OSCR

Functional impact of genetic background on variable expressivity in neurodevelopmental disorders.

Code ↔ Paper

27 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 27 matches · 10 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Weighted gene co-expression network analysis (WGCNA) ↔ RNA_seq_analysis/utils/WGCNA.R, lines 57–107 · score 0.91 · TOMType, mergeCutHeight, minModuleSize, pickSoftThreshold, signed hybrid, WGCNA
  2. [2] § Methods › Whole genome sequencing (WGS) sample processing and analysis ↔ WGS_analysis/utils/call_CNV/6_region_filter.sh, the whole file · a weak match · score 0.85 · UCSC Unusual Regions, GRC Exclusions, ENCODE Blacklist, centromeres, gnomAD, CNVs
  3. [3] § Methods › Whole genome sequencing (WGS) sample processing and analysis ↔ WGS_analysis/utils/call_STR/4_locus_filter.sh, the whole file · a weak match · score 0.83 · dumpSTR, ensemblTR, low quality, locus, MergeSTR, Browser
  4. [4] § Methods › Detection of alternative isoform usage ↔ RNA_seq_analysis/utils/IsoformSwitchAnalyzeR.R, lines 1–46 · score 0.78 · Isoform switches, importIsoformExpression, prefilter, imported, Kallisto, gene expression
  5. [5] § Results › Combined effects on gene expression related to neurodevelopment ↔ WGS_analysis/utils/Fisher_exact_test_rare_variant.py, lines 61–90 · score 0.78 · confidence intervals, outlier expression, odds ratio, RNA seq, rare variants, CI
  6. [6] § Methods › Whole genome sequencing (WGS) sample processing and analysis ↔ WGS_analysis/utils/call_SNV/2_GATK_preprocessing.sh, lines 112–172 · score 0.76 · quality score recalibration, MarkDuplicates, GATK, indels, SNVs, sequences
  7. [7] § Methods › Whole genome sequencing (WGS) sample processing and analysis ↔ WGS_analysis/utils/call_SNV/9_organize_variants.py, lines 116–126 · score 0.76 · splice LOF, noncoding variants, missense, MutationAssessor, UTR, intron
  8. [8] § Methods › Whole genome sequencing (WGS) sample processing and analysis ↔ WGS_analysis/utils/call_STR/2_dumpstr.sh, the whole file · a weak match · score 0.74 · badCI, dumpSTR, spanbound, GangSTR, DP, VCF
  9. [9] § Results › Variable neural phenotypes across genetic backgrounds ↔ Figure_code/utils/generate_heatmap.R, lines 199–285 · score 0.70 · GL_007, CRISPR deletion, DLX1, LHX6, LHX8, GABA
  10. [10] § Methods › RNA-sequencing sample processing and analysis ↔ Figure_code/utils/volcanoplot.R, the whole file · a weak match · score 0.70 · EEF2K, POLR3E, DESeq2, PDZD9, CDR2, UQCRC2
  11. [11] § Methods › Whole genome sequencing (WGS) sample processing and analysis ↔ WGS_analysis/utils/call_CNV/4_download_regions.sh, the whole file · a weak match · score 0.69 · UCSC Genome Browser, low confidence, centromeres, bin, CNVs, WGS
  12. [12] § Results › Connectivity of genes with secondary variants in PPI network ↔ Figure_code/utils/densityplot.R, the whole file · a weak match · score 0.68 · Anderson Darling, P1C_077, p1C_007, P2C_079, Density, POLR3E
  13. [13] § Methods › Protein-protein interaction network analysis ↔ Figure_code/utils/densityplot.R, the whole file · a weak match · score 0.65 · Anderson Darling, ad.test, density
  14. [14] § Results › Connectivity of genes with secondary variants in PPI network ↔ Figure_code/utils/generate_dotplot.R, lines 173–211 · score 0.65 · P1C_077, P1C_007, P2C_079, polr3e, UQCRC2, MOSMO
  15. [15] § Results › CRISPR activation of individual 16p12.1 genes reverses transcriptomic and neural defects ↔ Figure_code/utils/generate_dotplot.R, lines 173–211 · score 0.63 · P1C_077, P1C_007, P2C_079, polr3e, MOSMO
  16. [16] § Methods › ATAC-seq peak annotation ↔ ATAC_seq_analysis/utils/get_nearest_gene.py, lines 18–24 · score 0.62 · nearest gene, ATAC seq, closest, GENCODE, Pybedtools, peaks
  17. [17] § Results › Variable neural phenotypes across genetic backgrounds ↔ RNA_seq_analysis/utils/WGCNA.R, lines 109–175 · score 0.62 · eigengene scores, gene modules, RNA seq, weighted, WGCNA, models
  18. [18] § Methods › Whole genome sequencing (WGS) sample processing and analysis ↔ WGS_analysis/utils/call_STR/6_annovar.sh, the whole file · a weak match · score 0.61 · Func.wgEncodeGencodeBasicV38, ANNOVAR, Genic, exonic, Alleles, loci
  19. [19] § Results › CRISPR activation of individual 16p12.1 genes reverses transcriptomic and neural defects ↔ Figure_code/utils/generate_heatmap.R, lines 2–57 · score 0.60 · cytokine signaling, signaling pathways, interferon, Wnt, immune, TGF
  20. [20] § Methods › Whole genome sequencing (WGS) sample processing and analysis ↔ WGS_analysis/utils/Fisher_exact_test_QTL.r, lines 123–239 · score 0.59 · regression slopes, PsychENCODE, GTEx, eQTLs, WGS, gene
  21. [21] § Methods › ATAC-seq sample processing and analysis ↔ ATAC_seq_analysis/utils/run_mea_homer.sh, the whole file · a weak match · score 0.58 · findMotifsGenome.pl, ATAC seq, HOMER, peaks
  22. [22] § Methods › Whole genome sequencing (WGS) sample processing and analysis ↔ WGS_analysis/utils/call_STR/7_finalize_strs.py, lines 5–11 · score 0.55 · intronic, ANNOVAR, UTR3, UTR5, exonic, func
  23. [23] § Methods › RNA-sequencing sample processing and analysis ↔ RNA_seq_analysis/utils/doTrimmomatic.sh, the whole file · a weak match · score 0.55 · Trimmomatic, slidingwindow, trailing, minlen, Illumina, trimming
  24. [24] § Results › Combined effects on gene expression related to neurodevelopment ↔ Figure_code/utils/generate_heatmap.R, lines 199–285 · score 0.54 · CRISPR control, CRISPR deletion, DLX5, iMN, HD, DEGs
  25. [25] § Methods › Analysis of combined effects on gene expression ↔ WGS_analysis/utils/Fisher_exact_test_rare_variant.py, lines 61–90 · score 0.54 · outlier expression, Rare variants, TPM, MN, DEGs, genes
  26. [26] § Methods › Weighted gene co-expression network analysis (WGCNA) ↔ RNA_seq_analysis/utils/WGCNA.R, lines 109–175 · score 0.53 · module eigengene scores, Weighted, WGCNA, network
  27. [27] § Results › Variable neural phenotypes across genetic backgrounds ↔ Figure_code/utils/barplot.R, lines 62–133 · score 0.51 · chase hours, positive cells, backgrounds

Paper

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

The paper is loaded when this pane is shown.

The authors' code

R · 176 lines · 5.5 KB · GPL-3.0 · 3 matches

  1. library(WGCNA)
  2. library(FactoMineR)
  3. library(factoextra)
  4. library(tidyverse)
  5. library(data.table)
  6. enableWGCNAThreads(nThreads = 0.75*parallel::detectCores())
  8. tpm <- family_npc_counts_tpm #input tpm
  9. datTraits <- datTraits_npc # input sample information
  11. data <- log2(tpm+1)
  12. keep_data <- data[order(apply(data,1,mad), decreasing = T)[1:10000],]
  14. datExpr0 <- as.data.frame(t(keep_data))
  16. #quality_check
  17. sampleTree <- hclust(dist(datExpr0), method = "average")
  18. par(mar = c(0,5,2,0))
  19. plot(sampleTree, main = "Sample clustering", sub="", xlab="", cex.lab = 2,
  20. cex.axis = 1, cex.main = 1,cex.lab=1)
  21. sample_colors <- numbers2colors(as.numeric(factor(datTraits$group)),
  22. colors = rainbow(length(table(datTraits$group))),
  23. signed = FALSE)
  24. par(mar = c(1,4,3,1),cex=0.8)
  25. pdf("Sample dendrogram and trait.pdf",width = 8,height = 6)
  26. plotDendroAndColors(sampleTree, sample_colors,
  27. groupLabels = "trait",
  28. cex.dendroLabels = 0.8,
  29. marAll = c(1, 4, 3, 1),
  30. cex.rowText = 0.01,
  31. main = "Sample dendrogram and trait" )
  32. dev.off()
  34. group_list <- datTraits$group
  35. dat.pca <- PCA(datExpr0, graph = F)
  36. pca <- fviz_pca_ind(dat.pca,
  37. title = "Principal Component Analysis",
  38. legend.title = "Groups",
  39. geom.ind = c("point","text"), #"point","text"
  40. pointsize = 2,
  41. labelsize = 4,
  42. repel = TRUE,
  43. col.ind = group_list,
  44. axes.linetype=NA, # remove axeslines
  45. mean.point=F
  46. ) +
  47. theme(legend.position = "none")+ # "none" REMOVE legend
  48. coord_fixed(ratio = 1)
  49. pca
  50. ggsave(pca,filename= "Sample PCA analysis.pdf", width = 8, height = 8)
  53. datExpr <- datExpr0
  54. nGenes <- ncol(datExpr)
  55. nSamples <- nrow(datExpr)
  57. #pick_power_for_unsigend
  59. R.sq_cutoff = 0.8
  60. if(T){
  61. # Call the network topology analysis function
  63. powers <- c(seq(1,20,by = 1), seq(22,30,by = 2))
  64. sft <- pickSoftThreshold(datExpr,
  65. networkType = "unsigned",# can also be signed or signed hybrid
  66. powerVector = powers,
  67. RsquaredCut = R.sq_cutoff,
  68. verbose = 5)
  69. #SFT.R.sq > 0.8 , slope ≈ -1
  70. pdf("power-value_unsigned.pdf",width = 16,height = 12)
  72. par(mfrow = c(1,2));
  73. cex1 = 0.9;
  74. # Scale-free topology fit index as a function of the soft-thresholding power
  75. plot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
  76. xlab="Soft Threshold (power)",ylab="Scale Free Topology Model Fit,signed R^2",type="n")
  77. text(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
  78. labels=powers,cex=cex1,col="red")
  79. # this line corresponds to using an R^2 cut-off of h
  80. abline(h=R.sq_cutoff ,col="red")
  81. # Mean connectivity as a function of the soft-thresholding power
  82. plot(sft$fitIndices[,1], sft$fitIndices[,5],
  83. xlab="Soft Threshold (power)",ylab="Mean Connectivity", type="n")
  84. text(sft$fitIndices[,1], sft$fitIndices[,5], labels=powers, cex=cex1,col="red")
  85. abline(h=100,col="red")
  86. dev.off()
  87. }
  89. power = sft$powerEstimate
  92. #build_network
  93. allowWGCNAThreads()
  94. net <- blockwiseModules(
  95. datExpr,
  96. power = power,
  97. maxBlockSize = ncol(datExpr),
  98. corType = "pearson",
  99. networkType = "unsigned", #use signed or signed hybrid with the setting when picking power.
  100. TOMType = "unsigned",
  101. minModuleSize = 100,
  102. mergeCutHeight = 0.25,
  103. numericLabels = TRUE,
  104. saveTOMs = F,
  105. verbose = 3
  106. )
  107. table(net$colors)
  109. # Convert labels to colors for plotting
  110. moduleColors <- labels2colors(net$colors)
  111. table(moduleColors)
  112. # Plot the dendrogram and the module colors underneath
  113. pdf("genes-modules_ClusterDendrogram_unsigned.pdf",width = 16,height = 12)
  114. plotDendroAndColors(net$dendrograms[[1]], moduleColors[net$blockGenes[[1]]],
  115. "Module colors",
  116. dendroLabels = FALSE, hang = 0.03,
  117. addGuide = TRUE, guideHang = 0.05)
  118. dev.off()
  120. gene_module <- data.frame(gene=colnames(datExpr),
  121. module=moduleColors)
  122. write.csv(gene_module,file = "gene_moduleColors_unsigned.csv",row.names = F)
  123. MES0 <- moduleEigengenes(datExpr, moduleColors)$eigengnes #calculate the eigengene score
  124. MEs_unsigned <- orderMEs(MES0) #put close eigenvectors next to each other
  125. write.csv(MEs_unsigned,file = "MEs_unsigned.csv")
  127. #draw heatmap using MEs such as Suppfig 6b
  128. datTraits$group <- as.factor(datTraits$group)
  129. design <- model.matrix(~0+datTraits$group)
  130. colnames(design) <- levels(datTraits$group) #get the group
  131. MES0 <- moduleEigengenes(datExpr,moduleColors)$eigengenes #Calculate module eigengenes.
  132. MEs <- orderMEs(MES0) #Put close eigenvectors next to each other
  133. moduleTraitCor <- cor(MEs,design,use = "p")
  134. moduleTraitPvalue <- corPvalueStudent(moduleTraitCor,nSamples)
  135. textMatrix <- paste0(signif(moduleTraitCor,2),"\n(",
  136. signif(moduleTraitPvalue,1),")")
  137. dim(textMatrix) <- dim(moduleTraitCor)
  138. pdf("step4_Module-trait-relationship_heatmap.pdf",
  139. width = 2*length(colnames(design)),
  140. height = 0.6*length(names(MEs)) )
  141. par(mar=c(5, 9, 3, 3))
  142. labeledHeatmap(Matrix = moduleTraitCor,
  143. xLabels = colnames(design),
  144. yLabels = names(MEs),
  145. ySymbols = names(MEs),
  146. colorLabels = F,
  147. colors = blueWhiteRed(50),
  148. textMatrix = textMatrix,
  149. setStdMargins = F,
  150. cex.text = 0.5,
  151. zlim = c(-1,1),
  152. main = "Module-trait relationships")
  153. dev.off()
  155. #output files for cytoscape such as Suppfig 11b
  156. gene <- colnames(datExpr)
  157. inModule <- moduleColors==module #input module color of interest
  158. modgene <- gene[inModule]
  160. TOM <- TOMsimilarityFromExpr(datExpr,power=power)
  161. modTOM <- TOM[inModule,inModule]
  162. dimnames(modTOM) <- list(modgene,modgene)
  164. nTop = 100
  165. IMConn = softConnectivity(datExpr[, modgene]) #calculate connectivity
  166. top = (rank(-IMConn) <= nTop) #filter for the top
  167. filter_modTOM <- modTOM[top, top]
  169. cyt <- exportNetworkToCytoscape(filter_modTOM,
  170. edgeFile = paste("CytoscapeInput-edges-", paste(module, collapse="-"), ".txt", sep=""),
  171. nodeFile = paste("CytoscapeInput-nodes-", paste(module, collapse="-"), ".txt", sep=""),
  172. weighted = TRUE,
  173. threshold = 0.15, #can be changed
  174. nodeNames = modgene[top],
  175. nodeAttr = moduleColors[inModule][top])

WGCNA.R at commit a20e59e, under GPL-3.0 · at the source

Overview

Authors: Jiawan Sun1,2, Serena Noss1,2, Corrine Smolen1,2, Venkata Hemanjani Bhavana1, Deepro Banerjee1,2, Maitreya Das1,2, Belinda Giardine1, Anisha Prabhu1, David J. Amor3,4, Kate Pope4, Paul J. Lockhart3,4, Santhosh Girirajan1,2
ORCID iDs: Jiawan Sun, Serena Noss, Venkata Hemanjani Bhavana, Belinda Giardine, Anisha Prabhu, David J. Amor, Paul J. Lockhart, Santhosh Girirajan
  1. Department of Biochemistry and Molecular Biology, Pennsylvania State University,University Park, PA USA
  2. Huck Institutes of the Life Sciences, University Park, PA USA
  3. Department of Paediatrics, University of Melbourne,Parkville, VIC Australia
  4. Bruce Lefroy Centre, Murdoch Children’s Research Institute,Parkville, VIC Australia
Institutions: Pennsylvania State University (United States); The University of Melbourne (Australia); Murdoch Children's Research Institute (Australia)
Journal: Nature communications, volume 17, issue 1, article 5917
Dates: received 11 August 2025; accepted 21 April 2026; published online 1 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-72598-z · PMID 42062284 · PMCID PMC13338134 · OpenAlex W7159618449
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), other condition (population)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, Connectivity, Graphs
Keywords: Medical genomics, Molecular medicine, Neuronal development
MeSH: Neurodevelopmental Disorders*, Animals, Cell Proliferation, Chromatin, CRISPR-Cas Systems, Forkhead Transcription Factors, Humans, Induced Pluripotent Stem Cells, Neural Stem Cells, Neurodevelopment, Neurons, Phenotype, Signal Transduction (* major topic)
Topic: Genetics and Neurodevelopmental Disorders (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NIGMS (GM121907); NINDS (NS122398)
Citations: not cited yet (Europe PMC); 137 references in the paper
Research resources: Lenti dCAS-VP64_Blast RRID:Addgene_61425, lenti MS2-P65-HSF1_Hygro RRID:Addgene_61426, RRID:Addgene_61427

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 27 matches between paragraphs and lines of code.

ENCODE-DCC/atac-seq-pipeline

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 47ba8dff9c332e24b48e767303e9fcac98589cf2, 15 February 2024
Languages: Python (45), Shell (9)
Size: 204 files, 54 scripts
Software Heritage: not archived
Found in: the text, “ATAC-seq sample processing and analysis”
Holds: README, license file, environment (scripts/requirements.macs2.txt, scripts/requirements.python2.txt, scripts/requirements.spp.txt, scripts/requirements.txt, dev/docker_image/Dockerfile), tests, continuous integration, documentation
Not found: CITATION.cff
Tools: Matplotlib (6 files), NumPy (6 files), pandas (3 files), SAMtools (2 files), SciPy (2 files), BEDTools (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
56 files
At the source: github.com/ENCODE-DCC/atac-seq-pipeline

Jiawan1023/iPSC_integrated_framework

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: a20e59e0560420e7a9048334f9541f40ff4c16e3, 2 April 2026
Languages: Shell (20), R (19), Python (16), Perl (1)
Size: 73 files, 56 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: pandas (14 files), tidyverse (14 files), ggplot2 (11 files), BEDTools (5 files), NumPy (4 files), BCFtools (2 files), clusterProfiler (2 files), data.table (2 files), DESeq2 (2 files), pheatmap (2 files), SciPy (2 files), statsmodels (2 files), ComplexHeatmap (1 file), deepTools (1 file), ggpubr (1 file), Matplotlib (1 file), NetworkX (1 file), patchwork (1 file), SAMtools (1 file), seaborn (1 file), WGCNA (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
58 files
At the source: github.com/Jiawan1023/iPSC_integrated_framework

Code availability statement

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

Read it in the paper: doi.org/10.1038/s41467-026-72598-z.

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;
  • 110 scripts, each with its path and the digest of its content;
  • 27 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

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:

Read it in the paper: doi.org/10.1038/s41467-026-72598-z.

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, 12 authors, 3 keywords, 13 MeSH terms, 2 funders, 136 references, 3 RRIDs.

Cite

This paper

Sun, J., Noss, S., Smolen, C., Bhavana, V. H., Banerjee, D., Das, M., Giardine, B., Prabhu, A., Amor, D. J., Pope, K., Lockhart, P. J., & Girirajan, S. (2026). Functional impact of genetic background on variable expressivity in neurodevelopmental disorders. Nature communications, 17(1), 5917. https://doi.org/10.1038/s41467-026-72598-z

BibTeX

@article{sun2026functional,
author = {Sun, Jiawan and Noss, Serena and Smolen, Corrine and Bhavana, Venkata Hemanjani and Banerjee, Deepro and Das, Maitreya and Giardine, Belinda and Prabhu, Anisha and Amor, David J. and Pope, Kate and Lockhart, Paul J. and Girirajan, Santhosh},
title = {{Functional impact of genetic background on variable expressivity in neurodevelopmental disorders}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {5917},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-72598-z},
url = {https://doi.org/10.1038/s41467-026-72598-z},
pmid = {42062284},
pmcid = {PMC13338134}
}

RIS

TY - JOUR
AU - Sun, Jiawan
AU - Noss, Serena
AU - Smolen, Corrine
AU - Bhavana, Venkata Hemanjani
AU - Banerjee, Deepro
AU - Das, Maitreya
AU - Giardine, Belinda
AU - Prabhu, Anisha
AU - Amor, David J.
AU - Pope, Kate
AU - Lockhart, Paul J.
AU - Girirajan, Santhosh
TI - Functional impact of genetic background on variable expressivity in neurodevelopmental disorders
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/01
VL - 17
IS - 1
SP - 5917
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-72598-z
UR - https://doi.org/10.1038/s41467-026-72598-z
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-72598-z",
"type": "article-journal",
"title": "Functional impact of genetic background on variable expressivity in neurodevelopmental disorders",
"container-title": "Nature communications",
"author": [
{
"family": "Sun",
"given": "Jiawan"
},
{
"family": "Noss",
"given": "Serena"
},
{
"family": "Smolen",
"given": "Corrine"
},
{
"family": "Bhavana",
"given": "Venkata Hemanjani"
},
{
"family": "Banerjee",
"given": "Deepro"
},
{
"family": "Das",
"given": "Maitreya"
},
{
"family": "Giardine",
"given": "Belinda"
},
{
"family": "Prabhu",
"given": "Anisha"
},
{
"family": "Amor",
"given": "David J."
},
{
"family": "Pope",
"given": "Kate"
},
{
"family": "Lockhart",
"given": "Paul J."
},
{
"family": "Girirajan",
"given": "Santhosh"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "5917",
"DOI": "10.1038/s41467-026-72598-z",
"PMID": "42062284",
"PMCID": "PMC13338134",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-72598-z",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
1
]
]
}
}

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

Similar papers

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

[1] doi:10.1038/s41467-026-76675-1 [code]
Long-read proteogenomic atlas of human neuronal differentiation reveals isoform diversity informing neurodevelopmental risk mechanisms.
Journal: Nature communications
In common: WGCNA, BEDTools, SAMtools, 13 other tools, 5 references
[2] doi:10.1093/bioinformatics/btag592 [code]
Network-based stratification of allele-specific expression reveals patient subgroups in Huntington's disease.
Journal: Bioinformatics (Oxford, England)
In common: BCFtools, WGCNA, SAMtools, 15 other tools, other condition, 2 references
[3] doi:10.1016/j.celrep.2026.117073 [code]
Single-cell epigenomics uncovers heterochromatin instability and transcription factor dysfunction during mouse brain aging.
Journal: Cell reports
In common: BEDTools, SAMtools, DESeq2, 13 other tools, 5 references
[4] doi:10.1038/s42003-026-10957-8 [code]
Brain defence by the extracellular matrix protein Cochlin.
Journal: Communications biology
In common: BCFtools, WGCNA, SAMtools, 16 other tools
[5] doi:10.1038/s41467-026-69944-6 [code]
Multi-modal dissection of cell-type specific TDP-43 pathology in the motor cortex.
Journal: Nature communications
In common: deepTools, BEDTools, SAMtools, 13 other tools, other condition, 3 references
[6] doi:10.1038/s41467-026-71790-5 [code]
Recurrent DNA break clusters drive replication-stress-induced copy number variants and genome diversification.
Journal: Nature communications
In common: BCFtools, BEDTools, SAMtools, 12 other tools, 2 references
[7] doi:10.1016/j.xcrm.2026.102766 [code]
A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma.
Journal: Cell reports. Medicine
In common: WGCNA, DESeq2, clusterProfiler, 13 other tools, other condition, 2 references
[8] doi:10.1126/sciadv.aed2952 [code]
Activation of transposable elements is linked to a region- and cell type-specific interferon response in Parkinson's disease.
Journal: Science advances
In common: deepTools, BEDTools, SAMtools, 13 other tools, 1 reference
[9] doi:10.1038/s41467-026-71803-3 [code]
Charting the transition from in vitro gliogenesis to the in vivo maturation of human glial progenitor cells transplanted into the hypomyelinated mouse brain.
Journal: Nature communications
In common: deepTools, BEDTools, SAMtools, 12 other tools, 1 reference
[10] doi:10.21203/rs.3.rs-9927928/v1 [code]
Genome-wide and allele-resolved maps of the radial architecture of the mouse genome
Journal: Research Square (preprint)
In common: BCFtools, BEDTools, SAMtools, 12 other tools, 1 reference

Contribute

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

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

Request its removal

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

Discussion, reproductions, activity

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

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

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