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Stage-specific epigenetic priming amplifies gene activation during lineage commitment.

Code ↔ Paper

13 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 13 matches · 1 of them tie a paragraph to a whole file without given lines: a weak match, whose lines are not tinted
  1. [1] § MATERIALS AND METHODS › Bioinformatic analysis › RNA sequencing ↔ Package_install.Rmd, lines 12 to 81 · score 0.90 · ComplexHeatmap, clusterProfiler, Bioconductor package, TxDb.Mmusculus.UCSC.mm10.knownGene, Known Genes, ggplot2 · Report a mismatch
  2. [2] § RESULTS › MSL1 depletion impairs neuronal transcriptional programs and morphogenesis in vitro ↔ Fig_2/scripts/Temporal_GO_Term_Analysis.Rmd, lines 86 to 106 · score 0.75 · axon guidance, dendrite development, synapse assembly, GO terms, regulated, cells · Report a mismatch
  3. [3] § MATERIALS AND METHODS › Bioinformatic analysis › Micro-C ↔ MicroC/workflows/rules/scripts/cooltools.ipynb, lines 388 to 439 · score 0.73 · cooltools.dots, max_nans_tolerated, FDR, cooler, mcool, resolution · Report a mismatch
  4. [4] § MATERIALS AND METHODS › Bioinformatic analysis › CUT&RUN ↔ Fig_3/scripts/Boxplots_H4K16ac.Rmd, lines 25 to 124 · score 0.67 · MSL1 V5 peaks, boxplots, H4K16ac, deepTools, enrichment, bam · Report a mismatch
  5. [5] § RESULTS › Msl1 KO disrupts embryonic development ↔ scripts/download_public_data.py, lines 1 to 38 · score 0.61 · Single Cell Multiome, ATAC seq, RNA seq, accessible · Report a mismatch
  6. [6] § MATERIALS AND METHODS › Bioinformatic analysis › RNA sequencing ↔ Fig_4/scripts/GO_term_analysis.Rmd, lines 18 to 52 · score 0.59 · compareCluster, enrichGO, enriched, clusterProfiler, Genes · Report a mismatch
  7. [7] § RESULTS › MSL1 depletion impairs neuronal transcriptional programs and morphogenesis in vitro ↔ Fig_2/scripts/Heatmap_D14_seeding_densities.Rmd, lines 127 to 141 · score 0.57 · synapse assembly genes, seeding density, Figure 2 · Report a mismatch
  8. [8] § MATERIALS AND METHODS › Bioinformatic analysis › CUT&RUN ↔ Fig_3/scripts/RELACS_marks_on_MSL1_peaks.Rmd, lines 12 to 42 · score 0.56 · MSL1 V5 peaks, computeMatrix, bam, bed, mapping, filtered · Report a mismatch
  9. [9] § MATERIALS AND METHODS › Bioinformatic analysis › RNA sequencing ↔ Fig_4/scripts/MSLc_primed_genes_violin.Rmd, lines 47 to 76 · score 0.56 · DESeq2, log2fc, primed genes, auxin, thresholds, Padj · Report a mismatch
  10. [10] § RESULTS › Stage-specific MSLc activity governs neuronal maturation in vitro ↔ Fig_3/scripts/Boxplots_H4K16ac.Rmd, lines 25 to 124 · score 0.55 · MSL1 V5 peaks, H4K16ac, untreated, signals, treatment, AID · Report a mismatch
  11. [11] § MATERIALS AND METHODS › Bioinformatic analysis › UMI-4C ↔ UMI_4C/workflows/rules/scripts/counter.R, the whole file · a weak match · score 0.55 · contactsUMI4C, PAD, barcode, mm10, genome, cut · Report a mismatch
  12. [12] § MATERIALS AND METHODS › Bioinformatic analysis › Protein-protein interaction network analysis ↔ Fig_4/scripts/Protein_Interaction_Graph.Rmd, lines 22 to 36 · score 0.54 · STRINGdb, protein interactions, score · Report a mismatch
  13. [13] § MATERIALS AND METHODS › Bioinformatic analysis › RNA sequencing ↔ Fig_4/scripts/MSLc_primed_genes_violin.Rmd, lines 47 to 76 · score 0.50 · DESeq2, log2fc, primed genes, Padj, filtered, NPCs · Report a mismatch

Paper

Loaded from Europe PMC by your browser; OSCR does not store it: doi.org · Europe PMC

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The authors' code

R Markdown · 124 lines · 3.7 KB · MIT · 2 matches

  1. ---
  2. title: "Boxplots for H4K16ac signal in Peaks"
  3. author: "AF"
  4. date: "`r Sys.Date()`"
  5. output: html_document
  6. ---
  8. ```{r setup, include=FALSE}
  9. knitr::opts_chunk$set(echo = TRUE)
  10. ```
  12. ```{r}
  13. plot_save_as_svg <- function(plot, file_name) {
  14. dir.create(paste0(dirname(getSourceEditorContext()$path),"/../plots"), showWarnings = FALSE)
  15. file_save_name <- paste0(dirname(getSourceEditorContext()$path),"/../plots/",file_name,".svg")
  16. print(file_save_name)
  17. ggsave(file_save_name, plot, device = "svg",width=8.27,height=5.83,units="in")
  18. }
  22. ```
  25. I want to make boxplots of the H4K16ac signal isnide the MSL1 V5 peaks. To do this I first need to uncompress the H4K16ac matrices.
  28. #1kb around peak center and 50 bp binsize
  30. ```{r}
  31. library(ggplot2)
  32. library(rstudioapi)
  34. deeptools_matrix <- as.data.frame(read.table(file="/Volumes/akhtar-group4/foertsch/CUTRUN/1_4_24_NPC_CR_WT_KO_AID/snakeOut/deepTools_ChIP/bamCompare/heatmaps/matrix/uncompr_matrices/NPC_ctrl_V5_peaks_intersect_H4K16ac_500bp_bs50.tsv", skip = 1))
  37. rownames(deeptools_matrix) <- deeptools_matrix$V4
  38. deeptools_matrix <- deeptools_matrix[,-(1:6)]
  40. dim(deeptools_matrix)
  42. #separation of samples
  44. K16ac_ctrl_R1 <- deeptools_matrix[,1:20]
  45. K16ac_ctrl_R2 <- deeptools_matrix[,21:40]
  47. K16ac_4hAux_R1 <- deeptools_matrix[,41:60]
  48. K16ac_4hAux_R2 <- deeptools_matrix[,61:80]
  50. K16ac_24hAux_R1 <- deeptools_matrix[,81:100]
  51. K16ac_24hAux_R2 <- deeptools_matrix[,101:120]
  54. colnames(K16ac_ctrl_R1) <- seq(-450,500,50)
  55. colnames(K16ac_ctrl_R2) <- seq(-450,500,50)
  56. colnames(K16ac_4hAux_R1) <- seq(-450,500,50)
  57. colnames(K16ac_4hAux_R2) <- seq(-450,500,50)
  58. colnames(K16ac_24hAux_R1) <- seq(-450,500,50)
  59. colnames(K16ac_24hAux_R2) <- seq(-450,500,50)
  61. K16ac_ctrl_R1$sample <- "ctrl_R1"
  62. K16ac_ctrl_R2$sample <- "ctrl_R2"
  64. K16ac_4hAux_R1$sample <- "4hAux_R1"
  65. K16ac_4hAux_R2$sample <- "4hAux_R2"
  67. K16ac_24hAux_R1$sample <- "24hAux_R1"
  68. K16ac_24hAux_R2$sample <- "24hAux_R2"
  71. K16ac_ctrl_R1$condition <- "untreated"
  72. K16ac_ctrl_R2$condition <- "untreated"
  74. K16ac_4hAux_R1$condition <- "4hAux"
  75. K16ac_4hAux_R2$condition <- "4hAux"
  77. K16ac_24hAux_R1$condition <- "24hAux"
  78. K16ac_24hAux_R2$condition <- "24hAux"
  82. deeptools_matrix_rejoined <- rbind(K16ac_ctrl_R1,K16ac_ctrl_R2,K16ac_24hAux_R1,K16ac_24hAux_R2)
  83. #deeptools_matrix_rejoined_cut$sample <- deeptools_matrix_rejoined$sample
  86. data <- deeptools_matrix_rejoined
  87. sample_names <- data[, ncol(data)]
  89. # Remove the last column (sample names) from the data
  90. data <- data[, -ncol(data)]
  92. # Melt the data frame to long format
  93. melted_data <- reshape2::melt(data)
  95. # Add the sample names to the melted data
  96. melted_data$sample <- sample_names
  98. melted_data$sample <- factor(melted_data$sample, levels=c("untreated","24hAux"))
  101. plot <- ggplot(melted_data, aes( x= sample, y = value, fill= sample)) +
  102. geom_boxplot(notch = TRUE, outlier.shape = NA) + # Remove outlier arguments
  103. labs(y = expression(paste("Log"[2], "(Enrichment over IgG)")),fill="Treatment \ngroups") +
  104. scale_fill_manual( breaks = c("untreated", "24hAux"),
  105. values=c("untreated" = "#787575", "24hAux" = "#E84A1D"),
  106. labels=c("untreated","24h Auxin"))+
  107. scale_y_continuous(limits = c(-4.5, 4.5), breaks = c(-4.5,-3,-1.5,0,1.5,3,4.5)) +
  108. theme(axis.title.y = element_text(size=26),
  109. axis.title.x = element_text(size=0),
  110. axis.text.x = element_text(size=0,angle=0,vjust=-0.3,hjust=0.5),
  111. axis.text.y = element_text(size=26),
  112. legend.text = element_text( size = 18),
  113. legend.title = element_text( size = 24),
  114. strip.text.x = element_text(size = 20, angle = 0)) +
  115. theme_bw()
  118. plot
  120. #plot_save(plot,"H4K16ac_CR_signal_on_MSL1_V5_peaks_no_outliers","landscape")
  121. plot_save_as_svg(plot,"Boxplots_H4K16ac/H4K16ac_M1V5_peaks_clean")
  124. ```

Boxplots_H4K16ac.Rmd at commit 875df46, under MIT · at the source

Overview

Authors: Arion Foertsch1,2,3, Naama Hirsch1, Janine Seyfferth1, Ward Deboutte1, Herbert Holz1, Maria Shvedunova1, Remzi Karayol1, Thomas Stehle1, Laura Arrigoni1, Niyazi Umut Erdogdu1, Asifa Akhtar1,4
ORCID iDs: Arion Foertsch, Janine Seyfferth, Ward Deboutte, Herbert Holz, Maria Shvedunova, Remzi Karayol, Thomas Stehle, Laura Arrigoni, Niyazi Umut Erdogdu, Asifa Akhtar
  1. Max Planck Institute of Immunobiology and Epigenetics, 79108 Freiburg, Germany
  2. Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
  3. International Max Planck Research School for Epigenetics, Biophysics and Metabolism (IMPRS-EBM), 79108 Freiburg, Germany
  4. CIBSS Centre for Integrative Biological Signalling Studies, University of Freiburg, Schänzlestrasse 18, 79104 Freiburg, Germany
Institutions: University of Freiburg (Germany); Max Planck Institute of Immunobiology and Epigenetics (Germany)
Journal: Science advances, volume 12, issue 13, article eadz8889
Dates: received 18 June 2025; accepted 23 February 2026; published online 27 March 2026; in print March 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1126/sciadv.adz8889 · PMID 41894493 · PMCID PMC13025117 · OpenAlex W7141162112
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: mouse (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Connectivity, Graphs
MeSH: Cell Lineage*, Epigenesis, Genetic*, Gene Expression Regulation, Developmental*, Transcriptional Activation*, Animals, Cell Differentiation, Mice, Neural Stem Cells, Neurogenesis, Promoter Regions, Genetic (* major topic)
Journal subjects: Biomedicine and Life Sciences, Cell Biology, Genetics
Topic: Neurogenesis and neuroplasticity mechanisms (Developmental Neuroscience, Neuroscience), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (Germany’s Excellence Strategy (CIBSS – EXC-2189 – Project ID 390939984), SFB 1381 (Project ID 403222702)); Gottfried Wilhelm Leibniz Prize
Citations: not cited yet (Europe PMC); 63 references in the paper
Research resources: Henikoff RRID:Addgene_123461, RRID:IMSR_EM:13726

Abstract

Neural progenitor cells exhibit developmental plasticity as they can commit to distinct developmental trajectories. The male-specific lethal complex (MSLc) is linked to multiple developmental disorders, suggesting a role in neural fate commitment. To dissect MSLc function, we used a multipronged approach combining chronic and acute depletion models. Knockout of the MSLc scaffolding component MSL1 caused embryonic lethality by E10.5 (embryonic day 10.5), and single-cell multiomics revealed altered cell population composition across multiple germ layer–derived lineages, including neuroectoderm. Two-dimensional directed differentiation models showed that the MSLc facilitates accessibility at regulatory elements during early stages of neurogenesis. Neurodevelopmental genes displayed reduced enhancer-promoter contacts and failed to reach appropriate expression levels when the MSLc was absent early in neural differentiation. In contrast, MSLc loss at later stages did not recapitulate this phenotype, indicating that MSLc-mediated gene priming is a key mechanism enabling timely activation of lineage-specifying transcriptional programs.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 13 matches between paragraphs and lines of code.

foertsch/MSLc_Gene_Priming_public

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 875df4668364b9c63c9a877a1586d80cd7982255, 23 March 2026
Languages: R (35), Python (13), Jupyter (2)
Size: 217 files, 50 scripts
Software Heritage: not archived
Found in: “Data, code, and materials availability:”
Holds: README, license file, environment (MicroC/conf/env.yml, UMI_4C/conf/env.yml), 31 notebooks
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: tidyverse (25 files), ggplot2 (15 files), clusterProfiler (10 files), pandas (9 files), NumPy (7 files), circlize (5 files), ComplexHeatmap (5 files), Seurat (5 files), Matplotlib (4 files), reshape2 (4 files), seaborn (4 files), deepTools (2 files), Snakemake (2 files), ggpubr (1 file), igraph (1 file), SciPy (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
52 files
At the source: github.com/foertsch/MSLc_Gene_Priming_public

Zenodo 15471244

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 3 files
Software Heritage: not checked
Found in: “Data, code, and materials availability:”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
At the source: zenodo.org/records/15471244

The paper's code and data availability statement is in the Data section.

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Data

No dataset and no data link were found in the paper.

Data, code, and materials availability

All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. All sequencing datasets generated in this study have been deposited in the Gene Expression Omnibus under the following accession numbers: UMI-4C (GSE298000), ATAC-seq (GSE298001), CUT&RUN (GSE298002), RELACS (GSE298005), RNA-seq (GSE298006), Multiome (GSE298008), and Micro-C (GSE298010). Previously published software packages are detailed in the Materials and Methods section including parameters used for analysis. All custom code to create the figures is available at GitHub (https://github.com/foertsch/MSLc_Gene_Priming_public) and Zenodo (https://zenodo.org/records/15471244). Materials generated during this study will be made available upon reasonable request to the corresponding author.

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

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 10 MeSH terms, 2 funders, 62 references, 2 RRIDs.

Cite

This paper

Foertsch, A., Hirsch, N., Seyfferth, J., Deboutte, W., Holz, H., Shvedunova, M., Karayol, R., Stehle, T., Arrigoni, L., Erdogdu, N. U., & Akhtar, A. (2026). Stage-specific epigenetic priming amplifies gene activation during lineage commitment. Science advances, 12(13), eadz8889. https://doi.org/10.1126/sciadv.adz8889

BibTeX

@article{foertsch2026stage,
author = {Foertsch, Arion and Hirsch, Naama and Seyfferth, Janine and Deboutte, Ward and Holz, Herbert and Shvedunova, Maria and Karayol, Remzi and Stehle, Thomas and Arrigoni, Laura and Erdogdu, Niyazi Umut and Akhtar, Asifa},
title = {{Stage-specific epigenetic priming amplifies gene activation during lineage commitment}},
journal = {Science advances},
year = {2026},
month = mar,
volume = {12},
number = {13},
pages = {eadz8889},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.adz8889},
url = {https://doi.org/10.1126/sciadv.adz8889},
pmid = {41894493},
pmcid = {PMC13025117}
}

RIS

TY - JOUR
AU - Foertsch, Arion
AU - Hirsch, Naama
AU - Seyfferth, Janine
AU - Deboutte, Ward
AU - Holz, Herbert
AU - Shvedunova, Maria
AU - Karayol, Remzi
AU - Stehle, Thomas
AU - Arrigoni, Laura
AU - Erdogdu, Niyazi Umut
AU - Akhtar, Asifa
TI - Stage-specific epigenetic priming amplifies gene activation during lineage commitment
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/03/27
VL - 12
IS - 13
SP - eadz8889
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.adz8889
UR - https://doi.org/10.1126/sciadv.adz8889
LA - en
ER -

CSL-JSON

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