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Assessing molecular gene by treatment interactions using a population of neural progenitors exposed to valproic acid and lithium.

Code ↔ Paper

20 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 20 matches · 6 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Results › Regulatory elements with drug dependent effects on gene expression ↔ Peak_or_Gene_base/Peak_Gene_corr/corr.GenePeak.04.getCorr.R, lines 1–52 · score 0.79 · peak gene correlations, gene peak pairs, correlating chromatin accessibility, gene expression, Mb, transcription
  2. [2] § Results › Regulatory elements with drug dependent effects on gene expression ↔ Peak_or_Gene_base/Peak_Gene_corr/corr.GenePeak.01.R, the whole file · a weak match · score 0.76 · gene peak pairs, nearby peaks, correlating chromatin accessibility, gene expression, Mb, distance
  3. [3] § Materials and methods › RNA-seq data preprocessing ↔ Peak_or_Gene_base/DESeq_analysis/RNA/00.DEG.bran.mkRmList.R, lines 41–84 · score 0.71 · duplication rates, VerifyBamID, mapping rates, RIN, STAR, seq
  4. [4] § Materials and methods › ATAC-seq data preprocessing ↔ 00.Preprocessing/ATACseq/WASP.3.remapBWAmem.sh, the whole file · a weak match · score 0.71 · BWA MEM, ATAC seq, reference genome, remapped, WASP, hg38
  5. [5] § Materials and methods › ATAC-seq data preprocessing ↔ 00.Preprocessing/ATACseq/WASP.3.remapBWAmem.R, the whole file · a weak match · score 0.62 · BWA MEM, reference genome, remapped, WASP, seq, hg38
  6. [6] § Materials and methods › Chromatin accessibility quantitative trait loci (caQTL) mapping ↔ QTLs/caQTLs/RASQUAL_Output_Processing/00_ConditionSpecific_CombineandFilter_Output.R, the whole file · a weak match · score 0.59 · likelihood ratio, eigenMT, LiCl, RASQUAL, mapped, allele
  7. [7] § Results › Genetic effects on gene regulation altered by VPA or Li ↔ QTLs/eQTLs/11.prep.reqtl.step2.bran.R, lines 56–120 · score 0.57 · eSNP, eGene, SNPs, drug, VPA
  8. [8] § Results › TWAS highlights drug-dependent gene-trait associations ↔ QTLs/eQTLs/gwasOlap/gwasOlap.eQTLs.R, lines 43–102 · score 0.56 · Cortical surface area, bipolar disorder, thickness, Intelligence, ASD, variants
  9. [9] § Results › TWAS highlights drug-dependent gene-trait associations ↔ QTLs/eQTLs/gwasOlap/gwasOlap.reQTLs.R, lines 38–95 · score 0.56 · Cortical surface area, bipolar disorder, thickness, Intelligence, ASD, variants
  10. [10] § Results › Measuring alterations in gene regulation in hNPCs due to VPA and Li ↔ Peak_or_Gene_base/DESeq_analysis/ATAC/util/05.PartHerit.R, lines 315–375 · score 0.56 · brain structure, ADHD, PD, MDD, SCZ, BP
  11. [11] § Materials and methods › RNA-seq data preprocessing ↔ 00.Preprocessing/RNAseq/02_StarAlignment_01_indexCreation.sh, the whole file · a weak match · score 0.56 · RNA seq, reference genome, Ensembl, STAR, preprocessing
  12. [12] § Results › Measuring alterations in gene regulation in hNPCs due to VPA and Li ↔ QTLs/eQTLs/gwasOlap/gwasOlap.eQTLs.R, lines 43–102 · score 0.55 · cortical surface area, bipolar disorder, depressive, SCZ, BP, thickness
  13. [13] § Materials and methods › Gene expression quantitative trait loci (eQTL) mapping ↔ QTLs/eQTLs/12.batch.run.reQTL.bdl.R, lines 65–129 · score 0.55 · variant filtering, limix_qtl, kinship, covariates, interactions, mapping
  14. [14] § Materials and methods › Differential chromatin accessibility and gene expression analyses ↔ Peak_or_Gene_base/DESeq_analysis/ATAC/util/02.TF.enrich.bran.2023.R, lines 13–150 · score 0.54 · log2 fold change, shrinkage, binding, enrichment, FDR, gene
  15. [15] § Materials and methods › Gene expression quantitative trait loci (eQTL) mapping ↔ QTLs/caQTLs/RASQUAL_Output_Processing/00_ConditionSpecific_CombineandFilter_Output.R, the whole file · a weak match · score 0.54 · Benjamini Hochberg, status, LiCl, positions, matrix, genotype
  16. [16] § Results › Genetic effects on gene regulation altered by VPA or Li ↔ QTLs/eQTLs/gwasOlap/gwasOlap.eQTLs.ecav.03.input.R, lines 1–89 · score 0.52 · eGene, eQTLs, SNPs, drug, Li, VPA
  17. [17] § Results › DARs linked to brain-trait heritability ↔ QTLs/eQTLs/gwasOlap/gwasOlap.reQTLs.R, lines 38–95 · score 0.52 · cortical surface area, bipolar disorder, thickness, SNP
  18. [18] § Results › Characteristics of Condition Dependent Regulatory QTLs ↔ QTLs/caQTLs/ClinicalvsWnt/Plot_pLOEUFScores.R, lines 43–131 · score 0.52 · LOEUF scores, gnomAD, loss, metric, QTLs, genes
  19. [19] § Results › Characteristics of Condition Dependent Regulatory QTLs ↔ QTLs/caQTLs/ClinicalvsWnt/Plot_pLOEUFScores_Comparison.R, lines 43–89 · score 0.51 · LOEUF scores, gnomAD, loss, metric, QTLs, genes
  20. [20] § Materials and methods › Differential chromatin accessibility and gene expression analyses ↔ Peak_or_Gene_base/DESeq_analysis/ATAC/util/04.GO.bran.R, lines 7–91 · score 0.50 · log2 fold change, binding, threshold, enrichment, FDR, gene

Paper

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The authors' code

R · 57 lines · 3.7 KB · no license · 2 matches

  1. #Script for filtering of RASQUAL output for eigenMT input
  2. #JMV 10/21
  4. library(dplyr);
  5. library(stringr);
  7. rm(list=ls())
  8. options(stringsAsFactors=FALSE);
  10. #################################
  11. #Loops for input file production#
  12. #################################
  14. Conditions <- c("Veh","LiCl", "VPA")
  15. Chromosomes <- c(1:22, "X") #Not currently running X chromosome
  17. for (j in 1:3){
  19. Well_Condition <- Conditions[j]
  20. print(paste0("Condition:", Well_Condition))
  21. #Creating dataframe to hold combined RASQUAL output for each condition
  22. Full_condition <- as.data.frame(matrix(0, nrow=1, ncol=26))
  23. colnames(Full_condition) <- c("Feature", "rs ID", "Chromosome", "SNP Position", "Ref Allele", "Alt allele", "Allele Frequency", "HWE Chi_Square Statistic", "Imputation Quality (IA)", "Log_10 Benjamini-Hochberg Q-value", "Chi square statistic (2 x log Likelihood ratio)", "Effect Size", "Sequencing/mapping error rate (Delta)", "Reference allele mapping bias (Phi)", "Overdispersion", "SNP ID within the region", "No. of feature SNPs", "No. of tested SNPs", "No. of iterations for null hypothesis", "No. of iterations for alternative hypothesis", "Random location of ties", "Log likelihood of the null hypothesis", "Convergence status", "Squared correlation between prior and posterior genotypes (fSNPs)", "Squared correlation between prior and posterior genotypes (rSNP)", "PValue")
  25. for (c in Chromosomes){
  27. print(paste0("Chr:", c))
  29. ###########################
  30. #Loading in RASQUAL Output#
  31. ###########################
  32. RASQUAL_File <- paste0("/work/users/j/m/jmvalone/00_Wnt_Clinical/rasqual/2024/Results/", Well_Condition, "/00_RAW/chr", c, "_", Well_Condition, "_RASQUALResults_25kb.txt")
  33. RASQUAL <- read.delim(RASQUAL_File, header = FALSE)
  34. colnames(RASQUAL) <- c("Feature", "rs ID", "Chromosome", "SNP Position", "Ref Allele", "Alt allele", "Allele Frequency", "HWE Chi_Square Statistic", "Imputation Quality (IA)", "Log_10 Benjamini-Hochberg Q-value", "Chi square statistic (2 x log Likelihood ratio)", "Effect Size", "Sequencing/mapping error rate (Delta)", "Reference allele mapping bias (Phi)", "Overdispersion", "SNP ID within the region", "No. of feature SNPs", "No. of tested SNPs", "No. of iterations for null hypothesis", "No. of iterations for alternative hypothesis", "Random location of ties", "Log likelihood of the null hypothesis", "Convergence status", "Squared correlation between prior and posterior genotypes (fSNPs)", "Squared correlation between prior and posterior genotypes (rSNP)")
  36. #Calculating pvalue
  37. RASQUAL$PValue <- pchisq(RASQUAL$`Chi square statistic (2 x log Likelihood ratio)`, 1, lower.tail = F)
  39. #Filtering out skipped entries
  40. RASQUAL <- filter(RASQUAL, RASQUAL$`SNP Position` != "-1")
  42. #Filtering for r2 between prior and posterior
  43. RASQUAL <- filter(RASQUAL, RASQUAL$`Squared correlation between prior and posterior genotypes (rSNP)` >= 0.8)
  45. #Writing out filtered version
  46. Output_File <- paste0("/work/users/j/m/jmvalone/00_Wnt_Clinical/rasqual/2024/Results/", Well_Condition, "/01_Filtered/chr", c, "_", Well_Condition, "RASQUALResults_25kb.txt")
  47. write.table(RASQUAL, Output_File, col.names = TRUE, row.names = FALSE)
  49. #Adding to final full data frame
  50. Full_condition <- rbind(Full_condition, RASQUAL)
  51. }
  52. #Trimming off empty placeholder row
  53. Full_condition <- Full_condition[-1,]
  54. #Writing out full condition RASQUAL output table
  55. FullOutput_File <- paste0("/work/users/j/m/jmvalone/00_Wnt_Clinical/rasqual/2024/Results/", Well_Condition, "/01_Filtered/", Well_Condition, "_FullFilteredRASQUALResults_25kb.txt")
  56. write.csv(Full_condition, FullOutput_File, col.names = TRUE, row.names = FALSE)
  57. }

00_ConditionSpecific_CombineandFilter_Output.R at commit a961ab4, no license · at the source

Overview

Authors: Jordan M. Valone1,2, Brandon D. Le1,2, Nana Matoba1,2, Jessica T. Mory1,2, Justin M. Wolter1,2,3,4, Michael I. Love1,5, Jason L. Stein1,2,3
ORCID iDs: Jordan M. Valone, Nana Matoba, Jason L. Stein
  1. Department of Genetics, University of North Carolina at Chapel Hill,Chapel Hill, NC USA
  2. UNC Neuroscience Center, University of North Carolina at Chapel Hill,Chapel Hill, NC USA
  3. Carolina Institute for Developmental Disabilities,Carrboro, NC USA
  4. Present Address: University of Wisconsin–Madison, Genetics,Madison, WI USA
  5. Department of Biostatistics, University of North Carolina at Chapel Hill,Chapel Hill, NC USA
Institutions: University of North Carolina at Chapel Hill (United States); University of Wisconsin–Madison (United States)
Journal: Molecular psychiatry, volume 31, issue 8, pages 4759-4773
Dates: received 30 September 2025; accepted 20 March 2026; published online 4 April 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41380-026-03578-4 · PMID 41935183 · PMCID PMC13364665 · OpenAlex W4414175504
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.09.12.25335653
Status: code verified
Categories: genetics / omics (modality), human (organism), cellular / molecular (subfield)
Methods: Statistics
Keywords: Genetics, Molecular biology
MeSH: Lithium*, Neural Stem Cells*, Valproic Acid*, Female, Folic Acid, Gene-Environment Interaction, Genome-Wide Association Study, Humans, Mental Disorders, Polymorphism, Single Nucleotide (* major topic)
Topic: Genetics and Neurodevelopmental Disorders (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Institute of Mental Health (R01MH118349, R01MH120125, R01MH121433); NIH (T32GM067553, T32GM135123)
Citations: not cited yet (Europe PMC); 105 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.

Repository

Its files are read in the Code ↔ Paper reader above, with 20 matches between paragraphs and lines of code.

steinlabunc/clinical_rqtls

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: a961ab430b09f802067d09b115b0270cc00c0409, 10 January 2026
Languages: R (125), Shell (74)
Size: 200 files, 199 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (105 files), data.table (59 files), DESeq2 (20 files), ggplot2 (20 files), ggpubr (18 files), patchwork (16 files), lmerTest (12 files), SAMtools (11 files), reshape2 (7 files), lme4 (6 files), FastQC (4 files), STAR (3 files), BEDTools (2 files), Subread (featureCounts) (2 files), ComplexHeatmap (1 file), edgeR (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
200 files
At the source: bitbucket.org/steinlabunc/clinical_rqtls/

Code availability statement

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Read it in the paper: doi.org/10.1038/s41380-026-03578-4.

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  • 199 scripts, each with its path and the digest of its content;
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Data

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Data availability statement

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Read it in the paper: doi.org/10.1038/s41380-026-03578-4.

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Version 2, 28 September 2026

  • Publisher: n/a → Springer Nature

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 2 keywords, 10 MeSH terms, 2 funders, 104 references.

Cite

This paper

Valone, J. M., Le, B. D., Matoba, N., Mory, J. T., Wolter, J. M., Love, M. I., & Stein, J. L. (2026). Assessing molecular gene by treatment interactions using a population of neural progenitors exposed to valproic acid and lithium. Molecular psychiatry, 31(8), 4759-4773. https://doi.org/10.1038/s41380-026-03578-4

BibTeX

@article{valone2026assessing,
author = {Valone, Jordan M. and Le, Brandon D. and Matoba, Nana and Mory, Jessica T. and Wolter, Justin M. and Love, Michael I. and Stein, Jason L.},
title = {{Assessing molecular gene by treatment interactions using a population of neural progenitors exposed to valproic acid and lithium}},
journal = {Molecular psychiatry},
year = {2026},
month = apr,
volume = {31},
number = {8},
pages = {4759--4773},
publisher = {Springer Nature},
issn = {1359-4184},
doi = {10.1038/s41380-026-03578-4},
url = {https://doi.org/10.1038/s41380-026-03578-4},
pmid = {41935183},
pmcid = {PMC13364665}
}

RIS

TY - JOUR
AU - Valone, Jordan M.
AU - Le, Brandon D.
AU - Matoba, Nana
AU - Mory, Jessica T.
AU - Wolter, Justin M.
AU - Love, Michael I.
AU - Stein, Jason L.
TI - Assessing molecular gene by treatment interactions using a population of neural progenitors exposed to valproic acid and lithium
T2 - Molecular psychiatry
J2 - Mol Psychiatry
PY - 2026
DA - 2026/04/04
VL - 31
IS - 8
SP - 4759
EP - 4773
SN - 1359-4184
PB - Springer Nature
DO - 10.1038/s41380-026-03578-4
UR - https://doi.org/10.1038/s41380-026-03578-4
LA - en
ER -

CSL-JSON

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