.0 # temporary hack
def makeEditLines(seq, pamSeqs, winStart, winEnd, guideScores):
" create the lines that show the possible baseEditor edits "
editInfos = []
for i in range(0, len(seq)):
editInfos.append(defaultdict(list))
upSeq = seq.upper()
for pamId, pamStart, guideStart, strand, guideSeq, pamSeq, pamPlusSeq in pamSeqs:
specScore = guideScores[pamId]
if strand=="+":
fromPos = guideStart+winStart
toPos = guideStart+winEnd
fromNucl = "C"
toNucl = "T"
else:
guideEnd = guideStart+GUIDELEN
fromPos = guideEnd-winEnd
toPos = guideEnd-winStart
fromNucl = "G"
toNucl = "A"
for pos in range(fromPos, toPos):
# position of mutated nucl on forw strand guide
if strand=="+":
mutPos = pos-guideStart
else:
mutPos = GUIDELEN - (pos - guideStart) - 1
if upSeq[pos]==fromNucl:
beScore = calcKomorScore(guideSeq, mutPos)
editInfos[pos][toNucl].append((pamId, guideSeq, pamSeq, mutPos, beScore, specScore))
altNucls = ["A", "T"]
editLabels = []
for an in altNucls:
editLabels.append("Edits to "+an)
editLines = []
for i in range(0, len(altNucls)):
editLines.append([" "]*len(seq))
# rearrange into lines of text + JSON
jsonData = defaultdict(list)
for pos, eiDict in enumerate(editInfos):
if not eiDict:
continue
jsonData[pos] = eiDict
for nucl, guideData in eiDict.items():
yPos = altNucls.index(nucl)
editLines[yPos][pos] = "<d pos=%d>%s</d>" % (pos, nucl)
ret = []
for label, lineChars in zip(editLabels, editLines):
ret.append( (label, None, "".join(lineChars)) )
return ret, jsonData
def makePamLines(lines, maxY, pamIdToSeq, guideScores):
for y in range(0, maxY+1):
texts = []
lastEnd = 0
for start, end, name, strand, pamId in lines[y]:
guideSeq = pamIdToSeq.get(pamId)
if guideSeq==None:
# when there is an N in the guide, the PAM is valid, but the guide is not
continue
classStr = cssClassesFromSeq(guideSeq, suffix="Seq")
spacer = "".join([" "]*((start-lastEnd)))
lastEnd = end
texts.append(spacer)
score = guideScores[pamId]
# XX How can this happen for non-Cpf1 enzymes? Can this ever happen?
if score is None and not pamIsFirst:
continue
color = scoreToColor(score)[0]
texts.append('''<a class='%s' style="text-shadow: 1px 1px 1px #bbb; color: %s" id="list%s" href="#%s">''' % (classStr, color, pamId,pamId))
texts.append(name)
texts.append("</a>")
yield ("", None, ''.join(texts))
def getBeWin(winVal):
" return (start, end) of base editor window given CGI variable "
fs = winVal.split("-")
if len(fs)!=2:
errAbort("parameter beWin must contain only one dash")
start = fs[0].strip()
end = fs[1].strip()
if not start.isdigit() or not end.isdigit():
errAbort("parameter beWin must be two dash-separated numbers")
start = int(start)
end = int(end)
return start, end
def printLines(lines, labelLen):
" print list of (label, string) such that label is at least labelLen characters long "
for label, mouseOver, line in lines:
if mouseOver is not None:
labelStr =('<span class="tooltipsterInteract" title="{:s}">{:'+str(labelLen)+'s} </span>').format(label, mouseOver)
else:
labelStr = ('{:'+str(labelLen)+'s} ').format(label)
print((labelStr), end=' ')
print(line)
def getMaxLen(lines):
" given a list of tuples where first element is the label, return the longest label len "
maxLen = 0
for l in lines:
label = l[0]
maxLen = max(maxLen, len(label))
return maxLen
def printJson(name, obj):
print("<script>")
print((name), end=' ')
print(("="), end=' ')
print((json.dumps(obj)))
print("</script>")
def showSeqAndPams(org, seq, startDict, pam, guideScores, varHtmls, varDbs, varDb, minFreq, position, pamIdToSeq):
" show the sequence and the PAM sites underneath in a sequence viewer "
pamSeqs = list(flankSeqIter(seq, startDict, len(pam), True))
lines, maxY = distrOnLines(seq.upper(), startDict, len(pam), pam)
posLabel = "Position"
varLabel = "Variants"
seqLabel = "Sequence"
exonLabelLen = 0
editLines = []
exonLines = []
geneModels, selGeneModel, selTransId = getSelGeneModel(org)
#selGeneModel = None
#geneModels = None
if baseEditor:
beWinStart, beWinEnd = getBeWin(cgiParams.get("beWin", DEFAULTBEWIN))
editLines, jsonData = makeEditLines(seq, pamSeqs, beWinStart, beWinEnd, guideScores)
printJson("editData", jsonData)
pamLines = list(makePamLines(lines, maxY, pamIdToSeq, guideScores))
labelLen = max(len(varLabel), len(seqLabel), len(posLabel), getMaxLen(pamLines))
if selGeneModel!=None:
exonInfo, maxTransIdLen = getExonInfo(org, selGeneModel, position)
labelLen = max(labelLen, maxTransIdLen)
if baseEditor:
labelLen = max(labelLen, getMaxLen(editLines))
if selGeneModel:
labelLen = max(labelLen, exonLabelLen)
print("<div class='substep'>")
print('<a id="seqStart"></a>')
print("Your input sequence is %d bp long. It contains %d possible guide sequences.<br>" % (len(seq), len(guideScores)))
if not pamIsFirst:
print("Shown below are their PAM sites and the expected cleavage position located -3bp 5' of the PAM site.<br>")
print("Click on a match for the PAM %s below to show its %d bp-long guide sequence. " % (pam, GUIDELEN))
print("(Need help? Look at the <a target=_blank href='manual/#annotseq'>CRISPOR manual</a>)<br>")
print('''Colors <span style="color:#32cd32; text-shadow: 1px 1px 1px #bbb">green</span>, <span style="color:#ffff00; text-shadow: 1px 1px 1px #888">yellow</span> and <span style="text-shadow: 1px 1px 1px #f01; color:#aa0014">red</span> indicate high, medium and low specificity of the PAM's guide sequence in the genome.<p>''')
else:
print("Click on a match for the PAM %s below to show its %d bp-long guide sequence.<br>" % (pam, GUIDELEN))
if baseEditor or varDb or selGeneModel:
print(("""<form style="display:inline" id="paramForm" action="%s" method="GET">""" % basename(__file__)))
if geneModels:
print ("Gene Models:")
printDropDown("geneModel", geneModels, selGeneModel, style="width:20em")
if selGeneModel!="noGenes":
print ("Transcript:")
transIdInfo = [("allTrans", "All Transcripts")]
for transId, sym in list(exonInfo.keys()):
transIdInfo.append( (transId, sym+" / "+transId) )
printDropDown("transId", transIdInfo, selTransId, style="width:20em")
# XX XXXXXX
exonLines = makeExonLines(exonInfo, seq, selTransId)
#exonLines = []
print("""<input style="height:18px;margin:0px;font-size:10px;line-height:normal" type="submit" name="submit" value="Update">""")
print("""<br>""")
if baseEditor:
print ("Base Editor modification window:")
print(("""<input type="text" name="beWin" size="10" value="%s">""" % DEFAULTBEWIN))
print("""<input style="height:18px;margin:0px;font-size:10px;line-height:normal" type="submit" name="submit" value="Update">""")
print("<br>")
if varDb is not None:
print ("Variant database:")
varDbList = [(b,c) for a,b,c,d in varDbs] # only keep fname+label
printDropDown("varDb", varDbList, varDb)
if minFreq==0.0:
minFreq="0.0"
else:
minFreq = str(minFreq)
# pull out the hasAF field for this varDb
varDbHasAF = False
for shortLabel, fname, desc, hasAF in varDbs:
if fname==varDb:
varDbHasAF = hasAF
break
if varDbHasAF:
print("""  Min. frequency: """)
print(("""<input type="text" name="minFreq" size="8" value="%s">""" % minFreq))
print("""<input style="height:18px;margin:0px;font-size:10px;line-height:normal" type="submit" name="submit" value="Update">""")
print(("<small style='margin-left:30px'><a href='mailto:%s'>Missing a variant database? We can add it.</a></small>" % contactEmail))
if position=="?":
print("<small style=''>Input sequence not in genome, cannot show genome variants.</small>")
elif varDb is None:
print(("<small style=''><a href='mailto:%s'>Suggest a genome variants database to show on this page</a></small>" % contactEmail))
print("</div>")
print('''<div class="blueHighlight" style="text-align: left; overflow-x:scroll; width:98vw; background:#DDDDDD; border-style: solid; border-width: 1px">''')
print('''<pre style="font-family: Source Code Pro; font-size: 80%; display:inline; line-height: 0.95em; text-align:left">''')
print(('{:'+str(labelLen)+'s} ').format(posLabel), end=' ')
print(rulerString(len(seq)))
if varHtmls is not None:
print(('{:'+str(labelLen)+'s} ').format(varLabel), end=' ')
print("".join(varHtmls))
print(('{:'+str(labelLen)+'s} ').format(seqLabel), end=' ')
print (seq)
printLines(exonLines, labelLen)
if baseEditor:
printLines(editLines, labelLen)
printLines(pamLines, labelLen)
print("</pre><br>")
print('''</div>''')
#printSeqForCopy(seq)
if pamIsCas12max(pam):
print('<div style="line-height: 1.0; padding-top: 5px; font-size: 15px">Cpf1 has a staggered site: cleavage occurs between the 14th and 16th base on the non-targeted strand (indicated by "\\" in the schema above). Cleavage mostly occurs after the 24rd base on the targeted strand (indicated by "/" in the schema above). See on <a target=_blank href="https://www.synthego.com/products/nuclease/hfcas12max-hifi">Synthego</a></div>')
elif pamIsCpf1(pam):
print('<div style="line-height: 1.0; padding-top: 5px; font-size: 15px">Cpf1 has a staggered site: cleavage occurs usually - but not always - after the 18th base on the non-targeted strand which has the TTTV PAM motif (indicated by "\\" in the schema above). Cleavage mostly occurs after the 23rd base on the targeted strand which has the AAAN motif (indicated by "/" in the schema above). See <a target=_blank href="http://www.sciencedirect.com/science/article/pii/S0092867415012003">Zetsche et al 2015</a>, in particular <a target=_blank href="http://www.sciencedirect.com/science?_ob=MiamiCaptionURL&_method=retrieve&_eid=1-s2.0-S0092867415012003&_image=1-s2.0-S0092867415012003-gr3.jpg&_cid=272196&_explode=defaultEXP_LIST&_idxType=defaultREF_WORK_INDEX_TYPE&_alpha=defaultALPHA&_ba=&_rdoc=1&_fmt=FULL&_issn=00928674&_pii=S0092867415012003&md5=11771263f3e390e444320cacbcfae323">Fig 3</a>.</div>')
elif pamIsCasX(pam):
print('<div style="line-height: 1.0; padding-top: 5px; font-size: 15px">We have no description yet on how exactly the CasX cleavage looks like. Please contact [email hidden] if you have an idea how to describe the cleavage site.</div>')
def iterOneDelSeqs(seq):
""" given a seq, create versions with each bp removed. Avoid duplicates
yields (delPos, seq)
>>> list(iterOneDelSeqs("AATGG"))
[(0, 'ATGG'), (2, 'AAGG'), (3, 'AATG')]
"""
doneSeqs = set()
for i in range(0, len(seq)):
delSeq = seq[:i]+seq[i+1:]
if delSeq not in doneSeqs:
yield i, delSeq
doneSeqs.add(delSeq)
def flankSeqIter(seq, startDict, pamLen, doFilterNs):
""" given a seq and dictionary of pamPos -> strand and the length of the pamSite
yield tuples of (name, pamStart, guideStart, strand, flankSeq, pamSeq)
flankSeq is the guide sequence (=flanking the PAM).
if doFilterNs is set, will not return any sequences that contain an N character
pamPlusSeq are the 5bp after the PAM. If not enough space, pamPlusSeq is None
"""
startList = sorted(startDict.keys())
for pamStart in startList:
strand = startDict[pamStart]
pamPlusSeq = None
if pamIsFirst: # Cpf1: get the sequence to the right of the PAM
if strand=="+":
guideStart = pamStart+pamLen
flankSeq = seq[guideStart:guideStart+GUIDELEN]
pamSeq = seq[pamStart:pamStart+pamLen]
if pamStart-pamPlusLen >= 0:
pamPlusSeq = seq[pamStart-pamPlusLen:pamStart]
else: # strand is minus
guideStart = pamStart-GUIDELEN
flankSeq = revComp(seq[guideStart:pamStart])
pamSeq = revComp(seq[pamStart:pamStart+pamLen])
if pamStart+pamLen+pamPlusLen < len(seq):
pamPlusSeq = revComp(seq[pamStart+pamLen:pamStart+pamLen+pamPlusLen])
else: # common case: get the sequence on the left side of the PAM
if strand=="+":
guideStart = pamStart-GUIDELEN
flankSeq = seq[guideStart:pamStart]
pamSeq = seq[pamStart:pamStart+pamLen]
if pamStart+pamLen+pamPlusLen < len(seq):
pamPlusSeq = seq[pamStart+pamLen:pamStart+pamLen+pamPlusLen]
else: # strand is minus
guideStart = pamStart+pamLen
flankSeq = revComp(seq[guideStart:guideStart+GUIDELEN])
pamSeq = revComp(seq[pamStart:pamStart+pamLen])
if pamStart-pamPlusLen >= 0:
pamPlusSeq = revComp(seq[pamStart-pamPlusLen:pamStart])
if "N" in flankSeq and doFilterNs:
continue
yield "s%d%s" % (pamStart, strand), pamStart, guideStart, strand, flankSeq, pamSeq, pamPlusSeq
def makeBrowserLink(dbInfo, pos, text, title, cssClasses, ctUrl=None):
" return link to genome browser (ucsc or ensembl) at pos, with given text "
if dbInfo is None:
errAbort("Your batchID relates to a genome that is not present anymore. You will have to change the version of the site. Or contact us and send us the full URL of this page.")
if dbInfo.server.startswith("Ensembl"):
baseUrl = "www.ensembl.org"
urlLabel = "Ensembl"
# link back to archive, if possible
if dbInfo.description.startswith("Ensembl "):
ensVersion = dbInfo.description.split()[1]
if ensVersion.isdigit():
baseUrl = "e%s.ensembl.org" % ensVersion
elif dbInfo.server=="EnsemblPlants":
baseUrl = "plants.ensembl.org"
elif dbInfo.server=="EnsemblMetazoa":
baseUrl = "metazoa.ensembl.org"
elif dbInfo.server=="EnsemblProtists":
baseUrl = "protists.ensembl.org"
org = dbInfo.scientificName.replace(" ", "_")
pos = pos.replace(":+","").replace(":-","") # remove the strand
url = "http://%s/%s/Location/View?r=%s" % (baseUrl, org, pos)
elif dbInfo.server=="ucsc" or dbInfo.name.startswith("GCA_") or dbInfo.name.startswith("GCF_"):
urlLabel = "UCSC"
if len(pos)>0 and pos[0].isdigit():
pos = "chr"+pos
# remove the strand
pos = pos.replace(":+","").replace(":-","")
url = "http://genome.ucsc.edu/cgi-bin/hgTracks?db=%s&position=%s" % (dbInfo.name, pos)
if ctUrl is not None:
url+= "&hgt.customText=%s" % ctUrl
# some limited support for gbrowse
elif dbInfo.server.startswith("http://"):
urlLabel = "GBrowse"
chrom, start, end, strand = parsePos(pos)
start = start+1
url = "%s/?name=%s:%d..%d" % (dbInfo.server, chrom, start, end)
else:
chrom, start, end, strand = parsePos(pos)
if chrom is not None and chrom.startswith("NC_"):
start = start+1
url = "https://www.ncbi.nlm.nih.gov/nuccore/%s?report=graph&log$=seqview&v=%d-%d" % \
(chrom, start, end)
urlLabel = "NCBI "
else:
#return "unknown genome browser server %s, please email [email hidden]" % dbInfo.server
urlLabel = None
url = "javascript:void(0)"
classStr = ""
if len(cssClasses)!=0:
classStr = ' class="%s"' % (" ".join(cssClasses))
if title is None:
if urlLabel != None:
title = "Link to %s Genome Browser" % urlLabel
else:
title = "No Genome Browser link available yet for this organism"
return '''<a title="%s"%s target="_blank" href="%s">%s</a>''' % (title, classStr, url, text)
def highlightMismatches(guide, offTarget, pamLen):
" return a string that marks mismatches between guide and offtarget with * "
if pamLen!=0:
if pamIsFirst:
offTarget = offTarget[pamLen:]
else:
offTarget = offTarget[:-pamLen]
assert(len(guide)==len(offTarget))
s = []
for x, y in zip(guide, offTarget):
if x==y:
s.append(".")
else:
s.append("*")
return "".join(s)
def parseNewAlias(ifh):
" part of parseAlias(): IGV-compatible format: first is UCSC, all other columns are aliases "
toUcsc = {}
for line in ifh:
if line.startswith("#"):
continue
row = line.rstrip("\n").split("\t")
for i in range(1, len(row)):
toUcsc[row[i]] = row[0]
return toUcsc
def parseAlias(fname):
" parse tsv file with at least two columns, orig chrom name and new chrom name. copied from chromToUcsc script from the UCSC tools. "
logging.debug("alias file is in IGV-format")
toUcsc = {}
if fname.startswith("http://") or fname.startswith("https://"):
ifh = urlopen(fname)
if fname.endswith(".gz"):
data = gzip.GzipFile(fileobj=ifh).read().decode()
ifh = data.splitlines()
elif fname.endswith(".gz"):
ifh = gzip.open(fname, "rt")
else:
ifh = open(fname)
firstLine = True
for line in ifh:
if line.startswith("#") and firstLine:
return parseNewAlias(ifh)
if line.startswith("alias"):
continue
row = line.rstrip("\n").split("\t")
toUcsc[row[0]] = row[1]
firstLine = False
return toUcsc
chromAlias = None
def applyChromAlias(db, chrom):
" if chrom is in chromAlias, return the human-readable name "
global chromAlias
if chromAlias==-1: # == chromAlias file not present
return chrom
elif chromAlias is None:
chromAliasFname = join("genomes", db, db+".chromAlias.txt")
if not isfile(chromAliasFname):
chromAlias = -1
return chrom
else:
chromAlias = parseAlias(chromAliasFname)
return chromAlias.get(chrom, chrom)
def makeAlnStr(org, seq1, seq2, pam, mitScore, cfdScore, posStr, chromDist):
""" given two strings of equal length, return a html-formatted string of several lines
that show the two sequences and a line that highlights where they differ
"""
lines = [ [], [], [] ]
last12MmCount = 0
inLinkage = False
hlSeed = False
if pamIsSpCas9(pam):
hlSeed = True
if pamIsFirst:
lines[0].append("<i>"+seq1[:len(pam)]+"</i> ")
lines[1].append("<i>"+seq2[:len(pam)]+"</i> ")
lines[2].append("".join([" "]*(len(pam)+1)))
if pamIsFirst:
guideStart = len(pam)
guideEnd = len(seq1)
else:
guideStart = 0
guideEnd = len(seq1)-len(pam)
for i in range(guideStart, guideEnd):
if hlSeed and i==10:
lines[1].append("<u>")
if seq1[i]==seq2[i]:
lines[0].append(seq1[i])
lines[1].append(seq2[i])
lines[2].append(" ")
else:
lines[0].append("<b>%s</b>" % seq1[i])
lines[1].append("<b>%s</b>" % seq2[i])
lines[2].append("*")
if i>7:
last12MmCount += 1
if hlSeed and i==guideEnd-1:
lines[1].append("</u>")
if not pamIsFirst:
lines[0].append(" <i>"+seq1[-len(pam):]+"</i>")
lines[1].append(" <i>"+seq2[-len(pam):]+"</i>")
lines = ["".join(l) for l in lines]
chrom, chromPos, strand = posStr.split(":")
chrom = applyChromAlias(org, chrom)
posStr = ":".join((chrom, chromPos, strand))
if len(posStr)>1 and posStr[0].isdigit():
posStr = "chr"+posStr
htmlText1 = "<small><pre>guide: %s<br>off-target: %s<br> %s</pre>" \
% (lines[0], lines[1], lines[2])
if pamIsCpf1(pam) or pamIsCasX(pam):
htmlText2 = "Cpf1/CasX: No off-target scores available</small>"
elif saCas9Mode:
htmlText2 = "SaCas9 Tycko Score: %s" % mitScore
else:
if cfdScore==None:
cfdStr = "Cannot calculate CFD score on non-ACTG characters"
else:
cfdStr = "%f" % cfdScore
htmlText2 = "CFD Off-target score: %s<br>MIT Off-target score: %.2f<br>Position: %s</small>" % (cfdStr, mitScore, posStr)
if chromDist!=None and org!=None:
htmlText2 += "<br><small>Distance from target: %.3f Mbp</small>" % (float(chromDist)/1000000.0)
if org.startswith("mm") or org.startswith("hg") or org.startswith("rn"):
if chromDist > 20000000:
htmlText2 += "<br><small>>20Mbp = unlikely to be in linkage with target</small>"
else:
htmlText2 += "<br><small><20Mbp= likely to be in linkage with "
"target! Even if no linkage: beware of chromosomal rearrangements "
"when using this guide!</small>"
inLinkage = True
hasLast12Mm = last12MmCount>0
return htmlText1+htmlText2, hasLast12Mm, inLinkage
def parsePos(text):
""" parse a string of format chr:start-end:strand and return a 4-tuple
Strand defaults to + and end defaults to start+23
"""
if text!=None and len(text)!=0 and text!="?":
fields = text.split(":")
if len(fields)==2:
chrom, posRange = fields
strand = "+"
else:
chrom, posRange, strand = fields
posRange = posRange.replace(",","")
if "-" in posRange:
start, end = posRange.split("-")
start, end = int(start), int(end)
if start > end:
start, end = end, start
strand = "-"
else:
# if the end position is not specified (as by default done by UCSC outlinks), use start+23
start = int(posRange)
end = start+23
else:
chrom, start, end, strand = "", 0, 0, "+"
return chrom, start, end, strand
def annotateOfftargets(org, countDict, guideSeq, pam, inputPos):
""" for a given guide sequence, return a list of tuples that
describes the offtargets sorted by score and a string to describe the offtargets in the
format x/y/z/w of mismatch counts
inputPos has format "chrom:start-end:strand". All 0MM matches in this range
are ignored from scoring ("ontargets")
Also return the same description for just the last 12 bp and the score
of the guide sequence (calculated using all offtargets).
"""
inChrom, inStart, inEnd, inStrand = parsePos(inputPos)
count = 0
otCounts = []
posList = []
mitOtScores = []
cfdScores = []
last12MmCounts = []
ontargetDesc = ""
repCount = 0 # if repCount for a guide is !=0, then the guide should not be used. repCount is then the number
# of matches for the guide in the genome (not looking at the PAM)
# for each edit distance, get the off targets and iterate over them
foundOneOntarget = False
isSaCas9 = pamIsSaCas9(pam)
isCpf1 = pamIsCpf1(pam)
for editDist in range(0, maxMMs+1):
#print countDict,"<p>"
matches = countDict.get(editDist, [])
#print otCounts,"<p>"
last12MmOtCount = 0
# create html and score for every offtarget
otCount = 0
for chrom, start, end, otSeq, strand, segType, geneNameStr, totalAlnCount, isRep in matches:
# if repCount is > 0, then this means that the guide should not be used and we cannot
# even get any off-targets
if (totalAlnCount > MAXOCC) or (totalAlnCount > 1 and isRep):
repCount = totalAlnCount # any off-target with this condition will trigger the whole guide to be suppressed
# skip on-targets
if segType!="":
segTypeDesc = segTypeConv[segType]
geneDesc = segTypeDesc+":"+geneNameStr
geneDesc = geneDesc.replace("|", "-")
else:
geneDesc = geneNameStr
# is this not an off-target but the on-target?
# if we got a genome position, use it. Otherwise use a random off-target with 0MMs
# as the on-target ("auto-ontarget" mode)
if editDist==0 and \
repCount==0 and \
((chrom==inChrom and start >= inStart and end <= inEnd) \
or (inChrom=='' and foundOneOntarget==False)):
foundOneOntarget = True
ontargetDesc = geneDesc
continue
otCount += 1
guideNoPam = guideSeq[:len(guideSeq)-len(pam)]
otSeqNoPam = otSeq[:len(otSeq)-len(pam)]
if len(otSeqNoPam)==19:
otSeqNoPam = "A"+otSeqNoPam # should not change the score a lot, weight0 is very low
guideNoPam = "A"+guideNoPam
if isCpf1:
# Cpf1 has no off-target scores yet
mitScore=0.0
cfdScore=0.0
elif isSaCas9:
mitScore = calcSaHitScore(guideNoPam, otSeqNoPam)
cfdScore = -1
else:
# MIT score must not include the PAM
mitScore = calcHitScore(guideNoPam, otSeqNoPam)
# this is a heuristic based on the guideSeq data where alternative
# PAMs represent only ~10% of all cleaveage events.
# We divide the MIT score by 5 to make sure that these off-targets
# are not ranked among the top but still appear in the list somewhat
if pam=="NGG" and otSeq[-2:]!="GG":
mitScore = mitScore * 0.2
# CFD score must include the PAM
cfdScore = calcCfdScore(guideSeq, otSeq)
mitOtScores.append(mitScore)
if cfdScore != -1:
cfdScores.append(cfdScore)
posStr = "%s:%d-%s:%s" % (chrom, start+1,end, strand)
if (chrom==inChrom):
dist = abs(start-inStart)
else:
dist = None
parNum = isInPar(org, chrom, start, end)
if parNum is not None:
posStr += " PAR%s" % parNum
alnHtml, hasLast12Mm, inLinkage = makeAlnStr(org, guideSeq, otSeq,
pam, mitScore, cfdScore, posStr, dist)
if not hasLast12Mm:
last12MmOtCount+=1
posList.append( (otSeq, mitScore, cfdScore, editDist, posStr, geneDesc,
alnHtml, inLinkage) )
last12MmCounts.append(str(last12MmOtCount))
# create a list of number of offtargets for this edit dist
otCounts.append( str(otCount) )
# calculate the guide scores
if pamIsCpf1(pam):
guideScore = -1
guideCfdScore = -1
else:
if repCount>0:
guideScore = 0
guideCfdScore = 0
else:
guideScore = calcMitGuideScore(sum(mitOtScores))
if doCfdFix:
guideCfdScore = calcCfdGuideScore(sum(cfdScores))
else:
guideCfdScore = calcMitGuideScore(sum(cfdScores))
# obtain the off-target info: coordinates, descriptions and off-target counts
if repCount>0:
posList = []
ontargetDesc = ""
last12DescStr = ""
otDescStr = ""
else:
otDescStr = " - ".join(otCounts)
last12DescStr = " - ".join(last12MmCounts)
if pamIsCpf1(pam):
# sort by edit dist if using Cfp1
posList.sort(key=operator.itemgetter(3))
else:
# sort by CFD score if we have it
posList.sort(reverse=True, key=operator.itemgetter(2))
return posList, otDescStr, guideScore, guideCfdScore, last12DescStr, \
ontargetDesc, repCount
# --- START OF SCORING ROUTINES
saGuide = None
saScorer = None
def calcSaHitScore(guideSeq, otSeq):
"""
saCas9 offtarget scoring from Tycko et al, https://www.nature.com/articles/s41467-018-05391-2
see bin/src/pairwise-library-screen/
"""
global saScorer
global saGuide
if guideSeq!=saGuide:
sys.path.append("bin/src/pairwise-library-screen")
import predictSingle
saGuide = guideSeq
saScorer = predictSingle.SaCas9Scorer(len(guideSeq))
# to be compatible with the MIT score, has to be in the range 0-100
# for the MIT aggregate guide specificity score
return 100.0*saScorer.calcScore(guideSeq, otSeq)
# MIT offtarget scoring, "Hsu score"
# aka Matrix "M"
hitScoreM = [0,0,0.014,0,0,0.395,0.317,0,0.389,0.079,0.445,0.508,0.613,0.851,0.732,0.828,0.615,0.804,0.685,0.583]
def calcHitScore(string1,string2):
" see 'Scores of single hits' on http://crispr.mit.edu/about "
# The Patrick Hsu weighting scheme
# S. aureus requires 21bp long guides. We fudge by using only last 20bp
matrixStart = 0
maxDist = 19
assert(string1[0].isupper())
assert(len(string1)==len(string2))
#for nmCas9 and a few others with longer guides, we limit ourselves to 20bp
if len(string1)>20:
string1 = string1[-20:]
string2 = string2[-20:]
# for 19bp guides, we fudge a little, but first pos has no weight anyways
elif len(string1)==19:
string1 = "A"+string1
string2 = "A"+string2
# for shorter guides, I'm not sure if this score makes sense anymore, we force things
elif len(string1)<19:
matrixStart = 20-len(string1)
maxDist = len(string1)-1
assert(len(string1)==len(string2))
dists = [] # distances between mismatches, for part 2
mmCount = 0 # number of mismatches, for part 3
lastMmPos = None # position of last mismatch, used to calculate distance
score1 = 1.0
for pos in range(matrixStart, len(string1)):
if string1[pos]!=string2[pos]:
mmCount+=1
if lastMmPos!=None:
dists.append(pos-lastMmPos)
score1 *= 1-hitScoreM[pos]
lastMmPos = pos
# 2nd part of the score
if mmCount<2: # special case, not shown in the paper
score2 = 1.0
else:
avgDist = sum(dists)/len(dists)
score2 = 1.0 / (((maxDist-avgDist)/float(maxDist)) * 4 + 1)
# 3rd part of the score
if mmCount==0: # special case, not shown in the paper
score3 = 1.0
else:
score3 = 1.0 / (mmCount**2)
score = score1 * score2 * score3 * 100
return score
def calcMitGuideScore(hitSum):
""" Sguide defined on http://crispr.mit.edu/about
Input is the sum of all off-target hit scores. Returns the specificity of the guide.
"""
score = 100 / (100+hitSum)
score = int(round(score*100))
return score
def calcCfdGuideScore(hitSum):
" suggested by Nicholas Parkinson "
norm_score = 100.* 100.0 / (hitSum)
return norm_score
# === SOURCE CODE cfd-score-calculator.py provided by John Doench =====
# The CFD score is an improved specificity score
def get_mm_pam_scores():
"""
"""
import pickle
dataDir = join(dirname(__file__), 'CFD_Scoring')
mm_scores = pickle.load(open(join(dataDir, 'mismatch_score.pkl'),'rb'))
pam_scores = pickle.load(open(join(dataDir, 'pam_scores.pkl'),'rb'))
return (mm_scores,pam_scores)
#Reverse complements a given string
def revcom(s):
basecomp = {'A': 'T', 'C': 'G', 'G': 'C', 'T': 'A','U':'A'}
letters = list(s[::-1])
letters = [basecomp[base] for base in letters]
return ''.join(letters)
#Calculates CFD score
def calc_cfd(wt,sg,pam):
#mm_scores,pam_scores = get_mm_pam_scores()
score = 1
sg = sg.replace('T','U')
wt = wt.replace('T','U')
s_list = list(sg)
wt_list = list(wt)
for i,sl in enumerate(s_list):
if wt_list[i] == sl:
score*=1
else:
key = 'r'+wt_list[i]+':d'+revcom(sl)+','+str(i+1)
score*= mm_scores[key]
score*=pam_scores[pam]
return (score)
mm_scores, pam_scores = None, None
def calcCfdScore(guideSeq, otSeq):
""" based on source code provided by John Doench
>>> calcCfdScore("GGGGGGGGGGGGGGGGGGGGGGG", "GGGGGGGGGGGGGGGGGAAAGGG")
0.4635989007074176
>>> calcCfdScore("GGGGGGGGGGGGGGGGGGGGGGG", "GGGGGGGGGGGGGGGGGGGGGGG")
1.0
>>> calcCfdScore("GGGGGGGGGGGGGGGGGGGGGGG", "aaaaGaGaGGGGGGGGGGGGGGG")
0.5140384614450001
# mismatches: * !!
>>> calcCfdScore("ATGGTCGGACTCCCTGCCAGAGG", "ATGGTGGGACTCCCTGCCAGAGG")
0.5
# mismatches: * ** *
>>> calcCfdScore("ATGGTCGGACTCCCTGCCAGAGG", "ATGATCCAAATCCCTGCCAGAGG")
0.53625000020625
>>> calcCfdScore("ATGTGGAGATTGCCACCTACCGG", "ATCTGGAGATTGCCACCTACAGG")
0.384615385
"""
global mm_scores, pam_scores
if mm_scores is None:
mm_scores,pam_scores = get_mm_pam_scores()
wt = guideSeq.upper()
off = otSeq.upper()
m_wt = re.search('[^ATCG]',wt)
m_off = re.search('[^ATCG]',off)
if (m_wt is None) and (m_off is None):
pam = off[-2:]
sg = off[:20]
cfd_score = calc_cfd(wt,sg,pam)
if doCfdFix:
cfd_score = cfd_score*100.0
return cfd_score
return -1
# ==== END CFD score source provided by John Doench
# --- END OF SCORING ROUTINES
def getSizeFname(genome):
" return name of chrom.sizes file "
genomeDir = genomesDir # make local
sizeFname = "%(genomeDir)s/%(genome)s/%(genome)s.sizes" % locals()
return sizeFname
def parseChromSizes(genome):
" return chrom sizes as dict chrom -> size "
sizeFname = getSizeFname(genome)
ret = {}
for line in open(sizeFname).read().splitlines():
fields = line.split()
chrom, size = fields[:2]
ret[chrom] = int(size)
return ret
def extendAndGetSeq(db, chrom, start, end, strand, oldSeq, flank=FLANKLEN):
""" extend (start, end) by flank and get sequence for it using twoBitTwoFa.
Return None if not possible to extend.
#>>> extendAndGetSeq("hg19", "chr21", 10000000, 10000005, "+", flank=3)
#'AAGGAATGTAG'
"""
assert("|" not in chrom) # we are using | to split info in BED files. | is not allowed in the fasta
chromSizes = parseChromSizes(db)
maxEnd = chromSizes[chrom]+1
start -= flank
end += flank
if start < 0 or end > maxEnd:
return None
genomeDir = genomesDir
twoBitFname = "%(genomeDir)s/%(db)s/%(db)s.2bit" % locals()
progDir = binDir
genome = db
cmd = "%(progDir)s/twoBitToFa %(genomeDir)s/%(genome)s/%(genome)s.2bit stdout -seq='%(chrom)s' -start=%(start)s -end=%(end)s" % locals()
proc = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, encoding="utf8")
seqStr = proc.stdout.read()
proc.wait()
if proc.returncode!=0:
errAbort("Could not run '%s'. Return code %s" % (cmd, str(proc.returncode)))
faFile = StringIO(seqStr)
seqs = parseFasta(faFile)
assert(len(seqs)==1)
seq = list(seqs.values())[0].upper()
if strand=="-":
seq = revComp(seq)
genomeSeq = seq[FLANKLEN:(FLANKLEN+len(oldSeq))].upper()
if oldSeq.upper() not in genomeSeq:
logging.warn("Input sequence has SNPs compared to genome, not returning extended seq:")
logging.warn("- Input sequence: %s" % oldSeq)
logging.warn("- Genome sequence: %s" % genomeSeq)
logging.warn("- Diff String : %s" % highlightMismatches(oldSeq, genomeSeq, 0))
return None
# ? make sure that user annotations, like added Ns, are retained in the long sequence
#fixedSeq = seq[:100]+oldSeq+seq[-100:]
#assert(len(fixedSeq)==len(seq))
return seq
def getExtSeq(seq, start, end, strand, extUpstream, extDownstream, extSeq=None, extFlank=FLANKLEN):
""" extend (start,end) by extUpstream and extDownstream and return the subsequence
at this position in seq.
Return None if there is not enough space to extend (start, end).
extSeq is a sequence with extFlank additional flanking bases on each side. It can be provided
optionally and is used if needed to return a subseq.
Careful: returned sequence might contain lowercase letters.
>>> getExtSeq("AACCTTGG", 2, 4, "+", 2, 4)
'AACCTTGG'
>>> getExtSeq("CCAACCTTGGCC", 4, 6, "-", 2, 3)
'AAGGTTG'
>>> getExtSeq("AA", 0, 2, "+", 2, 3)
>>> getExtSeq("AA", 0, 2, "+", 2, 3, extSeq="CAGAATGA", extFlank=3)
'AGAATGA'
>>> getExtSeq("AA", 0, 2, "-", 2, 3, extSeq="CAGAATGA", extFlank=3)
'CATTCTG'
"""
assert(start>=0)
assert(end<=len(seq))
# extend
if strand=="+":
extStart, extEnd = start-extUpstream, end+extDownstream
else:
extStart, extEnd = start-extDownstream, end+extUpstream
# check for out of bounds and get seq
if extStart >= 0 and extEnd <= len(seq):
logging.debug("using input seq, pos %d-%d" % (extStart, extEnd))
subSeq = seq[extStart:extEnd]
else:
if extSeq==None:
return None
# lift to extSeq coords and get seq
extStart += extFlank
extEnd += extFlank
assert(extStart >= 0)
assert(extEnd <= len(extSeq))
subSeq = extSeq[extStart:extEnd]
logging.debug("using extended seq, pos %d-%d" % (extStart, extEnd))
if strand=="-":
logging.debug("revcomp'ing result")
subSeq = revComp(subSeq)
# check that the extended sequence really contains the whole input seq
# e.g. when user has added nucleotides to a otherwise matching sequence
#if seq.upper() not in subSeq.upper():
#debug("seq is not in extSeq")
#subSeq = None
logging.debug("Got -%d/+%d-extended seq for (%d, %d, %s) = %s. Result: %s." %
(extUpstream, extDownstream, start, end, strand, seq[start:end], subSeq))
return subSeq
def pamStartToGuideRange(startPos, strand, pamLen):
""" given a PAM start position and its strand, return the (start,end) of the guide.
Coords can be negative or exceed the length of the input sequence.
"""
if not pamIsFirst:
if strand=="+":
return (startPos-GUIDELEN, startPos)
else: # strand is minus
return (startPos+pamLen, startPos+pamLen+GUIDELEN)
else:
if strand=="+":
return (startPos+pamLen, startPos+pamLen+GUIDELEN)
else: # strand is minus
return (startPos-GUIDELEN, startPos)
def htmlHelp(text):
" show help text with tooltip or modal dialog "
className = "tooltipster"
if "href" in text:
className = "tooltipsterInteract"
print('''<img style="padding-bottom: 3px; height:1.1em; width:1.0em" src="%simage/info-small.png" class="help %s" title="%s" />''' % (HTMLPREFIX, className, text))
def htmlWarn(text):
" show help text with tooltip "
print('''<img style="height:0.9em; width:0.8em; padding-bottom: 2px" src="%simage/warning-32.png" class="help tooltipster" title="%s" />''' % (HTMLPREFIX, text))
def readRestrEnzymes():
""" parse restrSites.txt and
return as dict length -> list of (name, suppliers, seq) """
fname = "restrSites.txt"
enzList = {}
for line in open(join(baseDir, fname)):
if line.startswith("#"):
continue
seq, name, suppliers = line.rstrip("\n").rstrip("\r").split("\t")
suppliers = tuple(suppliers.split(","))
enzList.setdefault(len(seq), []).append( (name, suppliers, seq) )
return enzList
def patMatch(seq, pat, notDegPos=None):
""" return true if pat matches seq, both have to be same length
do not match degenerate codes at position notDegPos (0-based)
"""
assert(len(seq)==len(pat))
for x in range(0, len(pat)):
patChar = pat[x]
nuc = seq[x]
assert(patChar in "MKYRACTGNWSDVBH")
assert(nuc in "MKYRACTGNWSDX")
if notDegPos!=None and x==notDegPos and patChar!=nuc:
return False
if nuc=="X":
return False
if patChar=="N":
continue
if patChar=="H" and nuc in "ACT":
continue
if patChar=="D" and nuc in "AGT":
continue
if patChar=="B" and nuc in "CGT":
continue
if patChar=="V" and nuc in "ACG":
continue
if patChar=="W" and nuc in "AT":
continue
if patChar=="S" and nuc in "GC":
continue
if patChar=="M" and nuc in "AC":
continue
if patChar=="K" and nuc in "TG":
continue
if patChar=="R" and nuc in "AG":
continue
if patChar=="Y" and nuc in "CT":
continue
if patChar!=nuc:
return False
return True
def findSite(seq, restrSite):
""" return the positions where restrSite matches seq
seq can be longer than restrSite
Do not allow degenerate characters to match at position len(restrSite) in seq
"""
posList = []
for i in range(0, len(seq)-len(restrSite)+1):
subseq = seq[i:i+len(restrSite)]
# JP does not want any potential site to be suppressed
#if i<len(restrSite):
#isMatch = patMatch(subseq, restrSite, len(restrSite)-i-1)
#else:
#isMatch = patMatch(subseq, restrSite)
isMatch = patMatch(subseq, restrSite)
if isMatch:
posList.append( (i, i+len(restrSite)) )
return posList
def matchRestrEnz(allEnzymes, guideSeq, pamSeq, pamPlusSeq, pamPat):
""" return list of enzymes that overlap the -3 position in guideSeq
returns dict (name, pattern, suppliers) -> list of matching positions
"""
matches = defaultdict(set)
if pamPlusSeq is None:
pamPlusSeq = "XXXXX" # make sure that we never match a restriction site outside the seq boundaries
fullSeq = concatGuideAndPam(guideSeq, pamSeq, pamPlusSeq)
#print guideSeq, pamSeq, pamPlusSeq, fullSeq, "<br>"
for siteLen, sites in allEnzymes.items():
if pamIsCpf1(pamPat):
# most modified position: 4nt from the end
# see http://www.nature.com/nbt/journal/v34/n8/full/nbt.3620.html
# Figure 1
startSeq = len(fullSeq)-4-pamPlusLen-(siteLen)+1
else:
# most modified position for Cas9: 3bp from the end
startSeq = len(fullSeq)-len(pamSeq)-3-pamPlusLen-(siteLen)+1
seq = fullSeq[startSeq:].upper()
for name, suppliers, restrSite in sites:
posList = findSite(seq, restrSite)
if len(posList)!=0:
liftOffset = startSeq
posList = [(liftOffset+x, liftOffset+y) for x,y in posList]
matches.setdefault((name, restrSite, suppliers), set()).update(posList)
return matches
def mergeGuideInfo(seq, startDict, pamPat, otMatches, inputPos, effScores, sortBy=None, org=None):
"""
merges guide information from the sequence, the efficiency scores and the off-targets.
creates rows with too many fields. needs refactoring.
for each pam in startDict, retrieve the guide sequence next to it and score it
sortBy can be "effScore", "mhScore", "oofScore" or "pos"
"""
allEnzymes = readRestrEnzymes()
guideData = []
guideScores = {}
hasNotFound = False
pamIdToSeq = {}
pamSeqs = list(flankSeqIter(seq.upper(), startDict, len(pamPat), True))
for pamId, pamStart, guideStart, strand, guideSeq, pamSeq, pamPlusSeq in pamSeqs:
# matches in genome
# one desc in last column per OT seq
if pamId in otMatches:
pamMatches = otMatches[pamId]
guideSeqFull = concatGuideAndPam(guideSeq, pamSeq)
mutEnzymes = matchRestrEnz(allEnzymes, guideSeq, pamSeq, pamPlusSeq, pamPat)
posList, otDesc, guideScore, guideCfdScore, last12Desc, ontargetDesc, \
repCount = \
annotateOfftargets(org, pamMatches, guideSeqFull, pamPat, inputPos)
if repCount!=0:
guideScore = 0
guideCfdScore = 0
# no off-targets found?
else:
posList, otDesc, guideScore = None, "Not found", -1
guideCfdScore = -1
last12Desc = ""
hasNotFound = True
mutEnzymes = []
ontargetDesc = ""
repCount = 0
seq34Mer = None
guideRow = [guideScore, guideCfdScore, effScores.get(pamId, {}), pamStart, guideStart, strand, pamId, guideSeq, pamSeq, posList, otDesc, last12Desc, mutEnzymes, ontargetDesc, repCount]
guideData.append( guideRow )
guideScores[pamId] = guideScore
pamIdToSeq[pamId] = guideSeq
if sortBy == "pos":
sortFunc = (lambda row: row[3])
reverse = False
elif sortBy == "offCount":
sortFunc = (lambda row: len(row[9]))
reverse = False
elif sortBy == "cfdSpec":
sortFunc = operator.itemgetter(1)
reverse = True
elif sortBy == "spec" or sortBy is None:
sortFunc = (lambda row: row[0])
reverse = True
elif sortBy is not None and not sortBy.endswith("pec"):
sortFunc = (lambda row: row[2].get(sortBy, 0))
reverse = True
else:
errAbort("Unknown sortBy value. This is a bug. Please contact us.")
guideData.sort(reverse=reverse, key=sortFunc)
return guideData, guideScores, hasNotFound, pamIdToSeq
def printDownloadTableLinks(batchId, addTsv=False):
print('<div id="downloads" style="text-align:left">')
print("Download as Excel tables: ", end=' ')
print('<a href="crispor.py?batchId=%s&download=guides&format=xls">Guides</a> / ' % batchId, end=' ')
if not pamIsFirst and not saCas9Mode:
print('<a href="crispor.py?batchId=%s&showAllScores=1&download=guides&format=xls">Guides, all scores</a> / ' % batchId, end=' ')
print('<a href="crispor.py?batchId=%s&download=offtargets&format=xls">Off-targets</a> / ' % batchId, end=' ')
print(('<a href="crispor.py?batchId=%s&satMut=1">Saturating mutagenesis assistant</a><br>' % batchId))
#print "<small>Plasmid Editor: ",
#print '<a href="crispor.py?batchId=%s&download=genbank">Guides</a></small>' % batchId,
if addTsv:
print("<small>Tab-sep format: ", end=' ')
print('<a href="crispor.py?batchId=%s&download=guides&format=tsv">Guides</a> / ' % batchId, end=' ')
print('<a href="crispor.py?batchId=%s&download=offtargets&format=tsv">Off-targets</a></small>' % batchId, end=' ')
print('</div>')
def hasGeneModels(org):
" return true if this organism has gene model information "
geneFname = join(genomesDir, org, org+".segments.bed")
return isfile(geneFname)
def printTableHead(pam, batchId, chrom, org, varHtmls, showColumns):
" print guide score table description and columns "
# one row per guide sequence
if not pamIsCpf1(pam):
print('''<div class='substep'>Ranked by default from highest to lowest specificity score (<a target='_blank' href='http://dx.doi.org/10.1038/nbt.2647'>Hsu et al., Nat Biot 2013</a>). Click on a column title to rank by a score.<br>''')
#print("""<b>Our recommendation:</b> Use Fusi for in-vivo (U6) transcribed guides, Moreno-Mateos for in-vitro (T7) guides injected into Zebrafish/Mouse oocytes.<br>""")
print('''If you use this website, please cite our <a href="https://academic.oup.com/nar/article/46/W1/W242/4995687">paper in NAR 2018</a>.''')
print("Too much information? Look at the <a target=_blank href='manual/'>CRISPOR manual</a>.<p>")
print('</div>')
printDownloadTableLinks(batchId)
print("""
<script type="text/javascript">
function allRows() {
$("guideRow").show();
}
//function copySeq() {
//var c = new ClipboardJS('#seqAsText');
//var copyText = document.getElementById("seqAsText");
//var selRes = copyText.select();
//var val = copyText.value;
//var res = document.execCommand("copy");
//alert("The input sequence is now in your clipboard. You can paste it into other programs.");
//}
$(document).ready( function() {
//#$('#copyLink').click( copySeq );
var clipboard = new ClipboardJS('#copyLink');
clipboard.on('success', function(e) {
alert("The input sequence is now in your clipboard. You can paste it into other programs.");
console.info('Action:', e.action);
console.info('Text:', e.text);
console.info('Trigger:', e.trigger);
e.clearSelection();
});
$('d').mouseenter( onEditHover );
$('d').mouseleave ( onEditOut );
});
function onEditOut() {
$('#editHover').hide();
}
function colorChar(str, pos) {
/* put a span-color tag around the char at pos in str and return result */
var prefix = str.substring(0, pos);
var hlChar = str[pos];
var suffix = str.substring(pos+1);
return prefix+"<mut>"+hlChar+"</mut>"+suffix;
}
function onEditHover(ev) {
/* user hovers over an edit letter */
ev.preventDefault();
var oldEl = document.getElementById("editHover");
if (oldEl)
oldEl.remove();
console.log(ev.target);
const boundBox = ev.target.getBoundingClientRect();
var x = boundBox.left;
var y = boundBox.top;
y += 14;
var div = document.createElement('div');
div.id = "editHover";
div.style.width="400px";
div.style.height="200px";
div.style.border="1px solid black";
div.style.padding="10px";
div.style.position="fixed";
div.style.backgroundColor="white";
div.style.left=x+"px";
div.style.top=y+"px";
var pos = parseInt(this.getAttribute("pos"));
var nucl = this.textContent;
if (nucl.toUpperCase()==="T")
origNucl = "C";
else
origNucl = "G";
var htmls=[];
htmls.push("The following guides can mutate "+origNucl+" to "+nucl+" at position "+pos+":<br>");
htmls.push("<table class='editTable'>");
htmls.push("<tr><th>Guide ID</th><th>Guide Sequence</th><th>Komor score</th><th>Spec. Score</th></tr>");
var guides = editData[pos][nucl];
guides.sort( function (a, b) { a[4] - b[4] } ); // sort by komor score
for (var i=0; i<guides.length; i++) {
guide = guides[i];
pamId = guide[0];
guideSeq = guide[1];
pam = guide[2];
mutPos = guide[3];
beScore = guide[4];
specScore = guide[5];
htmls.push("<tr>");
htmls.push("<td>"+pamId+"</td>");
htmls.push("<td><tt>"+colorChar(guideSeq, mutPos)+" "+pam+"</tt></td>");
htmls.push("<td>"+beScore.toFixed(2)+"</td>");
htmls.push("<td>"+specScore+"</td>");
htmls.push("</tr>");
}
htmls.push("</table>");
$(div).append(htmls.join(""));
document.body.appendChild(div);
}
function onlyWith(doPrefix) {
/* show only guide rows and guide sequence viewer features that start with a prefix */
if ($("#onlyWith"+doPrefix+"Box").prop("checked"))
{
$(".prefixBox").prop("checked", false);
$("#onlyWith"+doPrefix+"Box").prop("checked", true);
//$(".guideRow").show();
$(".guideRow").css("visibility", "visible");
$(".guideRowNoPrefix"+doPrefix).hide();
// special handling for sequence viewer: hide() would destroy the layout there
$(".guideRowNoPrefix"+doPrefix+"Seq").css("visibility", "hidden");
}
else
{
$(".prefixBox").prop("checked", false);
$(".guideRow").show();
$(".guideRowNoPrefix"+doPrefix+"Seq").css("visibility", "visible");
}
}
function displayClass(className, dispVal) {
/* hide in a loop, works around Safari stack size limits that crash jquery functions */
var els = document.getElementsByClassName(className);
for (var el of els) {
el.style.display = dispVal;
}
}
function onlyExons() {
/* show only off-targets in exons */
if ($("#onlyExonBox").prop("checked")) {
$(".otMore").show();
$(".otMoreLink").hide();
$(".otLessLink").hide();
displayClass("notExon", "none");
}
else {
if ($("#onlySameChromBox").prop("checked")) {
$(".notExon:not(.diffChrom)").show();
}
else {
displayClass("notExon", "block");
$(".otMoreLink").show();
$(".otMore").hide();
}
}
}
function onlySameChrom() {
if ($("#onlySameChromBox").prop("checked"))
{
$(".otMore").show();
$(".otMoreLink").hide();
$(".otLessLink").hide();
$(".diffChrom").hide();
}
else {
if ($("#onlyExonBox").prop("checked")) {
$(".diffChrom:not(.notExon)").show();
}
else {
$(".diffChrom").show();
$(".otMoreLink").show();
$(".otMore").hide();
}
}
}
function showAllOts(classId) {
$("#"+classId).show();
$("#"+classId+"MoreLink").hide();
$("#"+classId+"LessLink").show();
}
function showLessOts(classId) {
$("#"+classId).hide();
$("#"+classId+"MoreLink").show();
$("#"+classId+"LessLink").hide();
}
</script>
""")
print('<table id="otTable" style="background:white;table-layout:fixed; overflow:scroll; width:100%">')
print('<thead>')
print('<tr style="border-bottom:none; border-left:5px solid black; background-color:#F0F0F0">')
print('<th style="width:80px; border-bottom:none"><a href="crispor.py?batchId=%s&sortBy=pos" class="tooltipster" title="Click to sort the table by the position of the PAM site">Position/<br>Strand</a>' % batchId)
htmlHelp("You can click on the links in this column to highlight the <br>PAM site in the sequence viewer at the top of the page.")
print('</th>')
print('<th style="width:235px; border-bottom:none">Guide Sequence + <i>PAM</i><br>')
print ('+ Restriction Enzymes')
htmlHelp("Restriction enzymes can be very useful for screening mutations induced by the guide RNA using PCR and Restrictrion frament length polymorphism (RFLP).<br>Enzyme sites shown here overlap the main cleavage site 3bp 5' to the PAM.<br>Digestion of the PCR product with these enzymes will not cut the product if the genome was mutated by Cas9. This is a lot easier than screening with the T7 assay, Surveyor or sequencing.")
print('<br>')
if varHtmls is not None:
print(' + Variants')
htmlHelp("Variants that overlap the guide sequence are shown. You can change the variant database with the drop-down box above the sequence viewer at the top of the page.")
print('<br>')
print('''<small>''')
print('''<input type="checkbox" class="prefixBox" id="onlyWithGBox" onchange="onlyWith('G')">Only G-''')
print('''<input type="checkbox" class="prefixBox" id="onlyWithGGBox" onchange="onlyWith('GG')">Only GG-''')
print('''<input type="checkbox" class="prefixBox" id="onlyWithABox" onchange="onlyWith('A')">Only A-''')
htmlHelp("The three checkboxes allow you to show only guides that start with GG-, G- or A-. While we recommend prefixing a 20bp guide with G for U6 expression with spCas9, some protocols recommend using only guides with a G- prefix for U6 and A- for U3.")
print('''</small>''')
if not pamIsCpf1(pam):
print('<th style="width:80px; border-bottom:none"><a href="crispor.py?batchId=%s&sortBy=spec" class="tooltipster" title="Click to sort the table by specificity score. Hover over the (i) bubble on the right to get more information about the specificity score.">MIT Specificity Score</a>' % batchId)
if pamIsSaCas9(pam):
htmlHelp("The higher the specificity score, the lower are off-target effects in the genome.<br>This specificity score has been adapted for SaCas9 and based on the off-target scores shown on mouse-over. The algorithm was provided by Josh Tycko. Like the MIT score for spCas9, it is aggregated from all off-target scores and ranges 0-100. See <a href='https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6063963/'>Tycko et al. Nat Comm 2018</a> for details.")
else:
htmlHelp("The higher the specificity score, the lower are off-target effects in the genome.<br>The specificity score ranges from 0-100 and measures the uniqueness of a guide in the genome. See <a href='http://dx.doi.org/10.1038/nbt.2647'>Hsu et al. Nat Biotech 2013</a>. We recommend values >50, where possible. See <a target=_blank href='manual/#offs'>the CRISPOR manual</a>")
print("</th>")
if "cfdGuideScore" in showColumns:
print('<th style="width:60px; border-bottom:none"><a href="crispor.py?batchId=%s&sortBy=cfdSpec" class="tooltipster" title="Click to sort the table by CFD specificity score">CFD Spec. score</a>' % batchId)
htmlHelp("The CFD specificity score, inspired like guidescan.com, behaves like the MIT specificity score, but it is based on the more accurate CFD off-target model, from <a href='http://www.nature.com/nbt/journal/v34/n2/full/nbt.3437.html'>Doench 2016</a>, which is also used by Crispor to rank the off-targets. The CFD specificity score correlates better than the MIT score with the total off-target cleavage fraction of a guide, see <a target=_blank href='https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6731277/'>Tycko et al, Nat Comm 2019</a> and also the <a target=_blank href='/manual/#faq'>CRISPOR manual</a>.")
print("</th>")
if len(scoreNames)==2 or pamIsCpf1(pam) or pamIsSaCas9(pam):
print('<th style="width:150px; height:100px; border-bottom:none" colspan="%d">Predicted Efficiency' % (len(scoreNames)))
else:
print('<th style="width:270px; border-bottom:none" colspan="%d">Predicted Efficiency' % (len(scoreNames))) # -1 because proxGc is in scoreNames but has no column
htmlHelp("The higher the efficiency score, the more likely is cleavage at this position. For details on the scores, mouseover their titles below.<br>Note that these predictions are not very accurate, they merely enrich for more efficient guides by a factor of 2-3 so you have to test a few guides to see the effect. <a target=_blank href='manual/#onEff'>Read the CRISPOR manual</a>")
if not pamIsCpf1(pam) and not pamIsSaCas9(pam):
if cgiParams.get("showAllScores", "0")=="0":
print(("""<br><a style="font-size:12px" href="%s" class="tooltipsterInteract" title="By default, only the two most relevant scores are shown, based on our study <a href='http://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1012-2'>Haeussler et al. 2016</a>. Click this link to show all efficiency scores.">Show all scores</a>""" % cgiGetSelfUrl({"showAllScores":"1"}, anchor="otTable")))
scoreDescs["crisprScan"][0] = "Mor.-Mateos"
else:
print(("""<br><a style="font-size:12px" href="%s" class="tooltipsterInteract" title="Show only the two main scores">Show main scores</a>""" % cgiGetSelfUrl({"showAllScores":None}, anchor="otTable")))
print('</th>')
mhColName="Outcome"
if not baseEditor:
if len(mutScoreNames)<=1:
mhColName = ""
oofWidth=45
#oofDesc = "Click on score to show micro-homology"
#oofDesc = ""
else:
oofWidth=67
#oofDesc = "Click score for details"
colSpan = len(mutScoreNames)
print('<th colspan=%d style="width:%dpx; border-bottom:none"><a href="crispor.py?batchId=%s&sortBy=oof" class="tooltipster" title="Prediction of the DNA sequence after strand break repair. Click to sort the table by frameshift/out-of-frame scores. Hover over the score names to show information about a particular score. Click a score number to see the predicted indel pattern around the guide.">%s</a>' % (colSpan, oofWidth, batchId, mhColName))
#htmlHelp(scoreDescs["oof"][1])
#print "<small>%s</small>" % oofDesc
print('</th>')
print('<th style="width:117px; border-bottom:none"><a href="crispor.py?batchId=%s&sortBy=offCount" class="tooltipster" title="Click to sort the table by number of off-targets">Off-targets for <br>0-1-2-3-4 mismatches<br></a><span style="color:grey">+ next to PAM </span>' % (batchId))
altPamsHelp = [pam]
if pam in offtargetPams:
altPamsHelp.extend(offtargetPams[pam])
htmlHelp("For each number of mismatches, the number of off-targets is indicated.<br>Example: 1-3-20-50-60 means 1 off-target with 0 mismatches, 3 off-targets with 1 mismatch, <br>20 off-targets with 2 mismatches, etc.<br>The CRISPOR website only searches up to four mismatches (use the command line version for 5 or 6). Off-targets are considered if they are flanked by one of these motifs: %s .<br>Shown in grey are the off-targets that have no mismatches in the 12 bp adjacent to the PAM. These are the most likely off-targets." % (", ".join(altPamsHelp)))
print("</th>")
print('<th style="width:*; border-bottom:none">Genome Browser links to matches sorted by CFD off-target score')
htmlHelp("For each off-target the number of mismatches is indicated and linked to a genome browser. <br>Matches are ranked by CFD off-target score (see Doench 2016 et al) from most to least likely.<br>Matches can be filtered to show only off-targets in exons or on the same chromosome as the input sequence.<br>On most organisms, you can click the links below to open a window with a genome browser at this position.")
print('<br><small>')
print('<input type="hidden" name="batchId" value="%s">' % batchId)
if hasGeneModels(org):
print('''<input type="checkbox" id="onlyExonBox" onchange="onlyExons()">exons only''')
else:
print('<small title="When this genome was loaded into CRISPOR, gene models were not available. Contact us if you want to filter for off-targets in exons and think that a gene models are now available for this genome." style="color:grey">No exons.</small>')
if chrom!="":
if chrom[0].isdigit():
chrom = "chrom "+chrom
print('''<input type="checkbox" id="onlySameChromBox" onchange="onlySameChrom()">%s only''' % chrom)
else:
print('<small style="color:grey"> No match, no chrom filter</small>')
print("</small>")
print("</th>")
print("</tr>")
# subheaders
print('<tr style="border-top:none; border-left: solid black 5px; background-color:#F0F0F0">')
print('<th style="border-top:none"></th>')
print('<th style="border-top:none"></th>')
if "cfdGuideScore" in showColumns:
print('<th style="border-top:none"></th>')
if not pamIsCpf1(pam):
print('<th style="border-top:none"></th>')
for scoreName in scoreNames:
if scoreName in ["oof", "proxGc"] or "oof" in scoreName:
continue
scoreLabel, scoreDesc = scoreDescs[scoreName]
print('<th style="width: 10px; border: none; border-top:none; border-right: none" class="rotate"><div><span><a title="%s" class="tooltipsterInteract" href="crispor.py?batchId=%s&sortBy=%s">%s</a></span></div></th>' % (scoreDesc, batchId, scoreName, scoreLabel))
if "proxGc" in scoreNames:
# the ProxGC score comes next
print('''<th style="border: none; border-top:none; border-right: none; border-left:none" class="rotate">''')
print('''<div><span style="border-bottom:none">''')
print('''<a title="This column shows two heuristics based on observations rather than computational models: <a href='http://www.cell.com/cell-reports/abstract/S2211-1247%2814%2900827-4'>Ren et al</a> 2014 obtained the highest cleavage in Drosophila when the final 6bp contained >= 4 GCs, based on data from 39 guides. <a href='http://www.genetics.org/content/early/2015/02/18/genetics.115.175166.abstract'>Farboud et al.</a> obtained the highest cleavage in C. elegans for the 10 guides that ended with -GG, out of the 50 guides they tested.<br>The column contains + if the final GC count is >= 4 and GG if the guide ends with GG." href="crispor.py?batchId=%s&sortBy=finalGc6" class="tooltipsterInteract">Prox GC</span></div></th>''' % (batchId))
# these are empty cells to fill up the row and avoid white space
for scoreName in mutScoreNames:
scoreLabel, scoreDesc = scoreDescs[scoreName]
print('<th style="width: 10px; border-top:none; border-right: none" class="rotate"><div><span><a title="%s" class="tooltipsterInteract" href="crispor.py?batchId=%s&sortBy=%s">%s</a></span></div></th>' % (scoreDesc, batchId, scoreName, scoreLabel))
print('<th style="border-top:none"></th>')
print('<th style="border-top:none"></th>')
print("</tr>")
print('</thead>')
def scoreToColor(guideScore):
if guideScore is None:
color = ("#000000", "black")
elif guideScore > 50:
color = ("#32cd32", "green")
elif guideScore > 20:
color = ("#ffff00", "yellow")
elif guideScore==-1:
color = ("#000000", "black")
else:
color = ("#aa0114", "red")
return color
def hexToRgb(hexCode):
" convert hex color to RGB in UCSC format, https://stackoverflow.com/questions/29643352/converting-hex-to-rgb-value-in-python "
hexCode = hexCode.lstrip("#")
return ",".join(tuple(str(int(hexCode[i:i+2], 16)) for i in (0, 2 ,4)))
def makeOtBrowserLinks(otData, chrom, dbInfo, pamId):
" return a list with the html texts of the offtarget links "
links = []
i = 0
for otSeq, score, cfdScore, editDist, pos, gene, alnHtml, inLinkage in otData:
cssClasses = ["tooltipster"]
if not gene.startswith("exon:"):
cssClasses.append("notExon")
if pos.split(":")[0]!=chrom:
cssClasses.append("diffChrom")
if inLinkage:
cssClasses.append("inLinkage")
classStr = ""
if len(cssClasses)!=0:
classStr = ' class="%s"' % " ".join(cssClasses)
link = makeBrowserLink(dbInfo, pos, gene, alnHtml, ["tooltipster"])
editDist = str(editDist)
links.append( '''<div%(classStr)s>%(editDist)s:%(link)s</div>''' % locals() )
return links
def filterOts(otDatas, minScore):
" remove all offtargets with score < minScore "
newList = []
for otData in otDatas:
score = otData[1]
if score > minScore:
newList.append(otData)
return newList
def findOtCutoff(otData):
" try cutoffs 0.5, 1.0, 2.0, 3.0 until not more than 20 offtargets left "
for cutoff in [0.3, 0.5, 1.0, 2.0, 3.0, 10.0, 99.9]:
otData = filterOts(otData, cutoff)
if len(otData)<=30:
return otData, cutoff
if len(otData)>30:
return otData[:30], None
return otData, 1000
def printNote(s):
print('<div style="text-align:left; background-color: aliceblue; padding:5px; border: 1px solid black"><strong>Note:</strong>')
print(s)
print("</div>")
def printWarning(s):
print('<div style="text-align:left; background-color: #FFDDDD; padding:5px; border: 1px solid black"><strong>Warning:</strong>')
print(s)
print('</div>')
def printNoEffScoreFoundWarn(effScoresCount, pam):
if effScoresCount==0 and not pamIsCpf1(pam):
note = "No guide could be scored for efficiency. This happens when the input sequence is shorter than 100bp and there is no genome available to extend it or if there is simply not guide socring method. In the first case, please add flanking 50bp on both sides of the input sequence and submit this new, longer sequence. For the second case, you can contact me and suggest an efficiency scoring method, send me the published paper in this case."
printNote(note)
def showGuideTable(guideData, pam, otMatches, dbInfo, batchId, org, chrom, varHtmls):
" shows table of all PAM motif matches "
print("<br><div class='title'>Predicted guide sequences for PAMs</div>")
global scoreNames
if (cgiParams.get("showAllScores", "0")=="1"):
scoreNames = allScoreNames
showColumns = set()
# show the CFD guide score?
if pamIsSpCas9(pam):
showColumns.add("cfdGuideScore")
showPamWarning(pam)
showNoGenomeWarning(dbInfo)
printTableHead(pam, batchId, chrom, org, varHtmls, showColumns)
count = 0
effScoresCount = 0
showProxGcCol = ("proxGc" in scoreNames)
for guideRow in guideData:
guideScore, guideCfdScore, effScores, pamStart, guideStart, strand, pamId, guideSeq, \
pamSeq, otData, otDesc, last12Desc, mutEnzymes, ontargetDesc, repCount = guideRow
color = scoreToColor(guideScore)[0]
classStr = cssClassesFromSeq(guideSeq)
print('<tr id="%s" class="%s" style="border-left: 5px solid %s">' % (pamId, classStr, color))
# position and strand
#print '<td id="%s">' % pamId
print('<td>')
print('<a href="#list%s">' % (pamId))
print(str(pamStart+1)+" /")
if strand=="+":
print('fw')
else:
print('rev')
print('</a>')
print("</td>")
# sequence with variants and PCR primer link
print("<td>")
print("<small>")
# guide sequence + PAM sequence
if pamIsFirst:
fullGuideHtml = "<tt><i>"+pamSeq+"</i> " + guideSeq+"</tt>"
spacePos = len(pamSeq)
else:
fullGuideHtml = "<tt>"+guideSeq + " <i>" + pamSeq+"</i></tt>"
spacePos = len(guideSeq)
print(fullGuideHtml)
print("<br>")
# variant-string
if varHtmls is not None:
varFound = False
varStrs = []
guideHtmlStart = min(guideStart, pamStart)
guideHtmls = varHtmls[guideHtmlStart:guideHtmlStart+len(guideSeq)+len(pamSeq)]
if strand=="-":
guideHtmls = list(reversed(guideHtmls))
for i in range(len(guideHtmls)):
html = guideHtmls[i]
if html!=".":
varFound = True
if i==spacePos:
varStrs.append(" ")
varStrs.append(html)
print(("<tt style='color:#888888'>%s</tt><br>" % ("".join(varStrs))))
if "TTTT" in guideSeq.upper():
text = "This guide contains the sequence TTTT. It cannot be transcribed with a U6 or U3 promoter, as TTTT terminates the transcription."
htmlWarn(text)
print(' Not with U6/U3')
print("<br>")
if pam=="NGG":
grafType = crisporEffScores.getGrafType(guideSeq)
if grafType:
if grafType=="tt":
grafText = "The guide ends with TTC or TTT or contains only T and C in the last four nucleotides and more than 2 Ts or at least one TT and one T or C ('TT-motif'). These guides should be avoided in polymerase III (Pol III)-based gene editing experiments requiring high sgRNA expression levels."
elif grafType=="gcc":
grafText = "The guide ends with [AGT]GCC or GCCT ('GCC motif'). These sgRNAs appear to be inefficient irrespective of the delivery method and should thus be generally avoided."
text = "This guide contains one of the motifs described by <a target=_blank href='https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6352712/'>Graf et al, Cell Reports 2019</a>. %s " % grafText
htmlWarn(text)
print(' Inefficient')
print("<br>")
if gcContent(guideSeq)>0.75:
text = "This sequence has a GC content higher than 75%.<br>In the data of Tsai et al Nat Biotech 2015, the two guide sequences with a high GC content had almost as many off-targets as all other sequences combined. We do not recommend using guide sequences with such a high GC content."
htmlWarn(text)
print(' High GC content')
print("<br>")
if gcContent(guideSeq)<0.25:
text = "This sequence has a GC content lower than 25%.<br>In the data of Wang/Sabatini/Lander Science 2014, guides with a very low GC content had low cleavage efficiency."
htmlWarn(text)
print(' Low GC content<br>')
print("<br>")
if len(mutEnzymes)!=0:
print("<div style='margin-top: 3px'>Enzymes: <i>", end=' ')
print(", ".join([x.split("/")[0] for x,y,z in list(mutEnzymes.keys())]))
print("</i></div>")
scriptName = basename(__file__)
if otData!=None and repCount == 0:
print((' <a href="%s?batchId=%s&pamId=%s&pam=%s" target="_blank"><strong>Cloning / PCR primers</strong></a>' % (scriptName, batchId, urllib.parse.quote(str(pamId)), pam) ))
print("</small>")
print("</td>")
# off-target score, aka specificity score aka MIT score
if not pamIsCpf1(pam):
print("<td>")
if guideScore==None:
print("No matches")
else:
print("%d" % guideScore)
print("</td>")
# guide score based on CFD scores, aka guidescan score
if "cfdGuideScore" in showColumns:
print("<td>")
if guideCfdScore==None:
print("No matches")
else:
print("%d" % guideCfdScore)
print("</td>")
# eff scores
if effScores==None:
print('<td colspan="%d">Too close to end</td>' % len(scoreNames))
htmlHelp("The efficiency scores require some flanking sequence<br>This guide does not have enough flanking sequence in your input sequence and could not be extended as it was not found in the genome.<br>")
else:
for scoreName in scoreNames:
# out-of-frame and prox. gc need special treatment
if scoreName in ["oof", "proxGc"]:
continue
score = effScores.get(scoreName, None)
if score!=None:
effScoresCount += 1
if score==None:
print('''<td>--</td>''')
elif scoreName=="ssc":
# save some space
numStr = '%.1f' % (float(score))
print('''<td style="font-size:small">%s</td>''' % numStr)
elif scoreDigits.get(scoreName, 0)==0:
print('''<td>%d</td>''' % int(score))
else:
print('''<td>%0.1f</td>''' % (float(score)))
#print "" % seq30Mer
if showProxGcCol:
print("<td>")
# close GC > 4
finalGc = int(effScores.get("finalGc6", -1))
if finalGc==1:
print("+")
elif finalGc==0:
print("-")
else:
print("--")
# main motif is "NGG" and last nucleotides are GGNGG
if int(effScores.get("finalGg", 0))==1:
print("<br>")
print("<small>-GG</small>")
print("</td>")
if not baseEditor:
for mutScoreName in mutScoreNames:
print("<td>")
oofScore = str(effScores.get(mutScoreName, None))
if mutScoreName=="oof":
scoreDesc = "out-of-frame deletions"
else:
scoreDesc = "frameshift mutations"
if oofScore==None or oofScore=="None":
print("--")
else:
print("""<a href="%s?batchId=%s&pamId=%s&showMh=%s" target=_blank class="tooltipster" title="This score indicates how likely %s are. Click to show the induced deletions based on the micro-homology around the cleavage site.">%s</a>""" % (myName, batchId, urllib.parse.quote(pamId), mutScoreName, scoreDesc, oofScore))
#print """<br><br><small><a href="%s?batchId=%s&pamId=%s&showMh=1" target=_blank class="tooltipster">Micro-homology</a></small>""" % (myName, batchId, pamId)
print("</td>")
# mismatch description
print("<td>")
#otCount = sum([int(x) for x in otDesc.split("/")])
if otData==None:
# no genome match
print(otDesc)
htmlHelp("This exact sequence was not found in the genome.<br>If you have pasted a cDNA multi-exon sequence, note that sequences that overlap a splice site cannot be used as guide sequences. If you only have a cDNA sequence, please BLAST or BLAT your sequence first against the genome, then use the resulting exon from the genome for CRISPOR.<br>This warning also appears if you have selected the wrong or no genome.")
elif repCount > 0:
print ("Repeat")
htmlHelp("At <= 4 mismatches, %d alignments were found in the genome for this sequence, without looking at the PAM sequence around these alignments.<br>This guide is a repeated region, it is too unspecific.<br>Usually, CRISPR cannot be used to target repeats. Also, note that sequences that include long repeats will make the CRISPOR website slow. You can mask repeats with Ns to speed up the search." % repCount)
else:
print(otDesc)
print("<br>")
# mismatch description, last 12 bp
print('<small style="color:grey">'+last12Desc+"</small><br>")
otCount = len(otData)
print("<br><small>%d off-targets</small>" % otCount)
print("</td>")
# links to offtargets
print("<td><small>")
if otData!=None:
if len(otData)>500 and len(guideData)>1:
otData, cutoff = findOtCutoff(otData)
if cutoff==None:
print("More than 1000 off-targets, showing only top "+str(len(otData)))
else:
print("More than 500 off-targets, showing %d with score >%0.1f " % (len(otData), cutoff))
htmlHelp("This guide sequence has a high number of off-targets, its use is discouraged.<br>To show all off-targets, paste only the guide sequence into the input sequence box.")
otLinks = makeOtBrowserLinks(otData, chrom, dbInfo, pamId)
print("\n".join(otLinks[:3]))
if len(otLinks)>3:
cssPamId = pamId.replace("-","minus").replace("+","plus") # +/-: not valid in css
cssPamId = cssPamId+"More"
print('<div id="%s" class="otMore" style="display:none; width:100%%">' % cssPamId)
print("\n".join(otLinks[3:]))
print('''<a style="float:right;text-decoration:underline" href="%s?batchId=%s&pamId=%s&otPrimers=1" id="%s">''' % (myName, batchId, urllib.parse.quote(pamId), cssPamId))
print('<strong>Off-target primers</strong></a>')
print('</div>')
print('''<a id="%sMoreLink" class="otMoreLink" onclick="showAllOts('%s')">''' % (cssPamId, cssPamId))
print('show all...</a>')
print('''<a id="%sLessLink" class="otLessLink" style="display:none" onclick="showLessOts('%s')">''' % (cssPamId, cssPamId))
print('show less...</a>')
print("</small></td>")
print("</tr>")
count = count+1
print("</table>")
printDownloadTableLinks(batchId, addTsv=True)
printNoEffScoreFoundWarn(effScoresCount, pam)
def linkLocalFiles(listFname):
""" write a <link> statement for each filename in listFname. Version them via mtime
(-> browser cache)
"""
for fname in open(listFname).read().splitlines():
fname = fname.strip()
if not isfile(fname):
fname = join(HTMLDIR, fname)
if not isfile(fname):
print("missing: %s<br>" % fname)
continue
mTime = str(os.path.getmtime(fname)).split(".")[0] # seconds is enough
if fname.endswith(".css"):
#url = fname.replace("/var/www/", "http://tefor.net/")
print("<link rel='stylesheet' media='screen' type='text/css' href='%s%s?%s'/>" % (HTMLPREFIX, fname, mTime))
def printHeader(batchId, title):
" print the html header "
print('''<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">''')
print("<html><head>")
if title==None:
if batchName!="":
print("""<title>CRISPOR - %s</title>""" % batchName)
else:
print("""<title>CRISPOR</title>""")
else:
print("""<title>%s</title>""" % title)
print("""
<meta name='description' content='Design CRISPR guides with off-target and efficiency predictions, for more than 100 genomes.'/>
<meta http-equiv='Content-Type' content='text/html; charset=utf-8' />
<meta property='fb:admins' content='692090743' />
<meta name="google-site-verification" content="OV5GRHyp-xVaCc76rbCuFj-CIizy2Es0K3nN9FbIBig" />
<meta property='og:type' content='website' />
<meta property='og:url' content='http://crispor.gi.ucsc.edu/' />
<meta property='og:image' content='http://crispor.gi.ucsc.edu/image/CRISPOR.png' />
<script src="https://cdn.jsdelivr.net/npm/clipboard@2/dist/clipboard.min.js"></script>
""")
# load jquery from local copy, not from CDN, for offline use
print(("""<script src='%sjs/jquery.min.js'></script>
<script src='%sjs/jquery-ui.min.js'></script>
""" % (HTMLPREFIX, HTMLPREFIX)))
#print('<link rel="stylesheet" href="//fonts.googleapis.com/css?family=Roboto:300,300italic,700,700italic" />')
#print('<link rel="stylesheet" type="text/css" href="https://cdnjs.cloudflare.com/ajax/libs/normalize/5.0.0/normalize.min.css" />')
#print('<link rel="stylesheet" href="//cdn.rawgit.com/milligram/milligram/master/dist/milligram.min.css">')
linkLocalFiles("includes.txt")
print('<link rel="stylesheet" type="text/css" href="%sstyle/tooltipster.css" />' % HTMLPREFIX)
print('<link rel="stylesheet" type="text/css" href="%sstyle/tooltipster-shadow.css" />' % HTMLPREFIX)
print('<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/chosen/1.6.2/chosen.css" />')
print('<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/source-code-pro@2.38.0/source-code-pro.css" />')
# the UFD combobox, https://code.google.com/p/ufd/wiki/Usage
# patched to allow mouse wheel
# https://code.google.com/p/ufd/issues/detail?id=86&q=mouse%20wheel
print('<script type="text/javascript" src="%sjs/jquery.ui.ufd.js"></script>' % HTMLPREFIX)
#print '<link rel="stylesheet" type="text/css" href="%sstyle/ufd-base.css" />' % HTMLPREFIX
print('<link rel="stylesheet" type="text/css" href="%sstyle/plain.css" />' % HTMLPREFIX)
print('<link rel="stylesheet" type="text/css" href="%sstyle/jquery-ui.css" />' % HTMLPREFIX)
print('<script type="text/javascript" src="js/jquery.tooltipster.min.js"></script>')
print('<script src="https://cdnjs.cloudflare.com/ajax/libs/chosen/1.6.2/chosen.jquery.min.js"></script>')
# override the main TEFOR css
print("""
<style>
select { font-size: 80%; }
body {
text-align: left;
/* float: left; */
}
p {
}
ul {
-webkit-margin-before: 0;
-webkit-margin-after: 0;
}
.editTable {
border: 1px solid black;
background-color: white;
}
mut {
color: blue;
background-color: yellow;
}
.editTable th {
background-color: #F0F0F0;
}
tt { font-size: 90% }
div.contentcentral { text-align: left; float: left}
/* for chosen.js */
.chosen-container { width: 600px }
.chosen-container .chosen-results li.active-result { float: left}
</style>
""")
# activate tooltipster
#theme: 'tooltipster-shadow',
# activate jqueryUI tooltips
print ("""
<script>
$(function () {
$(".tooltip").tooltip({
relative : true,
tooltipClass : "alignStyle",
content: function () {
return '<div style="width:300px">'+$(this).prop('title')+"</div>";
}
});
});
$(function () {
$(".tooltipAuto").tooltip({
contentAsHtml : true
});
});
</script>""")
# style of Jquery UI tooltips, default style is div.ui-tooltip
print("""<style>
.alignStyle {
background-color: #FFFFFF;
width: 350px;
max-width: 400px;
height: 110px;
position : absolute;
text-align: left;
border:1px solid #cccccc;
}
</style>""")
# style from https://css-tricks.com/rotated-table-column-headers/ to rotate table headers
print("""<style>
th.rotate {
/* Something you can count on */
/* height: 10px; */
white-space: nowrap;
}
th.rotate > div {
float:left;
white-space: nowrap;
position: relative;
border-style: none;
""")
# if we're showing all scores, we have very little space, so turn by
# 90degrees. otherwise, we can afford 45 degrees, which is easier to read.
#if cgiParams.get("showAllScores", "0")=="1":
print("""
-webkit-transform: rotate(-90);
-moz-transform: rotate(270deg);
-ms-transform: rotate(270deg);
-o-transform: rotate(270deg);
transform: rotate(270deg);
width: 25px;""")
#else:
#print("""
#-webkit-transform: rotate(-45deg);
#-moz-transform: rotate(315deg);
#-ms-transform: rotate(315deg);
#-o-transform: rotate(315deg);
#transform: rotate(315deg);
#width: 25px;""")
print("""
}
th.rotate > div > span {
/* border-bottom: 1px solid #ccc; */
padding: 0px 3px;
white-space: nowrap;
}
</style>""")
print("</head>")
print('<body id="wrapper">')
def firstFreeLine(lineMasks, y, start, end):
" recursively search for first free line to place a feature (start, end) "
#print "first free line called with y", y, "<br>"
if y>=len(lineMasks):
return None
lineMask = lineMasks[y]
for x in range(start, end):
#print "checking pos", x, "<br>"
if lineMask[x]!=0:
return firstFreeLine(lineMasks, y+1, start, end)
return y
#return None
def distrOnLines(seq, startDict, featLen, pam):
""" given a dict with start -> (start,end,name,strand) and a motif len, create lines of annotations such that
the motifs don't overlap on the lines
"""
# max number of lines in y direction to draw
MAXLINES = 18
# amount of free space around each feature
SLOP = 2
# bitmask, one per line, 1 = we have a feature here, 0 = no feature here
lineMasks = []
for i in range(0, MAXLINES):
lineMasks.append( [0]* (len(seq)+10) )
# dict with lineCount (0...MAXLINES) -> list of (start, strand) tuples
ftsByLine = defaultdict(list)
maxY = 0
for start in sorted(startDict):
end = start+featLen
strand = startDict[start]
# Cannot use Unicode here: these symbols are not part of the
# monospace font on some platforms and therefore their width
# is not the same as the other characters
#arrNE = u'\u2197'
#arrSE = u'\u2198'
arrNE = '/'
arrSE = '\\'
#arrNE = u'\u2a3c' # hebrew
#arrSE = u'\ufb27' # math
ftSeq = seq[start:end]
if strand=="+":
if pamIsFirst:
if pamIsCas12max(pam): #modify cleavage sites to 14-16 (target strand) and 24nt (non-target strand) / hfCas12Max
label = '%s'%(ftSeq)+'.............%s%s%s.......%s' % (arrNE, arrNE, arrNE, arrSE)
else:
label = '%s'%(ftSeq)+'.................%s....%s' % (arrNE, arrSE)
startFt = start
endFt = start+len(label)
else:
#label = '%s..%s'%(seq[start-3].lower(), ftSeq)
#label = '---%s'%(ftSeq)
#label = '---%s'%(ftSeq)
label = '−−−%s'%(ftSeq)
startFt = start - 3
endFt = end
else:
if pamIsFirst:
if pamIsCas12max(pam): #modify cleavage sites to 14-16 (target strand) and 24nt (non-target strand) / hfCas12Max
spc1 = "......."
spc2 = "............."
labelPrefix = '%s%s%s%s%s%s' % (arrSE, spc1, arrNE, arrNE, arrNE, spc2)
label = labelPrefix + ftSeq
else:
spc1 = "...."
spc2 = "................."
labelPrefix = '%s%s%s%s' % (arrSE, spc1, arrNE, spc2)
label = labelPrefix + ftSeq
startFt = start - len(labelPrefix)
endFt = startFt+len(label)
else:
#label = '%s..%s'%(ftSeq, seq[end+2].lower())
label = '%s---'%(ftSeq)
startFt = start
endFt = end + 3
#print "feature", strand, start, startFt, endFt, SLOP,"<br>"
#print "mask", lineMasks[0][startFt:endFt], "<br>"
y = firstFreeLine(lineMasks, 0, startFt, endFt)
#print "free line: %s<br>" % y
if y==None:
errAbort("not enough space to plot features")
# fill the current mask
mask = lineMasks[y]
maskStart = max(startFt-SLOP, 0)
maskEnd = min(endFt+SLOP, len(seq))
#print "mask:", maskStart, maskEnd
for i in range(maskStart, maskEnd):
mask[i]=1
maxY = max(y, maxY)
pamId = "s%d%s" % (start, strand)
ft = (startFt, endFt, label, strand, pamId)
#print "labelLen: %d<br>" % len(label)
#print "ft: %s<br>" % repr(ft)
ftsByLine[y].append(ft )
return ftsByLine, maxY
def writePamFlank(seq, startDict, pam, faFname):
" write pam flanking sequences to fasta file, optionally with versions where each nucl is removed "
#print "writing pams to %s<br>" % faFname
faFh = open(faFname, "w")
for pamId, pamStart, guideStart, strand, flankSeq, pamSeq, pamPlusSeq in flankSeqIter(seq, startDict, len(pam), True):
faFh.write(">%s\n%s\n" % (pamId, flankSeq))
faFh.close()
def runCmd(cmd, ignoreExitCode=False, useShell=True):
" run shell command, check ret code, replaces BIN and SCRIPTS special variables "
if useShell:
cmd = cmd.replace("$BIN", binDir)
cmd = cmd.replace("$PYTHON", sys.executable)
cmd = cmd.replace("$SCRIPT", scriptDir)
cmd = "set -o pipefail; " + cmd
executable = "/bin/bash"
else:
cmd = [x.replace("$BIN", binDir).replace("$PYTHON", sys.executable).replace("$SCRIPT", scriptDir) for x in cmd]
executable=None
debug("Running %s" % cmd)
ret = subprocess.call(cmd, shell=useShell, executable=executable)
if ret!=0 and not ignoreExitCode:
if not useShell:
cmd = " ".join(cmd)
if commandLineMode:
logging.error("Error: could not run command %s." % cmd)
sys.exit(1)
else:
print("Server error: could not run command %s, error %d.<p>" % (cmd, ret))
print("please send us an email, we will fix this error as quickly as possible. %s " % contactEmail)
raise
sys.exit(0)
def isAltChrom(chrom):
""" return true is chrom name looks like it's not on the primary assembly. This is mostly relevant for hg38.
examples: chr6_*_alt (hg38)
"""
return chrom.endswith("_alt")
def parseOfftargets(db, batchId, onTargetChrom=""):
""" parse a bed file with annotataed off target matches from overlapSelect,
has two name fields, one with the pam position/strand and one with the
overlapped segment
return as dict pamId -> editDist -> (chrom, start, end, seq, strand, segType, segName, totalAlnCount, isRep)
segType is "ex" "int" or "ig" (=intergenic)
if intergenic, geneNameStr is two genes, split by |
The isRep flag is true if BWA reported more than one alignment with X0+X1 but didn't report these with the
XA tag. It means that we can't get the alignments for this sequence from BWA (=repeats).
"""
# edge case: target is on chr6_alt -> we remove a single off-target with 0 mismatches on chr6
targetIsAlt = isAltChrom(onTargetChrom)
# keep track of pamIds already handled for this edge case
skippedPams = set()
# ideally we would check if the offtarget falls into the chrom area that gave rise to the alt
# but that would mean parsing yet another non-small file and this case should be sufficiently rare
# to not bother 99% of users.
batchBase = join(batchDir, batchId)
bedFname = batchBase+".bed.gz"
# example input:
# chrIV 9864393 9864410 s41-|-|5|ACTTGACTG|0 chrIV 9864303 9864408 ex:K07F5.16
# chrIV 9864393 9864410 s41-|-|5|ACTGTAGCTAGCT|9999 chrIV 9864408 9864470 in:K07F5.16
debug("reading offtargets from %s" % bedFname)
# first sort into dict (pamId,chrom,start,end,editDist,strand)
# -> (segType, segName)
pamData = {}
#ifh = open(bedFname) # switched to gzip compression in Dec 2018, converted old files with bash script
try:
ifh = gzip.open(bedFname, "rt")
except FileNotFoundError:
print("Off-target results from this link were temporarily removed to save space. ")
linkUrl = "crispor.py?batchId="+batchId
print("Please go back to <a href='%s'>your job page</a>, which will rerun the job, then try the link again." % linkUrl)
exit(0)
maxOtLines = 500000
count = 0
for line in ifh:
fields = line.rstrip("\n").split("\t")
count +=1
if count > maxOtLines:
print("Error: More than %d off-targets. CRISPR has trouble with handling extremely unspecific inputs. Please email us at "
"%s and discuss. Your input sequence most likely includes a recent L1HS, SVA or similar repetitive elements. "
"It is hard to design guides for these, you can try to re-run your input sequence, but without the repetitive element. "%
(maxOtLines, contactEmail))
sys.exit(1)
chrom, start, end, name, segment = fields
logging.debug("off-target: %s" % name)
# hg38: ignore alternate chromosomes otherwise the
# regions on the main chroms look as if they could not be
# targeted at all with Cas9
if isAltChrom(chrom):
logging.debug("skipping off-target: on alt-chromosome")
continue
nameFields = name.split("|")
pamId, strand, editDist, seq = nameFields[:4]
#print pamId, strand, editDist, seq, chrom, start, end, name, segment, "<br>"
if targetIsAlt:
if editDist=='0' and onTargetChrom.split("_")[0]==chrom.split("_")[0] and not pamId in skippedPams:
logging.debug("altChrom edge case: target is on alt-chrom, skipping a single 0-mismatch off-target on primary chrom")
skippedPams.add(pamId)
continue
isRep = 0
totalAlnCount = 0
# for compatibility with old bed files, only parse these fields if they are present
# note: for some reason, the MIT hitScore was always written to these files
# However, it's not parsed here and never was. In order to not break the old files
# I kept it in the files, but am not reading it here
# these are the different formats until now:
# seqId+"|"+strand+"|"+editDist+"|"+seq+"|"+str(hitScore) # five fields
# seqId+"|"+strand+"|"+editDist+"|"+seq+"|"+x1Score+"|"+str(hitScore) # six fields
# (x1Score was roughly the alnCount, similar enough for practical purposes, fixed in 2019)
# seqId+"|"+strand+"|"+editDist+"|"+seq+"|"+alnCount+"|"+str(hitScore)+"|"+isRep # seven fields
if len(nameFields)>5:
totalAlnCount = int(nameFields[4])
if len(nameFields)>6:
isRep = bool(int(nameFields[6]))
editDist = int(editDist)
# some gene models include colons
if ":" in segment:
segType, segName = segment.split(":", maxsplit=1)
else:
segType, segName = "", segment
start, end = int(start), int(end)
otKey = (pamId, chrom, start, end, editDist, seq, strand, totalAlnCount, isRep)
# if an offtarget is in the PAR region, we keep only the chrY off-target
parNum = isInPar(db, chrom, start, end)
# keep only matches on chrX
if parNum is not None and chrom=="chrX":
logging.debug("off-target on PAR region, skipping")
continue
# if a offtarget overlaps an intron/exon or ig/exon boundary it will
# appear twice; in this case, we only keep the exon offtarget
if otKey in pamData and segType!="ex":
logging.debug("skipping off-target: ex/ig boundary edge case")
continue
pamData[otKey] = (segType, segName)
# index by pamId and edit distance
indexedOts = defaultdict(dict)
for otKey, otVal in pamData.items():
pamId, chrom, start, end, editDist, seq, strand, totalAlnCount, isRep = otKey
segType, segName = otVal
otTuple = (chrom, start, end, seq, strand, segType, segName, totalAlnCount, isRep)
indexedOts[pamId].setdefault(editDist, []).append( otTuple )
return indexedOts
class ConsQueue:
""" a pseudo job queue that does nothing but report progress to the console """
def startStep(self, batchId, desc, label):
logging.info("Progress %s - %s - %s" % (batchId, desc, label))
def annotateBedWithPos(inBed, outBed, genome):
"""
given an input bed4 and an output bed filename, add an additional column 5 to the bed file
that is a descriptive text of the chromosome pos (e.g. chr1:1.23 Mbp).
"""
ofh = gzip.open(outBed, "wt")
for line in open(inBed):
chrom, start = line.split("\t")[:2]
chrom = applyChromAlias(genome, chrom)
start = int(start)
if start>1000000:
startStr = "%.2f Mbp" % (float(start)/1000000)
else:
startStr = "%.2f Kbp" % (float(start)/1000)
desc = "%s %s" % (chrom, startStr)
ofh.write(line.rstrip("\n"))
ofh.write("\t")
ofh.write(desc)
ofh.write("\n")
ofh.close()
def findAllGuides(seq, pam):
startDict, endSet = findAllPams(seq, pam)
pamInfo = list(flankSeqIter(seq, startDict, len(pam), False))
return pamInfo
def extractMutScores(scoreDict, pamIds):
" make a list of the guide-related outcome scores in the order of pamIds "
res = []
for pamId in pamIds:
res.append(scoreDict[pamId][0])
return res
def calcSaveEffScores(batchId, seq, extSeq, pam, queue):
""" given a sequence and an extended sequence, get all potential guides
with pam, extend them to 100mers and score them with various eff. scores.
Return a
list of rows [headers, (guideSeq, 100mer, score1, score2, score3,...), ... ]
Also write the results to a database so they can be retrieved later.
extSeq can be None, if we were unable to extend the sequence
"""
seq = seq.upper()
if extSeq:
extSeq = extSeq.upper()
pamInfo = findAllGuides(seq, pam)
pamIds = []
guides = []
longSeqs = []
for pamId, startPos, guideStart, strand, guideSeq, pamSeq, pamPlusSeq in pamInfo:
logging.debug("PAM ID: %s - guideSeq %s" % (pamId, guideSeq))
gStart, gEnd = pamStartToGuideRange(startPos, strand, len(pam))
longSeq = getExtSeq(seq, gStart, gEnd, strand, 50-GUIDELEN, 50, extSeq) # +-50 bp from the end of the guide
if longSeq!=None:
longSeqs.append(longSeq)
pamIds.append(pamId)
guides.append(guideSeq+pamSeq)
if len(longSeqs)>0 and doEffScoring:
enz = None
if pamIsCpf1(pam) and not pam=="NGTN":
enz = "cpf1"
elif pamIsSaCas9(pam):
enz = "sacas9"
# for spcas9, we use the extended list for the calculation
global scoreNames
if enz is None:
scoreNames = allScoreNames
effScores = crisporEffScores.calcAllScores(longSeqs, enzyme=enz, scoreNames=scoreNames)
# these are slow algorithms, so store the results for later
queue.startStep(batchId, "outcome", "Calculating editing outcomes")
mutScores = crisporEffScores.calcMutSeqs(pamIds, longSeqs, enz, scoreNames=mutScoreNames)
saveOutcomeData(batchId, mutScores)
# for output and sorting, it's easier to treat the outcome-derived scores like an efficiency score
for mutScoreName in mutScoreNames:
if mutScoreName in mutScores:
effScores[mutScoreName] = extractMutScores(mutScores[mutScoreName], pamIds)
# make sure the "N bug" reported by Alberto does never happen again:
# we must get back as many scores as we have sequences
for scoreName, scores in effScores.items():
if len(scores)!=len(longSeqs):
print("Internal error when calculating score %s" % scoreName)
assert(False)
else:
effScores = {}
activeScoreNames = list(effScores.keys())
# reformat to rows, write all scores to file
assert(len(pamIds)==len(guides)==len(longSeqs))
rows = []
for i, (guideId, guide, longSeq) in enumerate(zip(pamIds, guides, longSeqs)):
row = [guideId, guide, longSeq]
for scoreName in activeScoreNames:
scoreList = effScores[scoreName]
if len(scoreList) > 0:
row.append(scoreList[i])
else:
row.append("noScore?")
rows.append(row)
headerRow = ["guideId", "guide", "longSeq"]
headerRow.extend(activeScoreNames)
rows.insert(0, headerRow)
return rows
def writeRow(ofh, row):
" write list to file as tab-sep row "
row = [str(x) for x in row]
ofh.write("\t".join(row))
ofh.write("\n")
def createBatchEffScoreTable(batchId, queue):
""" annotate all potential guides with efficiency scores and write to file.
tab-sep file for easier debugging, no pickling
"""
outFname = join(batchDir, batchId+".effScores.tab")
# Todo: why don't we get these from the caller as arguments instead of reading the batch?
batchInfo = readBatchAsDict(batchId)
seq = batchInfo["seq"]
extSeq = batchInfo.get("extSeq") # cannot always extend a sequence, e.g. when no perfect match
pam = batchInfo["pam"]
pam = setupPamInfo(pam)
seq = seq.upper()
if extSeq:
extSeq = extSeq.upper()
guideRows = calcSaveEffScores(batchId, seq, extSeq, pam, queue)
guideFh = open(outFname, "w")
for row in guideRows:
writeRow(guideFh, row)
guideFh.close()
logging.info("Wrote eff scores to %s" % guideFh.name)
def readEffScores(batchId):
" parse eff scores from tab sep file and return as dict pamId -> dict of scoreName -> value "
effScoreFname = join(batchDir, batchId)+".effScores.tab"
seqToScores = {}
if isfile(effScoreFname):
for row in lineFileNext(open(effScoreFname)):
scoreDict = {}
rowDict = row._asdict()
# the first three fields are the pamId, shortSeq, longSeq, they are not scores
allScoreNames = row._fields[3:]
for scoreName in allScoreNames:
score = rowDict[scoreName]
if score=="None":
score = "NA"
elif "." in score or "e" in score:
score = float(score)
else:
score = int(score)
scoreDict[scoreName] = score
seqToScores[row.guideId] = scoreDict
return seqToScores
def findOfftargetsBwa(queue, batchId, batchBase, faFname, genome, pamDesc, bedFname):
" align faFname to genome and create matchedBedFname "
matchesBedFname = batchBase+".matches.bed"
saFname = batchBase+".sa"
pam = setupPamInfo(pamDesc)
pamLen = len(pam)
genomeDir = genomesDir # make var local, see below
open(matchesBedFname, "w") # truncate to 0 size
# increase MAXOCC if there is only a single query, but only in CGI mode
#if len(parseFasta(open(faFname)))==1 and not commandLineMode:
#global MAXOCC
#global maxMMs
#MAXOCC=max(HIGH_MAXOCC, MAXOCC)
#maxMMs=HIGH_maxMMs
maxDiff = maxMMs
queue.startStep(batchId, "bwa", "Alignment of potential guides, mismatches <= %d" % maxDiff)
convertMsg = "Converting alignments"
seqLen = GUIDELEN
bwaM = MFAC*MAXOCC # -m is queue size in bwa
cmd = "$BIN/bwa aln -o 0 -m %(bwaM)s -n %(maxDiff)d -k %(maxDiff)d -N -l %(seqLen)d %(genomeDir)s/%(genome)s/%(genome)s.fa %(faFname)s > %(saFname)s" % locals()
runCmd(cmd)
queue.startStep(batchId, "saiToBed", convertMsg)
maxOcc = MAXOCC # create local var from global
# EXTRACTION OF POSITIONS + CONVERSION + SORT/CLIP
# the sorting should improve the twoBitToFa runtime
python = sys.executable
cmd = "$BIN/bwa samse -n %(maxOcc)d %(genomeDir)s/%(genome)s/%(genome)s.fa %(saFname)s %(faFname)s | $SCRIPT/xa2multi.pl | %(python)s $SCRIPT/samToBed %(pam)s %(seqLen)d | sort -k1,1 -k2,2n | $BIN/bedClip stdin %(genomeDir)s/%(genome)s/%(genome)s.sizes stdout >> %(matchesBedFname)s " % locals()
runCmd(cmd)
filtMatchesBedFname = batchBase+".filtMatches.bed"
queue.startStep(batchId, "filter", "Removing matches without a PAM motif")
altPats = ",".join(offtargetPams.get(pam, ["na"]))
bedFnameTmp = bedFname+".tmp"
altPamMinScore = str(ALTPAMMINSCORE)
shmFaFname = join("/dev/shm", genome+".fa")
# EXTRACTION OF SEQUENCES + ANNOTATION - big headache!!
# twoBitToFa was 15x slower than python's twobitreader, after markd's fix it is better
# but bedtools uses an fa.idx file and also mmap, so is a LOT faster
# arguments: guideSeq, mainPat, altPats, altScore, passTotalAlnCount
if isfile(shmFaFname):
logging.info("Using bedtools and genome fasta on ramdisk, %s" % shmFaFname)
cmd = "time bedtools getfasta -s -name -fi %(shmFaFname)s -bed %(matchesBedFname)s -fo /dev/stdout | $SCRIPT/filterFaToBed %(faFname)s %(pam)s %(altPats)s %(altPamMinScore)s > %(filtMatchesBedFname)s" % locals()
else:
cmd = "time $BIN/twoBitToFa %(genomeDir)s/%(genome)s/%(genome)s.2bit stdout -bed=%(matchesBedFname)s | %(python)s $SCRIPT/filterFaToBed %(faFname)s %(pam)s %(altPats)s %(altPamMinScore)s > %(filtMatchesBedFname)s" % locals()
#cmd = "$SCRIPT/twoBitToFaPython %(genomeDir)s/%(genome)s/%(genome)s.2bit %(matchesBedFname)s | $SCRIPT/filterFaToBed %(faFname)s %(pam)s %(altPats)s %(altPamMinScore)s %(maxOcc)d > %(filtMatchesBedFname)s" % locals()
runCmd(cmd)
segFname = "%(genomeDir)s/%(genome)s/%(genome)s.segments.bed" % locals()
# if we have gene model segments, annotate them, otherwise just use the chrom position
if isfile(segFname):
queue.startStep(batchId, "genes", "Annotating matches with genes")
cmd = "cat %(filtMatchesBedFname)s | $BIN/overlapSelect %(segFname)s stdin stdout -mergeOutput -selectFmt=bed -inFmt=bed | cut -f1,2,3,4,8 | gzip > %(bedFnameTmp)s " % locals()
runCmd(cmd)
else:
queue.startStep(batchId, "chromPos", "Annotating matches with chromosome position")
annotateBedWithPos(filtMatchesBedFname, bedFnameTmp, genome)
# make sure the final bed file is never in a half-written state,
# as it is our signal that the job is complete
shutil.move(bedFnameTmp, bedFname)
queue.startStep(batchId, "done", "Job completed")
# remove the temporary files
tempFnames = [saFname, matchesBedFname, filtMatchesBedFname]
if not DEBUG:
for tfn in tempFnames:
if isfile(tfn):
os.remove(tfn)
return bedFname
def makeVariants(seq):
" generate all possible variants of sequence at 1bp-distance"
seqs = []
for i in range(0, len(seq)):
for l in "ACTG":
if l==seq[i]:
continue
newSeq = seq[:i]+l+seq[i+1:]
seqs.append((i, seq[i], l, newSeq))
return seqs
def expandIupac(seq):
""" expand all IUPAC characters to nucleotides, returns list.
>>> expandIupac("NY")
['GC', 'GT', 'AC', 'AT', 'TC', 'TT', 'CC', 'CT']
"""
# http://stackoverflow.com/questions/27551921/how-to-extend-ambiguous-dna-sequence
d = {'A': 'A', 'C': 'C', 'B': 'CGT', 'D': 'AGT', 'G': 'G', \
'H': 'ACT', 'K': 'GT', 'M': 'AC', 'N': 'GATC', 'S': 'CG', \
'R': 'AG', 'T': 'T', 'W': 'AT', 'V': 'ACG', 'Y': 'CT', 'X': 'GATC'}
seqs = []
for i in product(*[d[j] for j in seq]):
seqs.append("".join(i))
return seqs
def writeBowtieSequences(inFaFname, outFname, pamPat):
""" write the sequence and one-bp-distant-sequences + all possible PAM sequences to outFname
Return dict querySeqId -> querySeq and a list of all
possible PAMs, as nucleotide sequences (not IUPAC-patterns)
"""
ofh = open(outFname, "w")
outCount = 0
inCount = 0
guideSeqs = {} # 20mer guide sequences
qSeqs = {} # 23mer query sequences for bowtie, produced by expanding guide sequences
allPamSeqs = expandIupac(pamPat)
for seqId, seq in parseFastaAsList(open(inFaFname)):
inCount += 1
guideSeqs[seqId] = seq
for pamSeq in allPamSeqs:
# the input sequence + the PAM
newSeqId = "%s.%s" % (seqId, pamSeq)
newFullSeq = seq+pamSeq
ofh.write(">%s\n%s\n" % (newSeqId, newFullSeq))
qSeqs[newSeqId] = newFullSeq
# all one-bp mutations of the input sequence + the PAM
for nPos, fromNucl, toNucl, newSeq in makeVariants(seq):
newSeqId = "%s.%s.%d:%s>%s" % (seqId, pamSeq, nPos, fromNucl, toNucl)
newFullSeq = newSeq+pamSeq
ofh.write(">%s\n%s\n" % (newSeqId, newFullSeq))
qSeqs[newSeqId] = newFullSeq
outCount += 1
ofh.close()
logging.debug("Wrote %d variants+expandedPam of %d sequences to %s" % (outCount, inCount, outFname))
return guideSeqs, qSeqs, allPamSeqs
def applyModifStr(seq, modifStrs, strand):
""" bowtie: given a list of pos:toNucl>fromNucl and a seq, return the original seq.
position is 0-based
>>> applyModifStr("ACAATAAGACATAAACATATCGG", "14:T>A,21:A>G,22:C>G".split(","), "+")
'ACAATAAGACATAATCATATCAC'
"""
seq = list(seq)
for modifStr in modifStrs:
#logging.debug( modifStr)
pos, toFromNucl = modifStr.split(":")
fromNucl, toNucl = toFromNucl.split(">")
pos = int(pos)
if strand=="-":
fromNucl = revComp(fromNucl)
seq[pos] = fromNucl
return "".join(seq)
def parseRefout(tmpDir, guideSeqs, pamLen):
""" parse all .map file in tmpDir and return as list of chrom,start,end,strand,guideSeq,tSeq
"""
fnames = glob.glob(join(tmpDir, "*.map"))
# while parsing, make sure we keep only the hit with the lowest number of mismatches
# to the guide. Saves time when parsing.
posToHit = {}
hitBestMismCount = {}
for fname in fnames:
for line in open(fname):
# s20+.17:A>G - chr8 26869044 CCAGCACGTGCAAGGCCGGCTTC IIIIIIIIIIIIIIIIIIIIIII 7 4:C>G,13:T>G,15:C>G
guideIdWithMod, strand, chrom, start, tSeq, weird, someScore, alnModifStr = \
line.rstrip("\n").split("\t")
guideId = guideIdWithMod.split(".")[0]
modifParts = alnModifStr.split(",")
if modifParts==['']:
modifParts = []
mismCount = len(modifParts)
hitId = (guideId, chrom, start, strand)
oldMismCount = hitBestMismCount.get(hitId, 9999)
if mismCount < oldMismCount:
hit = (mismCount, guideIdWithMod, strand, chrom, start, tSeq, modifParts)
posToHit[hitId] = hit
hitBestMismCount[hitId] = mismCount # thanks to github user mbsimonovic
ret = []
for guideId, hit in posToHit.items():
mismCount, guideIdWithMod, strand, chrom, start, tSeq, modifParts = hit
if strand=="-":
tSeq = revComp(tSeq)
guideId = guideIdWithMod.split(".")[0]
guideSeq = guideSeqs[guideId]
genomeSeq = applyModifStr(tSeq, modifParts, strand)
start = int(start)
bedRow = (guideId, chrom, start, start+GUIDELEN+pamLen, strand, guideSeq, genomeSeq)
ret.append( bedRow )
return ret
def getEditDist(str1, str2):
""" return edit distance between two strings of equal length
>>> getEditDist("HIHI", "HAHA")
2
"""
assert(len(str1)==len(str2))
str1 = str1.upper()
str2 = str2.upper()
editDist = 0
for c1, c2 in zip(str1, str2):
if c1!=c2:
editDist +=1
return editDist
def findOfftargetsBowtie(queue, batchId, batchBase, faFname, genome, pamPat, bedFname):
" align guides with pam in faFname to genome and write off-targets to bedFname "
tmpDir = batchBase+".bowtie.tmp"
os.mkdir(tmpDir)
# make sure this directory gets removed, no matter what
global tmpDirsDelExit
tmpDirsDelExit.append(tmpDir)
if not DEBUG:
atexit.register(delTmpDirs)
# write out the sequences for bowtie
queue.startStep(batchId, "seqPrep", "preparing sequences")
bwFaFname = abspath(join(tmpDir, "bowtieIn.fa"))
guideSeqs, qSeqs, allPamSeqs = writeBowtieSequences(faFname, bwFaFname, pamPat)
genomePath = abspath(join(genomesDir, genome, genome))
oldCwd = os.getcwd()
# run bowtie
queue.startStep(batchId, "bowtie", "aligning with bowtie")
os.chdir(tmpDir) # bowtie writes to hardcoded output filenames with --refout
# -v 3 = up to three mismatches
# -y = try hard
# -t = print time it took
# -k = output up to X alignments
# -m = do not output any hit if a read has more than X hits
# --max = write all reads that exceed -m to this file
# --refout = output in bowtie format, not SAM
# --maxbts=2000 maximum number of backtracks
# -p 4 = use four threads
# --mm = use mmap
maxOcc = MAXOCC # meaning in BWA: includes any PAM, in bowtie we have the PAM in the input sequence
cmd = "$BIN/bowtie -e 1000 %(genomePath)s -f %(bwFaFname)s -v 3 -y -t -k %(maxOcc)d -m %(maxOcc)d dummy --max tooManyHits.txt --mm --refout --maxbts=2000 -p 4" % locals()
runCmd(cmd)
os.chdir(oldCwd)
queue.startStep(batchId, "parse", "parsing alignments")
pamLen = len(pamPat)
hits = parseRefout(tmpDir, guideSeqs, pamLen)
queue.startStep(batchId, "scoreOts", "scoring off-targets")
# make the list of alternative PAM sequences
altPats = offtargetPams.get(pamPat, [])
altPamSeqs = []
for altPat in altPats:
altPamSeqs.extend(expandIupac(altPat))
# iterate over bowtie hits and write to a BED file with scores
# if the hit looks OK (right PAM + score is high enough)
tempBedPath = join(tmpDir, "bowtieHits.bed")
tempFh = open(tempBedPath, "w")
offTargets = {}
isSaCas9 = pamIsSaCas9(pamPat)
for guideIdWithMod, chrom, start, end, strand, _, tSeq in hits:
guideId = guideIdWithMod.split(".")[0]
guideSeq = guideSeqs[guideId]
genomePamSeq = tSeq[-pamLen:]
logging.debug( "PAM seq: %s of %s" % (genomePamSeq, tSeq))
if genomePamSeq in altPamSeqs:
minScore = ALTPAMMINSCORE
elif genomePamSeq in allPamSeqs:
minScore = MINSCORE
else:
logging.debug("Skipping off-target for %s: %s:%d-%d" % (guideId, chrom, start, end))
continue
logging.debug("off-target minScore = %f" % minScore )
# check if this match passes the off-target score limit
if pamIsCpf1(pamPat):
otScore = 0.0
else:
tSeqNoPam = tSeq[:-pamLen]
if isSaCas9:
otScore = calcSaHitScore(guideSeq, tSeqNoPam)
else:
otScore = calcHitScore(guideSeq, tSeqNoPam)
if otScore < minScore:
logging.debug("off-target not accepted")
continue
editDist = getEditDist(guideSeq, tSeqNoPam)
guideHitCount = 0
guideId = guideId.split(".")[0] # full guide ID looks like s33+.0:A>T
name = guideId+"|"+strand+"|"+str(editDist)+"|"+tSeq+"|"+str(guideHitCount)+"|"+str(otScore)
row = [chrom, str(start), str(end), name]
# this way of collecting the features will remove the duplicates
otKey = (chrom, start, end, strand, guideId)
logging.debug("off-target key is %s" % str(otKey))
offTargets[ otKey ] = row
for rowKey, row in offTargets.items():
tempFh.write("\t".join(row))
tempFh.write("\n")
tempFh.flush()
# create a tempfile which is moved over upon success
# makes sure we do not leave behind a half-written file if
# we crash later
tmpFd, tmpAnnotOffsPath = tempfile.mkstemp(dir=tmpDir, prefix="annotOfftargets")
tmpFh = open(tmpAnnotOffsPath, "w")
# get name of file with genome locus names
genomeDir = genomesDir # make local var
segFname = "%(genomeDir)s/%(genome)s/%(genome)s.segments.bed" % locals()
# annotate with genome locus names
cmd = "$BIN/overlapSelect %(segFname)s %(tempBedPath)s stdout -mergeOutput -selectFmt=bed -inFmt=bed | cut -f1,2,3,4,8 > %(tmpAnnotOffsPath)s" % locals()
runCmd(cmd)
shutil.move(tmpAnnotOffsPath, bedFname)
queue.startStep(batchId, "done", "Job completed")
if DEBUG:
logging.info("debug mode: Not deleting %s" % tmpDir)
else:
shutil.rmtree(tmpDir)
def processSubmission(faFname, genome, pamDesc, bedFname, batchBase, batchId, queue):
""" search fasta file against genome, filter for pam matches and write to bedFName
optionally write status updates to work queue. Remove faFname.
"""
batchInfo = readBatchAsDict(batchId)
if genome=="noGenome":
posStr = "?"
elif "batchName" in batchInfo and batchInfo["batchName"].count(":")==2: # chrom:start-end:strand
posStr = batchInfo["batchName"]
else:
queue.startStep(batchId, "bwasw", "Searching genome for one 100% identical match to input sequence")
posStr = findPerfectMatch(batchId)
batchInfo["posStr"] = posStr
if posStr!="?":
# get a 100bp-extended version of the input seq
chrom, start, end, strand = parsePos(posStr)
extSeq = extendAndGetSeq(genome, chrom, start, end, strand, batchInfo["seq"])
if extSeq is None:
# this can only happen if there is a 100%-M match but small SNPs in it compared to the input sequence
# so the extension of the input fails.
# in this case, we also invalidate the position, as there was no perfect match and the user
# has to do something to fix it
batchInfo["posStr"] = "?"
else:
logging.debug("100pb-extended seq (len: %d) is: %s" % (len(extSeq), extSeq))
batchInfo["extSeq"] = extSeq
# must save the batch again, as otherwise display won't work, we need the position saved
writeBatchAsDict(batchInfo, batchId)
if doEffScoring:
queue.startStep(batchId, "effScores", "Calculating guide efficiency scores")
createBatchEffScoreTable(batchId, queue)
if genome=="noGenome":
# skip the off-target search entirely
open(bedFname, "w") # create a 0-byte file to signal job completion
queue.startStep(batchId, "done", "Job completed")
return
if useBowtie:
findOfftargetsBowtie(queue, batchId, batchBase, faFname, genome, pamDesc, bedFname)
else:
findOfftargetsBwa(queue, batchId, batchBase, faFname, genome, pamDesc, bedFname)
if not DEBUG:
os.remove(faFname)
return bedFname
def lineFileNext(fh):
"""
parses tab-sep file with headers as field names
yields collection.namedtuples
strips "#"-prefix from header line
"""
line1 = fh.readline()
while line1.startswith("##"):
line1 = fh.readline()
line1 = line1.strip("\n").strip("#")
headers = line1.split("\t")
Record = namedtuple('tsvRec', headers)
for line in fh:
line = line.rstrip("\n")
fields = line.split("\t")
try:
rec = Record(*fields)
except Exception as msg:
logging.error("Exception occured while parsing line, %s" % msg)
logging.error("Filename %s" % fh.name)
logging.error("Line was: %s" % repr(line))
logging.error("Does number of fields match headers?")
logging.error("Headers are: %s" % headers)
#raise Exception("wrong field count in line %s" % line)
continue
# convert fields to correct data type
yield rec
allGenomes = None
def readGenomes():
" return list of all genomes supported "
global allGenomes
if allGenomes:
return allGenomes
genomes = {}
myDir = dirname(__file__)
genomesDir = join(myDir, "genomes")
inFnames = []
globalFname = join(genomesDir, "genomeInfo.all.tab")
if isfile(globalFname):
inFnames = [globalFname]
else:
for subDir in os.listdir(genomesDir):
infoFname = join(genomesDir, subDir, "genomeInfo.tab")
if isfile(infoFname):
inFnames.append(infoFname)
for infoFname in inFnames:
for row in lineFileNext(open(infoFname)):
# add a note to identify UCSC genomes
if row.server.startswith("ucsc"):
addStr="UCSC "
else:
addStr = ""
genomes[row.name] = row.scientificName+" - "+row.genome+" - "+addStr+row.description
genomes = list(genomes.items())
genomes.sort(key=operator.itemgetter(1))
allGenomes = genomes
return allGenomes
def printOrgDropDown(lastorg, genomes):
" print the organism drop down box. "
print('<select id="genomeDropDown" class style="max-width:600px" name="org" tabindex="2">')
print('<option ')
if lastorg == "noGenome":
print('selected ')
print('value="noGenome">-- No Genome: no specificity, only cleavage efficiency scores (max. len 25kbp)</option>')
for db, desc in genomes:
print('<option ')
if db == lastorg :
print('selected ')
print('value="%s">%s</option>' % (db, desc))
print("</select>")
#print ('''
#<script type="text/javascript">
#$("#genomeDropDown").ufd({maxWidth:350, listWidthFixed:false});
#</script>''')
print ('''<br>''')
print ("""<script>
$("#genomeDropDown").chosen();
$(".chosen-choices li").css("background","red");
</script>
""")
def printPamDropDown(lastpam):
print('<select style="float:left" name="pam" tabindex="3">')
for key,value in pamDesc:
print('<option ')
if key == lastpam :
print('selected ')
print('value="%s">%s</option>' % (key, value))
print("</select>")
def printForm(params):
" print html input form "
scriptName = basename(__file__)
genomes = readGenomes()
haveHuman = False
for g in genomes:
if g[0]=="hg19":
haveHuman = True
# The returned cookie is available in the os.environ dictionary
cookies=http.cookies.SimpleCookie(os.environ.get('HTTP_COOKIE'))
if "lastorg" in cookies and "lastseq" in cookies and "lastpam" in cookies:
lastorg = cookies['lastorg'].value
lastseq = cookies['lastseq'].value
lastpam = cookies['lastpam'].value
else:
if not haveHuman:
global DEFAULTSEQ
global DEFAULTORG
DEFAULTSEQ = ALTSEQ
DEFAULTORG = ALTORG
lastorg = DEFAULTORG
lastseq = DEFAULTSEQ
lastpam = DEFAULTPAM
# SerialCloner is sending us the sequence via a HTTP get parameter
if "seq" in params:
lastseq = params["seq"]
if "org" in params:
lastorg = params["org"]
seqName = ""
if "seqName" in params:
seqName = params["seqName"]
printTeforBodyStart()
#print('''March 6 2023: Sorry, no CRISPOR on the new UCSC-based-server (with RS3 scores) today. Too many performance problems on the new server. We were able to renew the old server. Please use the <a href="http://37.187.154.234">old server</a> temporarily.''')
#sys.exit(0)
print("""
<form id="main-form" method="post" action="%s">
<div style="text-align:left; margin-left: 10px">
CRISPOR (<a href="https://academic.oup.com/nar/article/46/W1/W242/4995687">citation</a>) is a program that helps design, evaluate and clone guide sequences for the CRISPR/Cas9 system. <a target=_blank href="/manual/">CRISPOR Manual</a>
<br><i>July 2025: Added hasCas12Max and e-SpotOn. Also allowing old primer links to Crispor to work again. See <a href="doc/changes.html">Full list of changes</a></i><br>
</div>
<div class="windowstep subpanel" style="width:40%%;">
<div class="substep">
<div class="title">
Step 1
</div>
Planning a lentiviral gene knockout screen? Use <a href="crispor.py?libDesign=1">CRISPOR Batch</a><br>
Sequence name (optional): <input type="text" name="name" size="20" value="%s"><br>
Enter a single genomic sequence, < %d bp, typically an exon
<img src="%simage/info-small.png" title="CRISPOR conserves the lowercase and uppercase format of your sequence, allowing to highlight sequence features of interest such as ATG or STOP codons.<br>Avoid using cDNA sequences as input, CRISPR guides that straddle splice sites are unlikely to work.<br>You can paste a single >23bp sequence and even multiple sequences, separated by N characters." class="tooltipster">
<br>
<small><a href="javascript:clearInput()">Clear Box</a> - </small>
<small><a href="javascript:resetToExample()">Reset to default</a></small>
</div>
<textarea tabindex="1" style="width:100%%" name="seq" rows="12"
placeholder="Paste here the genomic - not a cDNA - sequence of the exon you want to target. The sequence has to include the PAM site for your enzyme of interest, e.g. NGG. Maximum size %d bp. If you only have a cDNA, please BLAST or BLAT the cDNA first to find the right exon sequence for CRISPOR.">%s</textarea>
<small>Text case is preserved, e.g. you can mark ATGs with lowercase.<br>Instead of a sequence, you can paste a chromosome range, e.g. chr1:11,130,540-11,130,751</small>
</div>
<div class="windowstep subpanel" style="width:50%%">
<div class="substep" style="margin-bottom: 1px">
<div class="title" style="cursor:pointer;" onclick="$('#helpstep2').toggle('fast')">
Step 2
</div>
Select a genome
</div>
"""% (scriptName, seqName, MAXSEQLEN, HTMLPREFIX, MAXSEQLEN, lastseq))
printOrgDropDown(lastorg, genomes)
print("""
<div id="trackHubNote" style="margin-bottom:5px">
<small>Note: pre-calculated exonic guides for this species are on the <a id='hgTracksLink' target=_blank href="">UCSC Genome Browser</a>.</small>
</div>
""")
print('<small style="float:left">We have %d genomes, but not yours? Search <a href="https://www.ncbi.nlm.nih.gov/assembly">NCBI assembly</a> and send a GCF_/GCA_ ID to <a href="mailto:%s">CRISPOR support</a>.</small>' % (len(genomes), contactEmail))
print("""
</div>
<div class="windowstep subpanel" style="width:50%%; height:158px">
<div class="substep">
<div class="title" style="cursor:pointer;" onclick="$('#helpstep3').toggle('fast')">
Step 3
<img src="%simage/info-small.png" title="The most common system uses the NGG PAM recognized by Cas9 from S. <i>pyogenes</i>. The VRER and VQR mutants were described by <a href='http://www.nature.com/nature/journal/vaop/ncurrent/abs/nature14592.html' target='_blank'>Kleinstiver et al</a>, Cas9-HF1 by <a href='https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4851738/'>Kleinstiver 2016</a>, eSpCas1.1 by <a href='https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4714946/'>Slaymaker 2016</a>, Cpf1 by <a href='http://www.cell.com/abstract/S0092-8674(15)01200-3'>Zetsche 2015</a>, SaCas9 by <a href='https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25830891/'>Ran 2015</a> and KKH-SaCas9 by <a href='https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/26524662/'>Kleinstiver 2015</a>, modified As-Cpf1s by <a href='http://biorxiv.org/content/early/2016/12/04/091611'>Gao et al. 2017</a>." class="tooltipsterInteract">
</div>
Select a Protospacer Adjacent Motif (PAM)
</div>
""" % HTMLPREFIX)
printPamDropDown(lastpam)
print("""<br>See <a target=_blank href="manual/manual.html#enzymes">notes on enzymes</a> in the manual.<br>""")
print("""
<div style="width:40%; margin-top: 10px; margin-left:50px; text-align:center; display:block">
<input type="submit" name="submit" value="SUBMIT" tabindex="4"/>
</div>
</div>
""")
print("""
<script>
/* set the dropbox to hg19 and paste the example sequence into the input box. */
function resetToExample() {
$("textarea[name='seq']").val("%s");
$("#genomeDropDown").val("%s");
$("select[name='pam']").val("NGG");
}
/* clear the sequence input box */
function clearInput() {
$("textarea[name='seq']").val("");
}
</script>
<script>
/* hide the track hub note if genome is not hg19 */
ucscTrackDbs=['hg19', 'hg38', 'rn5', 'mm10', 'mm9', 'ci2', 'danRer7', 'sacCer3', 'dm6'];
function showHideHubNote() {
var valSel = $("#genomeDropDown").val();
if (jQuery.inArray(valSel, ucscTrackDbs)!=-1)
{
$("#trackHubNote").css('visibility', 'visible');
$("#hgTracksLink").attr("href", "http://genome.ucsc.edu/cgi-bin/hgTracks?db="+valSel+"&crispr=show");
}
else
$("#trackHubNote").css('visibility', 'hidden');
}
$("#genomeDropDown").on('change', showHideHubNote);
showHideHubNote();
</script>
</form>
""" % (DEFAULTSEQ, DEFAULTORG))
def readBatchAsDict(batchId):
" return contents of batch as a dictionary or None "
batchBase = join(batchDir, batchId)
jsonFname = batchBase+".json"
if isfile(jsonFname):
params = json.load(open(jsonFname))
else:
db = sqlite3.connect(batchArchive)
c = db.cursor()
c.execute("select data from jobArchive where id=?", (batchId,))
data = None
for row in c.fetchall():
data = row[0]
db.close()
if data is None:
return None
jsonStr = gzip.decompress(data).decode("utf8")
params = json.loads(jsonStr)
if "batchName" in params:
global batchName
batchName = params["batchName"]
return params
def writeBatchAsDict(batchInfo, batchId):
batchBase = join(batchDir, batchId)
tmpFname = batchBase+".json.tmp"
ofh = open(tmpFname, "w")
json.dump(batchInfo, ofh)
ofh.close()
jsonFname = batchBase+".json"
os.rename(tmpFname, jsonFname)
logging.debug("Wrote batch info to %s: %s" % (jsonFname, batchInfo))
def readBatchParams(batchId):
""" given a batchId, return the genome, the pam, the input sequence and the
chrom pos and extSeq, a 100bp-extended version of the input sequence.
Returns None for pos if not found. """
params = readBatchAsDict(batchId)
if params != None:
return params["seq"], params["org"], params["pam"], params.get("posStr"), params.get("extSeq")
# FROM HERE UP TO END OF FUNCTION: legacy cold for old batches pre-end-2016 (no json files back then)
# remove in 2017
batchBase = join(batchDir, batchId)
inputFaFname = batchBase+".input.fa"
if not isfile(inputFaFname):
errAbort('Could not find the batch %s. We cannot keep Crispor runs for more than '
'a few months. Please resubmit your input sequence via'
' <a href="crispor.py">the query input form</a>' % batchId)
ifh = open(inputFaFname, encoding="utf8")
ifhFields = ifh.readline().replace(">","").strip().split()
if len(ifhFields)==2:
genome, pamSeq = ifhFields
position = None
else:
genome, pamSeq, position = ifhFields
inSeq = ifh.readline().strip()
ifh.seek(0)
seqs = parseFasta(ifh)
ifh.close()
extSeq = None
if "extSeq" in seqs:
extSeq = seqs["extSeq"]
return inSeq, genome, pamSeq, position, extSeq
def gzipStr(s):
" compress a string with gzip and return "
out = StringIO()
with gzip.GzipFile(fileobj=out, mode="w") as f:
f.write(s)
return out.getvalue()
def gunzipStr(s):
" uncompress a string with gzip and return "
print(len(s), type(s), dir(s))
f = gzip.GzipFile(StringIO(s))
result = f.read()
f.close()
return result
def openDbm(dbFname, mode):
" some distributions don't include the dbm module anymore "
#import dbm.ndbm
#dbMod = dbm
#import dbm.gnu
#dbMod = gdbm
#import semidbm
# lmdbm is faster than everything else: https://pypi.org/project/lmdbm/
# though semidbm is not bad either
# Also see leveldb Wiki page
#import lmdb
#import dbm.dumb
from lmdbm import Lmdb
db = Lmdb.open(dbFname+".lmdb", mode)
if mode=="c":
# hack, I don't know how to set the permissions on the open call
cmd = ["chmod", "-R", "a+rw",dbFname+".lmdb"]
runCmd(cmd, useShell=False)
return db
def saveOutcomeData(batchId, data):
""" save outcome data of batch. data is a dictionary with key = score name """
batchBase = join(batchDir, batchId)
dbFname = batchBase
db = openDbm(dbFname, "c")
#conn = sqlite3.connect(dbFname, "w")
#c = conn.cursor()
#c.execute('''CREATE TABLE outcomes (id text PRIMARY KEY, data blob))''' % scoreName)
#c.commit()
for scoreName, data in data.items():
#c.execute("INSERT INTO outcomes values (?, ?)", (scoreName, gzipStr(json.dumps(data))))
db[scoreName] = zlib.compress(json.dumps(data).encode("utf8"))
db.close()
#c.commit()
def readOutcomeData(batchId, scoreName):
""" open outcome data of batch, key is score name """
batchBase = join(batchDir, batchId)
#conn = sqlite3.connect(dbFname, "r")
#c = conn.cursor()
#binData = c.execute("SELECT data FROM outcomes where id=?", scoreName)
#try:
# import dbm.ndbm
# db = dbm.ndbm.open(batchBase, "r") # dbm always adds .db to the file name
#except:
# # old batches on crispor.org are still using gdbm
#dbFname = batchBase+".dbm"
# import dbm.gnu
# db = dbm.gnu.open(dbFname, "r")
#import dbm.dumb
#db = dbm.dumb.open(dbFname, "r") # dbm always adds .db to the file name
db = openDbm(batchBase, "r")
dbObj = db[scoreName]
jsonStr = zlib.decompress(dbObj)
data = json.loads(jsonStr)
db.close()
return data
def findAllPams(seq, pam):
""" find all matches for PAM and return as dict startPos -> strand and a set
of end positions. The start positions for the negative strand are for the
rev-complemented PAM
"""
seq = seq.upper()
startDict, endSet = findPams(seq, pam, "+", {}, set())
startDict, endSet = findPams(seq, revComp(pam), "-", startDict, endSet)
if pam in multiPams:
for pam2 in multiPams[pam]:
startDict, endSet = findPams(seq, pam2, "+", startDict, endSet)
startDict, endSet = findPams(seq, revComp(pam2), "-", startDict, endSet)
return startDict, endSet
def newBatch(batchName, seq, org, pam):
""" obtain a batch ID and write seq/org/pam to their files.
Return batchId.
"""
batchId = makeTempBase(seq, org, pam, batchName)
batchData = {}
batchData["org"] = org
batchData["pam"] = pam
batchData["batchName"] = batchName
batchData["seq"] = seq
batchData["posStr"] = ""
writeBatchAsDict(batchData, batchId)
return batchId
def readDbInfo(org):
" return a dbInfo object with the columsn in the genomeInfo.tab file "
myDir = dirname(__file__)
genomesDir = join(myDir, "genomes")
infoFname = join(genomesDir, org, "genomeInfo.tab")
if not isfile(infoFname):
return None
dbInfo = next(lineFileNext(open(infoFname)))
return dbInfo
def printQueryNotFoundNote(dbInfo):
print("<div class='title'>Query sequence, not found in the selected genome, %s (%s)</div>" % (dbInfo.scientificName, dbInfo.name))
print("<div class='substep' style='border: 1px black solid; padding:5px; background-color: aliceblue'>")
print("<strong>Warning:</strong> The query sequence was not found in the selected genome.")
print("This can be a valid query, e.g. a GFP sequence.<br>")
print("If not, you might want to check if you selected the right genome for your query sequence.<br>")
print("Use a tool like <a target=_blank href='http://genome.ucsc.edu/cgi-bin/hgBlat'>BLAT</a> to check if the " \
"sequence really has a 100% identical match in the target genome.<p>")
print("When reading the list of guide sequences and off-targets below, bear in mind that in case that the input sequence is really in the genome and just has a few differences, the software will use the first found match as the on-target as it cannot distinguish 0-mismatch off-targets from 0-mismatch on-targets. In this case, the specificity scores of guide sequences are too low. In other words, some guides may be fine, the problem may just be that the on-target is shown as an off-target. <br>")
print("Because there is no flanking sequence available, the guides in your sequence that are within 50bp of the ends will have no efficiency scores. The efficiency scores will instead be shown as '--'. Include more flanking sequence > 50bp to obtain these scores.")
print("</div>")
def getOfftargets(seq, org, pamDesc, batchId, startDict, queue):
""" write guides to fasta and run bwa or use cached results.
Return name of the BED file with the matches or None if not yet available.
Write progress status updates to queue object.
"""
pam = setupPamInfo(pamDesc)
assert('-' not in pam)
batchBase = join(batchDir, batchId)
otBedFname = batchBase+".bed.gz"
batchInfo = readBatchAsDict(batchId)
flagFile = batchBase+".running"
if isfile(flagFile):
errAbort("This sequence is still being processed. Please wait for ~20 seconds "
"and try again, e.g. by reloading this page. If you see this message for "
"more than 2-3 minutes, please send an email to %s. Thanks!" % contactEmail)
if not batchInfo or not isfile(otBedFname) or commandLineMode or not "posStr" in batchInfo or \
(batchInfo["posStr"]=="" and not batchInfo["org"]=="noGenome"): # pre-4.8 batches don't have a posStr at all
# write potential PAM sites to file
faFname = batchBase+".fa"
writePamFlank(seq, startDict, pam, faFname)
if commandLineMode:
processSubmission(faFname, org, pamDesc, otBedFname, batchBase, batchId, queue)
else:
q = JobQueue()
q.openSqlite()
ip = os.environ.get("REMOTE_ADDR", "noIp")
if ip=="195.176.112.240":
errAbort("IP address blocked.")
wasOk = q.addJob("search", batchId, "ip=%s,org=%s,pam=%s" % (ip, org, pamDesc))
if not wasOk:
print("CRISPOR job %s failed-running..." % batchId)
pass
q.close()
return None
return otBedFname
def showPamWarning(pam):
if pamIsCpf1(pam):
print('<div style="text-align:left; border: 1px solid; background-color: aliceblue; padding: 3px">')
print("<strong>Note:</strong> You are using the Cpf1 enzyme or related enzyme.")
print("While there is an efficiency score specificially for Cpf1, there is no off-target ranking algorithm available in the literature, to our knowledge. We use Hsu and CFD scores below for off-target ranking, but they were developed for spCas9. There is not enough data yet to support their usefulness for Cpf1. Contact us for more info if you need to rank Cpf1 off-targets for validation or if you have a dataset that could elucidate this question. We are showing out-of-frame scores, but they are based on micro-homology that assumes a spCas9 cut site, so most likely the out-of-frame scores are not accurate for the staggered cut of Cpf1 either.")
print('</div>')
#elif pamIsSaCas9(pam):
#print '<div style="text-align:left; border: 1px solid; background-color: aliceblue; padding: 3px">'
#print "<strong>Note:</strong> Your query is using a Cas9 from S. aureus.<br>"
#print "Please note that while the efficiency scoring was built for saCas9, the off-target ranking below and specificity scores are based on CFD/Hsu models, which were developed for spCas9. The ranking of off-targets could be very inaccurate. If you have a saCas9 off-target dataset, you can contact us for further info, we are only aware of the BLESS dataset by <a href='https://www.nature.com/articles/nature14299' target=_blank>Ran et al. 2015</a>.<br>As for out-of-frame and micro-homology, this model is also based on spCas9, but <a target=_blank href='https://www.nature.com/articles/nature14299'>Ran et al 2015</a> showed that the saCas9 cleavage pattern looks identical to spCas9's, so the OOF micro-homology model should work with saCas9."
#print '</div>'
elif not pamIsSpCas9(pam) and not pamIsSaCas9(pam):
print('<div style="text-align:left; border: 1px solid; background-color: aliceblue; padding: 3px">')
print("<strong>Warning:</strong> Your query involves a Cas9 that is not from S. Pyogenes and is also not Cpf1 nor saCas9.")
print("Please bear in mind that specificity and efficiency scores were designed using data with S. Pyogenes Cas9 and will very likely not be applicable to this particular Cas9. There is nothing we can do about this, we are unaware of a published dataset for this enzyme. If you know one, please contact us. Also contact us if you think another one of the existing scoring model would be more appropriate for this enzyme.<br>")
print('</div>')
if pam=="NNNNACA":
printNote("You selected the old version of the CjCas9 PAM. You may want to select the more recent "+
"PAMs from the menu on the first page, based on the study by "+
"<a target=_blank href='https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5473640/'>Kim et al 2017</a>.")
if pam=="NGN":
printNote("You have selected the NGN pam for xCas9. While this PAM is documented to work, "+
"if you read the paper in detail, you will notice that the editing efficiency is much lower. "+
"For optimal efficiency, consider going back and switching to the 'high-efficiency' xCas9 PAM.")
if pam=="NGK":
printNote("You have selected the most efficient PAM for xCas9. You can also select the more general/flexible"+
" NGN PAM from the menu when you submit your job. If you read the xCas9 paper in detail, you will find "+
"that NGN is not as efficient though.")
if pam=="TTTN":
printWarning("You selected TTTN as the PAM for Cpf1. " +
"This is not the best PAM. The actual PAM is TTTV, as shown in Fig. 2a of " +
"<a href='https://www.ncbi.nlm.nih.gov/pubmed/27992409'>Kim HK et al. Nat Meth 2017</a>.<br>")
def showNoGe

crispor.py at commit 3aa198f, under other · at the source

Overview

Authors: Florine Roussange¹, Jacqueline Gide², Johana Tournois³, Michel Cailleret¹, Anne Boland⁴, Christophe Battail^4,5, Jean-François Deleuze⁴, Hélène Polvèche³, Didier Auboeuf⁶, Knut Brockmann⁷, Edor Kabashi⁸, Anca Marian⁸, Lina El Kassar³, Sophie Blondel², François Salachas⁹, Gaëlle Bruneteau^9,10, Marc Peschanski², Cécile Martinat^1,2, Sandrine Baghdoyan¹

ORCID iDs: Cécile Martinat, Sandrine Baghdoyan

Institutions: Inserm (France); Université Paris-Saclay (France); Institut des Cellules Souches pour le Traitement et l'Étude des Maladies Monogéniques (France); Université d'Évry Val-d'Essonne (France); Commissariat à l'Énergie Atomique et aux Énergies Alternatives (France); CEA Paris-Saclay - Etablissement de Saclay (France); Centre National de Recherche en Génomique Humaine (France); Université Grenoble Alpes (France); Lyon 1 Université (France); École Normale Supérieure de Lyon (France); Centre National de la Recherche Scientifique (France); Laboratoire de Biologie et Modélisation de la Cellule (France); Universitätsmedizin Göttingen (Germany); University of Göttingen (Germany); Université Paris Cité (France); Institut des Maladies Génétiques Imagine (France); Sorbonne Université (France); Assistance Publique – Hôpitaux de Paris (France); Pitié-Salpêtrière Hospital (France); Institut du Cerveau (France)

Journal: Stem cell reports, volume 21, issue 7, article 102977

Dates: received 25 November 2025; accepted 28 May 2026; published online 25 June 2026; in print July 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1016/j.stemcr.2026.102977 · PMID 42349423 · PMCID PMC13385447 · OpenAlex W7165873553

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: genetics / omics (modality), human (organism), zebrafish (organism), other condition (population), cellular / molecular (subfield)

Methods: Statistics

Keywords: pluripotent stem cells, drug repositioning, amyotrophic lateral sclerosis, motor neurons, autophagy, SQSTM1, repurposing, personalized therapies, gene expression mapping

MeSH: Amyotrophic Lateral Sclerosis*, Pluripotent Stem Cells*, Sequestosome-1 Protein*, Animals, Cell Line, Fibroblasts, Gene Expression Profiling, Gene Expression Regulation, Humans, Induced Pluripotent Stem Cells, Motor Neurons, Zebrafish (* major topic)

Topic: Amyotrophic Lateral Sclerosis Research (Neurology, Medicine), according to OpenAlex

Funding: European Organisation for Rare Diseases; Association française contre les myopathies; French National Research Agency

Citations: cited by 1 paper (Europe PMC); 54 references in the paper

Research resources: Rabbit monoclonal anti-NANOG RRID:AB_10559205, SSEA3 RRID:AB_10564070, Rabbit monoclonal anti-ACTB RRID:AB_1850027, Goat polyclonal anti-ISLET1 RRID:AB_2126324, TUJ1/TUBB3 RRID:AB_2313773, anti-LC3B antibody RRID:AB_3750600, Mouse monoclonal anti-LAMP2 RRID:AB_470709, Mouse monoclonal anti-Oct3/4 RRID:AB_628051, Rabbit polyclonal anti-LC3B RRID:AB_669581, Rabbit polyclonal anti-PLP RRID:AB_776593, Mouse monoclonal anti-SQSTM1 RRID:AB_945626

Abstract

The classical paradigm of drug screening often faces significant limitations due to the challenges associated with identifying molecular or cellular read-outs that are relevant to specific genetic diseases. To remedy this, an alternative approach of reverse phenotypic mapping was tested: Compounds were evaluated for their effects on gene expression and alternative splicing in a healthy cell model, and the resulting data were matched to molecular signatures of diseases. A subset of 50 drugs was tested on mesenchymal stem cells derived from a human pluripotent stem cell line. Over half of the compounds altered gene expression, many affecting pathways linked to monogenic diseases. One hit, increased SQSTM1 expression induced by prazosin, was further validated in FTD/ALS type 3 models caused by SQSTM1 haploinsufficiency, including patient-derived fibroblasts, SQSTM1-depleted hiPSC-derived motor neurons, and a zebrafish model. Extending this paradigm could involve testing diverse cell types and larger drug libraries.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

maximilianh/crisporWebsite

License: other

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 3aa198f1371bb15733acd98aaa097aad052cdbe3, 26 September 2026

Languages: C/C++ (278), C (275), JavaScript (98), Python (86), C++ (67), Perl (33), Java (8), Shell (8), R (4), Stan (1)

Size: 3,120 files, 858 scripts

Software Heritage: not archived

Found in: the resources table

Holds: README, license file, environment (requirements.txt, docker/Dockerfile, bin/Azimuth-2.0/setup.cfg, bin/Azimuth-2.0/setup.py, bin/src/lindel/setup.py, bin/src/ViennaRNA-2.1.9/interfaces/Python/setup.py), tests, documentation

Not found: CITATION.cff, continuous integration

Tools: NumPy (45 files), pandas (24 files), SciPy (19 files), scikit-learn (15 files), Biopython (11 files), Matplotlib (11 files), Keras (2 files), Stan (2 files), limma (1 file), reshape2 (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

860 files

CFD_Scoring/cfd-score-calculator.py, Python, 69 lines
bin/Azimuth-2.0/azimuth/__init__.py, Python, 1 line
bin/Azimuth-2.0/azimuth/cli_run_model.py, Python, 54 lines
bin/Azimuth-2.0/azimuth/cluster_job.py, Python, 118 lines
bin/Azimuth-2.0/azimuth/corrstats.py, Python, 120 lines
bin/Azimuth-2.0/azimuth/features/__init__.py, Python, 1 line
bin/Azimuth-2.0/azimuth/features/featurization.py, Python, 552 lines
bin/Azimuth-2.0/azimuth/features/microhomology.py, Python, 102 lines
bin/Azimuth-2.0/azimuth/load_data.py, Python, 481 lines
bin/Azimuth-2.0/azimuth/local_multiprocessing.py, Python, 30 lines
bin/Azimuth-2.0/azimuth/metrics.py, Python, 655 lines
bin/Azimuth-2.0/azimuth/model_comparison.py, Python, 649 lines
bin/Azimuth-2.0/azimuth/models/DNN.py, Python, 74 lines
bin/Azimuth-2.0/azimuth/models/GP.py, Python, 114 lines
bin/Azimuth-2.0/azimuth/models/__init__.py, Python, 1 line
bin/Azimuth-2.0/azimuth/models/baselines.py, Python, 97 lines
bin/Azimuth-2.0/azimuth/models/ensembles.py, Python, 212 lines
bin/Azimuth-2.0/azimuth/models/gpy_ssk.py, Python, 56 lines
bin/Azimuth-2.0/azimuth/models/regression.py, Python, 287 lines
bin/Azimuth-2.0/azimuth/models/ssk.py, Python, 38 lines
bin/Azimuth-2.0/azimuth/predict.py, Python, 370 lines
bin/Azimuth-2.0/azimuth/tests/test_saved_models.py, Python, 22 lines
bin/Azimuth-2.0/azimuth/util.py, Python, 1,331 lines
bin/Azimuth-2.0/model_comparison.py, Python, 649 lines
bin/Azimuth-2.0/setup.py, Python, 15 lines
bin/Darwin/WU-CRISPR/libsvm-2.82/svm-predict.c, C, 177 lines
bin/Darwin/WU-CRISPR/libsvm-2.82/svm-scale.c, C, 308 lines
bin/Darwin/WU-CRISPR/libsvm-2.82/svm-toy/gtk/callbacks.cpp, C++, 422 lines
bin/Darwin/WU-CRISPR/libsvm-2.82/svm-toy/gtk/callbacks.h, C/C++, 54 lines
bin/Darwin/WU-CRISPR/libsvm-2.82/svm-toy/gtk/interface.c, C, 164 lines
bin/Darwin/WU-CRISPR/libsvm-2.82/svm-toy/gtk/interface.h, C/C++, 14 lines
bin/Darwin/WU-CRISPR/libsvm-2.82/svm-toy/gtk/main.c, C, 23 lines
bin/Darwin/WU-CRISPR/libsvm-2.82/svm-toy/qt/svm-toy.cpp, C++, 433 lines
bin/Darwin/WU-CRISPR/libsvm-2.82/svm-toy/windows/svm-toy.cpp, C++, 456 lines
bin/Darwin/WU-CRISPR/libsvm-2.82/svm-train.c, C, 318 lines
bin/Darwin/WU-CRISPR/libsvm-2.82/svm.cpp, C++, 3,075 lines
bin/Darwin/WU-CRISPR/libsvm-2.82/svm.h, C/C++, 70 lines
bin/Darwin/WU-CRISPR/wu-crispr.pl, Perl, 688 lines
bin/Linux-aarch64/WU-CRISPR/wu-crispr.pl, Perl, 688 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/java/libsvm/svm.java, Java, 2,811 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/java/libsvm/svm_model.java, Java, 21 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/java/libsvm/svm_node.java, Java, 6 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/java/libsvm/svm_parameter.java, Java, 47 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/java/libsvm/svm_problem.java, Java, 7 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/java/svm_predict.java, Java, 149 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/java/svm_toy.java, Java, 471 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/java/svm_train.java, Java, 299 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/python/cross_validation.py, Python, 31 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/python/svm.py, Python, 283 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/python/svm_test.py, Python, 57 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/python/svmc_wrap.c, C, 3,569 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/python/test_cross_validation.py, Python, 27 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/svm-predict.c, C, 177 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/svm-scale.c, C, 308 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/svm-toy/gtk/callbacks.cpp, C++, 422 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/svm-toy/gtk/callbacks.h, C/C++, 54 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/svm-toy/gtk/interface.c, C, 164 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/svm-toy/gtk/interface.h, C/C++, 14 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/svm-toy/gtk/main.c, C, 23 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/svm-toy/qt/svm-toy.cpp, C++, 433 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/svm-toy/windows/svm-toy.cpp, C++, 456 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/svm-train.c, C, 318 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/svm.cpp, C++, 3,075 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/svm.h, C/C++, 70 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/tools/easy.py, Python, 78 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/tools/grid.py, Python, 350 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/tools/grid_bk.py, Python, 349 lines
bin/Linux-x86_64/WU-CRISPR/libsvm-2.82/tools/subset.py, Python, 144 lines
bin/Linux-x86_64/WU-CRISPR/wu-crispr.pl, Perl, 688 lines
bin/azimuthMoreno/predict_moreno.py, Python, 56 lines
bin/deepCpf1/DeepCpf1-orig.py, Python, 106 lines
bin/deepCpf1/DeepCpf1.py, Python, 140 lines
bin/fusiDoench/analysis/features/__init__.py, Python, 1 line
bin/fusiDoench/analysis/features/featurization.py, Python, 465 lines
bin/fusiDoench/analysis/features/microhomology.py, Python, 102 lines
bin/fusiDoench/analysis/load_data.py, Python, 365 lines
bin/fusiDoench/analysis/local_multiprocessing.py, Python, 30 lines
bin/fusiDoench/analysis/metrics.py, Python, 376 lines
bin/fusiDoench/analysis/model_comparison.py, Python, 609 lines
bin/fusiDoench/analysis/models/DNN.py, Python, 74 lines
bin/fusiDoench/analysis/models/GP.py, Python, 114 lines
bin/fusiDoench/analysis/models/__init__.py, Python, 1 line
bin/fusiDoench/analysis/models/baselines.py, Python, 76 lines
bin/fusiDoench/analysis/models/ensembles.py, Python, 157 lines
bin/fusiDoench/analysis/models/gpy_ssk.py, Python, 56 lines
bin/fusiDoench/analysis/models/regression.py, Python, 257 lines
bin/fusiDoench/analysis/models/ssk.py, Python, 38 lines
bin/fusiDoench/analysis/predict.py, Python, 337 lines
bin/fusiDoench/analysis/rs2_score_calculator.py, Python, 50 lines
bin/fusiDoench/analysis/util.py, Python, 786 lines
bin/fusiDoench/analysis/util_new.py, Python, 162 lines
bin/najm2018/saureus_scoring_v1.py, Python, 39 lines
bin/src/SSC0.1/include/performance.h, C/C++, 17 lines
bin/src/SSC0.1/include/words.h, C/C++, 22 lines
bin/src/SSC0.1/src/Fasta2Spacer.c, C, 286 lines
bin/src/SSC0.1/src/SSC.c, C, 274 lines
bin/src/SSC0.1/src/performance.c, C, 32 lines
bin/src/SSC0.1/src/words.c, C, 133 lines
bin/src/ViennaRNA-2.1.9/Cluster/AD_main.c, C, 85 lines
bin/src/ViennaRNA-2.1.9/Cluster/AS_main.c, C, 294 lines
bin/src/ViennaRNA-2.1.9/Cluster/PS3D.c, C, 143 lines
bin/src/ViennaRNA-2.1.9/Cluster/PS3D.h, C/C++, 2 lines
bin/src/ViennaRNA-2.1.9/Cluster/StrEdit_CostMatrix.h, C/C++, 107 lines
bin/src/ViennaRNA-2.1.9/Cluster/cluster.c, C, 281 lines
bin/src/ViennaRNA-2.1.9/Cluster/cluster.h, C/C++, 16 lines
bin/src/ViennaRNA-2.1.9/Cluster/distance_matrix.c, C, 568 lines
bin/src/ViennaRNA-2.1.9/Cluster/distance_matrix.h, C/C++, 14 lines
bin/src/ViennaRNA-2.1.9/Cluster/split.c, C, 332 lines
bin/src/ViennaRNA-2.1.9/Cluster/split.h, C/C++, 14 lines
bin/src/ViennaRNA-2.1.9/Cluster/statgeom.c, C, 515 lines
bin/src/ViennaRNA-2.1.9/Cluster/statgeom.h, C/C++, 5 lines
bin/src/ViennaRNA-2.1.9/Cluster/treeplot.c, C, 358 lines
bin/src/ViennaRNA-2.1.9/Cluster/treeplot.h, C/C++, 1 line
bin/src/ViennaRNA-2.1.9/H/2Dfold.h, C/C++, 134 lines
bin/src/ViennaRNA-2.1.9/H/2Dpfold.h, C/C++, 173 lines
bin/src/ViennaRNA-2.1.9/H/LPfold.h, C/C++, 164 lines
bin/src/ViennaRNA-2.1.9/H/Lfold.h, C/C++, 81 lines
bin/src/ViennaRNA-2.1.9/H/MEA.h, C/C++, 32 lines
bin/src/ViennaRNA-2.1.9/H/PKplex.h, C/C++, 25 lines
bin/src/ViennaRNA-2.1.9/H/PS_dot.h, C/C++, 197 lines
bin/src/ViennaRNA-2.1.9/H/ProfileAln.h, C/C++, 14 lines
bin/src/ViennaRNA-2.1.9/H/RNAstruct.h, C/C++, 152 lines
bin/src/ViennaRNA-2.1.9/H/ali_plex.h, C/C++, 40 lines
bin/src/ViennaRNA-2.1.9/H/alifold.h, C/C++, 378 lines
bin/src/ViennaRNA-2.1.9/H/aln_util.h, C/C++, 10 lines
bin/src/ViennaRNA-2.1.9/H/cofold.h, C/C++, 182 lines
bin/src/ViennaRNA-2.1.9/H/convert_epars.h, C/C++, 91 lines
bin/src/ViennaRNA-2.1.9/H/data_structures.h, C/C++, 775 lines
bin/src/ViennaRNA-2.1.9/H/dist_vars.h, C/C++, 51 lines
bin/src/ViennaRNA-2.1.9/H/duplex.h, C/C++, 28 lines
bin/src/ViennaRNA-2.1.9/H/edit_cost.h, C/C++, 53 lines
bin/src/ViennaRNA-2.1.9/H/energy_const.h, C/C++, 33 lines
bin/src/ViennaRNA-2.1.9/H/energy_par.h, C/C++, 100 lines
bin/src/ViennaRNA-2.1.9/H/findpath.h, C/C++, 53 lines
bin/src/ViennaRNA-2.1.9/H/fold.h, C/C++, 604 lines
bin/src/ViennaRNA-2.1.9/H/fold_vars.h, C/C++, 221 lines
bin/src/ViennaRNA-2.1.9/H/gquad.h, C/C++, 726 lines
bin/src/ViennaRNA-2.1.9/H/inverse.h, C/C++, 67 lines
bin/src/ViennaRNA-2.1.9/H/loop_energies.h, C/C++, 661 lines
bin/src/ViennaRNA-2.1.9/H/mm.h, C/C++, 21 lines
bin/src/ViennaRNA-2.1.9/H/move_set.h, C/C++, 89 lines
bin/src/ViennaRNA-2.1.9/H/naview.h, C/C++, 16 lines
bin/src/ViennaRNA-2.1.9/H/pair_mat.h, C/C++, 148 lines
bin/src/ViennaRNA-2.1.9/H/params.h, C/C++, 134 lines
bin/src/ViennaRNA-2.1.9/H/part_func.h, C/C++, 444 lines
bin/src/ViennaRNA-2.1.9/H/part_func_co.h, C/C++, 235 lines
bin/src/ViennaRNA-2.1.9/H/part_func_up.h, C/C++, 141 lines
bin/src/ViennaRNA-2.1.9/H/plex.h, C/C++, 80 lines
bin/src/ViennaRNA-2.1.9/H/plot_layouts.h, C/C++, 106 lines
bin/src/ViennaRNA-2.1.9/H/profiledist.h, C/C++, 58 lines
bin/src/ViennaRNA-2.1.9/H/read_epars.h, C/C++, 57 lines
bin/src/ViennaRNA-2.1.9/H/ribo.h, C/C++, 8 lines
bin/src/ViennaRNA-2.1.9/H/snofold.h, C/C++, 58 lines
bin/src/ViennaRNA-2.1.9/H/snoop.h, C/C++, 284 lines
bin/src/ViennaRNA-2.1.9/H/stringdist.h, C/C++, 30 lines
bin/src/ViennaRNA-2.1.9/H/subopt.h, C/C++, 117 lines
bin/src/ViennaRNA-2.1.9/H/svm_utils.h, C/C++, 46 lines
bin/src/ViennaRNA-2.1.9/H/treedist.h, C/C++, 42 lines
bin/src/ViennaRNA-2.1.9/H/utils.h, C/C++, 615 lines
bin/src/ViennaRNA-2.1.9/Kinfold/Laplace/extract_data.pl, Perl, 16 lines
bin/src/ViennaRNA-2.1.9/Kinfold/Laplace/laplace.sh, Shell, 26 lines
bin/src/ViennaRNA-2.1.9/Kinfold/Laplace/to_boxplot.R, R, 6 lines
bin/src/ViennaRNA-2.1.9/Kinfold/baum.c, C, 764 lines
bin/src/ViennaRNA-2.1.9/Kinfold/baum.h, C/C++, 20 lines
bin/src/ViennaRNA-2.1.9/Kinfold/cache.c, C, 133 lines
bin/src/ViennaRNA-2.1.9/Kinfold/cache_util.h, C/C++, 33 lines
bin/src/ViennaRNA-2.1.9/Kinfold/cmdline.c, C, 1,689 lines
bin/src/ViennaRNA-2.1.9/Kinfold/cmdline.h, C/C++, 259 lines
bin/src/ViennaRNA-2.1.9/Kinfold/config.h, C/C++, 91 lines
bin/src/ViennaRNA-2.1.9/Kinfold/globals.c, C, 584 lines
bin/src/ViennaRNA-2.1.9/Kinfold/globals.h, C/C++, 79 lines
bin/src/ViennaRNA-2.1.9/Kinfold/main.c, C, 224 lines
bin/src/ViennaRNA-2.1.9/Kinfold/nachbar.c, C, 440 lines
bin/src/ViennaRNA-2.1.9/Kinfold/nachbar.h, C/C++, 21 lines
bin/src/ViennaRNA-2.1.9/Progs/RNA2Dfold.c, C, 344 lines
bin/src/ViennaRNA-2.1.9/Progs/RNA2Dfold_cmdl.c, C, 1,939 lines
bin/src/ViennaRNA-2.1.9/Progs/RNA2Dfold_cmdl.h, C/C++, 308 lines
bin/src/ViennaRNA-2.1.9/Progs/RNALalifold.c, C, 393 lines
bin/src/ViennaRNA-2.1.9/Progs/RNALalifold_cmdl.c, C, 1,681 lines
bin/src/ViennaRNA-2.1.9/Progs/RNALalifold_cmdl.h, C/C++, 338 lines
bin/src/ViennaRNA-2.1.9/Progs/RNALfold.c, C, 186 lines
bin/src/ViennaRNA-2.1.9/Progs/RNALfold_cmdl.c, C, 1,502 lines
bin/src/ViennaRNA-2.1.9/Progs/RNALfold_cmdl.h, C/C++, 289 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAPKplex.c, C, 520 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAPKplex_cmdl.c, C, 1,417 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAPKplex_cmdl.h, C/C++, 268 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAaliduplex.c, C, 172 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAaliduplex_cmdl.c, C, 1,428 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAaliduplex_cmdl.h, C/C++, 262 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAalifold.c, C, 700 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAalifold_cmdl.c, C, 1,995 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAalifold_cmdl.h, C/C++, 428 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAcofold.c, C, 687 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAcofold_cmdl.c, C, 1,718 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAcofold_cmdl.h, C/C++, 327 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAdistance.c, C, 548 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAdistance_cmdl.c, C, 1,216 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAdistance_cmdl.h, C/C++, 207 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAduplex.c, C, 204 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAduplex_cmdl.c, C, 1,458 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAduplex_cmdl.h, C/C++, 268 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAeval.c, C, 244 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAeval_cmdl.c, C, 1,367 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAeval_cmdl.h, C/C++, 251 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAfold.c, C, 415 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAfold_cmdl.c, C, 1,754 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAfold_cmdl.h, C/C++, 340 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAheat.c, C, 253 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAheat_cmdl.c, C, 1,506 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAheat_cmdl.h, C/C++, 289 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAinverse.c, C, 249 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAinverse_cmdl.c, C, 1,513 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAinverse_cmdl.h, C/C++, 293 lines
bin/src/ViennaRNA-2.1.9/Progs/RNApaln.c, C, 314 lines
bin/src/ViennaRNA-2.1.9/Progs/RNApaln_cmdl.c, C, 1,591 lines
bin/src/ViennaRNA-2.1.9/Progs/RNApaln_cmdl.h, C/C++, 318 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAparconv.c, C, 111 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAparconv_cmdl.c, C, 1,517 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAparconv_cmdl.h, C/C++, 310 lines
bin/src/ViennaRNA-2.1.9/Progs/RNApdist.c, C, 326 lines
bin/src/ViennaRNA-2.1.9/Progs/RNApdist_cmdl.c, C, 1,531 lines
bin/src/ViennaRNA-2.1.9/Progs/RNApdist_cmdl.h, C/C++, 280 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAplex.c, C, 2,390 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAplex_cmdl.c, C, 1,689 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAplex_cmdl.h, C/C++, 346 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAplfold.c, C, 434 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAplfold_cmdl.c, C, 1,642 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAplfold_cmdl.h, C/C++, 338 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAplot.c, C, 144 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAplot_cmdl.c, C, 1,198 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAplot_cmdl.h, C/C++, 211 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAsnoop.c, C, 1,083 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAsnoop_cmdl.c, C, 1,886 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAsnoop_cmdl.h, C/C++, 446 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAsubopt.c, C, 383 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAsubopt_cmdl.c, C, 1,745 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAsubopt_cmdl.h, C/C++, 338 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAup.c, C, 1,142 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAup_cmdl.c, C, 2,082 lines
bin/src/ViennaRNA-2.1.9/Progs/RNAup_cmdl.h, C/C++, 354 lines
bin/src/ViennaRNA-2.1.9/RNAforester/config.h, C/C++, 80 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/demo/demo_cpp.cpp, C++, 40 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/demo/g2_anim.c, C, 60 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/demo/g2_arc.c, C, 148 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/demo/g2_splines_demo.c, C, 143 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/demo/g2_test.c, C, 254 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/demo/handles.c, C, 58 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/demo/penguin.c, C, 63 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/demo/pointer.c, C, 46 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/demo/simple_FIG.c, C, 30 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/demo/simple_PS.c, C, 30 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/demo/simple_X11.c, C, 31 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/demo/simple_gd.c, C, 31 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/demo/simple_win32.c, C, 31 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/g2_perl/Makefile.PL, Perl, 10 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/g2_perl/test.pl, Perl, 213 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/include/g2.h, C/C++, 200 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/include/g2_FIG.h, C/C++, 54 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/include/g2_PS.h, C/C++, 118 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/include/g2_X11.h, C/C++, 45 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/include/g2_gd.h, C/C++, 67 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/perl/Makefile.PL, Perl, 58 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/perl/g2_wrap.c, C, 2,172 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/FIG/g2_FIG.c, C, 486 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/FIG/g2_FIG.h, C/C++, 54 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/FIG/g2_FIG_P.h, C/C++, 82 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/FIG/g2_FIG_funix.h, C/C++, 56 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/GD/g2_gd.c, C, 348 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/GD/g2_gd.h, C/C++, 67 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/GD/g2_gd_P.h, C/C++, 118 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/GD/g2_gd_funix.h, C/C++, 54 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/PS/g2_PS.c, C, 624 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/PS/g2_PS.h, C/C++, 118 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/PS/g2_PS_P.h, C/C++, 99 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/PS/g2_PS_definitions.h, C/C++, 104 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/PS/g2_PS_funix.h, C/C++, 56 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/Win32/g2_win32.c, C, 671 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/Win32/g2_win32.h, C/C++, 68 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/Win32/g2_win32_P.h, C/C++, 119 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/Win32/g2_win32_funix.h, C/C++, 56 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/Win32/g2_win32_thread.c, C, 213 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/Win32/g2res.c, C, 7 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/Win32/resource.h, C/C++, 21 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/X11/g2_X11.c, C, 797 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/X11/g2_X11.h, C/C++, 45 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/X11/g2_X11_P.h, C/C++, 94 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/X11/g2_X11_funix.h, C/C++, 60 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/g2.h, C/C++, 200 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/g2_config.h, C/C++, 42 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/g2_control_pd.c, C, 314 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/g2_control_pd.h, C/C++, 39 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/g2_device.c, C, 206 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/g2_device.h, C/C++, 64 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/g2_fif.c, C, 499 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/g2_funix.h, C/C++, 160 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/g2_graphic_pd.c, C, 684 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/g2_graphic_pd.h, C/C++, 66 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/g2_physical_device.c, C, 78 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/g2_physical_device.h, C/C++, 61 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/g2_splines.c, C, 880 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/g2_ui_control.c, C, 612 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/g2_ui_device.c, C, 196 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/g2_ui_graphic.c, C, 1,045 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/g2_ui_virtual_device.c, C, 149 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/g2_util.c, C, 224 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/g2_util.h, C/C++, 48 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/g2_virtual_device.c, C, 82 lines
bin/src/ViennaRNA-2.1.9/RNAforester/g2-0.70/src/g2_virtual_device.h, C/C++, 32 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/Arguments.cpp, C++, 220 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/Arguments.h, C/C++, 110 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/algebra.h, C/C++, 66 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/alignment.h, C/C++, 94 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/alignment.t.cpp, C++, 1,045 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/debug.h, C/C++, 30 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/fold.h, C/C++, 8 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/fold_vars.h, C/C++, 37 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/glib.c, C, 355 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/graphtypes.h, C/C++, 89 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/main.cpp, C++, 1,127 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/matrix.h, C/C++, 61 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/misc.h, C/C++, 21 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/misc.t.cpp, C++, 37 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/pairs.c, C, 140 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/part_func.h, C/C++, 13 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/pointer.c, C, 80 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/ppforest.h, C/C++, 124 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/ppforest.t.cpp, C++, 226 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/ppforestali.h, C/C++, 52 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/ppforestbase.cpp, C++, 203 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/ppforestbase.h, C/C++, 225 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/ppforestsz.h, C/C++, 69 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/ppforestsz.t.cpp, C++, 77 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/progressive_align.cpp, C++, 343 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/progressive_align.h, C/C++, 32 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/readgraph.c, C, 129 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/rna_algebra.cpp, C++, 89 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/rna_algebra.h, C/C++, 638 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/rna_alignment.cpp, C++, 191 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/rna_alignment.h, C/C++, 87 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/rna_alphabet.cpp, C++, 33 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/rna_alphabet.h, C/C++, 99 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/rna_profile_alignment.cpp, C++, 1,294 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/rna_profile_alignment.h, C/C++, 147 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/rnaforest.cpp, C++, 183 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/rnaforest.h, C/C++, 61 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/rnaforester_options.cpp, C++, 244 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/rnaforester_options.h, C/C++, 140 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/rnaforestsz.cpp, C++, 114 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/rnaforestsz.h, C/C++, 49 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/rnafuncs.cpp, C++, 1,127 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/rnafuncs.h, C/C++, 68 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/term.c, C, 66 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/treeedit.h, C/C++, 72 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/treeedit.t.cpp, C++, 174 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/types.h, C/C++, 7 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/unpairs.c, C, 112 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/utils.h, C/C++, 52 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/wmatch.c, C, 147 lines
bin/src/ViennaRNA-2.1.9/RNAforester/src/wmatch.h, C/C++, 40 lines
bin/src/ViennaRNA-2.1.9/Readseq/macinit.c, C, 302 lines
bin/src/ViennaRNA-2.1.9/Readseq/macinit.r, R, 412 lines
bin/src/ViennaRNA-2.1.9/Readseq/readseq.c, C, 1,139 lines
bin/src/ViennaRNA-2.1.9/Readseq/ureadasn.c, C, 305 lines
bin/src/ViennaRNA-2.1.9/Readseq/ureadseq.c, C, 1,876 lines
bin/src/ViennaRNA-2.1.9/Readseq/ureadseq.h, C/C++, 169 lines
bin/src/ViennaRNA-2.1.9/Utils/b2ct.c, C, 152 lines
bin/src/ViennaRNA-2.1.9/Utils/b2mt.pl, Perl, 42 lines
bin/src/ViennaRNA-2.1.9/Utils/cmount.pl, Perl, 152 lines
bin/src/ViennaRNA-2.1.9/Utils/coloraln.pl, Perl, 564 lines
bin/src/ViennaRNA-2.1.9/Utils/colorrna.pl, Perl, 111 lines
bin/src/ViennaRNA-2.1.9/Utils/ct2b.pl, Perl, 27 lines
bin/src/ViennaRNA-2.1.9/Utils/ct2db.c, C, 198 lines
bin/src/ViennaRNA-2.1.9/Utils/ct2db_cmdl.c, C, 1,127 lines
bin/src/ViennaRNA-2.1.9/Utils/ct2db_cmdl.h, C/C++, 183 lines
bin/src/ViennaRNA-2.1.9/Utils/dpzoom.pl, Perl, 46 lines
bin/src/ViennaRNA-2.1.9/Utils/mountain.pl, Perl, 135 lines
bin/src/ViennaRNA-2.1.9/Utils/popt.c, C, 88 lines
bin/src/ViennaRNA-2.1.9/Utils/refold.pl, Perl, 327 lines
bin/src/ViennaRNA-2.1.9/Utils/relplot.pl, Perl, 200 lines
bin/src/ViennaRNA-2.1.9/Utils/rotate_ss.pl, Perl, 138 lines
bin/src/ViennaRNA-2.1.9/Utils/switch.pl, Perl, 608 lines
bin/src/ViennaRNA-2.1.9/config.h, C/C++, 163 lines
bin/src/ViennaRNA-2.1.9/doc/html/2Dfold_8h.js, JavaScript, 7 lines
bin/src/ViennaRNA-2.1.9/doc/html/2Dpfold_8h.js, JavaScript, 9 lines
bin/src/ViennaRNA-2.1.9/doc/html/LPfold_8h.js, JavaScript, 9 lines
bin/src/ViennaRNA-2.1.9/doc/html/Lfold_8h.js, JavaScript, 6 lines
bin/src/ViennaRNA-2.1.9/doc/html/MEA_8h.js, JavaScript, 4 lines
bin/src/ViennaRNA-2.1.9/doc/html/PS__dot_8h.js, JavaScript, 12 lines
bin/src/ViennaRNA-2.1.9/doc/html/RNAstruct_8h.js, JavaScript, 19 lines
bin/src/ViennaRNA-2.1.9/doc/html/alifold_8h.js, JavaScript, 21 lines
bin/src/ViennaRNA-2.1.9/doc/html/annotated.js, JavaScript, 41 lines
bin/src/ViennaRNA-2.1.9/doc/html/cofold_8h.js, JavaScript, 13 lines
bin/src/ViennaRNA-2.1.9/doc/html/convert__epars_8h.js, JavaScript, 26 lines
bin/src/ViennaRNA-2.1.9/doc/html/data__structures_8h.js, JavaScript, 36 lines
bin/src/ViennaRNA-2.1.9/doc/html/dir_97aefd0d527b934f1d99a682da8fe6a9.js, JavaScript, 12 lines
bin/src/ViennaRNA-2.1.9/doc/html/dir_d72344b28b4f2089ce25682c4e6eba22.js, JavaScript, 49 lines
bin/src/ViennaRNA-2.1.9/doc/html/dist__vars_8h.js, JavaScript, 9 lines
bin/src/ViennaRNA-2.1.9/doc/html/dynsections.js, JavaScript, 97 lines
bin/src/ViennaRNA-2.1.9/doc/html/energy__const_8h.js, JavaScript, 11 lines
bin/src/ViennaRNA-2.1.9/doc/html/files.js, JavaScript, 6 lines
bin/src/ViennaRNA-2.1.9/doc/html/findpath_8h.js, JavaScript, 6 lines
bin/src/ViennaRNA-2.1.9/doc/html/fold_8h.js, JavaScript, 30 lines
bin/src/ViennaRNA-2.1.9/doc/html/fold__vars_8h.js, JavaScript, 29 lines
bin/src/ViennaRNA-2.1.9/doc/html/globals_dup.js, JavaScript, 27 lines
bin/src/ViennaRNA-2.1.9/doc/html/gquad_8h.js, JavaScript, 7 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__centroid__fold.js, JavaScript, 5 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__class__fold.js, JavaScript, 5 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__cofold.js, JavaScript, 6 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__consensus__fold.js, JavaScript, 17 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__consensus__mfe__fold.js, JavaScript, 6 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__consensus__pf__fold.js, JavaScript, 7 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__consensus__stochbt.js, JavaScript, 4 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__dos.js, JavaScript, 4 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__energy__parameters.js, JavaScript, 12 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__energy__parameters__convert.js, JavaScript, 27 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__energy__parameters__rw.js, JavaScript, 7 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__eval.js, JavaScript, 10 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__folding__routines.js, JavaScript, 13 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__inverse__fold.js, JavaScript, 10 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__kl__neighborhood.js, JavaScript, 6 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__kl__neighborhood__mfe.js, JavaScript, 8 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__kl__neighborhood__pf.js, JavaScript, 8 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__kl__neighborhood__stochbt.js, JavaScript, 5 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__local__consensus__fold.js, JavaScript, 4 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__local__fold.js, JavaScript, 7 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__local__mfe__fold.js, JavaScript, 5 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__local__pf__fold.js, JavaScript, 9 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__mfe__cofold.js, JavaScript, 10 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__mfe__fold.js, JavaScript, 12 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__pf__cofold.js, JavaScript, 14 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__pf__fold.js, JavaScript, 22 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__subopt__fold.js, JavaScript, 7 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__subopt__stochbt.js, JavaScript, 8 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__subopt__wuchty.js, JavaScript, 8 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__subopt__zuker.js, JavaScript, 5 lines
bin/src/ViennaRNA-2.1.9/doc/html/group__up__cofold.js, JavaScript, 8 lines
bin/src/ViennaRNA-2.1.9/doc/html/inverse_8h.js, JavaScript, 9 lines
bin/src/ViennaRNA-2.1.9/doc/html/jquery.js, JavaScript, 77 lines
bin/src/ViennaRNA-2.1.9/doc/html/loop__energies_8h.js, JavaScript, 9 lines
bin/src/ViennaRNA-2.1.9/doc/html/modules.js, JavaScript, 5 lines
bin/src/ViennaRNA-2.1.9/doc/html/navtree.js, JavaScript, 542 lines
bin/src/ViennaRNA-2.1.9/doc/html/navtreeindex0.js, JavaScript, 253 lines
bin/src/ViennaRNA-2.1.9/doc/html/navtreeindex1.js, JavaScript, 253 lines
bin/src/ViennaRNA-2.1.9/doc/html/navtreeindex2.js, JavaScript, 253 lines
bin/src/ViennaRNA-2.1.9/doc/html/navtreeindex3.js, JavaScript, 56 lines
bin/src/ViennaRNA-2.1.9/doc/html/params_8h.js, JavaScript, 10 lines
bin/src/ViennaRNA-2.1.9/doc/html/part__func_8h.js, JavaScript, 27 lines
bin/src/ViennaRNA-2.1.9/doc/html/part__func__co_8h.js, JavaScript, 15 lines
bin/src/ViennaRNA-2.1.9/doc/html/part__func__up_8h.js, JavaScript, 7 lines
bin/src/ViennaRNA-2.1.9/doc/html/plot__layouts_8h.js, JavaScript, 9 lines
bin/src/ViennaRNA-2.1.9/doc/html/profiledist_8h.js, JavaScript, 8 lines
bin/src/ViennaRNA-2.1.9/doc/html/read__epars_8h.js, JavaScript, 5 lines
bin/src/ViennaRNA-2.1.9/doc/html/resize.js, JavaScript, 93 lines
bin/src/ViennaRNA-2.1.9/doc/html/stringdist_8h.js, JavaScript, 5 lines
bin/src/ViennaRNA-2.1.9/doc/html/structConcEnt.js, JavaScript, 6 lines
bin/src/ViennaRNA-2.1.9/doc/html/structSOLUTION.js, JavaScript, 5 lines
bin/src/ViennaRNA-2.1.9/doc/html/structTwoDfold__solution.js, JavaScript, 7 lines
bin/src/ViennaRNA-2.1.9/doc/html/structTwoDfold__vars.js, JavaScript, 16 lines
bin/src/ViennaRNA-2.1.9/doc/html/structTwoDpfold__solution.js, JavaScript, 6 lines
bin/src/ViennaRNA-2.1.9/doc/html/structTwoDpfold__vars.js, JavaScript, 15 lines
bin/src/ViennaRNA-2.1.9/doc/html/structcofoldF.js, JavaScript, 8 lines
bin/src/ViennaRNA-2.1.9/doc/html/structinteract.js, JavaScript, 12 lines
bin/src/ViennaRNA-2.1.9/doc/html/structintermediate__t.js, JavaScript, 7 lines
bin/src/ViennaRNA-2.1.9/doc/html/structmodel__detailsT.js, JavaScript, 12 lines
bin/src/ViennaRNA-2.1.9/doc/html/structpair__info.js, JavaScript, 9 lines
bin/src/ViennaRNA-2.1.9/doc/html/structparamT.js, JavaScript, 5 lines
bin/src/ViennaRNA-2.1.9/doc/html/structpf__paramT.js, JavaScript, 7 lines
bin/src/ViennaRNA-2.1.9/doc/html/structpu__contrib.js, JavaScript, 9 lines
bin/src/ViennaRNA-2.1.9/doc/html/structpu__out.js, JavaScript, 8 lines
bin/src/ViennaRNA-2.1.9/doc/html/subopt_8h.js, JavaScript, 9 lines
bin/src/ViennaRNA-2.1.9/doc/html/svgpan.js, JavaScript, 319 lines
bin/src/ViennaRNA-2.1.9/doc/html/treedist_8h.js, JavaScript, 7 lines
bin/src/ViennaRNA-2.1.9/doc/html/utils_8h.js, JavaScript, 65 lines
bin/src/ViennaRNA-2.1.9/doc/mainpage.h, C/C++, 848 lines
bin/src/ViennaRNA-2.1.9/interfaces/Perl/Makefile.PL, Perl, 51 lines
bin/src/ViennaRNA-2.1.9/interfaces/Perl/RNA_wrap.c, C, 7,231 lines
bin/src/ViennaRNA-2.1.9/interfaces/Perl/RNAfold.pl, Perl, 144 lines
bin/src/ViennaRNA-2.1.9/interfaces/Perl/test.pl, Perl, 166 lines
bin/src/ViennaRNA-2.1.9/interfaces/Python/RNA_wrap.c, C, 6,691 lines
bin/src/ViennaRNA-2.1.9/interfaces/Python/__init__.py, Python, 1,124 lines
bin/src/ViennaRNA-2.1.9/interfaces/Python/setup.py, Python, 35 lines
bin/src/ViennaRNA-2.1.9/interfaces/Python/version_test.py, Python, 10 lines
bin/src/ViennaRNA-2.1.9/lib/1.8.4_epars.h, C/C++, 366 lines
bin/src/ViennaRNA-2.1.9/lib/1.8.4_intloops.h, C/C++, 2,992 lines
bin/src/ViennaRNA-2.1.9/lib/2Dfold.c, C, 4,024 lines
bin/src/ViennaRNA-2.1.9/lib/2Dpfold.c, C, 4,489 lines
bin/src/ViennaRNA-2.1.9/lib/LPfold.c, C, 1,379 lines
bin/src/ViennaRNA-2.1.9/lib/Lfold.c, C, 1,284 lines
bin/src/ViennaRNA-2.1.9/lib/MEA.c, C, 278 lines
bin/src/ViennaRNA-2.1.9/lib/PS_dot.c, C, 2,277 lines
bin/src/ViennaRNA-2.1.9/lib/ProfileAln.c, C, 272 lines
bin/src/ViennaRNA-2.1.9/lib/ProfileDist.c, C, 247 lines
bin/src/ViennaRNA-2.1.9/lib/RNAstruct.c, C, 579 lines
bin/src/ViennaRNA-2.1.9/lib/aliLfold.c, C, 972 lines
bin/src/ViennaRNA-2.1.9/lib/ali_plex.c, C, 1,294 lines
bin/src/ViennaRNA-2.1.9/lib/alifold.c, C, 1,726 lines
bin/src/ViennaRNA-2.1.9/lib/alipfold.c, C, 1,290 lines
bin/src/ViennaRNA-2.1.9/lib/aln_util.c, C, 171 lines
bin/src/ViennaRNA-2.1.9/lib/c_plex.c, C, 1,172 lines
bin/src/ViennaRNA-2.1.9/lib/cofold.c, C, 1,510 lines
bin/src/ViennaRNA-2.1.9/lib/convert_epars.c, C, 950 lines
bin/src/ViennaRNA-2.1.9/lib/dist_vars.c, C, 9 lines
bin/src/ViennaRNA-2.1.9/lib/duplex.c, C, 565 lines
bin/src/ViennaRNA-2.1.9/lib/energy_par.c, C, 843 lines
bin/src/ViennaRNA-2.1.9/lib/findpath.c, C, 543 lines
bin/src/ViennaRNA-2.1.9/lib/fold.c, C, 2,792 lines
bin/src/ViennaRNA-2.1.9/lib/fold_vars.c, C, 91 lines
bin/src/ViennaRNA-2.1.9/lib/gquad.c, C, 1,051 lines
bin/src/ViennaRNA-2.1.9/lib/intl11.h, C/C++, 393 lines
bin/src/ViennaRNA-2.1.9/lib/intl11dH.h, C/C++, 393 lines
bin/src/ViennaRNA-2.1.9/lib/intl21.h, C/C++, 1,993 lines
bin/src/ViennaRNA-2.1.9/lib/intl21dH.h, C/C++, 1,993 lines
bin/src/ViennaRNA-2.1.9/lib/intl22.h, C/C++, 5,749 lines
bin/src/ViennaRNA-2.1.9/lib/intl22dH.h, C/C++, 5,749 lines
bin/src/ViennaRNA-2.1.9/lib/inverse.c, C, 533 lines
bin/src/ViennaRNA-2.1.9/lib/list.c, C, 411 lines
bin/src/ViennaRNA-2.1.9/lib/list.h, C/C++, 65 lines
bin/src/ViennaRNA-2.1.9/lib/mm.c, C, 99 lines
bin/src/ViennaRNA-2.1.9/lib/move_set.c, C, 1,130 lines
bin/src/ViennaRNA-2.1.9/lib/naview.c, C, 1,167 lines
bin/src/ViennaRNA-2.1.9/lib/params.c, C, 595 lines
bin/src/ViennaRNA-2.1.9/lib/part_func.c, C, 1,724 lines
bin/src/ViennaRNA-2.1.9/lib/part_func_co.c, C, 1,053 lines
bin/src/ViennaRNA-2.1.9/lib/part_func_up.c, C, 1,458 lines
bin/src/ViennaRNA-2.1.9/lib/plex.c, C, 2,853 lines
bin/src/ViennaRNA-2.1.9/lib/plex_functions.c, C, 301 lines
bin/src/ViennaRNA-2.1.9/lib/plot_layouts.c, C, 157 lines
bin/src/ViennaRNA-2.1.9/lib/read_epars.c, C, 1,075 lines
bin/src/ViennaRNA-2.1.9/lib/ribo.c, C, 1,104 lines
bin/src/ViennaRNA-2.1.9/lib/snofold.c, C, 1,287 lines
bin/src/ViennaRNA-2.1.9/lib/snoop.c, C, 2,586 lines
bin/src/ViennaRNA-2.1.9/lib/stringdist.c, C, 433 lines
bin/src/ViennaRNA-2.1.9/lib/subopt.c, C, 1,510 lines
bin/src/ViennaRNA-2.1.9/lib/svm_utils.c, C, 474 lines
bin/src/ViennaRNA-2.1.9/lib/treedist.c, C, 657 lines
bin/src/ViennaRNA-2.1.9/lib/utils.c, C, 1,154 lines
bin/src/ViennaRNA-2.1.9/libsvm-2.91/svm.cpp, C++, 3,069 lines
bin/src/ViennaRNA-2.1.9/libsvm-2.91/svm.h, C/C++, 76 lines
bin/src/ViennaRNA-2.1.9/man/cmdlopt.sh, Shell, 15 lines
bin/src/bwa-0.7.15/QSufSort.c, C, 402 lines
bin/src/bwa-0.7.15/QSufSort.h, C/C++, 45 lines
bin/src/bwa-0.7.15/bamlite.c, C, 210 lines
bin/src/bwa-0.7.15/bamlite.h, C/C++, 114 lines
bin/src/bwa-0.7.15/bntseq.c, C, 446 lines
bin/src/bwa-0.7.15/bntseq.h, C/C++, 92 lines
bin/src/bwa-0.7.15/bwa.c, C, 450 lines
bin/src/bwa-0.7.15/bwa.h, C/C++, 69 lines
bin/src/bwa-0.7.15/bwakit/bwa-postalt.js, JavaScript, 524 lines
bin/src/bwa-0.7.15/bwakit/typeHLA-selctg.js, JavaScript, 62 lines
bin/src/bwa-0.7.15/bwakit/typeHLA.js, JavaScript, 496 lines
bin/src/bwa-0.7.15/bwakit/typeHLA.sh, Shell, 49 lines
bin/src/bwa-0.7.15/bwamem.c, C, 1,201 lines
bin/src/bwa-0.7.15/bwamem.h, C/C++, 184 lines
bin/src/bwa-0.7.15/bwamem_extra.c, C, 140 lines
bin/src/bwa-0.7.15/bwamem_pair.c, C, 388 lines
bin/src/bwa-0.7.15/bwape.c, C, 783 lines
bin/src/bwa-0.7.15/bwase.c, C, 603 lines
bin/src/bwa-0.7.15/bwase.h, C/C++, 29 lines
bin/src/bwa-0.7.15/bwaseqio.c, C, 235 lines
bin/src/bwa-0.7.15/bwashm.c, C, 213 lines
bin/src/bwa-0.7.15/bwt.c, C, 469 lines
bin/src/bwa-0.7.15/bwt.h, C/C++, 130 lines
bin/src/bwa-0.7.15/bwt_gen.c, C, 1,632 lines
bin/src/bwa-0.7.15/bwt_lite.c, C, 98 lines
bin/src/bwa-0.7.15/bwt_lite.h, C/C++, 29 lines
bin/src/bwa-0.7.15/bwtaln.c, C, 320 lines
bin/src/bwa-0.7.15/bwtaln.h, C/C++, 153 lines
bin/src/bwa-0.7.15/bwtgap.c, C, 264 lines
bin/src/bwa-0.7.15/bwtgap.h, C/C++, 40 lines
bin/src/bwa-0.7.15/bwtindex.c, C, 324 lines
bin/src/bwa-0.7.15/bwtsw2.h, C/C++, 69 lines
bin/src/bwa-0.7.15/bwtsw2_aux.c, C, 776 lines
bin/src/bwa-0.7.15/bwtsw2_chain.c, C, 112 lines
bin/src/bwa-0.7.15/bwtsw2_core.c, C, 619 lines
bin/src/bwa-0.7.15/bwtsw2_main.c, C, 89 lines
bin/src/bwa-0.7.15/bwtsw2_pair.c, C, 268 lines
bin/src/bwa-0.7.15/example.c, C, 60 lines
bin/src/bwa-0.7.15/fastmap.c, C, 441 lines
bin/src/bwa-0.7.15/is.c, C, 223 lines
bin/src/bwa-0.7.15/kbtree.h, C/C++, 388 lines
bin/src/bwa-0.7.15/khash.h, C/C++, 614 lines
bin/src/bwa-0.7.15/kopen.c, C, 374 lines
bin/src/bwa-0.7.15/kseq.h, C/C++, 239 lines
bin/src/bwa-0.7.15/ksort.h, C/C++, 273 lines
bin/src/bwa-0.7.15/kstring.c, C, 39 lines
bin/src/bwa-0.7.15/kstring.h, C/C++, 115 lines
bin/src/bwa-0.7.15/ksw.c, C, 713 lines
bin/src/bwa-0.7.15/ksw.h, C/C++, 114 lines
bin/src/bwa-0.7.15/kthread.c, C, 146 lines
bin/src/bwa-0.7.15/kvec.h, C/C++, 94 lines
bin/src/bwa-0.7.15/main.c, C, 104 lines
bin/src/bwa-0.7.15/malloc_wrap.c, C, 57 lines
bin/src/bwa-0.7.15/malloc_wrap.h, C/C++, 47 lines
bin/src/bwa-0.7.15/maxk.c, C, 67 lines
bin/src/bwa-0.7.15/pemerge.c, C, 291 lines
bin/src/bwa-0.7.15/qualfa2fq.pl, Perl, 27 lines
bin/src/bwa-0.7.15/rle.c, C, 191 lines
bin/src/bwa-0.7.15/rle.h, C/C++, 77 lines
bin/src/bwa-0.7.15/rope.c, C, 318 lines
bin/src/bwa-0.7.15/rope.h, C/C++, 58 lines
bin/src/bwa-0.7.15/utils.c, C, 295 lines
bin/src/bwa-0.7.15/utils.h, C/C++, 111 lines
bin/src/bwa-0.7.15/xa2multi.pl, Perl, 25 lines
bin/src/cctop_standalone/CCTop.py, Python, 712 lines
bin/src/cctop_standalone/bedInterval.py, Python, 86 lines
bin/src/cufflinks-2.2.1/make_bin.sh, Shell, 54 lines
bin/src/cufflinks-2.2.1/src/GArgs.cpp, C++, 376 lines
bin/src/cufflinks-2.2.1/src/GArgs.h, C/C++, 98 lines
bin/src/cufflinks-2.2.1/src/GBase.cpp, C++, 780 lines
bin/src/cufflinks-2.2.1/src/GBase.h, C/C++, 458 lines
bin/src/cufflinks-2.2.1/src/GFaSeqGet.cpp, C++, 319 lines
bin/src/cufflinks-2.2.1/src/GFaSeqGet.h, C/C++, 136 lines
bin/src/cufflinks-2.2.1/src/GFastaIndex.cpp, C++, 170 lines
bin/src/cufflinks-2.2.1/src/GFastaIndex.h, C/C++, 79 lines
bin/src/cufflinks-2.2.1/src/GStr.cpp, C++, 1,354 lines
bin/src/cufflinks-2.2.1/src/GStr.h, C/C++, 223 lines
bin/src/cufflinks-2.2.1/src/abundances.cpp, C++, 5,165 lines
bin/src/cufflinks-2.2.1/src/abundances.h, C/C++, 806 lines
bin/src/cufflinks-2.2.1/src/assemble.cpp, C++, 568 lines
bin/src/cufflinks-2.2.1/src/assemble.h, C/C++, 44 lines
bin/src/cufflinks-2.2.1/src/biascorrection.cpp, C++, 835 lines
bin/src/cufflinks-2.2.1/src/biascorrection.h, C/C++, 118 lines
bin/src/cufflinks-2.2.1/src/bundles.cpp, C++, 2,104 lines
bin/src/cufflinks-2.2.1/src/bundles.h, C/C++, 405 lines
bin/src/cufflinks-2.2.1/src/clustering.cpp, C++, 130 lines
bin/src/cufflinks-2.2.1/src/clustering.h, C/C++, 168 lines
bin/src/cufflinks-2.2.1/src/codons.cpp, C++, 90 lines
bin/src/cufflinks-2.2.1/src/codons.h, C/C++, 54 lines
bin/src/cufflinks-2.2.1/src/common.cpp, C++, 507 lines
bin/src/cufflinks-2.2.1/src/common.h, C/C++, 817 lines
bin/src/cufflinks-2.2.1/src/compress_gtf.cpp, C++, 428 lines
bin/src/cufflinks-2.2.1/src/cuffcompare.cpp, C++, 2,684 lines
bin/src/cufflinks-2.2.1/src/cuffdiff.cpp, C++, 2,721 lines
bin/src/cufflinks-2.2.1/src/cufflinks.cpp, C++, 1,802 lines
bin/src/cufflinks-2.2.1/src/cuffnorm.cpp, C++, 1,995 lines
bin/src/cufflinks-2.2.1/src/cuffquant.cpp, C++, 1,623 lines
bin/src/cufflinks-2.2.1/src/differential.cpp, C++, 1,705 lines
bin/src/cufflinks-2.2.1/src/differential.h, C/C++, 217 lines
bin/src/cufflinks-2.2.1/src/filters.cpp, C++, 1,151 lines
bin/src/cufflinks-2.2.1/src/filters.h, C/C++, 42 lines
bin/src/cufflinks-2.2.1/src/gdna.cpp, C++, 90 lines
bin/src/cufflinks-2.2.1/src/gdna.h, C/C++, 15 lines
bin/src/cufflinks-2.2.1/src/genes.cpp, C++, 155 lines
bin/src/cufflinks-2.2.1/src/genes.h, C/C++, 217 lines
bin/src/cufflinks-2.2.1/src/gff.cpp, C++, 2,124 lines
bin/src/cufflinks-2.2.1/src/gff.h, C/C++, 1,094 lines
bin/src/cufflinks-2.2.1/src/gff_utils.cpp, C++, 676 lines
bin/src/cufflinks-2.2.1/src/gff_utils.h, C/C++, 610 lines
bin/src/cufflinks-2.2.1/src/gffread.cpp, C++, 1,060 lines
bin/src/cufflinks-2.2.1/src/graph_optimize.cpp, C++, 751 lines
bin/src/cufflinks-2.2.1/src/graph_optimize.h, C/C++, 94 lines
bin/src/cufflinks-2.2.1/src/gtf_to_sam.cpp, C++, 349 lines
bin/src/cufflinks-2.2.1/src/gtf_tracking.cpp, C++, 778 lines
bin/src/cufflinks-2.2.1/src/gtf_tracking.h, C/C++, 1,347 lines
bin/src/cufflinks-2.2.1/src/hits.cpp, C++, 1,276 lines
bin/src/cufflinks-2.2.1/src/hits.h, C/C++, 1,188 lines
bin/src/cufflinks-2.2.1/src/jensen_shannon.cpp, C++, 171 lines
bin/src/cufflinks-2.2.1/src/jensen_shannon.h, C/C++, 30 lines
bin/src/cufflinks-2.2.1/src/lemon/bfs.h, C/C++, 1,597 lines
bin/src/cufflinks-2.2.1/src/lemon/bin_heap.h, C/C++, 346 lines
bin/src/cufflinks-2.2.1/src/lemon/bipartite_matching.h, C/C++, 1,732 lines
bin/src/cufflinks-2.2.1/src/lemon/bits/alteration_notifier.h, C/C++, 485 lines
bin/src/cufflinks-2.2.1/src/lemon/bits/array_map.h, C/C++, 346 lines
bin/src/cufflinks-2.2.1/src/lemon/bits/base_extender.h, C/C++, 495 lines
bin/src/cufflinks-2.2.1/src/lemon/bits/debug_map.h, C/C++, 382 lines
bin/src/cufflinks-2.2.1/src/lemon/bits/default_map.h, C/C++, 181 lines
bin/src/cufflinks-2.2.1/src/lemon/bits/graph_adaptor_extender.h, C/C++, 742 lines
bin/src/cufflinks-2.2.1/src/lemon/bits/graph_extender.h, C/C++, 1,397 lines
bin/src/cufflinks-2.2.1/src/lemon/bits/invalid.h, C/C++, 54 lines
bin/src/cufflinks-2.2.1/src/lemon/bits/map_extender.h, C/C++, 321 lines
bin/src/cufflinks-2.2.1/src/lemon/bits/path_dump.h, C/C++, 174 lines
bin/src/cufflinks-2.2.1/src/lemon/bits/traits.h, C/C++, 346 lines
bin/src/cufflinks-2.2.1/src/lemon/bits/utility.h, C/C++, 140 lines
bin/src/cufflinks-2.2.1/src/lemon/bits/variant.h, C/C++, 508 lines
bin/src/cufflinks-2.2.1/src/lemon/bits/vector_map.h, C/C++, 243 lines
bin/src/cufflinks-2.2.1/src/lemon/bucket_heap.h, C/C++, 831 lines
bin/src/cufflinks-2.2.1/src/lemon/concept_check.h, C/C++, 105 lines
bin/src/cufflinks-2.2.1/src/lemon/concepts/bpugraph.h, C/C++, 1,004 lines
bin/src/cufflinks-2.2.1/src/lemon/concepts/graph.h, C/C++, 453 lines
bin/src/cufflinks-2.2.1/src/lemon/concepts/graph_components.h, C/C++, 2,093 lines
bin/src/cufflinks-2.2.1/src/lemon/concepts/heap.h, C/C++, 226 lines
bin/src/cufflinks-2.2.1/src/lemon/concepts/maps.h, C/C++, 208 lines
bin/src/cufflinks-2.2.1/src/lemon/concepts/matrix_maps.h, C/C++, 207 lines
bin/src/cufflinks-2.2.1/src/lemon/concepts/path.h, C/C++, 307 lines
bin/src/cufflinks-2.2.1/src/lemon/concepts/ugraph.h, C/C++, 702 lines
bin/src/cufflinks-2.2.1/src/lemon/dfs.h, C/C++, 1,543 lines
bin/src/cufflinks-2.2.1/src/lemon/error.h, C/C++, 683 lines
bin/src/cufflinks-2.2.1/src/lemon/fib_heap.h, C/C++, 464 lines
bin/src/cufflinks-2.2.1/src/lemon/graph_adaptor.h, C/C++, 2,720 lines
bin/src/cufflinks-2.2.1/src/lemon/graph_utils.h, C/C++, 3,179 lines
bin/src/cufflinks-2.2.1/src/lemon/list_graph.h, C/C++, 2,249 lines
bin/src/cufflinks-2.2.1/src/lemon/maps.h, C/C++, 1,633 lines
bin/src/cufflinks-2.2.1/src/lemon/math.h, C/C++, 63 lines
bin/src/cufflinks-2.2.1/src/lemon/smart_graph.h, C/C++, 1,163 lines
bin/src/cufflinks-2.2.1/src/lemon/tolerance.h, C/C++, 454 lines
bin/src/cufflinks-2.2.1/src/lemon/topology.h, C/C++, 1,590 lines
bin/src/cufflinks-2.2.1/src/locfit/adap.c, C, 195 lines
bin/src/cufflinks-2.2.1/src/locfit/ar_funs.c, C, 105 lines
bin/src/cufflinks-2.2.1/src/locfit/arith.c, C, 619 lines
bin/src/cufflinks-2.2.1/src/locfit/band.c, C, 383 lines
bin/src/cufflinks-2.2.1/src/locfit/c_args.c, C, 89 lines
bin/src/cufflinks-2.2.1/src/locfit/c_plot.c, C, 471 lines
bin/src/cufflinks-2.2.1/src/locfit/cmd.c, C, 801 lines
bin/src/cufflinks-2.2.1/src/locfit/dens_haz.c, C, 190 lines
bin/src/cufflinks-2.2.1/src/locfit/dens_int.c, C, 223 lines
bin/src/cufflinks-2.2.1/src/locfit/dens_odi.c, C, 515 lines
bin/src/cufflinks-2.2.1/src/locfit/density.c, C, 509 lines
bin/src/cufflinks-2.2.1/src/locfit/design.h, C/C++, 28 lines
bin/src/cufflinks-2.2.1/src/locfit/dist.c, C, 162 lines
bin/src/cufflinks-2.2.1/src/locfit/ev_atree.c, C, 204 lines
bin/src/cufflinks-2.2.1/src/locfit/ev_interp.c, C, 273 lines
bin/src/cufflinks-2.2.1/src/locfit/ev_kdtre.c, C, 332 lines
bin/src/cufflinks-2.2.1/src/locfit/ev_main.c, C, 287 lines
bin/src/cufflinks-2.2.1/src/locfit/ev_trian.c, C, 457 lines
bin/src/cufflinks-2.2.1/src/locfit/family.c, C, 607 lines
bin/src/cufflinks-2.2.1/src/locfit/fitted.c, C, 154 lines
bin/src/cufflinks-2.2.1/src/locfit/frend.c, C, 409 lines
bin/src/cufflinks-2.2.1/src/locfit/help.c, C, 88 lines
bin/src/cufflinks-2.2.1/src/locfit/imatlb.h, C/C++, 36 lines
bin/src/cufflinks-2.2.1/src/locfit/lf_dercor.c, C, 51 lines
bin/src/cufflinks-2.2.1/src/locfit/lf_fitfun.c, C, 258 lines
bin/src/cufflinks-2.2.1/src/locfit/lf_robust.c, C, 127 lines
bin/src/cufflinks-2.2.1/src/locfit/lf_vari.c, C, 157 lines
bin/src/cufflinks-2.2.1/src/locfit/lfcons.h, C/C++, 280 lines
bin/src/cufflinks-2.2.1/src/locfit/lfd.c, C, 319 lines
bin/src/cufflinks-2.2.1/src/locfit/lffuns.h, C/C++, 215 lines
bin/src/cufflinks-2.2.1/src/locfit/lfstr.c, C, 150 lines
bin/src/cufflinks-2.2.1/src/locfit/lfstruc.h, C/C++, 102 lines
bin/src/cufflinks-2.2.1/src/locfit/lfwin.h, C/C++, 117 lines
bin/src/cufflinks-2.2.1/src/locfit/linalg.c, C, 337 lines
bin/src/cufflinks-2.2.1/src/locfit/local.h, C/C++, 148 lines
bin/src/cufflinks-2.2.1/src/locfit/locfit.c, C, 263 lines
bin/src/cufflinks-2.2.1/src/locfit/m_chol.c, C, 72 lines
bin/src/cufflinks-2.2.1/src/locfit/m_eigen.c, C, 141 lines
bin/src/cufflinks-2.2.1/src/locfit/m_jacob.c, C, 118 lines
bin/src/cufflinks-2.2.1/src/locfit/m_max.c, C, 215 lines
bin/src/cufflinks-2.2.1/src/locfit/makecmd.c, C, 256 lines
bin/src/cufflinks-2.2.1/src/locfit/math.c, C, 158 lines
bin/src/cufflinks-2.2.1/src/locfit/minmax.c, C, 302 lines
bin/src/cufflinks-2.2.1/src/locfit/mutil.h, C/C++, 56 lines
bin/src/cufflinks-2.2.1/src/locfit/nbhd.c, C, 226 lines
bin/src/cufflinks-2.2.1/src/locfit/pcomp.c, C, 191 lines
bin/src/cufflinks-2.2.1/src/locfit/pout.c, C, 780 lines
bin/src/cufflinks-2.2.1/src/locfit/preplot.c, C, 222 lines
bin/src/cufflinks-2.2.1/src/locfit/random.c, C, 147 lines
bin/src/cufflinks-2.2.1/src/locfit/readfile.c, C, 81 lines
bin/src/cufflinks-2.2.1/src/locfit/scb.c, C, 326 lines
bin/src/cufflinks-2.2.1/src/locfit/scb_cons.c, C, 342 lines
bin/src/cufflinks-2.2.1/src/locfit/simul.c, C, 219 lines
bin/src/cufflinks-2.2.1/src/locfit/solve.c, C, 119 lines
bin/src/cufflinks-2.2.1/src/locfit/startlf.c, C, 447 lines
bin/src/cufflinks-2.2.1/src/locfit/strings.c, C, 140 lines
bin/src/cufflinks-2.2.1/src/locfit/vari.cpp, C++, 299 lines
bin/src/cufflinks-2.2.1/src/locfit/wdiag.c, C, 260 lines
bin/src/cufflinks-2.2.1/src/locfit/weight.c, C, 462 lines
bin/src/cufflinks-2.2.1/src/matching_merge.cpp, C++, 103 lines
bin/src/cufflinks-2.2.1/src/matching_merge.h, C/C++, 113 lines
bin/src/cufflinks-2.2.1/src/multireads.cpp, C++, 132 lines
bin/src/cufflinks-2.2.1/src/multireads.h, C/C++, 72 lines
bin/src/cufflinks-2.2.1/src/negative_binomial_distribution.h, C/C++, 223 lines
bin/src/cufflinks-2.2.1/src/progressbar.h, C/C++, 110 lines
bin/src/cufflinks-2.2.1/src/replicates.cpp, C++, 1,147 lines
bin/src/cufflinks-2.2.1/src/replicates.h, C/C++, 508 lines
bin/src/cufflinks-2.2.1/src/rounding.h, C/C++, 191 lines
bin/src/cufflinks-2.2.1/src/sampling.cpp, C++, 69 lines
bin/src/cufflinks-2.2.1/src/sampling.h, C/C++, 307 lines
bin/src/cufflinks-2.2.1/src/scaffold_graph.cpp, C++, 304 lines
bin/src/cufflinks-2.2.1/src/scaffold_graph.h, C/C++, 39 lines
bin/src/cufflinks-2.2.1/src/scaffolds.cpp, C++, 1,837 lines
bin/src/cufflinks-2.2.1/src/scaffolds.h, C/C++, 708 lines
bin/src/cufflinks-2.2.1/src/tokenize.cpp, C++, 39 lines
bin/src/cufflinks-2.2.1/src/tokenize.h, C/C++, 18 lines
bin/src/cufflinks-2.2.1/src/tracking.cpp, C++, 99 lines
bin/src/cufflinks-2.2.1/src/tracking.h, C/C++, 118 lines
bin/src/cufflinks-2.2.1/src/transitive_closure.h, C/C++, 391 lines
bin/src/cufflinks-2.2.1/src/transitive_reduction.h, C/C++, 129 lines
bin/src/cufflinks-2.2.1/src/update_check.h, C/C++, 113 lines
bin/src/libsvm-260/svm-predict.c, C, 177 lines
bin/src/libsvm-260/svm-scale.c, C, 290 lines
bin/src/libsvm-260/svm-train.c, C, 299 lines
bin/src/libsvm-260/svm.cpp, C++, 2,771 lines
bin/src/libsvm-260/svm.h, C/C++, 70 lines
bin/src/lindel/Lindel/Predictor.py, Python, 284 lines
bin/src/lindel/Lindel/__init__.py, Python, 1 line
bin/src/lindel/Lindel_prediction.py, Python, 30 lines
bin/src/lindel/sampleRun.sh, Shell, 1 line
bin/src/lindel/setup.py, Python, 24 lines
bin/src/pairwise-library-screen/encode_full_matrix.py, Python, 53 lines
bin/src/pairwise-library-screen/exp_model.stan, Stan, 44 lines
bin/src/pairwise-library-screen/filter_dataset.py, Python, 30 lines
bin/src/pairwise-library-screen/pairwise-library-analysis/PWL_library_features_annotate.py, Python, 120 lines
bin/src/pairwise-library-screen/pairwise-library-analysis/PWL_make_dataframe.py, Python, 78 lines
bin/src/pairwise-library-screen/pairwise-library-analysis/PWL_screen_analysis.py, Python, 460 lines
bin/src/pairwise-library-screen/predictSingle.py, Python, 74 lines
bin/src/pairwise-library-screen/predict_activity_single.py, Python, 92 lines
bin/src/pairwise-library-screen/train_rstan_model.R, R, 75 lines
bin/src/primer3-2.3.6/cmp_settings.pl, Perl, 113 lines
bin/src/primer3-2.3.6/src/dpal.c, C, 1,166 lines
bin/src/primer3-2.3.6/src/dpal.h, C/C++, 167 lines
bin/src/primer3-2.3.6/src/format_output.c, C, 950 lines
bin/src/primer3-2.3.6/src/format_output.h, C/C++, 61 lines
bin/src/primer3-2.3.6/src/libprimer3.c, C, 5,934 lines
bin/src/primer3-2.3.6/src/libprimer3.h, C/C++, 1,275 lines
bin/src/primer3-2.3.6/src/long_seq_tm_test_main.c, C, 88 lines
bin/src/primer3-2.3.6/src/ntdpal_main.c, C, 200 lines
bin/src/primer3-2.3.6/src/oligotm.c, C, 708 lines
bin/src/primer3-2.3.6/src/oligotm.h, C/C++, 203 lines
bin/src/primer3-2.3.6/src/oligotm_main.c, C, 226 lines
bin/src/primer3-2.3.6/src/p3_seq_lib.c, C, 473 lines
bin/src/primer3-2.3.6/src/p3_seq_lib.h, C/C++, 93 lines
bin/src/primer3-2.3.6/src/paranoid_tests.sh, Shell, 14 lines
bin/src/primer3-2.3.6/src/primer3_boulder_main.c, C, 565 lines
bin/src/primer3-2.3.6/src/print_boulder.c, C, 536 lines
bin/src/primer3-2.3.6/src/print_boulder.h, C/C++, 51 lines
bin/src/primer3-2.3.6/src/read_boulder.c, C, 1,302 lines
bin/src/primer3-2.3.6/src/read_boulder.h, C/C++, 89 lines
bin/src/primer3-2.3.6/src/thal.c, C, 2,829 lines
bin/src/primer3-2.3.6/src/thal.h, C/C++, 150 lines
bin/src/primer3-2.3.6/src/thal_main.c, C, 339 lines
bin/src/primer3-2.3.6/test/cmdline_test.pl, Perl, 357 lines
bin/src/primer3-2.3.6/test/dpal_test.pl, Perl, 278 lines
bin/src/primer3-2.3.6/test/long_seq_tm_test.pl, Perl, 17 lines
bin/src/primer3-2.3.6/test/oligotm_test.pl, Perl, 133 lines
bin/src/primer3-2.3.6/test/p3test.pl, Perl, 718 lines
bin/src/primer3-2.3.6/test/primer3_io_translator.pl, Perl, 231 lines
bin/src/primer3-2.3.6/test/thal_test.pl, Perl, 291 lines
bin/src/primer3-2.3.6/test/vgrep.pl, Perl, 9 lines
bin/src/sgRNA.Scorer.1.0/generateSVMFile.FASTA.py, Python, 48 lines
bin/src/sgRNA.Scorer.1.0/identifyPutativegRNASites.py, Python, 114 lines
bin/src/sgRNA.Scorer.1.0/makeFinalTable.py, Python, 64 lines
bin/src/sgRNA.Scorer.1.0/scoreMySites.py, Python, 100 lines
bin/src/sgRNA.Scorer.1.0/simplifyRankPerc.py, Python, 16 lines
bin/src/svm_light/kernel.h, C/C++, 40 lines
bin/src/svm_light/svm_classify.c, C, 197 lines
bin/src/svm_light/svm_common.c, C, 985 lines
bin/src/svm_light/svm_common.h, C/C++, 301 lines
bin/src/svm_light/svm_hideo.c, C, 1,062 lines
bin/src/svm_light/svm_learn.c, C, 4,147 lines
bin/src/svm_light/svm_learn.h, C/C++, 169 lines
bin/src/svm_light/svm_learn_main.c, C, 397 lines
bin/src/svm_light/svm_loqo.c, C, 211 lines
bin/src/wangSabatiniSvm/scorer.R, R, 16 lines
bin/twobitreader/__init__.py, Python, 698 lines
bin/twobitreader/__main__.py, Python, 3 lines
bin/twobitreader/download.py, Python, 50 lines
crispor.py, Python, 4,761 lines, 1 match
crisporEffScores.py, Python, 1,359 lines
doenchScore.py, Python, 54 lines
js/jquery-ui.min.js, JavaScript, 12 lines, 1 match
js/jquery.min.js, JavaScript, 4 lines
js/jquery.tooltipster.min.js, JavaScript, 1 line
js/jquery.ui.ufd.js, JavaScript, 1,320 lines
microHomScore.py, Python, 71 lines
scripts/xa2multi.pl, Perl, 25 lines
startWorkers.sh, Shell, 18 lines
stopWorkers.sh, Shell, 6 lines
tools/usrLocalBin/bed.py, Python, 474 lines
tools/usrLocalBin/tabfile.py, Python, 473 lines
LICENSE.txt, License, 42 lines
README.md, Text, 53 lines

At the source: github.com/maximilianh/crisporWebsite/

usadellab/trimmomatic

License: GPL-3.0

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: ef98d6252abeae80cfee36acf9e0e1055097da0b, 3 July 2026

Languages: Java (150)

Size: 178 files, 150 scripts

Software Heritage: not archived

Found in: the resources table

Holds: README, license file, environment (Dockerfile), tests, continuous integration

Not found: CITATION.cff, documentation

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

152 files

src/main/java/org/usadellab/trimmomatic/Pairomatic.java, Java, 181 lines
src/main/java/org/usadellab/trimmomatic/TrimStats.java, Java, 112 lines
src/main/java/org/usadellab/trimmomatic/Trimmomatic.java, Java, 153 lines
src/main/java/org/usadellab/trimmomatic/TrimmomaticPE.java, Java, 412 lines
src/main/java/org/usadellab/trimmomatic/TrimmomaticSE.java, Java, 234 lines
src/main/java/org/usadellab/trimmomatic/fasta/FastaParser.java, Java, 66 lines
src/main/java/org/usadellab/trimmomatic/fasta/FastaRecord.java, Java, 95 lines
src/main/java/org/usadellab/trimmomatic/fasta/FastaSerializer.java, Java, 46 lines
src/main/java/org/usadellab/trimmomatic/fastq/FastqParser.java, Java, 165 lines
src/main/java/org/usadellab/trimmomatic/fastq/FastqRecord.java, Java, 230 lines
src/main/java/org/usadellab/trimmomatic/fastq/FastqSerializer.java, Java, 54 lines
src/main/java/org/usadellab/trimmomatic/fastq/PairingValidator.java, Java, 112 lines
src/main/java/org/usadellab/trimmomatic/fastq/RecordNamePattern.java, Java, 79 lines
src/main/java/org/usadellab/trimmomatic/threading/BlockOfRecords.java, Java, 80 lines
src/main/java/org/usadellab/trimmomatic/threading/BlockOfWork.java, Java, 229 lines
src/main/java/org/usadellab/trimmomatic/threading/ExceptionHolder.java, Java, 23 lines
src/main/java/org/usadellab/trimmomatic/threading/parser/ParasiteSerialParser.java, Java, 21 lines
src/main/java/org/usadellab/trimmomatic/threading/parser/Parser.java, Java, 59 lines
src/main/java/org/usadellab/trimmomatic/threading/parser/SelfThreadedParser.java, Java, 69 lines
src/main/java/org/usadellab/trimmomatic/threading/pipeline/ParasiteSerialPipeline.java, Java, 20 lines
src/main/java/org/usadellab/trimmomatic/threading/pipeline/Pipeline.java, Java, 21 lines
src/main/java/org/usadellab/trimmomatic/threading/pipeline/ThreadedPipeline.java, Java, 57 lines
src/main/java/org/usadellab/trimmomatic/threading/serializer/ParasiteSerializer.java, Java, 43 lines
src/main/java/org/usadellab/trimmomatic/threading/serializer/SelfThreadedSerializer.java, Java, 55 lines
src/main/java/org/usadellab/trimmomatic/threading/serializer/SerializedBlock.java, Java, 47 lines
src/main/java/org/usadellab/trimmomatic/threading/serializer/SerializedBlockQueue.java, Java, 117 lines
src/main/java/org/usadellab/trimmomatic/threading/serializer/Serializer.java, Java, 86 lines
src/main/java/org/usadellab/trimmomatic/threading/trimlog/ParasiteTrimLogCollector.java, Java, 27 lines
src/main/java/org/usadellab/trimmomatic/threading/trimlog/SelfThreadedTrimLogCollector.java, Java, 67 lines
src/main/java/org/usadellab/trimmomatic/threading/trimlog/TrimLogCollector.java, Java, 54 lines
src/main/java/org/usadellab/trimmomatic/threading/trimlog/TrimLogRecord.java, Java, 3 lines
src/main/java/org/usadellab/trimmomatic/threading/trimstats/ParasiteTrimStatsCollector.java, Java, 29 lines
src/main/java/org/usadellab/trimmomatic/threading/trimstats/SelfThreadedTrimStatsCollector.java, Java, 69 lines
src/main/java/org/usadellab/trimmomatic/threading/trimstats/TrimStatsCollector.java, Java, 39 lines
src/main/java/org/usadellab/trimmomatic/trim/AbstractSingleRecordTrimmer.java, Java, 24 lines
src/main/java/org/usadellab/trimmomatic/trim/AvgQualTrimmer.java, Java, 32 lines
src/main/java/org/usadellab/trimmomatic/trim/BarcodeSplitter.java, Java, 110 lines
src/main/java/org/usadellab/trimmomatic/trim/BaseCountTrimmer.java, Java, 51 lines
src/main/java/org/usadellab/trimmomatic/trim/CropTrimmer.java, Java, 35 lines
src/main/java/org/usadellab/trimmomatic/trim/HeadCropTrimmer.java, Java, 95 lines
src/main/java/org/usadellab/trimmomatic/trim/IlluminaClippingTrimmer.java, Java, 968 lines
src/main/java/org/usadellab/trimmomatic/trim/LeadingTrimmer.java, Java, 29 lines
src/main/java/org/usadellab/trimmomatic/trim/MaxLenTrimmer.java, Java, 24 lines
src/main/java/org/usadellab/trimmomatic/trim/MaximumInformationTrimmer.java, Java, 111 lines
src/main/java/org/usadellab/trimmomatic/trim/MinLenTrimmer.java, Java, 24 lines
src/main/java/org/usadellab/trimmomatic/trim/SlidingWindowTrimmer.java, Java, 69 lines
src/main/java/org/usadellab/trimmomatic/trim/TailCropTrimmer.java, Java, 94 lines
src/main/java/org/usadellab/trimmomatic/trim/ToPhred33Trimmer.java, Java, 29 lines
src/main/java/org/usadellab/trimmomatic/trim/ToPhred64Trimmer.java, Java, 29 lines
src/main/java/org/usadellab/trimmomatic/trim/TrailingTrimmer.java, Java, 28 lines
src/main/java/org/usadellab/trimmomatic/trim/Trimmer.java, Java, 7 lines
src/main/java/org/usadellab/trimmomatic/trim/TrimmerFactory.java, Java, 70 lines
src/main/java/org/usadellab/trimmomatic/util/Logger.java, Java, 61 lines
src/main/java/org/usadellab/trimmomatic/util/PositionTrackingInputStream.java, Java, 100 lines
src/main/java/org/usadellab/trimmomatic/util/compression/BlockData.java, Java, 5 lines
src/main/java/org/usadellab/trimmomatic/util/compression/BlockOutputStream.java, Java, 25 lines
src/main/java/org/usadellab/trimmomatic/util/compression/Bzip2BlockData.java, Java, 10 lines
src/main/java/org/usadellab/trimmomatic/util/compression/Bzip2ParallelCompressor.java, Java, 48 lines
src/main/java/org/usadellab/trimmomatic/util/compression/CompressionFormat.java, Java, 76 lines
src/main/java/org/usadellab/trimmomatic/util/compression/ConcatGZIPInputStream.java, Java, 96 lines
src/main/java/org/usadellab/trimmomatic/util/compression/GzipBlockData.java, Java, 8 lines
src/main/java/org/usadellab/trimmomatic/util/compression/GzipParallelCompressor.java, Java, 155 lines
src/main/java/org/usadellab/trimmomatic/util/compression/ParallelCompressor.java, Java, 16 lines
src/main/java/org/usadellab/trimmomatic/util/compression/TunableGZIPOutputStream.java, Java, 12 lines
src/main/java/org/usadellab/trimmomatic/util/compression/UncompressedBlockData.java, Java, 63 lines
src/test/java/org/usadellab/trimmomatic/CompressionTest.java, Java, 88 lines
src/test/java/org/usadellab/trimmomatic/MultiThreadedCompressionTest.java, Java, 79 lines
src/test/java/org/usadellab/trimmomatic/TrimmomaticTest.java, Java, 22 lines
src/test/java/org/usadellab/trimmomatic/fasta/FastaParserTest.java, Java, 108 lines
src/test/java/org/usadellab/trimmomatic/fastq/CompressionIntegrationTest.java, Java, 97 lines
src/test/java/org/usadellab/trimmomatic/fastq/FastqParserAutoDetectTest.java, Java, 118 lines
src/test/java/org/usadellab/trimmomatic/fastq/FastqParserEdgeCasesTest.java, Java, 40 lines
src/test/java/org/usadellab/trimmomatic/fastq/FastqParserIteratorTest.java, Java, 43 lines
src/test/java/org/usadellab/trimmomatic/fastq/FastqParserMultiLineTest.java, Java, 34 lines
src/test/java/org/usadellab/trimmomatic/fastq/FastqParserNoNewlineTest.java, Java, 35 lines
src/test/java/org/usadellab/trimmomatic/fastq/FastqParserProgressTest.java, Java, 54 lines
src/test/java/org/usadellab/trimmomatic/fastq/FastqParserStrictnessTest.java, Java, 42 lines
src/test/java/org/usadellab/trimmomatic/fastq/FastqParserTest.java, Java, 194 lines
src/test/java/org/usadellab/trimmomatic/fastq/FastqParserTrailingNewlineTest.java, Java, 36 lines
src/test/java/org/usadellab/trimmomatic/fastq/FastqParserTruncatedTest.java, Java, 38 lines
src/test/java/org/usadellab/trimmomatic/fastq/FastqParserValidVariationsTest.java, Java, 38 lines
src/test/java/org/usadellab/trimmomatic/fastq/FastqParserWhitespaceTest.java, Java, 47 lines
src/test/java/org/usadellab/trimmomatic/fastq/FastqRecordGetLengthTest.java, Java, 215 lines
src/test/java/org/usadellab/trimmomatic/fastq/FastqRecordHeadPosTest.java, Java, 47 lines
src/test/java/org/usadellab/trimmomatic/fastq/FastqRecordTest.java, Java, 297 lines
src/test/java/org/usadellab/trimmomatic/fastq/FastqRecordToStringTest.java, Java, 17 lines
src/test/java/org/usadellab/trimmomatic/fastq/FastqSerializerTest.java, Java, 69 lines
src/test/java/org/usadellab/trimmomatic/fastq/PairingValidatorTest.java, Java, 77 lines
src/test/java/org/usadellab/trimmomatic/fastq/RecordNamePatternTest.java, Java, 31 lines
src/test/java/org/usadellab/trimmomatic/trim/AvgQualTrimmerTest.java, Java, 66 lines
src/test/java/org/usadellab/trimmomatic/trim/BarcodeSplitterTest.java, Java, 104 lines
src/test/java/org/usadellab/trimmomatic/trim/BaseCountTrimmerAdvancedTest.java, Java, 61 lines
src/test/java/org/usadellab/trimmomatic/trim/BaseCountTrimmerTest.java, Java, 70 lines
src/test/java/org/usadellab/trimmomatic/trim/CropTrimmerTest.java, Java, 98 lines
src/test/java/org/usadellab/trimmomatic/trim/GeneralOvertrimTest.java, Java, 151 lines
src/test/java/org/usadellab/trimmomatic/trim/HeadCropTrimmerTest.java, Java, 158 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaBitwiseExtendedTest.java, Java, 19 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaBitwiseTest.java, Java, 81 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaClippingTrimmerAdapterDimerTest.java, Java, 55 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaClippingTrimmerCaseTest.java, Java, 62 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaClippingTrimmerDirectionalTest.java, Java, 85 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaClippingTrimmerDuplicateTest.java, Java, 47 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaClippingTrimmerEdgeCasesTest.java, Java, 76 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaClippingTrimmerInterleaveTest.java, Java, 67 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaClippingTrimmerInvalidCharTest.java, Java, 54 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaClippingTrimmerLoadTest.java, Java, 111 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaClippingTrimmerMediumPartialTest.java, Java, 55 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaClippingTrimmerMergeTest.java, Java, 101 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaClippingTrimmerMinPrefixSimpleTest.java, Java, 55 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaClippingTrimmerOverlapCapTest.java, Java, 61 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaClippingTrimmerOverlapTest.java, Java, 58 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaClippingTrimmerOvertrimTest.java, Java, 125 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaClippingTrimmerPEvsSETest.java, Java, 83 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaClippingTrimmerPriorityTest.java, Java, 92 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaClippingTrimmerRobustnessExtendedTest.java, Java, 62 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaClippingTrimmerScoreTest.java, Java, 91 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaClippingTrimmerSeedMismatchTest.java, Java, 76 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaClippingTrimmerShortReadTest.java, Java, 45 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaClippingTrimmerSimpleShortOverlapTest.java, Java, 54 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaClippingTrimmerTest.java, Java, 284 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaClippingTrimmerTinyAdapterTest.java, Java, 49 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaClippingTrimmerWildcardTest.java, Java, 67 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaClippingTrimmerZeroThresholdTest.java, Java, 46 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaPalindromeMinLengthTest.java, Java, 86 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaPalindromeOverlapTest.java, Java, 83 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaPalindromeTest.java, Java, 83 lines
src/test/java/org/usadellab/trimmomatic/trim/IlluminaPrefixPairTest.java, Java, 67 lines
src/test/java/org/usadellab/trimmomatic/trim/LeadingTrailingTrimmerEdgeTest.java, Java, 64 lines
src/test/java/org/usadellab/trimmomatic/trim/LeadingTrimmerTest.java, Java, 65 lines
src/test/java/org/usadellab/trimmomatic/trim/MaxLenTrimmerTest.java, Java, 58 lines
src/test/java/org/usadellab/trimmomatic/trim/MaximumInformationTrimmerLongReadTest.java, Java, 41 lines
src/test/java/org/usadellab/trimmomatic/trim/MaximumInformationTrimmerTest.java, Java, 114 lines
src/test/java/org/usadellab/trimmomatic/trim/MinLenTrimmerTest.java, Java, 67 lines
src/test/java/org/usadellab/trimmomatic/trim/SensitivitySpecificityTest.java, Java, 107 lines
src/test/java/org/usadellab/trimmomatic/trim/SlidingWindowTrimmerEdgeTest.java, Java, 60 lines
src/test/java/org/usadellab/trimmomatic/trim/SlidingWindowTrimmerInvalidArgsTest.java, Java, 47 lines
src/test/java/org/usadellab/trimmomatic/trim/SlidingWindowTrimmerTest.java, Java, 114 lines
src/test/java/org/usadellab/trimmomatic/trim/TailCropTrimmerTest.java, Java, 95 lines
src/test/java/org/usadellab/trimmomatic/trim/ToPhred33TrimmerTest.java, Java, 53 lines
src/test/java/org/usadellab/trimmomatic/trim/ToPhred64TrimmerTest.java, Java, 53 lines
src/test/java/org/usadellab/trimmomatic/trim/TrailingTrimmerTest.java, Java, 90 lines
src/test/java/org/usadellab/trimmomatic/trim/TrimmerArrayProcessingTest.java, Java, 72 lines
src/test/java/org/usadellab/trimmomatic/trim/TrimmerFactoryEmptyTest.java, Java, 19 lines
src/test/java/org/usadellab/trimmomatic/trim/TrimmerFactoryFailureTest.java, Java, 45 lines
src/test/java/org/usadellab/trimmomatic/trim/TrimmerFactoryIlluminaClipFailureTest.java, Java, 65 lines
src/test/java/org/usadellab/trimmomatic/trim/TrimmerFactoryTest.java, Java, 188 lines
src/test/java/org/usadellab/trimmomatic/trim/TrimmerFactoryWhitespaceTest.java, Java, 20 lines
src/test/java/org/usadellab/trimmomatic/trim/TrimmerPipelineTest.java, Java, 107 lines
src/test/java/org/usadellab/trimmomatic/util/LoggerTest.java, Java, 44 lines
src/test/java/org/usadellab/trimmomatic/util/PositionTrackingInputStreamTest.java, Java, 59 lines
LICENSE, License, 677 lines
README.md, Text, 323 lines

At the source: github.com/usadellab/Trimmomatic/blob/main/README.md

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
1,008 scripts, each with its path and the digest of its content;
2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

geo:GSE261648, at NCBI GEO; found in “Data and code availability”

Data and code availability

Bulk RNA-seq data have been deposited at GEO: GSE261648 (https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE261648) accession number and are publicly available as of the date of publication.

Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

Authors: added Cécile Martinat (0000-0002-5234-1064); Sandrine Baghdoyan (0000-0002-5904-7394); removed Cécile Martinat; Sandrine Baghdoyan

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 19 authors, 9 keywords, 12 MeSH terms, 3 funders, 54 references, 11 RRIDs.

Cite

This paper

Roussange, F., Gide, J., Tournois, J., Cailleret, M., Boland, A., Battail, C., Deleuze, J.-F., Polvèche, H., Auboeuf, D., Brockmann, K., Kabashi, E., Marian, A., El Kassar, L., Blondel, S., Salachas, F., Bruneteau, G., Peschanski, M., Martinat, C., & Baghdoyan, S. (2026). Integrative analysis of drug-gene signatures in human pluripotent stem cells reveals prazosin as a novel SQSTM1 regulator for ALS therapeutics. Stem cell reports, 21(7), 102977. https://doi.org/10.1016/j.stemcr.2026.102977

BibTeX

@article{roussange2026integrative, author = {Roussange, Florine and Gide, Jacqueline and Tournois, Johana and Cailleret, Michel and Boland, Anne and Battail, Christophe and Deleuze, Jean-François and Polvèche, Hélène and Auboeuf, Didier and Brockmann, Knut and Kabashi, Edor and Marian, Anca and El Kassar, Lina and Blondel, Sophie and Salachas, François and Bruneteau, Gaëlle and Peschanski, Marc and Martinat, Cécile and Baghdoyan, Sandrine}, title = {{Integrative analysis of drug-gene signatures in human pluripotent stem cells reveals prazosin as a novel SQSTM1 regulator for ALS therapeutics}}, journal = {Stem cell reports}, year = {2026}, month = jun, volume = {21}, number = {7}, pages = {102977}, publisher = {Elsevier}, issn = {2213-6711}, doi = {10.1016/j.stemcr.2026.102977}, url = {https://doi.org/10.1016/j.stemcr.2026.102977}, pmid = {42349423}, pmcid = {PMC13385447} }

RIS

TY - JOUR AU - Roussange, Florine AU - Gide, Jacqueline AU - Tournois, Johana AU - Cailleret, Michel AU - Boland, Anne AU - Battail, Christophe AU - Deleuze, Jean-François AU - Polvèche, Hélène AU - Auboeuf, Didier AU - Brockmann, Knut AU - Kabashi, Edor AU - Marian, Anca AU - El Kassar, Lina AU - Blondel, Sophie AU - Salachas, François AU - Bruneteau, Gaëlle AU - Peschanski, Marc AU - Martinat, Cécile AU - Baghdoyan, Sandrine TI - Integrative analysis of drug-gene signatures in human pluripotent stem cells reveals prazosin as a novel SQSTM1 regulator for ALS therapeutics T2 - Stem cell reports J2 - Stem Cell Reports PY - 2026 DA - 2026/06/25 VL - 21 IS - 7 SP - 102977 SN - 2213-6711 PB - Elsevier DO - 10.1016/j.stemcr.2026.102977 UR - https://doi.org/10.1016/j.stemcr.2026.102977 LA - en ER -

CSL-JSON

{ "id": "10.1016/j.stemcr.2026.102977", "type": "article-journal", "title": "Integrative analysis of drug-gene signatures in human pluripotent stem cells reveals prazosin as a novel SQSTM1 regulator for ALS therapeutics", "container-title": "Stem cell reports", "author": [ { "family": "Roussange", "given": "Florine" }, { "family": "Gide", "given": "Jacqueline" }, { "family": "Tournois", "given": "Johana" }, { "family": "Cailleret", "given": "Michel" }, { "family": "Boland", "given": "Anne" }, { "family": "Battail", "given": "Christophe" }, { "family": "Deleuze", "given": "Jean-François" }, { "family": "Polvèche", "given": "Hélène" }, { "family": "Auboeuf", "given": "Didier" }, { "family": "Brockmann", "given": "Knut" }, { "family": "Kabashi", "given": "Edor" }, { "family": "Marian", "given": "Anca" }, { "family": "El Kassar", "given": "Lina" }, { "family": "Blondel", "given": "Sophie" }, { "family": "Salachas", "given": "François" }, { "family": "Bruneteau", "given": "Gaëlle" }, { "family": "Peschanski", "given": "Marc" }, { "family": "Martinat", "given": "Cécile" }, { "family": "Baghdoyan", "given": "Sandrine" } ], "container-title-short": "Stem Cell Reports", "volume": "21", "issue": "7", "page": "102977", "DOI": "10.1016/j.stemcr.2026.102977", "PMID": "42349423", "PMCID": "PMC13385447", "ISSN": "2213-6711", "publisher": "Elsevier", "URL": "https://doi.org/10.1016/j.stemcr.2026.102977", "language": "en", "issued": { "date-parts": [ [ 2026, 6, 25 ] ] } }

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s42003-026-10957-8 [code]: Brain defence by the extracellular matrix protein Cochlin.
Journal: Communications biology
In common: Stan, Biopython, limma, 7 other tools, zebrafish, cellular / molecular
[2] doi:10.1038/s41467-026-76675-1 [code]: Long-read proteogenomic atlas of human neuronal differentiation reveals isoform diversity informing neurodevelopmental risk mechanisms.
Journal: Nature communications
In common: Biopython, limma, reshape2, 5 other tools, genetics / omics, 2 references
[3] doi:10.1016/j.xcrm.2026.102766 [code]: A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma.
Journal: Cell reports. Medicine
In common: limma, Keras, reshape2, 5 other tools, genetics / omics, other condition, cellular / molecular, 1 reference
[4] doi:10.1038/s44318-026-00818-9 [code]: FAM134B-mediated ER-phagy degrades APP and suppresses Alzheimer's disease pathology.
Journal: The EMBO journal
In common: Biopython, limma, reshape2, 5 other tools, cellular / molecular
[5] doi:10.1186/s13293-026-00927-4 [code]: Gene regulatory network analysis identifies dysregulation of hypoxia pathways as contributing to glioblastoma treatment resistance in females.
Journal: Biology of sex differences
In common: Stan, limma, reshape2, 4 other tools, other condition, cellular / molecular
[6] doi:10.1038/s41592-026-03057-2 [code]: CREsted: modeling genomic and synthetic cell-type-specific enhancers across tissues and species.
Journal: Nature methods
In common: Biopython, Keras, scikit-learn, 4 other tools, zebrafish, genetics / omics
[7] doi:10.1038/s43587-026-01207-x [code]: A microprotein atlas of the human frontal cortex in Alzheimer's disease.
Journal: Nature aging
In common: Biopython, limma, reshape2, 4 other tools, genetics / omics
[8] doi:10.1126/sciadv.aed2952 [code]: Activation of transposable elements is linked to a region- and cell type-specific interferon response in Parkinson's disease.
Journal: Science advances
In common: Biopython, limma, reshape2, 4 other tools, cellular / molecular
[9] doi:10.1038/s41467-026-75700-7 [code]: Gene regulatory innovations from transposable elements in primate cerebellum development.
Journal: Nature communications
In common: Biopython, Keras, scikit-learn, 4 other tools, genetics / omics, cellular / molecular
[10] doi:10.1038/s41467-026-71391-2 [code]: Accelerating Leigh syndrome drug discovery through deep learning screening in brain organoids.
Journal: Nature communications
In common: scikit-learn, pandas, SciPy, 2 other tools, 3 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.