CRISPR-engineered deletion of POGZ alters transcription factor binding at promoters of genes involved in synaptic signaling.

Code ↔ Paper

17 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 17 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted

[1] § Material and methods › RNA-seq data analysis ↔ RNA-seq/runSTAR.sh, the whole file · a weak match · score 0.98 · GeneCounts, alignEndsType, alignIntronMax, outFilterMismatchNoverLmax, outSAMunmapped, quantMode
[2] § Material and methods › Enrichment analysis ↔ RNA-seq/RNAseq.analysis/R/read_pathway_db.R, the whole file · a weak match · score 0.85 · Human Phenotype Ontology, canonical pathways, transcription factor, mSigDB, SynGO, hs
[3] § Material and methods › ATAC-seq analysis › TF footprint ↔ ATAC-seq/Transcription_factor_footprint/5. call_TF_bindings.sh, the whole file · a weak match · score 0.80 · TF footprint, TF binding, merged peak, ATAC seq, transcription factor, motifs
[4] § Material and methods › Co-expression analysis ↔ RNA-seq/co-expression_analysis_allsamples.R, lines 53–95 · score 0.75 · Soft power, Module membership, Co expression, eigengene, outlier, network
[5] § Material and methods › ATAC-seq analysis › Data processing ↔ atac-seq-pipeline-1.5.4.zip/src/encode_task_qc_report.py, lines 277–421 · score 0.75 · alignment quality, GC bias, fragment length, PCR, duplicates, mitochondria
[6] § Material and methods › ATAC-seq analysis › Differential accessibility ↔ ATAC-seq/Differential_accessibility/diff_peaks_v2.R, lines 91–153 · score 0.74 · dba.contrast, model design, Treatment, peaks, DARs, accessibility
[7] § Material and methods › ATAC-seq analysis › Direct and indirect POGZ regulation ↔ ATAC-seq/Transcription_factor_footprint/tf_binding.Rmd, lines 181–248 · score 0.74 · predicted bound, log2 fold change, binding site, DTF, DEL, promoter
[8] § Material and methods › Co-expression analysis ↔ RNA-seq/RNAseq.analysis/R/co_expression_functions.R, lines 125–159 · score 0.74 · Soft power, scale free topology, Co expression, fit, outlier, network
[9] § Material and methods › ATAC-seq analysis › Data processing ↔ atac-seq-pipeline-1.1.7.zip/src/run_ataqc.py, lines 1470–1518 · score 0.74 · GC bias, naive overlap, overlapped peaks, duplicates, quality, fraction
[10] § Material and methods › ATAC-seq analysis › Differential accessibility ↔ ATAC-seq/Differential_accessibility/differential_accessibility.Rmd, lines 72–121 · score 0.68 · dba.contrast, DiffBind, Treatment, DARs, accessibility, DEL
[11] § Material and methods › ATAC-seq analysis › TF footprint ↔ ATAC-seq/Transcription_factor_footprint/tf_binding.Rmd, lines 181–248 · score 0.67 · TF binding, transcription factor, bound, footprint, predict, motifs
[12] § Material and methods › Enrichment analysis ↔ RNA-seq/RNAseq.analysis/R/enrichment.R, lines 53–123 · score 0.60 · co expression, KEGG, REACTOME, SynGO, Ontology, enrichments
[13] § Material and methods › RNA-seq data analysis ↔ RNA-seq/rnaseq_analysis_iN_final.Rmd, lines 32–56 · score 0.58 · wt c6, gene annotations, RNA seq, STAR, Ensembl, alignments
[14] § Material and methods › ATAC-seq analysis › Direct and indirect POGZ regulation ↔ ATAC-seq/Differential_accessibility/differential_accessibility.Rmd, lines 393–464 · score 0.57 · log2 fold change, POGZ target, cortex, DEL, binding, regulated
[15] § Results › POGZ direct regulatory targets modulated in edited NSCs are associated with chromatin regulation, cell cycle, and RNA processing ↔ ATAC-seq/Differential_accessibility/differential_accessibility.Rmd, lines 393–464 · score 0.57 · NSC DEGs, overlapping genes, POGZ target genes, binding, regulation, enrichment
[16] § Material and methods › Differential expression analysis ↔ RNA-seq/RNAseq.analysis/R/rnaseq_analysis_functions.R, lines 623–684 · score 0.56 · DESeq2, RNA seq, weighted, SVA, meta, scores
[17] § Results › Indirect effects are mediated by other TFs ↔ ATAC-seq/Transcription_factor_footprint/tf_binding.Rmd, lines 147–179 · score 0.53 · TF footprint, TF binding, probability, motif, scored, seq

Paper

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The authors' code

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---
title: "POGZ TF binding"
author: "Yating Liu"
date: "3/2/2023"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_knit$set(root.dir = "~/Documents/Talkowski/POGZ_project/ATAC_seq")
knitr::opts_chunk$set(echo = TRUE)
```
```{r message=FALSE}
library(ggplot2)
library(dplyr)
library(stringr)
library(biomaRt)
library(GenomicRanges)
options(stringsAsFactors = F)
```
## Get differential TF binding bw DEL vs WT from TF footprinting
### Read iN TF footprinting results into a list
```{r}
if (!file.exists("TF_footprint/iN_bindetect_results.rds")) {
iN_bindetect_results <- list()
iN_bindetect_results[["iN_GM_DEL2_vs_WT2"]] <- read.table("/Volumes/talkowski/Samples/POGZ/ATAC/TF_footprint/DTFB.iN_GM8330_Feb2021.DEL2_vs_WT2.conservative/bindetect_results.txt", header = T)
iN_bindetect_results[["iN_GM_DEL1_vs_WT1"]] <- read.table("/Volumes/talkowski/Samples/POGZ/ATAC/TF_footprint/DTFB.iN_GM8330_Mar2021.DEL1_vs_WT1.conservative/bindetect_results.txt", header = T)
iN_bindetect_results[["iN_MGH_DEL2_vs_WT2"]] <- read.table("/Volumes/talkowski/Samples/POGZ/ATAC/TF_footprint/DTFB.iN_MGH_Feb2021.DEL2_vs_WT2.conservative/bindetect_results.txt", header = T)
iN_bindetect_results[["iN_MGH_DEL1_vs_WT1"]] <- read.table("/Volumes/talkowski/Samples/POGZ/ATAC/TF_footprint/DTFB.iN_MGH_Mar2021.DEL1_vs_WT1.conservative/bindetect_results.txt", header = T)
iN_bindetect_results[["iN_GM_DEL_vs_WT"]] <- read.table("/Volumes/talkowski/Samples/POGZ/ATAC/TF_footprint/DTFB.iN_GM.DEL_vs_WT.conservative/bindetect_results.txt", header = T)
iN_bindetect_results[["iN_MGH_DEL_vs_WT"]] <- read.table("/Volumes/talkowski/Samples/POGZ/ATAC/TF_footprint/DTFB.iN_MGH.DEL_vs_WT.conservative/bindetect_results.txt", header = T)
iN_bindetect_results[["iN_GM_del1del2_vs_WT"]] <- read.table("/Volumes/talkowski/Samples/POGZ/ATAC/TF_footprint/DTFB.iN_GM8330_Sep2020.DEL1DEL2_vs_WT.conservative_with_pogz_motifs/bindetect_results.txt", header = T)
iN_bindetect_results[["iN_GM_del1_vs_WT"]] <- read.table("/Volumes/talkowski/Samples/POGZ/ATAC/TF_footprint/DTFB.iN_GM8330_Sep2020.DEL1_vs_WT.conservative_with_pogz_motifs/bindetect_results.txt", header = T)
saveRDS(iN_bindetect_results, "TF_footprint/iN_bindetect_results.rds")
}
iN_bindetect_results <- readRDS("TF_footprint/iN_bindetect_results.rds")
```
### Read NSC TF footprinting results into a list
```{r}
if (!file.exists("TF_footprint/NSC_bindetect_results.rds")) {
NSC_bindetect_results <- list()
NSC_bindetect_results[["NSC_GM_DEL1_vs_WT1"]] <- read.table("/Volumes/talkowski/Samples/POGZ/ATAC/TF_footprint/DTFB.NSC_GM8330_Nov2020.DEL1_vs_WT1.conservative/bindetect_results.txt", header = T)
NSC_bindetect_results[["NSC_GM_DEL2_vs_WT2"]] <- read.table("/Volumes/talkowski/Samples/POGZ/ATAC/TF_footprint/DTFB.NSC_GM8330_Nov2020.DEL2_vs_WT2.conservative/bindetect_results.txt", header = T)
NSC_bindetect_results[["NSC_MGH_DEL1_vs_WT1"]] <- read.table("/Volumes/talkowski/Samples/POGZ/ATAC/TF_footprint/DTFB.NSC_MGH_Nov2020.DEL1_vs_WT1.conservative/bindetect_results.txt", header = T)
NSC_bindetect_results[["NSC_MGH_DEL2_vs_WT2"]] <- read.table("/Volumes/talkowski/Samples/POGZ/ATAC/TF_footprint/DTFB.NSC_MGH_Nov2020.DEL2_vs_WT2.conservative/bindetect_results.txt", header = T)
NSC_bindetect_results[["NSC_GM_del1del2_vs_WT"]] <- read.table("/Volumes/talkowski/Samples/POGZ/ATAC/TF_footprint/DTFB.NSC_GM8330_Aug2020.DEL1DEL2_vs_WT.conservative_with_pogz_motifs/bindetect_results.txt", header = T)
NSC_bindetect_results[["NSC_GM_del1_vs_WT"]] <- read.table("/Volumes/talkowski/Samples/POGZ/ATAC/TF_footprint/DTFB.NSC_GM8330_Aug2020.DEL1_vs_WT.conservative_with_pogz_motifs/bindetect_results.txt", header = T)
NSC_bindetect_results[["NSC_GM_DEL_vs_WT"]] <- read.table("/Volumes/talkowski/Samples/POGZ/ATAC/TF_footprint/DTFB.NSC_GM.DEL_vs_WT.conservative/bindetect_results.txt", header = T)
NSC_bindetect_results[["NSC_MGH_DEL_vs_WT"]] <- read.table("/Volumes/talkowski/Samples/POGZ/ATAC/TF_footprint/DTFB.NSC_MGH.DEL_vs_WT.conservative/bindetect_results.txt", header = T)
saveRDS(NSC_bindetect_results, "TF_footprint/NSC_bindetect_results.rds")
}
NSC_bindetect_results <- readRDS("TF_footprint/NSC_bindetect_results.rds")
```
### Function to get differential TF bindings
threshold is set according to the TOBIAS paper: https://www.nature.com/articles/s41467-020-18035-1
All TFs with -log10(p-value) above the 95% quantile or differential binding scores smaller/larger than the 5%
and 95% quantiles (top 5% in each direction) are colored and shown with labels.
```{r}
identify_differential_TF <- function(bindetect_results, by = 0.05) {
pvalue_col <- colnames(bindetect_results)[str_detect(colnames(bindetect_results), "_pvalue")]
score_change_col <- colnames(bindetect_results)[str_detect(colnames(bindetect_results), "_change")]
sig_pvalue <- quantile(-log10(bindetect_results[[pvalue_col]]), probs = seq(0,1,by))
sig_pvalue <- unname(sig_pvalue[length(sig_pvalue)-1])
score_change <- quantile(bindetect_results[[score_change_col]], probs = seq(0,1,by))
score_change_small <- unname(score_change[2])
score_change_large <- unname(score_change[length(score_change)-1])
bindetect_results[["sig_change_up"]] <- bindetect_results[[score_change_col]] > 0 & (bindetect_results[[score_change_col]] > score_change_large | -log10(bindetect_results[[pvalue_col]]) > sig_pvalue)
bindetect_results[["sig_change_down"]] <- bindetect_results[[score_change_col]] < 0 & (bindetect_results[[score_change_col]] < score_change_small | -log10(bindetect_results[[pvalue_col]]) > sig_pvalue)
bindetect_results <- bindetect_results %>% mutate(sig_change = case_when(sig_change_up == TRUE ~ "up",
sig_change_down == TRUE ~"down",
TRUE ~"NA"))
#print(str(bindetect_results))
p <- ggplot(bindetect_results, aes(x = get(score_change_col), y = -log10(get(pvalue_col)), color = sig_change)) +
geom_point() +
scale_color_manual(breaks = c("up", "down", "NA"), values = c("red", "blue", "grey")) +
labs(x = "score_change", y = "-log10(pvalue)") +
geom_hline(yintercept = sig_pvalue, color = "purple", linetype = "dotted") +
geom_vline(xintercept = score_change_small, color = "blue", linetype = "dotted") +
geom_vline(xintercept = score_change_large, color = "red", linetype = "dotted")
print(p)
return (bindetect_results)
}
```
## Overlap function
```{r}
overlap_tf <- function(tf1, tf2, tf1_name, tf2_name) {
up <- intersect(tf1[tf1$sig_change == "up",]$motif_id, tf2[tf2$sig_change == "up",]$motif_id)
down <- intersect(tf1[tf1$sig_change == "down",]$motif_id, tf2[tf2$sig_change == "down",]$motif_id)
up_down <- intersect(tf1[tf1$sig_change == "up",]$motif_id, tf2[tf2$sig_change == "down",]$motif_id)
down_up <- intersect(tf1[tf1$sig_change == "down",]$motif_id, tf2[tf2$sig_change == "up",]$motif_id)
summary_stat <- matrix(0, nrow = 2, ncol = 2)
rownames(summary_stat) <- c(paste0(tf1_name, "_up"), paste0(tf1_name, "_down"))
colnames(summary_stat) <- c(paste0(tf2_name, "_up"), paste0(tf2_name, "_down"))
summary_stat[paste0(tf1_name, "_up"),paste0(tf2_name, "_up")] <- length(up)
summary_stat[paste0(tf1_name, "_down"),paste0(tf2_name, "_down")] <- length(down)
summary_stat[paste0(tf1_name, "_up"),paste0(tf2_name, "_down")] <- length(up_down)
summary_stat[paste0(tf1_name, "_down"),paste0(tf2_name, "_up")] <- length(down_up)
print(summary_stat)
return (list(up = up, down = down, up_down = up_down, down_up = down_up))
}
```
### iN DEL vs WT
```{r}
iN_GM_DEL_vs_WT <- identify_differential_TF(iN_bindetect_results$iN_GM_DEL_vs_WT)
iN_MGH_DEL_vs_WT <- identify_differential_TF(iN_bindetect_results$iN_MGH_DEL_vs_WT)
# iN GM het vs iN MGH het
iN_overlapping_DEL_vs_WT_GM_MGH <- overlap_tf(iN_GM_DEL_vs_WT, iN_MGH_DEL_vs_WT, "GM", "MGH")
saveRDS(iN_overlapping_DEL_vs_WT_GM_MGH, "TF_footprint/iN_GM_MGH_overlapped_DTF.rds")
iN_GM_del1del2_vs_WT <- identify_differential_TF(iN_bindetect_results$iN_GM_del1del2_vs_WT)
# iN GM het vs comp het
overlapping_DEL1DEL2_vs_WT_DEL_vs_WT <- overlap_tf(iN_GM_del1del2_vs_WT, iN_GM_DEL_vs_WT, "iN_GM_del1del2_vs_WT", "iN_GM_DEL_vs_WT")
```
### NSC DEL vs WT
```{r}
NSC_GM_DEL_vs_WT <- identify_differential_TF(NSC_bindetect_results$NSC_GM_DEL_vs_WT)
NSC_MGH_DEL_vs_WT <- identify_differential_TF(NSC_bindetect_results$NSC_MGH_DEL_vs_WT)
NSC_overlapping_DEL_vs_WT_GM_MGH <- overlap_tf(NSC_GM_DEL_vs_WT, NSC_MGH_DEL_vs_WT, "GM", "MGH")
saveRDS(NSC_overlapping_DEL_vs_WT_GM_MGH, "TF_footprint/NSC_GM_MGH_overlapped_DTF.rds")
NSC_GM_del1del2_vs_WT <- identify_differential_TF(NSC_bindetect_results$NSC_GM_del1del2_vs_WT)
# NSC GM het vs comp het
overlapping_DEL1_vs_WT1_DEL2_vs_WT2 <- overlap_tf(NSC_GM_del1del2_vs_WT, NSC_GM_DEL_vs_WT, "NSC_GM_del1del2_vs_WT", "NSC_GM_DEL_vs_WT")
```
#### POGZ motif change and pvalue
```{r}
pogz_motif_stat <- data.frame()
bindetect_lst <- list(iN_GM_DEL_vs_WT = iN_bindetect_results$iN_GM_DEL_vs_WT,
iN_MGH_DEL_vs_WT = iN_bindetect_results$iN_MGH_DEL_vs_WT,
NSC_GM_DEL_vs_WT = NSC_bindetect_results$NSC_GM_DEL_vs_WT,
NSC_MGH_DEL_vs_WT = NSC_bindetect_results$NSC_MGH_DEL_vs_WT)
for (bindetect_name in names(bindetect_lst)) {
message(bindetect_name)
bindetect_results <- bindetect_lst[[bindetect_name]]
by = 0.05
pvalue_col <- colnames(bindetect_results)[str_detect(colnames(bindetect_results), "_pvalue")]
score_change_col <- colnames(bindetect_results)[str_detect(colnames(bindetect_results), "_change")]
bindetect_results[["log10_pvalue"]] <- -log10(bindetect_results[[pvalue_col]])
sig_pvalue <- quantile(-log10(bindetect_results[[pvalue_col]]), probs = seq(0,1,by))
sig_pvalue_95 <- unname(sig_pvalue[length(sig_pvalue)-1])
sig_pvalue_90 <- unname(sig_pvalue[length(sig_pvalue)-2])
score_change <- quantile(bindetect_results[[score_change_col]], probs = seq(0,1,by))
score_change_small <- unname(score_change[2])
score_change_large <- unname(score_change[length(score_change)-1])
colnames(bindetect_results) <- str_remove_all(colnames(bindetect_results), "_GM|_MGH")
score_change_col <- colnames(bindetect_results)[str_detect(colnames(bindetect_results), "_change")]
pogz_motif_stat <- rbind(pogz_motif_stat, data.frame(bindetect_results[str_detect(bindetect_results$name, "pogz"),],
cell_type = bindetect_name,
log10_pvalue_percentile = sapply(bindetect_results[str_detect(bindetect_results$name, "pogz"),c("log10_pvalue")], function(x) {return(names(tail(sig_pvalue[x > sig_pvalue],n=1)))}),
score_change_percentile = sapply(bindetect_results[str_detect(bindetect_results$name, "pogz"),c(score_change_col)], function(x) {return(names(tail(score_change[x > score_change],n=1)))}),
log10_pvalue_95_percentile = sig_pvalue_95,
score_change_5_percentile = score_change_small,
score_change_95_percentile = score_change_large))
}
write_xlsx(pogz_motif_stat, "TF_footprint/pogz_motif_stat.xlsx")
```
## Get differential binding TFs target genes
### Function for get target genes for a table of motif ids and name, in the end overlap these target genes with DEG results
```{r}
## Get promoters of all genes
require(ensembldb)
edb <- EnsDb("data/Homo_sapiens.GRCh38.92.sqlite")
allpromoters <- promoters(edb, upstream = 1500, downstream = 500)
allpromoters <- allpromoters[seqnames(allpromoters) %in% c(1:22, "X", "Y")]
allpromoters <- keepStandardChromosomes(allpromoters)
gene_annotation <- readRDS("../iN_05_2021/results/gene_annotation.rds")
get_TF_target_genes <- function(motif_info, express_colname, allpromoters, gene_annotation, DTFB_path, del_vs_wt_final_degs, prefix) {
# get motif directory name
overlapped_TF_folder <- paste(motif_info$name, motif_info$motif_id, sep = "_")
overlapped_TF_folder <- str_remove(overlapped_TF_folder, "::")
overlapped_TF_folder <- str_remove_all(overlapped_TF_folder, "\$|\$")
genes_vs_TF_overlapping <- data.frame()
for (i in 1:length(overlapped_TF_folder)) {
bind_sites <- read.table(file.path(DTFB_path, overlapped_TF_folder[i], paste0(overlapped_TF_folder[i],"_overview.txt")), header = T)
bind_sites <- bind_sites[bind_sites$TFBS_chr %in% c(1:22, "X", "Y"),]
# if the TF is up-regulated, we use the TF bind regions that specific to DEL, otherwise use TF bind region specific to WT
del_bind_col <- which(str_detect(colnames(bind_sites), "DEL.*_bound"))
wt_bind_col <- which(str_detect(colnames(bind_sites), "WT.*_bound"))
# Filter bind sites by keeping the sites that are predicted bound within at least one condition (WT or DEL)
bind_sites <- bind_sites[bind_sites[[del_bind_col]] + bind_sites[[wt_bind_col]] > 0,]
# Filter annotated bind sites that have the log2 fold change between the footprint scores of the two conditions > 90% quantile or < 10% quantile
log2fc_col <- which(str_detect(colnames(bind_sites), "_log2fc"))
log2fc_quantile <- quantile(bind_sites[[log2fc_col]], prob = seq(0,1, by = 0.05))
# print(log2fc_quantile)
bind_sites <- bind_sites[bind_sites[[log2fc_col]] < log2fc_quantile[["10%"]] | bind_sites[[log2fc_col]] > log2fc_quantile[["90%"]],]
message(motif_info[i,]$name, ": number of bind sites in top quantitle: ", nrow(bind_sites))
# if (motif_info[i,]$sig_change == "up") {
# bind_sites <- bind_sites[bind_sites[[del_bind_col]] == 1 & bind_sites[[wt_bind_col]] == 0,]
# } else {
# bind_sites <- bind_sites[bind_sites[[del_bind_col]] == 0 & bind_sites[[wt_bind_col]] == 1,]
# }
bind_sites_gr <- GRanges(seqnames = bind_sites$TFBS_chr, ranges = IRanges(start = bind_sites$TFBS_start, end = bind_sites$TFBS_end), strand = bind_sites$TFBS_strand)
overlapped_genes <- unique(allpromoters[queryHits(findOverlaps(allpromoters, bind_sites_gr)),]$gene_id)
if (length(overlapped_genes) > 0) {
overlapped_genes <- gene_annotation[overlapped_genes,]
overlapped_genes <- cbind(overlapped_genes, motif_info[i,])
# overlapped_genes$motif_id <- motif_info[i,]$motif_id
# overlapped_genes$name <- motif_info[i,]$name
# overlapped_genes$sig_change <- motif_info[i,]$sig_change
genes_vs_TF_overlapping <- rbind(genes_vs_TF_overlapping, overlapped_genes)
}
}
write.csv(genes_vs_TF_overlapping, paste0(prefix, "TF_target_genes.csv"), row.names = F, quote = F)
# only pick the DTF that are expressed
message("Total num DTF: ", length(unique(genes_vs_TF_overlapping$motif_id)))
message("Expressed DTF: ", length(unique(genes_vs_TF_overlapping[genes_vs_TF_overlapping[[express_colname]] == "Yes",]$motif_id)))
genes_vs_TF_overlapping <- genes_vs_TF_overlapping[genes_vs_TF_overlapping[[express_colname]] == "Yes",]
# Overlap TF targets with DEGs
TF_genes <- genes_vs_TF_overlapping %>%
dplyr::select(ensembl_id, symbol, gene_type, motif_id, name, sig_change) %>%
group_by(ensembl_id, symbol, gene_type) %>%
summarise(motif_id = paste(motif_id, collapse = ";"), name = paste(name, collapse = ";"),sig_change = paste(sig_change, collapse = ";"))
TF_degs <- left_join(TF_genes[TF_genes$ensembl_id %in% intersect(TF_genes$ensembl_id, del_vs_wt_final_degs$ensembl_id),], del_vs_wt_final_degs[, which(str_detect(colnames(del_vs_wt_final_degs), "ensembl_id|padj|log2FC"))], by="ensembl_id")
write.csv(TF_genes, paste0(prefix, "TF_genes.csv"), row.names = F, quote = F)
write.csv(TF_degs, paste0(prefix, "TF_degs.csv"), row.names = F, quote = F)
return (TF_degs)
}
```
## all motif expression information (whether a motif is expressed in iN or NSC)
```{r}
all_motifs_expression_info <- readxl::read_excel("../POGZ_final/TF_footprinting/all_motifs_whether_expressed.xlsx")
```
## iN DTF target genes
```{r}
del_vs_wt_final_degs <- read.csv("../POGZ_paper/results/del_vs_wt_overlapped_GM_MGH.csv")
```
### Get differential binding GM TFs target genes
```{r}
DTFB_GM_path <- "/Volumes/talkowski/Samples/POGZ/ATAC/TF_footprint/DTFB.iN_GM.DEL_vs_WT.conservative"
DTFB_GM <- iN_GM_DEL_vs_WT[iN_GM_DEL_vs_WT$sig_change != "NA",]
DTFB_GM <- left_join(DTFB_GM, all_motifs_expression_info[, -c(which(colnames(all_motifs_expression_info) == "name"))], by = "motif_id")
writexl::write_xlsx(DTFB_GM, "TF_footprint/iN_GM_DTF.xlsx")
DTFB_GM_DEGs <- get_TF_target_genes(DTFB_GM, express_colname = "iN_expression", allpromoters, gene_annotation, DTFB_GM_path, del_vs_wt_final_degs, "TF_footprint/iN_GM_")
```
### Get differential binding MGH TFs target genes
```{r}
DTFB_MGH_path <- "/Volumes/talkowski/Samples/POGZ/ATAC/TF_footprint/DTFB.iN_MGH.DEL_vs_WT.conservative"
DTFB_MGH <- iN_MGH_DEL_vs_WT[iN_MGH_DEL_vs_WT$sig_change != "NA",]
DTFB_MGH <- left_join(DTFB_MGH, all_motifs_expression_info[, -c(which(colnames(all_motifs_expression_info) == "name"))], by = "motif_id")
writexl::write_xlsx(DTFB_MGH, "TF_footprint/iN_MGH_DTF.xlsx")
DTFB_MGH_DEGs <- get_TF_target_genes(DTFB_MGH, express_colname = "iN_expression", allpromoters, gene_annotation, DTFB_MGH_path, del_vs_wt_final_degs, "TF_footprint/iN_MGH_")
```
### Get overlapped TF targets DEGs across background
```{r}
del_vs_wt_GM <- read.csv("../POGZ_paper/results/DEL_vs_WT_GM__simple_model_sva/DEL_vs_WT_threshold_0.5_sv.csv", row.names = 1)
del_vs_wt_MGH <- read.csv("../POGZ_paper/results/DEL_vs_WT_MGH__simple_model_sva/DEL_vs_WT_threshold_0.5_sv.csv", row.names = 1)
analyzed_genes <- intersect(del_vs_wt_GM$ensembl_id, del_vs_wt_MGH$ensembl_id)
DTFB_GM <- readxl::read_excel("TF_footprint/iN_GM_DTF.xlsx")
DTFB_MGH <- readxl::read_excel("TF_footprint/iN_MGH_DTF.xlsx")
DTFB_GM_genes <- read.csv("TF_footprint/iN_GM_TF_genes.csv")
DTFB_MGH_genes <- read.csv("TF_footprint/iN_MGH_TF_genes.csv")
DTFB_GM_DEGs <- read.csv("TF_footprint/iN_GM_TF_degs.csv")
DTFB_GM_DEGs$deg_regulation <- ifelse(DTFB_GM_DEGs$log2FC_GM>0, "up", "down")
DTFB_MGH_DEGs <- read.csv("TF_footprint/iN_MGH_TF_degs.csv")
overlapped_tf_degs <- intersect(DTFB_MGH_DEGs$ensembl_id, DTFB_GM_DEGs$ensembl_id)
overlapped_tf_degs_info <- left_join(DTFB_GM_DEGs[, c("ensembl_id","symbol","gene_type","motif_id","name","sig_change", "deg_regulation")], DTFB_MGH_DEGs[, c("ensembl_id","symbol","gene_type","motif_id","name","sig_change")], by = c("ensembl_id"="ensembl_id", "symbol"="symbol", "gene_type"="gene_type"), suffix = c(".GM", ".MGH"))
overlapped_tf_degs_info <- overlapped_tf_degs_info[complete.cases(overlapped_tf_degs_info),]
write.csv(overlapped_tf_degs_info, "TF_footprint/iN_GM_MGH_overlapped_tf_degs_info.csv", row.names = F)
dtf_target_genes_gm <- length(intersect(DTFB_GM_genes$ensembl_id, analyzed_genes))
dtf_target_genes_mgh <- length(intersect(DTFB_MGH_genes$ensembl_id, analyzed_genes))
overlap_dtf_targets <- length(intersect(intersect(DTFB_GM_genes$ensembl_id, analyzed_genes), intersect(DTFB_MGH_genes$ensembl_id, analyzed_genes)))
fisher_test <- fisher.test(matrix(c(overlap_dtf_targets, dtf_target_genes_gm-overlap_dtf_targets, dtf_target_genes_mgh-overlap_dtf_targets, length(analyzed_genes)-dtf_target_genes_gm-dtf_target_genes_mgh+overlap_dtf_targets), nrow = 2, ncol = 2), alternative = "greater")
message("Number of GM DTF: ", nrow(DTFB_GM),
"\nNumber of GM DTF that are expressed: ", sum(DTFB_GM$iN_expression == "Yes"),
"\nNumber of MGH DTF: ", nrow(DTFB_MGH),
"\nNumber of MGH DTF that are expressed: ", sum(DTFB_MGH$iN_expression == "Yes"))
message("Number of DTF target genes in GM: ", length(intersect(DTFB_GM_genes$ensembl_id, analyzed_genes)),
"\nNumber of DTF target genes in MGH: ", length(intersect(DTFB_MGH_genes$ensembl_id, analyzed_genes)),
"\nNumber of DTF target genes in both GM and MGH: ", length(intersect(DTFB_GM_genes$ensembl_id,intersect(DTFB_MGH_genes$ensembl_id, analyzed_genes))),
"\nNum overlap DTF target genes in GM vs MGH ", overlap_dtf_targets,
" (fisher test pvalue = ", fisher_test$p.value, ")",
"\nNumber of DTF target DEGs in GM: ", nrow(DTFB_GM_DEGs), " up = ", sum(DTFB_GM_DEGs$log2FC_GM>0), " down = ", sum(DTFB_GM_DEGs$log2FC_GM<0),
"\nNumber of DTF target DEGs in MGH: ", nrow(DTFB_MGH_DEGs)," up = ", sum(DTFB_MGH_DEGs$log2FC_GM>0), " down = ", sum(DTFB_MGH_DEGs$log2FC_GM<0),
"\nNumber of overlapped DTF target DEGs: ", nrow(overlapped_tf_degs_info), " up = ", sum(overlapped_tf_degs_info$deg_regulation=="up"), " down = ", sum(overlapped_tf_degs_info$deg_regulation=="down"))
```
## iN comp DTF target genes
```{r}
del_vs_wt_final_degs <- read.csv("../POGZ_iN/results/8330_del1_del2_vs_8330_WT_threshold_0.5.csv")
del_vs_wt_final_degs$log2FC <- del_vs_wt_final_degs$log2FoldChange
del_vs_wt_final_degs <- del_vs_wt_final_degs[del_vs_wt_final_degs$padj<0.1,]
```
### Get differential binding GM TFs target genes
```{r}
iN_GM_del1del2_vs_WT <- identify_differential_TF(iN_bindetect_results$iN_GM_del1del2_vs_WT)
DTFB_path <- "/Volumes/talkowski/Samples/POGZ/ATAC/TF_footprint/DTFB.iN_GM8330_Sep2020.DEL1DEL2_vs_WT.conservative_with_pogz_motifs"
DTFB_comp <- iN_GM_del1del2_vs_WT[iN_GM_del1del2_vs_WT$sig_change != "NA",]
DTFB_comp <- left_join(DTFB_comp, all_motifs_expression_info[, -c(which(colnames(all_motifs_expression_info) == "name"))], by = "motif_id")
DTFB_DEGs <- get_TF_target_genes(DTFB_comp, express_colname = "iN_comp_het_expression", allpromoters, gene_annotation, DTFB_path, del_vs_wt_final_degs, "TF_footprint/iN_comp_")
```
## NSC DTF target genes
```{r}
del_vs_wt_final_degs <- read.csv("../POGZ_paper/results/NSC_del_vs_wt_overlapped_GM_MGH.csv")
```
### Get differential binding GM TFs target genes
```{r}
DTFB_GM_path <- "/Volumes/talkowski/Samples/POGZ/ATAC/TF_footprint/DTFB.NSC_GM.DEL_vs_WT.conservative"
DTFB_GM <- NSC_GM_DEL_vs_WT[NSC_GM_DEL_vs_WT$sig_change != "NA",]
DTFB_GM <- left_join(DTFB_GM, all_motifs_expression_info[, -c(which(colnames(all_motifs_expression_info) == "name"))], by = "motif_id")
writexl::write_xlsx(DTFB_GM, "TF_footprint/NSC_GM_DTF.xlsx")
DTFB_GM_DEGs <- get_TF_target_genes(DTFB_GM, express_colname = "NSC_expression", allpromoters, gene_annotation, DTFB_GM_path, del_vs_wt_final_degs, "TF_footprint/NSC_GM_")
```
### Get differential binding MGH TFs target genes
```{r}
DTFB_MGH_path <- "/Volumes/talkowski/Samples/POGZ/ATAC/TF_footprint/DTFB.NSC_MGH.DEL_vs_WT.conservative"
DTFB_MGH <- NSC_MGH_DEL_vs_WT[NSC_MGH_DEL_vs_WT$sig_change != "NA",]
DTFB_MGH <- left_join(DTFB_MGH, all_motifs_expression_info[, -c(which(colnames(all_motifs_expression_info) == "name"))], by = "motif_id")
writexl::write_xlsx(DTFB_MGH, "TF_footprint/NSC_MGH_DTF.xlsx")
DTFB_MGH_DEGs <- get_TF_target_genes(DTFB_MGH, express_colname = "NSC_expression", allpromoters, gene_annotation, DTFB_MGH_path, del_vs_wt_final_degs, "TF_footprint/NSC_MGH_")
```
### Get overlapped TF targets DEGs across background
```{r}
del_vs_wt_GM <- read.table("../NSC_03_2020/meta_analysis/POGZ_8330del1_2.metaAnalysis.txt", header = T)
del_vs_wt_MGH <- read.table("../NSC_03_2020/meta_analysis/POGZ_MGHdel1_2.metaAnalysis.txt", header = T)
analyzed_genes <- intersect(del_vs_wt_GM$ensemblid, del_vs_wt_MGH$ensemblid)
DTFB_GM <- readxl::read_excel("TF_footprint/NSC_GM_DTF.xlsx")
DTFB_MGH <- readxl::read_excel("TF_footprint/NSC_MGH_DTF.xlsx")
DTFB_GM_genes <- read.csv("TF_footprint/NSC_GM_TF_genes.csv")
DTFB_MGH_genes <- read.csv("TF_footprint/NSC_MGH_TF_genes.csv")
DTFB_GM_DEGs <- read.csv("TF_footprint/NSC_GM_TF_degs.csv")
DTFB_GM_DEGs$deg_regulation <- ifelse(DTFB_GM_DEGs$log2FC_GM>0, "up", "down")
DTFB_MGH_DEGs <- read.csv("TF_footprint/NSC_MGH_TF_degs.csv")
DTFB_MGH_DEGs$deg_regulation <- ifelse(DTFB_MGH_DEGs$log2FC_GM>0, "up", "down")
overlapped_tf_degs <- intersect(DTFB_MGH_DEGs$ensembl_id, DTFB_GM_DEGs$ensembl_id)
overlapped_tf_degs_info <- left_join(DTFB_GM_DEGs[, c("ensembl_id","symbol","gene_type","motif_id","name","sig_change", "deg_regulation")], DTFB_MGH_DEGs[, c("ensembl_id","symbol","gene_type","motif_id","name","sig_change")], by = c("ensembl_id"="ensembl_id", "symbol"="symbol", "gene_type"="gene_type"), suffix = c(".GM", ".MGH"))
overlapped_tf_degs_info <- overlapped_tf_degs_info[complete.cases(overlapped_tf_degs_info),]
write.csv(overlapped_tf_degs_info, "TF_footprint/NSC_GM_MGH_overlapped_tf_degs_info.csv", row.names = F)
dtf_target_genes_gm <- length(intersect(DTFB_GM_genes$ensembl_id, analyzed_genes))
dtf_target_genes_mgh <- length(intersect(DTFB_MGH_genes$ensembl_id, analyzed_genes))
overlap_dtf_targets <- length(intersect(intersect(DTFB_GM_genes$ensembl_id, analyzed_genes), intersect(DTFB_MGH_genes$ensembl_id, analyzed_genes)))
fisher_test <- fisher.test(matrix(c(overlap_dtf_targets, dtf_target_genes_gm-overlap_dtf_targets, dtf_target_genes_mgh-overlap_dtf_targets, length(analyzed_genes)-dtf_target_genes_gm-dtf_target_genes_mgh+overlap_dtf_targets), nrow = 2, ncol = 2), alternative = "greater")
message("Number of GM DTF: ", nrow(DTFB_GM),
"\nNumber of GM DTF that are expressed: ", sum(DTFB_GM$NSC_expression == "Yes"),
"\nNumber of MGH DTF: ", nrow(DTFB_MGH),
"\nNumber of MGH DTF that are expressed: ", sum(DTFB_MGH$NSC_expression == "Yes"))
message("Number of DTF target genes in GM: ", length(intersect(DTFB_GM_genes$ensembl_id, analyzed_genes)),
"\nNumber of DTF target genes in MGH: ", length(intersect(DTFB_MGH_genes$ensembl_id, analyzed_genes)),
"\nNumber of DTF target genes in both GM and MGH: ", length(intersect(DTFB_GM_genes$ensembl_id,intersect(DTFB_MGH_genes$ensembl_id, analyzed_genes))),
"\n Num overlap DTF target genes in GM vs MGH ", overlap_dtf_targets,
" (fisher test pvalue = ", fisher_test$p.value, ")",
"\nNumber of DTF target DEGs in GM: ", nrow(DTFB_GM_DEGs), " up = ", sum(DTFB_GM_DEGs$log2FC_GM>0), " down = ", sum(DTFB_GM_DEGs$log2FC_GM<0),
"\nNumber of DTF target DEGs in MGH: ", nrow(DTFB_MGH_DEGs)," up = ", sum(DTFB_MGH_DEGs$log2FC_GM>0), " down = ", sum(DTFB_MGH_DEGs$log2FC_GM<0),
"\nNumber of overlapped DTF target DEGs: ", nrow(overlapped_tf_degs_info), " up = ", sum(overlapped_tf_degs_info$deg_regulation=="up"), " down = ", sum(overlapped_tf_degs_info$deg_regulation=="down"))
```
## NSC comp DTF target genes
```{r}
del_vs_wt_final_degs <- read.csv("../POGZ_NSC/results/8330_del1_del2_vs_8330_WT_threshold_0.5.csv")
del_vs_wt_final_degs$log2FC <- del_vs_wt_final_degs$log2FoldChange
del_vs_wt_final_degs <- del_vs_wt_final_degs[del_vs_wt_final_degs$padj<0.1,]
```
### Get differential binding GM TFs target genes
```{r}
NSC_GM_del1del2_vs_WT <- identify_differential_TF(NSC_bindetect_results$NSC_GM_del1del2_vs_WT)
DTFB_path <- "/Volumes/talkowski/Samples/POGZ/ATAC/TF_footprint/DTFB.NSC_GM8330_Aug2020.DEL1DEL2_vs_WT.conservative_with_pogz_motifs"
DTFB_comp <- NSC_GM_del1del2_vs_WT[NSC_GM_del1del2_vs_WT$sig_change != "NA",]
DTFB_comp <- left_join(DTFB_comp, all_motifs_expression_info[, -c(which(colnames(all_motifs_expression_info) == "name"))], by = "motif_id")
DTFB_DEGs <- get_TF_target_genes(DTFB_comp, express_colname = "NSC_comp_het_expression", allpromoters, gene_annotation, DTFB_path, del_vs_wt_final_degs, "TF_footprint/NSC_comp_")
```
## Check whether promoter targets and DEGs are significant overlapped
### iN intersect with symbol
```{r}
del_vs_wt_GM <- read.csv("../POGZ_paper/results/DEL_vs_WT_GM__simple_model_sva/DEL_vs_WT_threshold_0.5_sv.csv", row.names = 1)
del_vs_wt_MGH <- read.csv("../POGZ_paper/results/DEL_vs_WT_MGH__simple_model_sva/DEL_vs_WT_threshold_0.5_sv.csv", row.names = 1)
analyzed_genes <- intersect(toupper(del_vs_wt_GM$symbol), toupper(del_vs_wt_MGH$symbol))
del_vs_wt_final_degs <- read.csv("../POGZ_paper/results/del_vs_wt_overlapped_GM_MGH.csv")
final_degs <- toupper(del_vs_wt_final_degs$symbol)
DTFB_GM_genes <- read.csv("TF_footprint/iN_GM_TF_genes.csv")
DTFB_MGH_genes <- read.csv("TF_footprint/iN_MGH_TF_genes.csv")
DTFB_GM_DEGs <- read.csv("TF_footprint/iN_GM_TF_degs.csv")
DTFB_MGH_DEGs <- read.csv("TF_footprint/iN_MGH_TF_degs.csv")
# iN_GM
target_genes <- intersect(toupper(DTFB_GM_genes$symbol), analyzed_genes)
target_degs <- intersect(target_genes, final_degs)
targets_vs_degs <- fisher.test(matrix(c(length(target_degs), length(target_genes)-length(target_degs), length(final_degs)-length(target_degs), length(analyzed_genes)-length(final_degs)-length(target_genes)+length(target_degs)), nrow=2),alternative = "greater")
message("target genes = ", length(target_genes), " target degs = ", length(target_degs))
print(targets_vs_degs)
require(VennDiagram)
venn.diagram(list("promoter_targets" = target_genes, "DEGs" = final_degs), main = "iN_GM_promoter_targets vs DEGs", sub = paste0("p.value = ", format(targets_vs_degs$p.value, digits=3)), filename = "TF_footprint/annotation_analysis/iN_GM_promoter_targes_vs_degs.png")
# iN_MGH
target_genes <- intersect(toupper(DTFB_MGH_genes$symbol), analyzed_genes)
target_degs <- intersect(target_genes, final_degs)
targets_vs_degs <- fisher.test(matrix(c(length(target_degs), length(target_genes)-length(target_degs), length(final_degs)-length(target_degs), length(analyzed_genes)-length(final_degs)-length(target_genes)+length(target_degs)), nrow=2),alternative = "greater")
message("target genes = ", length(target_genes), " target degs = ", length(target_degs))
print(targets_vs_degs)
venn.diagram(list("promoter_targets" = target_genes, "DEGs" = final_degs), main = "iN_MGH_promoter_targets vs DEGs", sub = paste0("p.value = ", format(targets_vs_degs$p.value, digits=3)), filename = "TF_footprint/annotation_analysis/iN_MGH_promoter_targes_vs_degs.png")
# iN GM and MGH overlapped DTFP
target_genes <- intersect(toupper(DTFB_GM_genes$symbol), intersect(toupper(DTFB_MGH_genes$symbol), analyzed_genes))
target_degs <- intersect(target_genes, final_degs)
targets_vs_degs <- fisher.test(matrix(c(length(target_degs), length(target_genes)-length(target_degs), length(final_degs)-length(target_degs), length(analyzed_genes)-length(final_degs)-length(target_genes)+length(target_degs)), nrow=2),alternative = "greater")
message("target genes = ", length(target_genes), " target degs = ", length(target_degs))
print(targets_vs_degs)
venn.diagram(list("promoter_targets" = target_genes, "DEGs" = final_degs), main = "iN_GM_and_MGH_overlapped promoter_targets vs DEGs", sub = paste0("p.value = ", format(targets_vs_degs$p.value, digits=3)), filename = "TF_footprint/annotation_analysis/iN_GM_MGH_overlapped_promoter_targes_vs_degs.png")
```
### NSC intersect with symbol
```{r}
del_vs_wt_GM <- read.table("../NSC_03_2020/meta_analysis/POGZ_8330del1_2.metaAnalysis.txt", header = T)
del_vs_wt_MGH <- read.table("../NSC_03_2020/meta_analysis/POGZ_MGHdel1_2.metaAnalysis.txt", header = T)
analyzed_genes <- intersect(toupper(del_vs_wt_GM$symbol), toupper(del_vs_wt_MGH$symbol))
del_vs_wt_final_degs <- read.csv("../POGZ_paper/results/NSC_del_vs_wt_overlapped_GM_MGH.csv")
final_degs <- toupper(del_vs_wt_final_degs$symbol)
DTFB_GM_genes <- read.csv("TF_footprint/NSC_GM_TF_genes.csv")
DTFB_MGH_genes <- read.csv("TF_footprint/NSC_MGH_TF_genes.csv")
DTFB_GM_DEGs <- read.csv("TF_footprint/NSC_GM_TF_degs.csv")
DTFB_MGH_DEGs <- read.csv("TF_footprint/NSC_MGH_TF_degs.csv")
# NSC_GM
target_genes <- intersect(toupper(DTFB_GM_genes$symbol), analyzed_genes)
target_degs <- intersect(target_genes, final_degs)
targets_vs_degs <- fisher.test(matrix(c(length(target_degs), length(target_genes)-length(target_degs), length(final_degs)-length(target_degs), length(analyzed_genes)-length(final_degs)-length(target_genes)+length(target_degs)), nrow=2),alternative = "greater")
message("target genes = ", length(target_genes), " target degs = ", length(target_degs))
print(targets_vs_degs)
require(VennDiagram)
venn.diagram(list("promoter_targets" = target_genes, "DEGs" = final_degs), main = "NSC_GM_promoter_targets vs DEGs", sub = paste0("p.value = ", format(targets_vs_degs$p.value, digits=3)), filename = "TF_footprint/annotation_analysis/NSC_GM_promoter_targes_vs_degs.png")
# NSC_MGH
target_genes <- intersect(toupper(DTFB_MGH_genes$symbol), analyzed_genes)
target_degs <- intersect(target_genes, final_degs)
targets_vs_degs <- fisher.test(matrix(c(length(target_degs), length(target_genes)-length(target_degs), length(final_degs)-length(target_degs), length(analyzed_genes)-length(final_degs)-length(target_genes)+length(target_degs)), nrow=2),alternative = "greater")
message("target genes = ", length(target_genes), " target degs = ", length(target_degs))
print(targets_vs_degs)
venn.diagram(list("promoter_targets" = target_genes, "DEGs" = final_degs), main = "NSC_MGH_promoter_targets vs DEGs", sub = paste0("p.value = ", format(targets_vs_degs$p.value, digits=3)), filename = "TF_footprint/annotation_analysis/NSC_MGH_promoter_targes_vs_degs.png")
# NSC GM and MGH overlapped DTFP
target_genes <- intersect(toupper(DTFB_GM_genes$symbol), intersect(toupper(DTFB_MGH_genes$symbol), analyzed_genes))
target_degs <- intersect(target_genes, final_degs)
targets_vs_degs <- fisher.test(matrix(c(length(target_degs), length(target_genes)-length(target_degs), length(final_degs)-length(target_degs), length(analyzed_genes)-length(final_degs)-length(target_genes)+length(target_degs)), nrow=2),alternative = "greater")
message("target genes = ", length(target_genes), " target degs = ", length(target_degs))
print(targets_vs_degs)
venn.diagram(list("promoter_targets" = target_genes, "DEGs" = final_degs), main = "NSC_GM_and_MGH_overlapped promoter_targets vs DEGs", sub = paste0("p.value = ", format(targets_vs_degs$p.value, digits=3)), filename = "TF_footprint/annotation_analysis/NSC_GM_MGH_overlapped_promoter_targes_vs_degs.png")
```
## Check if POGZ a DTF
```{r}
DTF_lst <- list(iN_GM_DTF = readxl::read_excel("TF_footprint/iN_GM_DTF.xlsx"),
iN_MGH_DTF = readxl::read_excel("TF_footprint/iN_MGH_DTF.xlsx"),
NSC_GM_DTF = readxl::read_excel("TF_footprint/NSC_GM_DTF.xlsx"),
NSC_MGH_DTF = readxl::read_excel("TF_footprint/NSC_MGH_DTF.xlsx"),
iN_comp_het_DTF <- readxl::read_excel("TF_footprint/"))
lapply(1:length(DTF_lst), function(i) {message(names(DTF_lst)[i], ": ", DTF_lst[[i]][str_detect(DTF_lst[[i]]$name, "POGZ|pogz"),]$name)})
```
## Run enrichment analysis on DTFP from NSC and iN
```{r}
source("../../MyTools/RNAseq.analysis/R/enrichment.R")
load("../../MyTools/enrichment_analysis/data/pathway_db/pathway_db.Rdata")
iN_del_vs_wt_GM <- read.csv("../POGZ_paper/results/DEL_vs_WT_GM__simple_model_sva/DEL_vs_WT_threshold_0.5_sv.csv", row.names = 1)
rownames(iN_del_vs_wt_GM) <- iN_del_vs_wt_GM$ensembl_id
iN_del_vs_wt_MGH <- read.csv("../POGZ_paper/results/DEL_vs_WT_MGH__simple_model_sva/DEL_vs_WT_threshold_0.5_sv.csv", row.names = 1)
NSC_del_vs_wt_GM <- read.table("../NSC_03_2020/meta_analysis/POGZ_8330del1_2.metaAnalysis.txt", sep = "\t", header = T)
NSC_del_vs_wt_MGH <- read.table("../NSC_03_2020/meta_analysis/POGZ_MGHdel1_2.metaAnalysis.txt", sep = "\t", header = T)
iN_DTFB_GM_DEGs <- read.csv("TF_footprint/iN_GM_TF_degs.csv")
iN_DTFB_MGH_DEGs <- read.csv("TF_footprint/iN_MGH_TF_degs.csv")
NSC_DTFB_GM_DEGs <- read.csv("TF_footprint/NSC_GM_TF_degs.csv")
NSC_DTFB_MGH_DEGs <- read.csv("TF_footprint/NSC_MGH_TF_degs.csv")
iN_DTFB_GM_DEGs_sig_terms <- run_enrichment_on_genelist(list(iN_DTFB_GM_DEGs=iN_DTFB_GM_DEGs$symbol), iN_del_vs_wt_GM$symbol, outfolder = "TF_footprint/enrichment_analysis/iN_DTFB_GM_DEGs", outplotfolder = "TF_footprint/enrichment_analysis/iN_DTFB_GM_DEGs/plots", skip_enrichment_analysis=F, fdr_threshold=0.1)
iN_DTFB_MGH_DEGs_sig_terms <- run_enrichment_on_genelist(list(iN_DTFB_MGH_DEGs=iN_DTFB_MGH_DEGs$symbol), iN_del_vs_wt_MGH$symbol, outfolder = "TF_footprint/enrichment_analysis/iN_DTFB_MGH_DEGs", outplotfolder = "TF_footprint/enrichment_analysis/iN_DTFB_MGH_DEGs/plots", skip_enrichment_analysis=F, fdr_threshold=0.1)
iN_GM_MGH_overlapped_DTFB_DEGs_sig_terms <- run_enrichment_on_genelist(list(iN_GM_MGH_overlapped_DTFB_DEGs=intersect(iN_DTFB_GM_DEGs$symbol, iN_DTFB_MGH_DEGs$symbol)), intersect(iN_del_vs_wt_GM$symbol,iN_del_vs_wt_MGH$symbol), outfolder = "TF_footprint/enrichment_analysis/iN_GM_MGH_overlapped_DTFB_DEGs", outplotfolder = "TF_footprint/enrichment_analysis/iN_GM_MGH_overlapped_DTFB_DEGs/plots", skip_enrichment_analysis=F, fdr_threshold=0.1)
NSC_DTFB_GM_DEGs_sig_terms <- run_enrichment_on_genelist(list(NSC_DTFB_GM_DEGs=NSC_DTFB_GM_DEGs$symbol), NSC_del_vs_wt_MGH$symbol, gene_to_remove = "POGZ", outfolder = "TF_footprint/enrichment_analysis/NSC_DTFB_GM_DEGs", outplotfolder = "TF_footprint/enrichment_analysis/NSC_DTFB_GM_DEGs/plots", skip_enrichment_analysis=F, fdr_threshold=0.1)
NSC_DTFB_MGH_DEGs_sig_terms <- run_enrichment_on_genelist(list(NSC_DTFB_MGH_DEGs=NSC_DTFB_MGH_DEGs$symbol), NSC_del_vs_wt_MGH$symbol, gene_to_remove = "POGZ", outfolder = "TF_footprint/enrichment_analysis/NSC_DTFB_MGH_DEGs", outplotfolder = "TF_footprint/enrichment_analysis/NSC_DTFB_MGH_DEGs/plots", skip_enrichment_analysis=F, fdr_threshold=0.1)
NSC_GM_MGH_overlapped_DTFB_DEGs_sig_terms <- run_enrichment_on_genelist(list(NSC_GM_MGH_overlapped_DTFB_DEGs=intersect(NSC_DTFB_GM_DEGs$symbol,NSC_DTFB_MGH_DEGs$symbol)), intersect(NSC_del_vs_wt_GM$symbol,NSC_del_vs_wt_MGH$symbol), gene_to_remove = "POGZ", outfolder = "TF_footprint/enrichment_analysis/NSC_GM_MGH_overlapped_DTFB_DEGs", outplotfolder = "TF_footprint/enrichment_analysis/NSC_GM_MGH_overlapped_DTFB_DEGs/plots", skip_enrichment_analysis=F, fdr_threshold=0.1)
sig_terms_lst <- list(iN_DTFB_GM_DEGs_sig_terms = iN_DTFB_GM_DEGs_sig_terms,
iN_DTFB_MGH_DEGs_sig_terms =iN_DTFB_MGH_DEGs_sig_terms,
iN_GM_MGH_overlapped_DTFB_DEGs_sig_terms = iN_GM_MGH_overlapped_DTFB_DEGs_sig_terms,
NSC_DTFB_GM_DEGs_sig_terms = NSC_DTFB_GM_DEGs_sig_terms,
NSC_DTFB_MGH_DEGs_sig_terms = NSC_DTFB_MGH_DEGs_sig_terms,
NSC_GM_MGH_overlapped_DTFB_DEGs_sig_terms = NSC_GM_MGH_overlapped_DTFB_DEGs_sig_terms)
for (datset in names(sig_terms_lst)) {
sig_terms <- sig_terms_lst[[datset]]
if (nrow(sig_terms) == 0) {
next
}
sig_terms_top10 <- slice_max(sig_terms, order_by = logBH, n = 10)
sig_terms_top10$name <- str_wrap(sig_terms_top10$name, width = 50)
sig_terms_top10$name <- factor(sig_terms_top10$name, levels = sig_terms_top10[order(sig_terms_top10$logBH),]$name)
ggplot(sig_terms_top10, aes(x = name, y = logBH)) +
geom_col(fill = "grey60") +
coord_flip() +
geom_text(aes(label = significant_genelist), hjust = 1) +
labs(x = "", title = paste0("Top 10 sig terms for ", str_remove(datset, "_sig_terms"))) +
theme(axis.text.y = element_text(size = 12), plot.title.position = "plot")
ggsave(file.path("TF_footprint/enrichment_analysis", paste0("top10_sig_terms_", str_remove(datset, "_sig_terms"), ".pdf")), height = 7, width = 6)
}
```

tf_binding.Rmd at commit 29381f6, no license · at the source

Overview

Authors: Mariana Moyses-Oliveira^1,2,3, Yating Liu^1,3,4, Serkan Erdin^1,3,4, Dadi Gao^1,2, Riya Bhavsar^1,3,4, Kiana Mohajeri^1,2,3,4,5, Kathryn O’Keefe¹, Philip M Boone^1,2,4,6, Gabriela Xavier^1,2,3, Calwing Liao^1,3,4,7, Aiqun Li^8,9,10,11,12, Rachita Yadav^1,2,3,4, Monica Salani¹, Diane Lucente¹, Benjamin Currall¹, Celine EF de Esch¹, Derek JC Tai^1,2,3,4, Douglas Ruderfer^13,14, Kristen J Brennand^{8,9,10,11,12,15}, James F Gusella^1,4,5,16,17, Michael E Talkowski^1,2,3,4,5

ORCID iDs: Mariana Moyses-Oliveira, Derek JC Tai

17 affiliations

Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA, USA
Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Division of Genetic Medicine, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Medical Center Dr., Nashville, TN 1211, USA
Department of Biomedical Informatics and Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Medical Center Dr., Nashville, TN 1211, USA
Department of Psychiatry, Yale University New Haven, New Haven, CT, USA
Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
Harvard Stem Cell Institute, Harvard University, Cambridge, MA, USA

Institutions: Broad Institute (United States); Massachusetts General Hospital (United States); Stanley Center for Psychiatric Research; Massachusetts Institute of Technology (United States); Harvard University (United States); Boston Children's Hospital (United States); Icahn School of Medicine at Mount Sinai (United States); Vanderbilt University Medical Center (United States); Yale University (United States); Harvard Stem Cell Institute (United States)

Journal: HGG advances, volume 7, issue 4, article 100652

Dates: received 11 March 2026; accepted 9 July 2026; published online 10 July 2026; in print July 2026

Type: Research article · Language: English

License: CC BY-NC-ND

Identifiers: DOI 10.1016/j.xhgg.2026.100652 · PMID 42433021 · PMCID PMC13429924 · OpenAlex W7167903738

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: human (organism), autism (population), cellular / molecular (subfield)

Methods: Statistics, Preprocessing, Evoked potentials, Single-unit activity, calcium imaging, Spectral & time-frequency

Keywords: autism spectrum disorder, neurodevelopment, chromatin regulator, synaptic function, POGZ

Topic: CRISPR and Genetic Engineering (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex

Funding: NIMH NIH HHS (R01 MH115957, R01 MH123155, RM1 MH132648); National Institutes of Health (R01HD096326, R01MH115957, R01MH123155, U01HG011755, RM1MH132648); Autism Speaks; Simons Foundation Autism Research Initiative (#1009802, #573206); NINDS NIH HHS (K08 NS117891, R00 NS118109); NHGRI NIH HHS (U01 HG011755); Massachusetts General Hospital; NICHD NIH HHS (R01 HD096326)

Citations: not cited yet (Europe PMC); 72 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 17 matches between paragraphs and lines of code.

Zenodo 3564813

License: CC-BY-4.0

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Size: 3 files

Software Heritage: not checked

Found in: the text, “Web resources”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: NumPy (10 files), Matplotlib (8 files), pandas (5 files), SAMtools (5 files), SciPy (4 files), BEDTools (3 files), pysam (2 files)

Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)

27 September 2026: the link answers (HTTP 200)

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atac-seq-pipeline-1.1.7.zip/conda/build_genome_data.sh, Shell, 255 lines
atac-seq-pipeline-1.1.7.zip/conda/install_dependencies.sh, Shell, 98 lines
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atac-seq-pipeline-1.1.7.zip/examples/local/ENCSR356KRQ_subsampled_sge_conda.sh, Shell, 69 lines
atac-seq-pipeline-1.1.7.zip/examples/local/ENCSR356KRQ_subsampled_sge_singularity.sh, Shell, 66 lines
atac-seq-pipeline-1.1.7.zip/examples/local/ENCSR356KRQ_subsampled_slurm_conda.sh, Shell, 65 lines
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atac-seq-pipeline-1.1.7.zip/examples/scg/ENCSR356KRQ_subsampled_scg_conda.sh, Shell, 65 lines
atac-seq-pipeline-1.1.7.zip/examples/scg/ENCSR356KRQ_subsampled_scg_singularity.sh, Shell, 62 lines
atac-seq-pipeline-1.1.7.zip/examples/sherlock/ENCSR356KRQ_subsampled_sherlock_conda.sh, Shell, 65 lines
atac-seq-pipeline-1.1.7.zip/examples/sherlock/ENCSR356KRQ_subsampled_sherlock_singularity.sh, Shell, 62 lines
atac-seq-pipeline-1.1.7.zip/genome/download_genome_data.sh, Shell, 201 lines
atac-seq-pipeline-1.1.7.zip/src/assign_multimappers.py, Python, 73 lines
atac-seq-pipeline-1.1.7.zip/src/detect_adapter.py, Python, 76 lines
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atac-seq-pipeline-1.1.7.zip/src/encode_bam2ta.py, Python, 156 lines
atac-seq-pipeline-1.1.7.zip/src/encode_blacklist_filter.py, Python, 80 lines
atac-seq-pipeline-1.1.7.zip/src/encode_bowtie2.py, Python, 209 lines
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atac-seq-pipeline-1.1.7.zip/src/encode_count_signal_track.py, Python, 81 lines
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atac-seq-pipeline-1.1.7.zip/src/encode_frip.py, Python, 107 lines
atac-seq-pipeline-1.1.7.zip/src/encode_idr.py, Python, 169 lines
atac-seq-pipeline-1.1.7.zip/src/encode_macs2_atac.py, Python, 219 lines
atac-seq-pipeline-1.1.7.zip/src/encode_macs2_signal_track_atac.py, Python, 161 lines
atac-seq-pipeline-1.1.7.zip/src/encode_naive_overlap.py, Python, 143 lines
atac-seq-pipeline-1.1.7.zip/src/encode_pool_ta.py, Python, 59 lines
atac-seq-pipeline-1.1.7.zip/src/encode_qc_report.py, Python, 617 lines
atac-seq-pipeline-1.1.7.zip/src/encode_reproducibility_qc.py, Python, 160 lines
atac-seq-pipeline-1.1.7.zip/src/encode_spr.py, Python, 132 lines
atac-seq-pipeline-1.1.7.zip/src/encode_trim_adapter.py, Python, 265 lines
atac-seq-pipeline-1.1.7.zip/src/encode_xcor.py, Python, 102 lines
atac-seq-pipeline-1.1.7.zip/src/run_ataqc.py, Python, 1,704 lines, 1 match
atac-seq-pipeline-1.1.7.zip/test/run_cromwell_server_on_gc.sh, Shell, 15 lines
atac-seq-pipeline-1.1.7.zip/test/test_task/download_hg38_fasta_for_test_ataqc.sh, Shell, 4 lines
atac-seq-pipeline-1.1.7.zip/test/test_task/test.sh, Shell, 55 lines
atac-seq-pipeline-1.1.7.zip/test/test_task/test_all.sh, Shell, 11 lines
atac-seq-pipeline-1.1.7.zip/test/test_workflow/test_atac.sh, Shell, 49 lines
atac-seq-pipeline-1.1.7.zip/utils/qc_jsons_to_tsv/qc_jsons_to_tsv.py, Python, 307 lines
atac-seq-pipeline-1.1.7.zip/utils/resumer/resumer.py, Python, 135 lines
atac-seq-pipeline-1.4.2.zip/conda/build_genome_data.sh, Shell, 255 lines
atac-seq-pipeline-1.4.2.zip/conda/config_conda_env.sh, Shell, 74 lines
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atac-seq-pipeline-1.4.2.zip/conda/update_conda_env.sh, Shell, 34 lines
atac-seq-pipeline-1.4.2.zip/examples/local/ENCSR356KRQ_subsampled_sge_conda.sh, Shell, 69 lines
atac-seq-pipeline-1.4.2.zip/examples/local/ENCSR356KRQ_subsampled_sge_singularity.sh, Shell, 66 lines
atac-seq-pipeline-1.4.2.zip/examples/local/ENCSR356KRQ_subsampled_slurm_conda.sh, Shell, 65 lines
atac-seq-pipeline-1.4.2.zip/examples/local/ENCSR356KRQ_subsampled_slurm_singularity.sh, Shell, 62 lines
atac-seq-pipeline-1.4.2.zip/examples/scg/ENCSR356KRQ_subsampled_scg_conda.sh, Shell, 66 lines
atac-seq-pipeline-1.4.2.zip/examples/scg/ENCSR356KRQ_subsampled_scg_singularity.sh, Shell, 62 lines
atac-seq-pipeline-1.4.2.zip/examples/sherlock/ENCSR356KRQ_subsampled_sherlock_conda.sh, Shell, 65 lines
atac-seq-pipeline-1.4.2.zip/examples/sherlock/ENCSR356KRQ_subsampled_sherlock_singularity.sh, Shell, 62 lines
atac-seq-pipeline-1.4.2.zip/genome/download_genome_data.sh, Shell, 201 lines
atac-seq-pipeline-1.4.2.zip/src/assign_multimappers.py, Python, 73 lines
atac-seq-pipeline-1.4.2.zip/src/detect_adapter.py, Python, 76 lines
atac-seq-pipeline-1.4.2.zip/src/encode_ataqc.py, Python, 469 lines
atac-seq-pipeline-1.4.2.zip/src/encode_bam2ta.py, Python, 156 lines
atac-seq-pipeline-1.4.2.zip/src/encode_blacklist_filter.py, Python, 80 lines
atac-seq-pipeline-1.4.2.zip/src/encode_bowtie2.py, Python, 214 lines
atac-seq-pipeline-1.4.2.zip/src/encode_common.py, Python, 285 lines
atac-seq-pipeline-1.4.2.zip/src/encode_common_genomic.py, Python, 406 lines
atac-seq-pipeline-1.4.2.zip/src/encode_common_html.py, Python, 211 lines
atac-seq-pipeline-1.4.2.zip/src/encode_common_log_parser.py, Python, 467 lines
atac-seq-pipeline-1.4.2.zip/src/encode_count_signal_track.py, Python, 81 lines
atac-seq-pipeline-1.4.2.zip/src/encode_filter.py, Python, 391 lines
atac-seq-pipeline-1.4.2.zip/src/encode_frip.py, Python, 107 lines
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atac-seq-pipeline-1.4.2.zip/src/encode_macs2_atac.py, Python, 134 lines
atac-seq-pipeline-1.4.2.zip/src/encode_macs2_signal_track_atac.py, Python, 163 lines
atac-seq-pipeline-1.4.2.zip/src/encode_naive_overlap.py, Python, 143 lines
atac-seq-pipeline-1.4.2.zip/src/encode_pool_ta.py, Python, 59 lines
atac-seq-pipeline-1.4.2.zip/src/encode_qc_report.py, Python, 633 lines
atac-seq-pipeline-1.4.2.zip/src/encode_reproducibility_qc.py, Python, 160 lines
atac-seq-pipeline-1.4.2.zip/src/encode_spr.py, Python, 132 lines
atac-seq-pipeline-1.4.2.zip/src/encode_trim_adapter.py, Python, 265 lines
atac-seq-pipeline-1.4.2.zip/src/encode_xcor.py, Python, 144 lines
atac-seq-pipeline-1.4.2.zip/src/run_ataqc.py, Python, 1,735 lines
atac-seq-pipeline-1.4.2.zip/test/run_cromwell_server_on_gc.sh, Shell, 15 lines
atac-seq-pipeline-1.4.2.zip/test/test_task/download_hg38_fasta_for_test_ataqc.sh, Shell, 4 lines
atac-seq-pipeline-1.4.2.zip/test/test_task/test.sh, Shell, 55 lines
atac-seq-pipeline-1.4.2.zip/test/test_task/test_all.sh, Shell, 11 lines
atac-seq-pipeline-1.4.2.zip/test/test_workflow/test_atac.sh, Shell, 50 lines
atac-seq-pipeline-1.4.2.zip/utils/qc_jsons_to_tsv/qc_jsons_to_tsv.py, Python, 326 lines
atac-seq-pipeline-1.5.4.zip/dev/build_on_dx.sh, Shell, 48 lines
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atac-seq-pipeline-1.5.4.zip/src/assign_multimappers.py, Python, 73 lines
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atac-seq-pipeline-1.5.4.zip/src/encode_task_post_align.py, Python, 88 lines
atac-seq-pipeline-1.5.4.zip/src/encode_task_post_call_peak_atac.py, Python, 88 lines
atac-seq-pipeline-1.5.4.zip/src/encode_task_post_call_peak_chip.py, Python, 91 lines
atac-seq-pipeline-1.5.4.zip/src/encode_task_preseq.py, Python, 165 lines
atac-seq-pipeline-1.5.4.zip/src/encode_task_qc_report.py, Python, 927 lines, 1 match
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atac-seq-pipeline-1.5.4.zip/src/encode_task_trim_adapter.py, Python, 232 lines
atac-seq-pipeline-1.5.4.zip/src/encode_task_trim_fastq.py, Python, 73 lines
atac-seq-pipeline-1.5.4.zip/src/encode_task_tss_enrich.py, Python, 178 lines
atac-seq-pipeline-1.5.4.zip/src/encode_task_xcor.py, Python, 156 lines
atac-seq-pipeline-1.5.4.zip/src/trimfastq.py, Python, 255 lines
atac-seq-pipeline-1.1.7.zip/LICENSE, License, 21 lines
atac-seq-pipeline-1.1.7.zip/README.md, Text, 67 lines
atac-seq-pipeline-1.4.2.zip/LICENSE, License, 21 lines
atac-seq-pipeline-1.4.2.zip/README.md, Text, 99 lines
atac-seq-pipeline-1.5.4.zip/LICENSE, License, 21 lines
atac-seq-pipeline-1.5.4.zip/README.md, Text, 81 lines

At the source: doi.org/10.5281/zenodo.3564813

talkowski-lab/POGZ

License: none: the authors keep all their rights

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 29381f6e1324f375ed9a8a75f09dee9231ab0d85, 22 May 2026

Languages: R (16), Shell (7)

Size: 28 files, 23 scripts

Software Heritage: not archived

Found in: “Data and code availability”

Holds: README, 3 notebooks

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: tidyverse (9 files), DESeq2 (5 files), ggplot2 (5 files), pheatmap (4 files), WGCNA (4 files), ggpubr (2 files), reshape2 (2 files), data.table (1 file), edgeR (1 file), STAR (1 file)

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

27 September 2026: the link answers

24 files

ATAC-seq/Differential_accessibility/diff_peaks_v2.R, R, 153 lines, 1 match
ATAC-seq/Differential_accessibility/differential_accessibility.Rmd, R, 763 lines, 3 matches
ATAC-seq/Transcription_factor_footprint/1. merge_ATACpeaks.sh, Shell, 15 lines
ATAC-seq/Transcription_factor_footprint/2. get_bamfile_list.sh, Shell, 13 lines
ATAC-seq/Transcription_factor_footprint/3. call_TF_footprints.sh, Shell, 16 lines
ATAC-seq/Transcription_factor_footprint/4. iN_merge_bigwigs.sh, Shell, 34 lines
ATAC-seq/Transcription_factor_footprint/5. call_TF_bindings.sh, Shell, 15 lines, 1 match
ATAC-seq/Transcription_factor_footprint/tf_binding.Rmd, R, 896 lines, 3 matches
RNA-seq/RNAseq.analysis/R/DESeq2_POGZ_NSC.R, R, 452 lines
RNA-seq/RNAseq.analysis/R/adjust_ModuleEigenegene_pvalues.R, R, 134 lines
RNA-seq/RNAseq.analysis/R/co_expression_functions.R, R, 868 lines, 1 match
RNA-seq/RNAseq.analysis/R/enrichment.R, R, 505 lines, 1 match
RNA-seq/RNAseq.analysis/R/functions_POGZ.R, R, 416 lines
RNA-seq/RNAseq.analysis/R/metaAnalysis_NSC.R, R, 65 lines
RNA-seq/RNAseq.analysis/R/read_pathway_db.R, R, 70 lines, 1 match
RNA-seq/RNAseq.analysis/R/rnaseq_analysis_functions.R, R, 685 lines, 1 match
RNA-seq/RNAseq.analysis/R/rnaseq_qc_functions.R, R, 212 lines
RNA-seq/RNAseq.analysis/R/runWGCNA.R, R, 529 lines
RNA-seq/co-expression_analysis_allsamples.R, R, 95 lines, 1 match
RNA-seq/co-expression_data.R, R, 102 lines
RNA-seq/rnaseq_analysis_iN_final.Rmd, R, 216 lines, 1 match
RNA-seq/runSTAR.sh, Shell, 44 lines, 1 match
RNA-seq/runTrimmomatic.sh, Shell, 7 lines
README.md, Text, 158 lines

At the source: github.com/talkowski-lab/POGZ

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;
164 scripts, each with its path and the digest of its content;
17 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:GSE204778, at NCBI GEO; found in “Data and code availability”

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

it points to a dataset: NCBI GEO GSE204778
it points to the authors' code: talkowski-lab/POGZ

Read it in the paper: doi.org/10.1016/j.xhgg.2026.100652.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 21 authors, 5 keywords, 8 funders, 72 references.

Cite

This paper

Moyses-Oliveira, M., Liu, Y., Erdin, S., Gao, D., Bhavsar, R., Mohajeri, K., O’Keefe, K., Boone, P. M., Xavier, G., Liao, C., Li, A., Yadav, R., Salani, M., Lucente, D., Currall, B., de Esch, C. E., Tai, D. J., Ruderfer, D., Brennand, K. J., . . . Talkowski, M. E. (2026). CRISPR-engineered deletion of POGZ alters transcription factor binding at promoters of genes involved in synaptic signaling. HGG advances, 7(4), 100652. https://doi.org/10.1016/j.xhgg.2026.100652

BibTeX

@article{moysesoliveira2026crispr, author = {Moyses-Oliveira, Mariana and Liu, Yating and Erdin, Serkan and Gao, Dadi and Bhavsar, Riya and Mohajeri, Kiana and O’Keefe, Kathryn and Boone, Philip M and Xavier, Gabriela and Liao, Calwing and Li, Aiqun and Yadav, Rachita and Salani, Monica and Lucente, Diane and Currall, Benjamin and de Esch, Celine EF and Tai, Derek JC and Ruderfer, Douglas and Brennand, Kristen J and Gusella, James F and Talkowski, Michael E}, title = {{CRISPR-engineered deletion of POGZ alters transcription factor binding at promoters of genes involved in synaptic signaling}}, journal = {HGG advances}, year = {2026}, month = jul, volume = {7}, number = {4}, pages = {100652}, publisher = {Elsevier}, issn = {2666-2477}, doi = {10.1016/j.xhgg.2026.100652}, url = {https://doi.org/10.1016/j.xhgg.2026.100652}, pmid = {42433021}, pmcid = {PMC13429924} }

RIS

TY - JOUR AU - Moyses-Oliveira, Mariana AU - Liu, Yating AU - Erdin, Serkan AU - Gao, Dadi AU - Bhavsar, Riya AU - Mohajeri, Kiana AU - O’Keefe, Kathryn AU - Boone, Philip M AU - Xavier, Gabriela AU - Liao, Calwing AU - Li, Aiqun AU - Yadav, Rachita AU - Salani, Monica AU - Lucente, Diane AU - Currall, Benjamin AU - de Esch, Celine EF AU - Tai, Derek JC AU - Ruderfer, Douglas AU - Brennand, Kristen J AU - Gusella, James F AU - Talkowski, Michael E TI - CRISPR-engineered deletion of POGZ alters transcription factor binding at promoters of genes involved in synaptic signaling T2 - HGG advances J2 - HGG Adv PY - 2026 DA - 2026/07/10 VL - 7 IS - 4 SP - 100652 SN - 2666-2477 PB - Elsevier DO - 10.1016/j.xhgg.2026.100652 UR - https://doi.org/10.1016/j.xhgg.2026.100652 LA - en ER -

CSL-JSON

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