OSCR

Complementary vertebrate <i>Wac</i> models exhibit phenotypes relevant to DeSanto-Shinawi Syndrome.

Code ↔ Paper

3 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 3 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Materials and methods › RNA-sequencing and bioinformatics analysis › Gene ontology enrichment analysis ↔ Gompers_NatNeuro_2017/1. Differential Gene Expression/Gompers_NatureNeuro_DE-GO-GSEA.R, lines 1–86 · score 0.73 · DE genes, genes expressed, db, GO, Ontology, background
  2. [2] § Materials and methods › RNA-sequencing and bioinformatics analysis › Differential expression (DE) analysis ↔ Gompers_NatNeuro_2017/1. Differential Gene Expression/Gompers_NatureNeuro_DE-GO-GSEA.R, lines 1–86 · score 0.55 · GLM, covariate, DE, edgeR, CPM, batch
  3. [3] § Materials and methods › RNA-sequencing and bioinformatics analysis ↔ Gompers_NatNeuro_2017/1. Differential Gene Expression/Gompers_NatureNeuro_RNA-seq_Alignment.sh, the whole file · a weak match · score 0.53 · featureCounts, STAR, PE, genome, RNA, gene

Paper

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The authors' code

R · 112 lines · 4.7 KB · no license · 2 matches

  1. #!/usr/bin/env Rscript
  3. ################################################################################
  5. ## Gompers et al. 2017 (Nature Neuroscience)
  6. ## Differential expression analysis
  7. ## topGO
  8. ## Permutation gene set enrichment analysis
  10. ################################################################################
  12. ## EDGER
  13. library(edgeR)
  15. cpm.sample.cutoff <- 2
  16. min.cpm <- 10
  18. y <- DGEList(counts=exp.data.matrix,group=group)
  19. keep <- rowSums(cpm(y)>min.cpm) >= cpm.sample.cutoff
  20. y <- y[keep, , keep.lib.sizes=FALSE]
  22. ## Perform simple exact test on genotype
  23. y <- calcNormFactors(y)
  24. y <- estimateCommonDisp(y)
  25. y <- estimateTagwiseDisp(y)
  26. pseudo.counts <- y$pseudo.counts
  27. sample.count.table <- y$counts
  29. ## Perform glm on genotype, with sex and batch as covariates
  30. design <- model.matrix(~as.factor(sex)+as.factor(seq.run)+as.factor(group))
  31. y <- estimateGLMCommonDisp(y,design)
  32. y <- estimateGLMTrendedDisp(y,design)
  33. y <- estimateGLMTagwiseDisp(y,design)
  34. fit <- glmFit(y,design)
  35. lrt <- glmLRT(fit)
  36. glm.output <- topTags(lrt, n=Inf)
  38. ## TOPGO
  39. ## All DE genes
  41. library(topGO)
  42. library(GO.db)
  43. BP.output.down <- list(length=length(module.names))
  44. BP.output.up <- list(length=length(module.names))
  45. test.gene.min <- 20
  46. FDR.criteria <- .2
  48. xx <- annFUN.org("BP", mapping = "org.Hs.eg.db", ID = "ensembl")
  49. background.genes <- final.set[,"Human.Ensembl.Gene.ID"]
  50. geneUniverse <- background.genes
  52. ## Down regulated for all DE
  53. for (module.index in 1:length(module.names)) {
  54. test.genes <- final.set[which(final.set[,"moduleColors"]==module.names[module.index] & final.set[,"FDR"]<FDR.criteria & final.set[,"logFC"]<0),"Human.Ensembl.Gene.ID"]
  55. if (length(test.genes)>test.gene.min) {
  56. genesOfInterest <- test.genes
  57. geneList <- factor(as.integer(geneUniverse %in% genesOfInterest))
  58. names(geneList) <- geneUniverse
  59. myGOdata <- new("topGOdata", description="My project", ontology="BP", allGenes=geneList, annot=annFUN.org, mapping="org.Hs.eg.db", ID = "ensembl", nodeSize=20)
  60. resultGO <- runTest(myGOdata, algorithm = "weight01", statistic="fisher")
  61. print(paste(module.names[module.index], "Down"))
  62. BP.output.down[[module.index]] <- GenTable(myGOdata, resultGO, topNodes=3315)
  63. print(BP.output.down[[module.index]][1:10,])
  64. } else {
  65. print(module.names[module.index])
  66. print("Too few genes")
  67. }
  68. }
  70. ## Up regulated for all DE
  71. for (module.index in 1:length(module.names)) {
  72. test.genes <- final.set[which(final.set[,"moduleColors"]==module.names[module.index] & final.set[,"FDR"]<FDR.criteria & final.set[,"logFC"]>0),"Human.Ensembl.Gene.ID"]
  73. if (length(test.genes)>test.gene.min) {
  74. genesOfInterest <- test.genes
  75. geneList <- factor(as.integer(geneUniverse %in% genesOfInterest))
  76. names(geneList) <- geneUniverse
  77. myGOdata <- new("topGOdata", description="My project", ontology="BP", allGenes=geneList, annot=annFUN.org, mapping="org.Hs.eg.db", ID = "ensembl", nodeSize=20)
  78. resultGO <- runTest(myGOdata, algorithm = "weight01", statistic="fisher")
  79. print(paste(module.names[module.index], "Up"))
  80. BP.output.up[[module.index]] <- GenTable(myGOdata, resultGO, topNodes=3315)
  81. print(BP.output.up[[module.index]][1:10,])
  82. } else {
  83. print(module.names[module.index])
  84. print("Too few genes")
  85. }
  86. }
  89. ## PERMUTATION GSEA
  90. ## Function to perform general permutation test and generate histogram output
  91. geneset.perm.test <- function(binary.score.reference, binary.score.test, iterations, plot.name) {
  92. binary.score.reference <- ifelse(is.na(binary.score.reference),0,binary.score.reference)
  93. binary.score.test <- ifelse(is.na(binary.score.test),0,binary.score.test)
  94. count.criteria <- vector(length=iterations)
  95. test.size <- length(binary.score.test)
  96. for (index in 1:iterations) {
  97. count.criteria[index] <- sum(sample(binary.score.reference, test.size, replace = F))
  98. }
  99. x.min <- min(c(count.criteria, sum(binary.score.test))) - 20
  100. if (x.min < 0) {x.min <- 0}
  101. x.max<- max(c(count.criteria, sum(binary.score.test))) + 20
  102. z <- abs(mean(count.criteria) - sum(binary.score.test))/sd(count.criteria)
  103. p <- 2*pnorm(-abs(z))
  104. e <- sum(binary.score.test)/mean(count.criteria)
  105. prop <- sum(binary.score.test)/length(binary.score.test)
  106. bg <- length(binary.score.reference)
  107. pdf(file=paste(plot.name, ".pdf", sep=""), height=6, width=10)
  108. hist(count.criteria, col="gray", xlab="Count", ylab="Frequency", main=paste(plot.name, " \ncount=", sum(binary.score.test), " of ", length(binary.score.test), "; p-value=", format(p, scientific = T, digits = 2), "; FE=", format(e, scientific = F, digits = 2), "; Prop=", format(prop, scientific = F, digits = 2), "; n(bg)=", bg, sep=""), xlim=c(x.min,x.max), cex=.75)
  109. abline(v=sum(binary.score.test), lwd=3, col="red")
  110. dev.off()
  111. return(list(count.criteria,z,p,e,prop,bg,sum(binary.score.test)))
  112. }

Gompers_NatureNeuro_DE-GO-GSEA.R at commit 93d28a1, no license · at the source

Overview

Authors: Kang-Han Lee1, April M Stafford2, Maria Pacheco-Vergara3, Karol Cichewicz4,5, Cesar P Canales4,5, Nicolas Seban4,5, Melissa Corea4,5, Darlene Rahbarian4,5, Kelly E Bonekamp6, Grant R Gillie6, Dariangelly Pacheco-Cruz2,7, Alyssa M Gill2, Hye-Eun Hwang1, Yeong-Eun Kim1, Katie L Uhl2, Tara E Jager8, Marwan Shinawi9, Xiaopeng Li2, Andre Obenaus10, Shane R Crandall6,7, Juhee Jeong3, Alex S Nord4,5, Cheol-Hee Kim1, Daniel Vogt2,7
ORCID iDs: Kang-Han Lee, Karol Cichewicz, Cesar P Canales, Dariangelly Pacheco-Cruz, Yeong-Eun Kim, Andre Obenaus, Shane R Crandall, Alex S Nord, Cheol-Hee Kim, Daniel Vogt
  1. Department of Biology, Chungnam National University, Daejeon, Republic of Korea
  2. Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, United States
  3. Department of Molecular Pathology, New York University College of Dentistry, New York, United States
  4. Department of Psychiatry and Behavioral Sciences, University of California Davis, Davis, United States
  5. Department of Neurobiology, Physiology and Behavior, University of California Davis, Davis, United States
  6. Department of Physiology, Michigan State University, East Lansing, United States
  7. Neuroscience Program, Michigan State University, East Lansing, United States
  8. Corewell Health, Grand Rapids, United States
  9. Division of Genetics and Genomic Medicine, Department of Pediatrics, Washington University School of Medicine, St. Louis, United States
  10. Director, Preclinical and Translational Imaging Center, School of Medicine, University of California Irvine, Irvine, United States
Institutions: Chungnam National University (South Korea); Michigan State University (United States); New York University (United States); University of California, Davis (United States); Corewell Health; Washington University in St. Louis (United States); University of California, Irvine (United States)
Journal: eLife, volume 14, article RP109104
Dates: published online 3 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.109104 · PMID 42690726 · PMCID PMC13541301 · OpenAlex W4417276952
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), zebrafish (organism), other condition (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Mouse, Zebrafish
MeSH: Intellectual Disability*, Zebrafish Proteins*, Animals, Brain, Disease Models, Animal, Female, GABAergic Neurons, Male, Mice, Phenotype, Zebrafish (* major topic)
Topic: Genomic variations and chromosomal abnormalities (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Korea National Institute of Health (2026-ER0807-00, 2026-ER0602-00, 2024-ER0703-00, 2024-ER0519-00); NIGMS NIH HHS (T32 GM142521); Cystic Fibrosis Foundation (LI19XX0); National Research Foundation of Korea (RS-2024-00349650, RS-2024-00443043); NIMH NIH HHS (R01 MH120513); NIH HHS (5T32GM142521, MH120513, DE026798, HL153165-01A1); NIDCR NIH HHS (R01 DE026798, R56 DE026798); NHLBI NIH HHS (R01 HL153165)
Citations: not cited yet (Europe PMC); 73 references in the paper
Research resources: RRID:SCR_002010

Abstract

Monogenic syndromes are associated with neurodevelopmental changes that result in cognitive impairments and neurobehavioral phenotypes, including autism and seizures. Limited studies and resources are available to make meaningful headway into the underlying molecular mechanisms that result in these symptoms. One such example is DeSanto-Shinawi Syndrome (DESSH), a rare disorder caused by pathogenic variants in the WAC gene. Individuals with DESSH syndrome exhibit a recognizable craniofacial gestalt, developmental delay/intellectual disability, neurobehavioral symptoms that include autism, ADHD, behavioral difficulties, and seizures. However, no thorough studies from a vertebrate model exist to understand how these changes occur. To overcome this, we developed both murine and zebrafish Wac/wac deletion mutants and studied whether their phenotypes recapitulate those described in individuals with DESSH syndrome. We first show that the two Wac models exhibit craniofacial and behavioral changes, reminiscent of abnormalities found in DESSH syndrome. In addition, each model revealed impacts on GABAergic neurons and further studies showed that the mouse mutants are susceptible to seizures, changes in brain volumes that are different between sexes and relevant behaviors. Finally, we uncovered transcriptional impacts of Wac loss-of-function in mice that will pave the way for future molecular studies into DESSH. These studies present two new vertebrate models that begin to uncover biological underpinnings of DESSH syndrome and elucidate the biology of Wac.

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.

nordneurogenomicslab/publications

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 93d28a15a5df195fef47ee85b213c9d00e829e9f, 31 August 2026
Languages: R (8), Perl (3), Shell (2)
Size: 85 files, 13 scripts
Software Heritage: not checked
Found in: the text, “Permutation test”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: edgeR (2 files), WGCNA (2 files), cowplot (1 file), FastQC (1 file), ggplot2 (1 file), reshape2 (1 file), STAR (1 file), Subread (featureCounts) (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
14 files
At the source: github.com/NordNeurogenomicsLab/Publications/tree/master/Lee_et.al._2024,_Complimentary_vertebrate_Wac_models_exhibit_phenotypes_relevant_to_DeSanto-Shinawi_Syndrome

Tracing map

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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;
  • 13 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

Data availability

RNA-sequencing data were performed and are embedded in the manuscript files.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 24 authors, 2 keywords, 11 MeSH terms, 8 funders, 72 references, 1 RRID.

Cite

This paper

Lee, K.-H., Stafford, A. M., Pacheco-Vergara, M., Cichewicz, K., Canales, C. P., Seban, N., Corea, M., Rahbarian, D., Bonekamp, K. E., Gillie, G. R., Pacheco-Cruz, D., Gill, A. M., Hwang, H.-E., Kim, Y.-E., Uhl, K. L., Jager, T. E., Shinawi, M., Li, X., Obenaus, A., . . . Vogt, D. (2026). Complementary vertebrate &lt;i&gt;Wac&lt;/i&gt; models exhibit phenotypes relevant to DeSanto-Shinawi Syndrome. eLife, 14, RP109104. https://doi.org/10.7554/elife.109104

BibTeX

@article{lee2026complementary,
author = {Lee, Kang-Han and Stafford, April M and Pacheco-Vergara, Maria and Cichewicz, Karol and Canales, Cesar P and Seban, Nicolas and Corea, Melissa and Rahbarian, Darlene and Bonekamp, Kelly E and Gillie, Grant R and Pacheco-Cruz, Dariangelly and Gill, Alyssa M and Hwang, Hye-Eun and Kim, Yeong-Eun and Uhl, Katie L and Jager, Tara E and Shinawi, Marwan and Li, Xiaopeng and Obenaus, Andre and Crandall, Shane R and Jeong, Juhee and Nord, Alex S and Kim, Cheol-Hee and Vogt, Daniel},
title = {{Complementary vertebrate \&lt;i\&gt;Wac\&lt;/i\&gt; models exhibit phenotypes relevant to DeSanto-Shinawi Syndrome}},
journal = {eLife},
year = {2026},
month = sep,
volume = {14},
pages = {RP109104},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.109104},
url = {https://doi.org/10.7554/elife.109104},
pmid = {42690726},
pmcid = {PMC13541301}
}

RIS

TY - JOUR
AU - Lee, Kang-Han
AU - Stafford, April M
AU - Pacheco-Vergara, Maria
AU - Cichewicz, Karol
AU - Canales, Cesar P
AU - Seban, Nicolas
AU - Corea, Melissa
AU - Rahbarian, Darlene
AU - Bonekamp, Kelly E
AU - Gillie, Grant R
AU - Pacheco-Cruz, Dariangelly
AU - Gill, Alyssa M
AU - Hwang, Hye-Eun
AU - Kim, Yeong-Eun
AU - Uhl, Katie L
AU - Jager, Tara E
AU - Shinawi, Marwan
AU - Li, Xiaopeng
AU - Obenaus, Andre
AU - Crandall, Shane R
AU - Jeong, Juhee
AU - Nord, Alex S
AU - Kim, Cheol-Hee
AU - Vogt, Daniel
TI - Complementary vertebrate &lt;i&gt;Wac&lt;/i&gt; models exhibit phenotypes relevant to DeSanto-Shinawi Syndrome
T2 - eLife
J2 - Elife
PY - 2026
DA - 2026/09/03
VL - 14
SP - RP109104
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.109104
UR - https://doi.org/10.7554/elife.109104
LA - en
ER -

CSL-JSON

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"id": "10.7554/elife.109104",
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"title": "Complementary vertebrate &lt;i&gt;Wac&lt;/i&gt; models exhibit phenotypes relevant to DeSanto-Shinawi Syndrome",
"container-title": "eLife",
"author": [
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"given": "Katie L"
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{
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"given": "Tara E"
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{
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