An optimized translating ribosome affinity purification protocol for low-abundance Drosophila tissues.

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[1] § STAR★Methods › Method details › Translating Ribosome Affinity Purification (TRAP) › RNA-Seq analysis ↔ multiqc/modules/trim_galore/trim_galore.py, lines 53 to 178 · score 0.60 · Trim Galore, quality trimming, adapter · Report a mismatch
[2] § STAR★Methods › Method details › Microscopy ↔ multiqc/templates/default/assets/js/packages/showdown.min.js, the whole file · a weak match · score 0.59 · microscope, oil, mounted, scissors, plates, medium · Report a mismatch
[3] § STAR★Methods › Method details › Translating Ribosome Affinity Purification (TRAP) › RNA-Seq analysis ↔ multiqc/modules/qualimap/QM_RNASeq.py, lines 232 to 301 · score 0.56 · GTF annotation, RNA seq, Alignments, genome, Genes, Mapped · Report a mismatch

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The authors' code

Python · 603 lines · 25 KB · GPL-3.0 · 1 match

import json
import logging
from collections import defaultdict
from typing import Any, Dict, List, Optional, Tuple, cast
from multiqc import config
from multiqc.base_module import BaseMultiqcModule, ModuleNoSamplesFound, SampleGroupingConfig
from multiqc.plots import bargraph, linegraph, table
from multiqc.types import ColumnKey, SampleName, SectionAlert
log = logging.getLogger(__name__)
SCHEMA_MAJOR_SUPPORTED = 1
class MultiqcModule(BaseMultiqcModule):
"""
[Trim Galore](https://github.com/FelixKrueger/TrimGalore) provides
consistent quality and adapter trimming for next-generation sequencing
data, with special handling for Reduced Representation Bisulfite
Sequencing (RRBS) and small-RNA libraries.
This MultiQC module supports Trim Galore v2.0, which is a Rust
rewrite of the original Perl-based v0.6 that has a new JSON output
file summarising results. The earlier v0.6 versions of Trim Galore
that wrapped Cutadapt are supported with reporting via the Cutadapt module.
The old log format is still produced, but the Cutadapt module search pattern
is configured to skip reports mentioning Trim Galore v2+. If you delete the
JSON but keep the v2 text file, the sample will not be reported by either module.
#### Paired-end sample grouping
R1 and R2 of a paired-end sample are grouped automatically into a single
row in the General Statistics and Pair Validation tables. Click the
expand arrow on a grouped row to see the per-read values. The grouping
is derived from the file list inside each JSON report, so it does not
depend on filename patterns.
To disable automatic grouping and show one row per read everywhere:
```yaml
trim_galore_config:
auto_group_pairs: false
```
Auto-grouping can be combined with the global
[`table_sample_merge`](../reports/customisation.md#sample-grouping)
config option to merge further — for example to group lanes of an
already-paired sample.
"""
def __init__(self):
super().__init__(
name="Trim Galore",
anchor="trim_galore",
href="https://github.com/FelixKrueger/TrimGalore",
info=(
"Quality and adapter trimming for next-generation sequencing data, "
"with special handling for RRBS libraries."
),
doi="10.5281/zenodo.5127898",
license="GNU General Public License v3.0",
license_url="https://github.com/FelixKrueger/TrimGalore/blob/master/LICENSE",
)
data_by_sample: Dict[str, Dict[str, Any]] = {}
self._pair_key_to_samples: Dict[Tuple[str, ...], List[str]] = {}
for f in self.find_log_files("trim_galore", filehandles=True):
parsed = self._parse_log(f)
if parsed is None:
continue
s_name, payload, pair_key = parsed
if self.is_ignore_sample(s_name):
continue
if s_name in data_by_sample:
log.debug(f"Duplicate sample name found! Overwriting: {s_name}")
data_by_sample[s_name] = payload
self._pair_key_to_samples.setdefault(pair_key, []).append(s_name)
self.add_data_source(f, s_name=s_name)
self.add_software_version(payload["trim_galore_version"], s_name)
if not data_by_sample:
raise ModuleNoSamplesFound
log.info(f"Found {len(data_by_sample)} reports")
auto_group_pairs = getattr(config, "trim_galore_config", {}).get("auto_group_pairs", True)
self._pair_display_by_key: Dict[Tuple[str, ...], str] = {}
explicit_groups: Optional[Dict[str, List[str]]] = None
if auto_group_pairs:
explicit_groups, self._pair_display_by_key = self._derive_auto_groups(data_by_sample)
self._general_stats_table(data_by_sample, explicit_groups)
self.add_section(
name="Filtered Reads",
anchor="trim_galore_filtered_reads",
description="Number of reads after trimming.",
helptext=(
"The `passing` bucket reflects reads that survived quality trimming, adapter trimming and N-content filtering "
"(i.e. what made it into the trimmed output); the remaining buckets show which filter rejected the rest."
),
plot=self._filtered_reads_plot(data_by_sample),
)
adapter_plot = self._adapter_length_plot(data_by_sample)
if adapter_plot is not None:
self.add_section(
name="Adapter Length Distribution",
anchor="trim_galore_adapter_lengths",
description="Histogram of adapter match lengths per read.",
helptext=(
"Note that a tall left tail at 1 bp is normal: Trim Galore's default `--stringency 1` accepts "
"single-base overlaps, which represent random hits rather than real adapter contamination."
),
plot=adapter_plot,
)
pair_validation_plot, pv_dropped = self._pair_validation_plot(data_by_sample)
if pair_validation_plot is not None:
self.add_section(
name="Pair Validation",
anchor="trim_galore_pair_validation",
description="Outcomes of paired-end validation",
helptext=(
"Shows pairs analysed, pairs removed (broken down by reason), and reads left unpaired after a partner "
"was discarded. R1 and R2 of a pair are collapsed into a single row (the data is identical between them)."
),
plot=pair_validation_plot,
alerts=SectionAlert(
message=(
f"**{len(pv_dropped)} sample{'s' if len(pv_dropped) != 1 else ''}** with less than 0.1% "
"of pairs affected hidden from this table."
),
affected_samples=pv_dropped,
)
if pv_dropped
else None,
)
poly_plot, poly_dropped = self._poly_trimming_plot(data_by_sample)
if poly_plot is not None:
self.add_section(
name="Poly-A / Poly-G Trimming",
anchor="trim_galore_poly_trimming",
description="Reads and bases removed by Trim Galore's poly-A and poly-G tail trimmers.",
plot=poly_plot,
alerts=SectionAlert(
message=(
f"**{len(poly_dropped)} sample{'s' if len(poly_dropped) != 1 else ''}** with no "
"poly-A/G trimming hidden from this table."
),
affected_samples=poly_dropped,
)
if poly_dropped
else None,
)
rrbs_plot, rrbs_dropped = self._rrbs_plot(data_by_sample)
if rrbs_plot is not None:
self.add_section(
name="RRBS Trimming",
anchor="trim_galore_rrbs",
description="Reduced Representation Bisulfite Sequencing (RRBS) end-repair trimming counts.",
plot=rrbs_plot,
alerts=SectionAlert(
message=(
f"**{len(rrbs_dropped)} sample{'s' if len(rrbs_dropped) != 1 else ''}** with no "
"RRBS trimming hidden from this table."
),
affected_samples=rrbs_dropped,
)
if rrbs_dropped
else None,
)
self.write_data_file(_flatten_for_data_file(data_by_sample), "multiqc_trim_galore")
def _parse_log(self, f) -> Optional[Tuple[str, Dict[str, Any], Tuple[str, ...]]]:
try:
payload = json.load(f["f"])
except json.JSONDecodeError as e:
log.warning(f"Could not parse {f['fn']!r}: {e}")
return None
# `tool` check uses .get() because we don't yet know this is a Trim
# Galore JSON — could be a foreign JSON that matched the filename glob.
if payload.get("tool") != "Trim Galore":
log.debug(f"Skipping {f['fn']!r}: tool field is {payload.get('tool')!r}, not Trim Galore")
return None
schema = payload["schema_version"]
major = int(str(schema).split(".")[0])
if major != SCHEMA_MAJOR_SUPPORTED:
log.error(
f"Skipping {f['fn']!r}: schema_version {schema!r} has incompatible major version "
f"(expected major v{SCHEMA_MAJOR_SUPPORTED}). This module needs updating to handle the new schema."
)
return None
# PE reports list both filenames in `input_filenames`; pick the one for
# this read_number so R1 and R2 produce distinct samples. SE reports
# have read_number=1 and a 1-element list, so the same logic works.
# `input_filenames` is byte-identical across R1 and R2 of the same pair,
# so it doubles as a stable pair key.
input_filenames = payload["input_filenames"]
read_number = payload["read_number"]
s_name = self.clean_s_name(input_filenames[read_number - 1], f)
return s_name, payload, tuple(input_filenames)
def _derive_auto_groups(
self,
data_by_sample: Dict[str, Dict[str, Any]],
) -> Tuple[Dict[str, List[str]], Dict[Tuple[str, ...], str]]:
"""Build module-supplied groups from tool-derived pair info.
Returns `(explicit_groups, pair_display_by_key)`. When the user has
`config.table_sample_merge` set, name patterns layer on top: each
auto-group's display name is run through `groups_for_sample` to
merge into a super-group.
"""
auto_groups: Dict[str, List[str]] = {}
pair_display_by_key: Dict[Tuple[str, ...], str] = {}
for pair_key, members in self._pair_key_to_samples.items():
sorted_members = sorted(members)
# _clean_fastq_pair needs two names; SE pairs have only one.
if len(sorted_members) >= 2:
display = self._clean_fastq_pair(sorted_members[0], sorted_members[1]) or "_".join(sorted_members)
else:
display = sorted_members[0]
auto_groups[display] = sorted_members
pair_display_by_key[pair_key] = display
if not config.table_sample_merge:
return auto_groups, pair_display_by_key
by_super: Dict[str, List[str]] = defaultdict(list)
for auto_display, samples in auto_groups.items():
super_name, _ = self.groups_for_sample(SampleName(auto_display))
by_super[str(super_name)].extend(samples)
return dict(by_super), pair_display_by_key
def _general_stats_table(
self,
data_by_sample: Dict[str, Dict[str, Any]],
explicit_groups: Optional[Dict[str, List[str]]] = None,
) -> None:
gen_stats: Dict[str, Dict[ColumnKey, Any]] = {}
for s_name, payload in data_by_sample.items():
rp = payload["read_processing"]
bp = payload["basepair_processing"]
total = rp["total_reads"]
total_bp = bp["total_bp_processed"]
gen_stats[s_name] = {
ColumnKey("tg_total_reads"): total,
ColumnKey("tg_pct_with_adapter"): _safe_pct(rp["reads_with_adapter"], total),
ColumnKey("tg_pct_passing"): _safe_pct(rp["reads_written"], total),
ColumnKey("tg_pct_quality_trimmed"): _safe_pct(bp["quality_trimmed_bp"], total_bp),
ColumnKey("tg_total_bp_written"): bp["total_bp_written"],
}
headers: Dict[str, Dict[str, Any]] = {
"tg_pct_with_adapter": {
"title": "% Adapter",
"description": "% reads where at least one adapter was detected",
"max": 100,
"min": 0,
"suffix": "%",
"scale": "OrRd",
"format": "{:,.1f}",
},
"tg_pct_passing": {
"title": "% Pass",
"description": "% reads passing all filters into trimmed output",
"max": 100,
"min": 0,
"suffix": "%",
"scale": "RdYlGn",
"format": "{:,.1f}",
},
"tg_pct_quality_trimmed": {
"title": "% Q-trim",
"description": "% bases removed by 3'-end quality trimming",
"max": 100,
"min": 0,
"suffix": "%",
"scale": "Blues",
"format": "{:,.1f}",
},
"tg_total_reads": {
"title": "Reads",
"description": "Total reads processed",
"scale": "Greys",
"shared_key": "read_count",
},
"tg_total_bp_written": {
"title": "BP written",
"description": "Total basepairs written to trimmed output",
"scale": "BuGn",
"shared_key": "base_count",
"hidden": True,
},
}
self.general_stats_addcols(
cast(Any, gen_stats),
headers,
group_samples_config=SampleGroupingConfig(
explicit_groups=explicit_groups,
cols_to_sum=[
ColumnKey("tg_total_reads"),
ColumnKey("tg_total_bp_written"),
],
cols_to_weighted_average=[
(ColumnKey("tg_pct_with_adapter"), ColumnKey("tg_total_reads")),
(ColumnKey("tg_pct_passing"), ColumnKey("tg_total_reads")),
(ColumnKey("tg_pct_quality_trimmed"), ColumnKey("tg_total_bp_written")),
],
),
)
def _filtered_reads_plot(self, data_by_sample: Dict[str, Dict[str, Any]]):
bar_data: Dict[str, Dict[str, int]] = {}
for s_name, payload in data_by_sample.items():
rp = payload["read_processing"]
bar_data[s_name] = {
"passing": rp["reads_written"],
"too_short": rp["reads_too_short"],
"too_long": rp["reads_too_long"],
"too_many_n": rp["reads_too_many_n"],
"discarded_untrimmed": rp["reads_discarded_untrimmed"],
}
cats = {
"passing": {"name": "Passed filters"},
"too_short": {"name": "Too short"},
"too_long": {"name": "Too long"},
"too_many_n": {"name": "Too many Ns"},
"discarded_untrimmed": {"name": "Discarded (untrimmed)"},
}
return bargraph.plot(
bar_data,
cats,
{
"id": "trim_galore_filtered_reads_plot",
"title": "Trim Galore: Read Filtering",
"ylab": "Number of reads",
"cpswitch_counts_label": "Number of reads",
},
)
def _adapter_length_plot(self, data_by_sample: Dict[str, Dict[str, Any]]):
line_data: Dict[str, Dict[int, int]] = {}
for s_name, payload in data_by_sample.items():
adapters = payload["adapter_trimming"]
for a in adapters:
key = f"{s_name} ({a['name']})" if len(adapters) > 1 else s_name
line_data[key] = {int(k): v for k, v in a["length_distribution"].items()}
if not line_data:
return None
return linegraph.plot(
line_data,
{
"id": "trim_galore_adapter_length_plot",
"title": "Trim Galore: Adapter Length Distribution",
"xlab": "Adapter overlap length (bp)",
"ylab": "Reads (count)",
"ymin": 0,
"tt_label": "{point.x} bp: {point.y:,} reads",
},
)
def _pair_validation_plot(self, data_by_sample: Dict[str, Dict[str, Any]]):
# pair_validation is identical between R1 and R2 JSONs of the same pair —
# collapse them by the tool-derived pair_key. Skip SE samples (no pv).
all_rows: Dict[str, Dict[str, int]] = {}
for pair_key, members in self._pair_key_to_samples.items():
pv = data_by_sample[members[0]]["pair_validation"]
if not pv: # SE samples — `pair_validation` is null
continue
display = self._pair_display_by_key.get(pair_key, members[0])
all_rows[display] = {
"pairs_analyzed": pv["pairs_analyzed"],
"pairs_removed": pv["pairs_removed"],
"pairs_removed_n": pv["pairs_removed_n"],
"pairs_removed_too_long": pv["pairs_removed_too_long"],
"r1_unpaired": pv["r1_unpaired"],
"r2_unpaired": pv["r2_unpaired"],
}
# `pairs_removed_*` are sub-reasons of `pairs_removed` so are not added
# to the affected-fraction sum here (would double-count).
def _kept(r: Dict[str, int]) -> bool:
return bool(r["pairs_analyzed"]) and (
(r["pairs_removed"] + r["r1_unpaired"] + r["r2_unpaired"]) / r["pairs_analyzed"] > 0.001
)
rows = {n: r for n, r in all_rows.items() if _kept(r)}
dropped = sorted(n for n, r in all_rows.items() if not _kept(r))
if not rows:
return None, dropped
headers: Dict[str, Dict[str, Any]] = {
"pairs_analyzed": {
"title": "Pairs analysed",
"description": "Total read pairs examined by pair validation",
"scale": "Greys",
"shared_key": "read_count",
},
"pairs_removed": {
"title": "Pairs removed",
"description": "Total read pairs removed by pair validation",
"scale": "OrRd",
"shared_key": "read_count",
},
"pairs_removed_n": {
"title": "Pairs removed (N content)",
"description": "Read pairs removed because they exceeded the N-content threshold",
"scale": "OrRd",
"shared_key": "read_count",
},
"pairs_removed_too_long": {
"title": "Pairs removed (too long)",
"description": "Read pairs removed because one or both reads exceeded the maximum length",
"scale": "OrRd",
"shared_key": "read_count",
},
"r1_unpaired": {
"title": "R1 unpaired",
"description": "R1 reads left unpaired after their R2 partner was discarded",
"scale": "Oranges",
"shared_key": "read_count",
},
"r2_unpaired": {
"title": "R2 unpaired",
"description": "R2 reads left unpaired after their R1 partner was discarded",
"scale": "Oranges",
"shared_key": "read_count",
},
}
return (
table.plot(
rows,
cast(Any, headers),
pconfig={
"namespace": self.name,
"id": "trim_galore_pair_validation_table",
"title": "Trim Galore: Pair Validation",
},
),
dropped,
)
def _poly_trimming_plot(self, data_by_sample: Dict[str, Dict[str, Any]]):
rows: Dict[str, Dict[str, int]] = {}
dropped: List[str] = []
for s_name, payload in data_by_sample.items():
pa = payload["poly_a_trimming"]
pg = payload["poly_g_trimming"]
row = {
"poly_a_reads_trimmed": pa["reads_trimmed"],
"poly_a_bases_removed": pa["bases_removed"],
"poly_g_reads_trimmed": pg["reads_trimmed"],
"poly_g_bases_removed": pg["bases_removed"],
}
if any(row.values()):
rows[s_name] = row
else:
dropped.append(s_name)
if not rows:
return None, sorted(dropped)
headers: Dict[str, Dict[str, Any]] = {
"poly_a_reads_trimmed": {
"title": "Poly-A reads trimmed",
"description": "Reads with a poly-A tail trimmed",
"scale": "Purples",
"shared_key": "read_count",
},
"poly_a_bases_removed": {
"title": "Poly-A bp removed",
"description": "Bases removed by poly-A trimming",
"scale": "Purples",
"shared_key": "base_count",
},
"poly_g_reads_trimmed": {
"title": "Poly-G reads trimmed",
"description": "Reads with a poly-G tail trimmed",
"scale": "Greens",
"shared_key": "read_count",
},
"poly_g_bases_removed": {
"title": "Poly-G bp removed",
"description": "Bases removed by poly-G trimming",
"scale": "Greens",
"shared_key": "base_count",
},
}
return (
table.plot(
rows,
cast(Any, headers),
pconfig={
"namespace": self.name,
"id": "trim_galore_poly_trimming_table",
"title": "Trim Galore: Poly-A / Poly-G Trimming",
},
),
sorted(dropped),
)
def _rrbs_plot(self, data_by_sample: Dict[str, Dict[str, Any]]):
rows: Dict[str, Dict[str, int]] = {}
dropped: List[str] = []
for s_name, payload in data_by_sample.items():
rr = payload["rrbs"]
row = {
"rrbs_trimmed_3prime": rr["trimmed_3prime"],
"rrbs_trimmed_5prime": rr["trimmed_5prime"],
"rrbs_r2_clipped_5prime": rr["r2_clipped_5prime"],
}
if any(row.values()):
rows[s_name] = row
else:
dropped.append(s_name)
if not rows:
return None, sorted(dropped)
headers: Dict[str, Dict[str, Any]] = {
"rrbs_trimmed_3prime": {
"title": "3' trimmed",
"description": "Reads trimmed at the 3' end for RRBS end-repair",
"scale": "Blues",
"shared_key": "read_count",
},
"rrbs_trimmed_5prime": {
"title": "5' trimmed",
"description": "Reads trimmed at the 5' end for RRBS end-repair",
"scale": "Blues",
"shared_key": "read_count",
},
"rrbs_r2_clipped_5prime": {
"title": "R2 5' clipped",
"description": "R2 reads with the 5' end clipped (directional RRBS)",
"scale": "Blues",
"shared_key": "read_count",
},
}
return (
table.plot(
rows,
cast(Any, headers),
pconfig={
"namespace": self.name,
"id": "trim_galore_rrbs_table",
"title": "Trim Galore: RRBS Trimming",
},
),
sorted(dropped),
)
def _flatten_for_data_file(data_by_sample: Dict[str, Dict[str, Any]]) -> Dict[str, Dict[str, Any]]:
flat: Dict[str, Dict[str, Any]] = {}
for s_name, p in data_by_sample.items():
rp = p["read_processing"]
bp = p["basepair_processing"]
pa = p["poly_a_trimming"]
pg = p["poly_g_trimming"]
rr = p["rrbs"]
# `pair_validation` is explicitly null for SE samples — keep `or {}`
# so the .get() calls below produce None for the SE pair columns.
pv = p["pair_validation"] or {}
flat[s_name] = {
"trim_galore_version": p["trim_galore_version"],
"mode": p["mode"],
"total_reads": rp["total_reads"],
"reads_with_adapter": rp["reads_with_adapter"],
"reads_written": rp["reads_written"],
"reads_too_short": rp["reads_too_short"],
"reads_too_long": rp["reads_too_long"],
"reads_too_many_n": rp["reads_too_many_n"],
"reads_discarded_untrimmed": rp["reads_discarded_untrimmed"],
"total_bp_processed": bp["total_bp_processed"],
"total_bp_written": bp["total_bp_written"],
"quality_trimmed_bp": bp["quality_trimmed_bp"],
"pairs_analyzed": pv.get("pairs_analyzed"),
"pairs_removed": pv.get("pairs_removed"),
"pairs_removed_n": pv.get("pairs_removed_n"),
"pairs_removed_too_long": pv.get("pairs_removed_too_long"),
"r1_unpaired": pv.get("r1_unpaired"),
"r2_unpaired": pv.get("r2_unpaired"),
"poly_a_reads_trimmed": pa["reads_trimmed"],
"poly_a_bases_removed": pa["bases_removed"],
"poly_g_reads_trimmed": pg["reads_trimmed"],
"poly_g_bases_removed": pg["bases_removed"],
"rrbs_trimmed_3prime": rr["trimmed_3prime"],
"rrbs_trimmed_5prime": rr["trimmed_5prime"],
"rrbs_r2_clipped_5prime": rr["r2_clipped_5prime"],
}
return flat
def _safe_pct(part: int, total: int) -> float:
if not total:
return 0.0
return 100.0 * part / total

trim_galore.py at commit dc565eb, under GPL-3.0 · at the source

Overview

Authors: Leonor Miller-Fleming¹, Wing Hei Au¹, Alexander J Whitworth¹

ORCID iDs: Alexander J Whitworth

MRC Mitochondrial Biology Unit, University of Cambridge, Cambridge Biomedical Campus, Cambridge CB2 0XY, UK

Institutions: University of Cambridge (United Kingdom); MRC Mitochondrial Biology Unit (United Kingdom)

Journal: iScience, volume 29, issue 5, article 115530

Dates: received 9 October 2025; accepted 26 March 2026; published online 30 March 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1016/j.isci.2026.115530 · PMID 42028032 · PMCID PMC13099348 · OpenAlex W7143345098

Open access: gold, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: drosophila (organism), cellular / molecular (subfield)

Methods: Statistics, Evoked potentials

Keywords: genetics, molecular biology, neuroscience

Topic: Toxin Mechanisms and Immunotoxins (Immunology, Immunology and Microbiology), according to OpenAlex

Funding: UK Research and Innovation Medical Research Council (MC_UU_0028/6, MR/V003933/1); Motor Neurone Disease Association (Whitworth/Apr17/857-79); Medical Research Center, Hamad Medical Corporation; European Research Council (309742)

Citations: not cited yet (Europe PMC); 42 references in the paper

Research resources: Monoclonal mouse anti-FLAG M2 antibody RRID:AB_262044, RRID:AB_95543, D. melanogaster: UAS-GFP RRID:BDSC_1522, D. melanogaster: OK371-GAL4 RRID:BDSC_26160, D. melanogaster: UAS-mCD8-GFP RRID:BDSC_5137, D. melanogaster: UAS-NLS-RFP RRID:BDSC_8547, RRID:BDSC_90315, D. melanogaster: UAS-Flag-TEV-GFP-RpL10 RRID:FlyBase_FBal0319196

Abstract

Localized protein translation enables spatially restricted cellular dynamics, particularly in neurons, where specific mRNAs are translated in axons and dendrites far from the cell body. Translating ribosome affinity purification (TRAP) has been used to study axonal translation in rodents and cell-type-specific translation in Drosophila, but existing protocols are not optimized for axons, where material is extremely limited. Here, we present a highly sensitive TRAP protocol for isolating ribosome-bound mRNAs from low-input samples, enabling recovery of axonal mRNAs from Drosophila larval and adult (leg) motor neurons. RNA-seq identified axonally translated transcripts, including mRNAs encoding ribosomal and mitochondrial proteins, similar to those reported in axons of other species, indicating conservation of axonal translation in Drosophila. This low-input method enables analysis of local translation with Drosophila genetics across developmental stages, genetic backgrounds, and disease models, and can be adapted for rare genotypes, other tissues and model systems requiring high sensitivity.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

MultiQC/MultiQC

License: GPL-3.0

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: dc565eb9eef7b22a289ab7eec78e5efb6bf297c3, 28 September 2026

Languages: Python (647), JavaScript (75), Shell (1)

Size: 1,215 files, 723 scripts

Software Heritage: archived

Found in: the resources table

Holds: README, license file, CITATION.cff, environment (Dockerfile, flake.nix, pyproject.toml, setup.py), tests, continuous integration, documentation

Tools: NumPy (14 files), Plotly (8 files), Pillow (2 files)

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

725 files

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multiqc/modules/ngsderive/ngsderive.py, Python, 474 lines
multiqc/modules/nonpareil/__init__.py, Python, 3 lines
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multiqc/modules/odgi/__init__.py, Python, 3 lines
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multiqc/modules/pairtools/__init__.py, Python, 3 lines
multiqc/modules/pairtools/pairtools.py, Python, 346 lines
multiqc/modules/pairtools/utils.py, Python, 276 lines
multiqc/modules/pangolin/__init__.py, Python, 3 lines
multiqc/modules/pangolin/pangolin.py, Python, 259 lines
multiqc/modules/pbmarkdup/__init__.py, Python, 3 lines
multiqc/modules/pbmarkdup/pbmarkdup.py, Python, 141 lines
multiqc/modules/peddy/__init__.py, Python, 3 lines
multiqc/modules/peddy/peddy.py, Python, 348 lines
multiqc/modules/percolator/__init__.py, Python, 3 lines
multiqc/modules/percolator/percolator.py, Python, 118 lines
multiqc/modules/phantompeakqualtools/__init__.py, Python, 3 lines
multiqc/modules/phantompeakqualtools/phantompeakqualtools.py, Python, 93 lines
multiqc/modules/picard/AlignmentSummaryMetrics.py, Python, 160 lines
multiqc/modules/picard/BaseDistributionByCycleMetrics.py, Python, 167 lines
multiqc/modules/picard/CrosscheckFingerprints.py, Python, 390 lines
multiqc/modules/picard/ExtractIlluminaBarcodes.py, Python, 168 lines
multiqc/modules/picard/GcBiasMetrics.py, Python, 143 lines
multiqc/modules/picard/HsMetrics.py, Python, 439 lines
multiqc/modules/picard/IlluminaBasecallingMetrics.py, Python, 126 lines
multiqc/modules/picard/IlluminaLaneMetrics.py, Python, 122 lines
multiqc/modules/picard/InsertSizeMetrics.py, Python, 205 lines
multiqc/modules/picard/MarkDuplicates.py, Python, 325 lines
multiqc/modules/picard/MarkIlluminaAdapters.py, Python, 64 lines
multiqc/modules/picard/OxoGMetrics.py, Python, 105 lines
multiqc/modules/picard/QualityByCycleMetrics.py, Python, 171 lines
multiqc/modules/picard/QualityScoreDistributionMetrics.py, Python, 60 lines
multiqc/modules/picard/QualityYieldMetrics.py, Python, 93 lines
multiqc/modules/picard/RnaSeqMetrics.py, Python, 213 lines
multiqc/modules/picard/RrbsSummaryMetrics.py, Python, 165 lines
multiqc/modules/picard/TargetedPcrMetrics.py, Python, 147 lines
multiqc/modules/picard/ValidateSamFile.py, Python, 345 lines
multiqc/modules/picard/VariantCallingMetrics.py, Python, 406 lines
multiqc/modules/picard/WgsMetrics.py, Python, 240 lines
multiqc/modules/picard/__init__.py, Python, 3 lines
multiqc/modules/picard/picard.py, Python, 312 lines
multiqc/modules/picard/tests/__init__.py, Python, 1 line
multiqc/modules/picard/tests/test_picard.py, Python, 54 lines
multiqc/modules/picard/util.py, Python, 190 lines
multiqc/modules/porechop/__init__.py, Python, 3 lines
multiqc/modules/porechop/porechop.py, Python, 286 lines
multiqc/modules/preseq/__init__.py, Python, 3 lines
multiqc/modules/preseq/preseq.py, Python, 489 lines
multiqc/modules/prinseqplusplus/__init__.py, Python, 3 lines
multiqc/modules/prinseqplusplus/prinseqplusplus.py, Python, 143 lines
multiqc/modules/prokka/__init__.py, Python, 3 lines
multiqc/modules/prokka/prokka.py, Python, 249 lines
multiqc/modules/purple/__init__.py, Python, 3 lines
multiqc/modules/purple/purple.py, Python, 225 lines
multiqc/modules/pychopper/__init__.py, Python, 3 lines
multiqc/modules/pychopper/pychopper.py, Python, 200 lines
multiqc/modules/pycoqc/__init__.py, Python, 3 lines
multiqc/modules/pycoqc/pycoqc.py, Python, 325 lines
multiqc/modules/qc3C/__init__.py, Python, 3 lines
multiqc/modules/qc3C/qc3C.py, Python, 1,025 lines
multiqc/modules/qorts/__init__.py, Python, 3 lines
multiqc/modules/qorts/qorts.py, Python, 315 lines
multiqc/modules/qualimap/QM_BamQC.py, Python, 765 lines
multiqc/modules/qualimap/QM_RNASeq.py, Python, 301 lines, 1 match
multiqc/modules/qualimap/__init__.py, Python, 115 lines
multiqc/modules/qualimap/qualimap.py, Python, 78 lines
multiqc/modules/quast/__init__.py, Python, 3 lines
multiqc/modules/quast/quast.py, Python, 409 lines
multiqc/modules/ribodetector/__init__.py, Python, 3 lines
multiqc/modules/ribodetector/ribodetector.py, Python, 162 lines
multiqc/modules/ribotish/__init__.py, Python, 3 lines
multiqc/modules/ribotish/ribotish.py, Python, 479 lines
multiqc/modules/ribotish/tests/__init__.py, Python, 1 line
multiqc/modules/ribotish/tests/test_ribotish.py, Python, 214 lines
multiqc/modules/ribowaltz/__init__.py, Python, 3 lines
multiqc/modules/ribowaltz/ribowaltz.py, Python, 561 lines
multiqc/modules/riker/__init__.py, Python, 3 lines
multiqc/modules/riker/alignment.py, Python, 288 lines
multiqc/modules/riker/basic.py, Python, 215 lines
multiqc/modules/riker/gcbias.py, Python, 132 lines
multiqc/modules/riker/hybcap.py, Python, 385 lines
multiqc/modules/riker/isize.py, Python, 152 lines
multiqc/modules/riker/riker.py, Python, 69 lines
multiqc/modules/riker/tests/__init__.py, Python, 1 line
multiqc/modules/riker/tests/test_riker.py, Python, 320 lines
multiqc/modules/riker/util.py, Python, 59 lines
multiqc/modules/riker/wgs.py, Python, 372 lines
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multiqc/modules/sincei/_helpers.py, Python, 15 lines
multiqc/modules/sincei/scCountQC.py, Python, 180 lines
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multiqc/modules/xenium/__init__.py, Python, 1 line
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multiqc/modules/xenome/__init__.py, Python, 3 lines
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multiqc/multiqc.py, Python, 749 lines
multiqc/plots/__init__.py, Python, 1 line
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multiqc/plots/box.py, Python, 563 lines
multiqc/plots/heatmap.py, Python, 775 lines
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multiqc/plots/plot.py, Python, 2,138 lines
multiqc/plots/scatter.py, Python, 568 lines
multiqc/plots/table.py, Python, 50 lines
multiqc/plots/table_object.py, Python, 1,387 lines
multiqc/plots/utils.py, Python, 71 lines
multiqc/plots/violin.py, Python, 1,198 lines
multiqc/report.py, Python, 1,270 lines
multiqc/templates/__init__.py, Python, 1 line
multiqc/templates/default/__init__.py, Python, 23 lines
multiqc/templates/default/assets/js/packages/FileSaver.min.js, JavaScript, 2 lines
multiqc/templates/default/assets/js/packages/jquery-3.1.1.min.js, JavaScript, 4 lines
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multiqc/templates/default/assets/js/packages/jquery.tablesorter.min.js, JavaScript, 1 line
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multiqc/templates/default/assets/js/packages/pako_inflate.min.js, JavaScript, 2 lines
multiqc/templates/default/assets/js/packages/plotly-3.1.2.custom.min.js, JavaScript, 18 lines
multiqc/templates/default/assets/js/packages/showdown.min.js, JavaScript, 3 lines, 1 match
multiqc/templates/default/compiled/js/multiqc.min.js, JavaScript, 1 line
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multiqc/templates/default/src/js/ai.js, JavaScript, 555 lines
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multiqc/templates/default/src/js/plots/violin.js, JavaScript, 455 lines
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multiqc/templates/default/src/js/statuses.js, JavaScript, 260 lines
multiqc/templates/default/src/js/tables.js, JavaScript, 628 lines
multiqc/templates/default/src/js/toolbox.js, JavaScript, 107 lines
multiqc/templates/default/src/js/toolbox/ai.js, JavaScript, 254 lines
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multiqc/templates/default/src/js/toolbox/constants.js, JavaScript, 60 lines
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multiqc/templates/default/src/js/toolbox/help.js, JavaScript, 46 lines
multiqc/templates/default/src/js/toolbox/hide.js, JavaScript, 155 lines
multiqc/templates/default/src/js/toolbox/highlights.js, JavaScript, 116 lines
multiqc/templates/default/src/js/toolbox/rename.js, JavaScript, 168 lines
multiqc/templates/default/src/js/toolbox/save-load.js, JavaScript, 445 lines
multiqc/templates/default/src/js/toolbox/utils.js, JavaScript, 141 lines
multiqc/templates/default/vite.config.js, JavaScript, 26 lines
multiqc/templates/disco/__init__.py, Python, 27 lines
multiqc/templates/disco/compiled/js/multiqc.min.js, JavaScript, 1 line
multiqc/templates/disco/src/js/main.js, JavaScript, 5 lines
multiqc/templates/disco/vite.config.js, JavaScript, 26 lines
multiqc/templates/gathered/__init__.py, Python, 15 lines
multiqc/templates/geo/__init__.py, Python, 18 lines
multiqc/templates/original/__init__.py, Python, 22 lines
multiqc/templates/original/assets/js/ai-helpers.js, JavaScript, 662 lines
multiqc/templates/original/assets/js/ai.js, JavaScript, 570 lines
multiqc/templates/original/assets/js/decompress.js, JavaScript, 47 lines
multiqc/templates/original/assets/js/flat.js, JavaScript, 42 lines
multiqc/templates/original/assets/js/multiqc.js, JavaScript, 103 lines
multiqc/templates/original/assets/js/packages/FileSaver.min.js, JavaScript, 2 lines
multiqc/templates/original/assets/js/packages/bootstrap.min.js, JavaScript, 7 lines
multiqc/templates/original/assets/js/packages/dompurify.min.js, JavaScript, 3 lines
multiqc/templates/original/assets/js/packages/jquery-3.1.1.min.js, JavaScript, 4 lines
multiqc/templates/original/assets/js/packages/jquery-ui.min.js, JavaScript, 7 lines
multiqc/templates/original/assets/js/packages/jquery.tablesorter.min.js, JavaScript, 1 line
multiqc/templates/original/assets/js/packages/jquery.toast.min.js, JavaScript, 1 line
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multiqc/templates/original/assets/js/packages/pako_inflate.min.js, JavaScript, 2 lines
multiqc/templates/original/assets/js/packages/plotly-2.27.0.js, JavaScript, 6,083 lines
multiqc/templates/original/assets/js/packages/plotly-2.27.0.min.js, JavaScript, 8 lines
multiqc/templates/original/assets/js/packages/showdown.min.js, JavaScript, 3 lines
multiqc/templates/original/assets/js/plots/bar.js, JavaScript, 193 lines
multiqc/templates/original/assets/js/plots/box.js, JavaScript, 210 lines
multiqc/templates/original/assets/js/plots/heatmap.js, JavaScript, 190 lines
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multiqc/types.py, Python, 147 lines
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multiqc/utils/config_schema.py, Python, 1,069 lines
multiqc/utils/material_icons.py, Python, 112 lines
multiqc/utils/megaqc.py, Python, 60 lines
multiqc/utils/mqc_colour.py, Python, 586 lines
multiqc/utils/pyaml_env.py, Python, 116 lines
multiqc/utils/report.py, Python, 12 lines
multiqc/utils/testing.py, Python, 16 lines
multiqc/utils/util_functions.py, Python, 393 lines
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tests/test_plots.py, Python, 1,543 lines
tests/test_rerun.py, Python, 455 lines
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tests/test_sample_renaming.py, Python, 219 lines
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LICENSE, License, 621 lines
README.md, Text, 148 lines

At the source: github.com/MultiQC/MultiQC

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:

1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
723 scripts, each with its path and the digest of its content;
3 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:GSE313171, at NCBI GEO; found in “Data and code availability”

Data and code availability

RNA-seq datasets have been deposited at GEO (GEO: GSE313171 (https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE313171)).

This paper does not report original code and data analysis methods are described in the STAR Methods.

Any additional information required to re-analyse the data reported in this study 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 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 3 keywords, 4 funders, 39 references, 8 RRIDs.

Cite

This paper

Miller-Fleming, L., Au, W. H., & Whitworth, A. J. (2026). An optimized translating ribosome affinity purification protocol for low-abundance Drosophila tissues. iScience, 29(5), 115530. https://doi.org/10.1016/j.isci.2026.115530

BibTeX

@article{millerfleming2026optimized, author = {Miller-Fleming, Leonor and Au, Wing Hei and Whitworth, Alexander J}, title = {{An optimized translating ribosome affinity purification protocol for low-abundance \<i\>Drosophila\</i\> tissues}}, journal = {iScience}, year = {2026}, month = mar, volume = {29}, number = {5}, pages = {115530}, publisher = {Elsevier}, issn = {2589-0042}, doi = {10.1016/j.isci.2026.115530}, url = {https://doi.org/10.1016/j.isci.2026.115530}, pmid = {42028032}, pmcid = {PMC13099348} }

RIS

TY - JOUR AU - Miller-Fleming, Leonor AU - Au, Wing Hei AU - Whitworth, Alexander J TI - An optimized translating ribosome affinity purification protocol for low-abundance Drosophila tissues T2 - iScience J2 - iScience PY - 2026 DA - 2026/03/30 VL - 29 IS - 5 SP - 115530 SN - 2589-0042 PB - Elsevier DO - 10.1016/j.isci.2026.115530 UR - https://doi.org/10.1016/j.isci.2026.115530 LA - en ER -

CSL-JSON

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An optimized translating ribosome affinity purification protocol for low-abundance <i>Drosophila</i> tissues.

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