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A scalable Tn5-based method for genome-wide DNA methylation profiling in development and disease.

Code ↔ Paper

18 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 18 matches · 4 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › EM-seq quantification of DNA methylation reduction upon inhibitor treatment ↔ figures/extfig5a_methylkit_bpDNAm.R, lines 45–80 · score 0.95 · bismarkCoverage, hi.perc, lo.count, lo.perc, methRead, methylKit
  2. [2] § Methods › EM-seq quantification of DNA methylation reduction upon inhibitor treatment ↔ figures/fig4e_methylKit_sankeyplot.R, lines 1–43 · score 0.90 · bismarkCoverage, hi.perc, lo.count, lo.perc, methRead, MethylDackel
  3. [3] § Methods › Chromatin state and ChIPseeker annotation of CUT&Tag peaks ↔ figures/extfig2e_chipseeker_addcpg.R, lines 21–60 · score 0.73 · gene body, CpG islands, UTRs, introns, Exons, ChIPseeker
  4. [4] § Methods › Preprocessing of CmeCUT&Tag-BS/EM ↔ snakePipes_methCnT/snakePipes_WGBS.sh, the whole file · a weak match · score 0.71 · WGBS pipeline, snakePipes, peak calling, aligner, BAM
  5. [5] § Methods › DNA methylation profiling in differential regions with CmeCUT&Tag-BS ↔ figures/fig4e_methylKit_sankeyplot.R, lines 45–104 · score 0.71 · methylKit, percMethylation, DiffBind regions, mid, 80 %, 20 %
  6. [6] § Methods › CG count and methylation profiling of CUT&Tag peaks ↔ figures/fig1f_enrichment_vs_WGBS.py, lines 118–120 · score 0.69 · 10–20 %, 90–100 %, bins, enrichment
  7. [7] § Methods › Pre-processing, alignment, and normalization of CUT&Tag data ↔ snakePipes_methCnT/snakePipes_WGBS.sh, the whole file · a weak match · score 0.69 · bamCoverage, snakePipes, aligner, deepTools, spike, genome
  8. [8] § Methods › Chromatin state and ChIPseeker annotation of CUT&Tag peaks ↔ utils/annotate_peaksets.R, lines 1–42 · score 0.68 · annoDb, annotatePeak, TSS, hs, gene, overlap
  9. [9] § Results › MBD–Tn5 fusion design for DNA methylation targeting ↔ figures/fig1e_plot_distributions.py, lines 44–67 · score 0.66 · xMeCP2, xMBD2, MBD1, NTD, IDR
  10. [10] § Results › MBD–Tn5 enriches CpG-rich methylated regions in iPSCs ↔ utils/intervene_constructs.sh, the whole file · a weak match · score 0.63 · xMeCP2, xMBD2, NTD, IDR, CpG, nuclei
  11. [11] § Methods › DNA methylation profiling in differential regions with CmeCUT&Tag-BS ↔ figures/extfig5a_methylkit_bpDNAm.R, lines 45–80 · score 0.63 · methylKit, percMethylation, DNAme, minCov, coverage
  12. [12] § Results › MBD–Tn5 fusion design for DNA methylation targeting ↔ utils/intervene_constructs.sh, the whole file · a weak match · score 0.62 · xMeCP2, xMBD2, NTD, IDR, DNA
  13. [13] § Results › MBD–Tn5 enriches CpG-rich methylated regions in iPSCs ↔ figures/fig1e_plot_distributions.py, lines 44–67 · score 0.61 · xMeCP2, xMBD2, NTD, IDR, CpG
  14. [14] § Results › MBD–Tn5 fusion design for DNA methylation targeting ↔ figures/extfig2e_chipseeker_addcpg.R, lines 96–138 · score 0.60 · MBDseq, MeCP2, RRBS, H9, MeDIP, histone
  15. [15] § Methods › crossNN prediction of tumor biopsies ↔ figures/fig4e_methylKit_sankeyplot.R, lines 1–43 · score 0.58 · Bismark coverage, MethylDackel, BS, hg38, filtering
  16. [16] § Results › MBD–Tn5 enriches CpG-rich methylated regions in iPSCs ↔ figures/extfig2e_chipseeker_addcpg.R, lines 21–60 · score 0.58 · gene bodies, CpG island, exons, promoters, overlap
  17. [17] § Methods › Single-cell CmeCUT&Tag ↔ sc_CmeCUT-Tag/sc_cme_cut-tag.Rmd, lines 194–197 · score 0.57 · FindClusters, FindNeighbors, clustering, Tag
  18. [18] § Methods › Comparison of MBD constructs ↔ figures/extfig2e_chipseeker_addcpg.R, lines 1–19 · score 0.53 · CpG island, UCSC, track, Browser, hg38, Genome

Paper

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The authors' code

R · 140 lines · 5.1 KB · no license · 4 matches

  1. # Load libraries
  2. library(ChIPseeker)
  3. library(org.Hs.eg.db)
  4. library(GenomicFeatures)
  5. library(dplyr)
  6. library(rtracklayer)
  7. library(clusterProfiler)
  8. library(stringr)
  9. library(ggplot2)
  10. library(purrr)
  11. txdb_ensembl <- makeTxDbFromEnsembl(organism="Homo sapiens", release='113')
  13. # Load CpG island annotation (see https://github.com/Bioconductor/AnnotationHub/issues/42)
  14. library(rtracklayer)
  15. session <- browserSession()
  16. genome(session) <- "hg38"
  17. query <- ucscTableQuery(session, table="cpgIslandExt")
  18. cpg_islands <- track(query) # GRanges object
  19. seqlevelsStyle(cpg_islands) <- seqlevelsStyle(txdb_ensembl)
  21. # Define a helper function to customize the annotation dataframe
  22. customize_anno <- function(peakAnno){
  23. # extract the annotation
  24. df <- peakAnno@anno %>% as.data.frame()
  25. # clean annotation
  26. df2 <- df %>%
  27. mutate(
  28. # remove trailing "(<=1kb)" etc
  29. annotation_minimal = annotation %>%
  30. str_remove_all("\\(.*?\\)") %>% # remove any parentheses content
  31. str_remove_all("\\s"),
  32. # extract numbers if present: "exon 1 of", "intron 2 of", etc.
  33. exon_no = str_extract(annotation, "(?<=exon )\\d+"),
  34. intron_no = str_extract(annotation, "(?<=intron )\\d+"),
  35. annotation_keepfirst = case_when(
  36. !is.na(exon_no) & exon_no == "1" ~ "1stExon",
  37. !is.na(exon_no) ~ "OtherExon",
  38. !is.na(intron_no) & intron_no == "1" ~ "1stIntron",
  39. !is.na(intron_no) ~ "OtherIntron",
  40. TRUE ~ annotation_minimal
  41. ),
  42. annotation_simple = case_when(
  43. str_detect(annotation, "Promoter") ~ "Promoter",
  44. str_detect(annotation, "exon|UTR|intron") ~ "GeneBody",
  45. TRUE ~ "Intergenic"
  46. )
  47. )
  49. # add cpg island info
  50. gr <- as.GRanges(peakAnno)
  51. hits <- findOverlaps(gr, cpg_islands)
  52. cpg_flag <- rep("-CpGisland", length(gr))
  53. cpg_flag[unique(queryHits(hits))] <- "+CpGisland"
  55. df2$CpG_island <- cpg_flag
  56. df2$annotation_minimal_CpG <- interaction(df2$annotation_minimal, df2$CpG_island, sep = '')
  57. df2$annotation_keepfirst_CpG <- interaction(df2$annotation_keepfirst, df2$CpG_island, sep = '')
  58. df2$annotation_simple_CpG <- interaction(df2$annotation_simple, df2$CpG_island, sep = '')
  59. return(df2)
  60. }
  63. # read anno
  64. peakAnnoList_all <- readRDS('all_peakAnnoList.rds')
  67. dfs <- lapply(peakAnnoList_all, customize_anno)
  68. saveRDS(dfs, "all_peakAnno_annotDFs.rds")
  70. # plot annotation of the list
  71. plot_annotation_bar <- function(dfs, col="annotation_simple_CpG"){
  72. if (is.null(names(dfs))) names(dfs) <- paste0("dataset_", seq_along(dfs))
  74. summarize_categories <- function(df, dataset, col) {
  75. df %>%
  76. count(.data[[col]], name = "n") %>%
  77. mutate(dataset = dataset, prop = n / sum(n)) %>%
  78. rename(category = !!col)
  79. }
  81. anno_bar_df <- map2_dfr(dfs, names(dfs), summarize_categories, col)
  83. p <- ggplot(anno_bar_df, aes(x = dataset, y = prop, fill = category)) +
  84. geom_bar(stat = "identity", position = "fill") +
  85. scale_y_continuous(labels = scales::percent_format()) +scale_fill_brewer(palette = "Set2") +
  86. labs(x = NULL, y = "Proportion", fill = "Annotation",
  87. title = "Annotation composition across datasets") +
  88. coord_flip() +
  89. theme_bw()
  90. return(list(
  91. anno_bar_df = anno_bar_df,
  92. plot=p
  93. ))
  94. }
  96. anno <- plot_annotation_bar(dfs)
  97. anno$plot
  99. # Subset for more meaningful plots
  100. histones <- dfs[names(dfs) %>% str_detect("H3")]
  101. cpg <- dfs[names(dfs) %>% str_detect("cpg")]
  102. bs <- dfs[names(dfs) %>% str_detect("ES")]
  103. cg_bs <- bs[names(bs) %>% str_detect("CG")]
  104. ch_bs<- bs[names(bs) %>% str_detect("CH")]
  105. rrbs <- bs[names(bs) %>% str_detect("RRBS")]
  106. high_bs <- dfs[names(dfs) %>% str_detect("CGN.h9_hESC_80-101|ES_80-101")]
  107. mbdseq <- dfs[names(dfs) %>% str_detect("MBDseq")]
  108. medip <- dfs[names(dfs) %>% str_detect("roadmap")]
  109. nuclei_constructs <- dfs[c('nuclei_2xMBD2.broadPeak_optimal','nuclei_2xMeCP2.broadPeak_optimal','nuclei_4xMeCP2.broadPeak_optimal','nuclei_NTD-MeCP2-IDR.broadPeak_optimal')]
  110. gDNA_constructs <- dfs[c('gDNA_2xMBD2.broadPeak_optimal','gDNA_2xMeCP2.broadPeak_optimal','gDNA_4xMeCP2.broadPeak_optimal','gDNA_NTD-MeCP2-IDR.broadPeak_optimal')]
  111. mbd2 <- dfs[c('gDNA_2xMBD2.broadPeak_optimal','nuclei_2xMBD2.broadPeak_optimal')]
  112. mbd2_intersect <- dfs[names(dfs) %>% str_detect("MBD2")]
  114. selected <- c(
  115. high_bs, cpg, histones, nuclei_constructs
  116. )
  117. names(selected) <- sub(".broadPeak_optimal", "", names(selected))
  118. pdf('cutomized_highBS_histone_nucleiConstructs.pdf',width = 8, height = 6)
  119. plot_annotation_bar(selected)$plot
  120. dev.off()
  122. selected <- c(
  123. cg_bs, rrbs, mbdseq, medip, mbd2
  124. )
  125. names(selected) <- sub(".broadPeak_optimal", "", names(selected))
  126. names(selected) <- sub("_chipr_optimal_filtered", "", names(selected))
  127. pdf('cutomized_MethCnT_MeDIP_MBDseq.pdf',width = 8, height = 6)
  128. plot_annotation_bar(selected)$plot
  129. dev.off()
  131. selected <- c(
  132. high_bs, mbdseq, medip, mbd2
  133. )
  134. names(selected) <- sub(".broadPeak_optimal", "", names(selected))
  135. names(selected) <- sub("_chipr_optimal_filtered", "", names(selected))
  136. pdf('cutomized_high_MethCnT_MeDIP_MBDseq.pdf', width = 8, height = 6)
  137. plot_annotation_bar(selected)$plot
  138. dev.off()

extfig2e_chipseeker_addcpg.R at commit f0f1063, no license · at the source

Overview

Authors: Hanrong Hu1,2, Nahuel Simonet1,2, Ece Naz Bilgiç1,2, Heather Murray1,2, Regina Reimann3, Markus Rechsteiner3, Fides Zenk1,2
ORCID iDs: Hanrong Hu, Nahuel Simonet, Ece Naz Bilgiç, Fides Zenk
  1. Ecole Polytechnique Federale de Lausanne (EPFL), School of Life Sciences, Brain Mind Institute, EpiGN—NeuroNA Chair in Epigenomics of Neurodevelopmental Disorders, Station 19, Lausanne, Switzerland
  2. Campus Biotech, Chemin des Mines 9, Geneve, Switzerland
  3. UniversitätsSpital Zürich, Schmelzbergstrasse 12, Zürich, Switzerland
Institutions: École Polytechnique Fédérale de Lausanne (Switzerland); University Hospital Zurich (Switzerland)
Journal: Nature communications, volume 17, issue 1, article 6736
Dates: received 31 August 2025; accepted 8 May 2026; published online 22 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-73325-4 · PMID 42173849 · PMCID PMC13385352 · OpenAlex W7162112536
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), zebrafish (organism), developmental (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions
Keywords: Epigenomics, Methylation analysis
MeSH: DNA Methylation*, Animals, Brain Neoplasms, Epigenesis, Genetic, High-Throughput Nucleotide Sequencing, Humans, Sequence Analysis, DNA, Zebrafish (* major topic)
Topic: Epigenetics and DNA Methylation (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Swiss National Science Foundation (218299)
Citations: not cited yet (Europe PMC); 66 references in the paper

Abstract

DNA methylation is a key epigenetic modification involved in development and disease, including cancer, and serves as a biomarker for diagnosis. Current detection methods, such as bisulfite sequencing, provide base-pair resolution but require high sequencing depth and cost. Here, we developed CmeCUT&Tag, a Tn5-based approach that uses methylation-binding domain fusion proteins to selectively target methylated DNA in chromatinized and isolated DNA. This enables adapter insertion into methylated regions, allowing low-depth sequencing for quantitative analysis or optional cytosine conversion for base-pair resolution. CmeCUT&Tag enables genome-wide DNA methylation profiling with reduced input and sequencing requirements. We demonstrate its performance in characterizing DNA methylation across development and disease in human stem cells, organoids, zebrafish embryogenesis, and tumor biopsies. The method shows strong concordance with bisulfite sequencing and supports the classification of brain tumor samples into methylation subtypes. These features make CmeCUT&Tag a scalable and cost-effective approach for epigenetic research and potential clinical applications.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 18 matches between paragraphs and lines of code.

EpiGN-EPFL/CmeCUT-Tag

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: f0f106319447018fc16798402e2051cbb7af3af7, 26 February 2026
Languages: R (8), Python (5), Shell (5)
Size: 37 files, 18 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (8 files), ggplot2 (7 files), pandas (6 files), Matplotlib (5 files), NumPy (5 files), reshape2 (4 files), seaborn (4 files), clusterProfiler (2 files), pheatmap (2 files), patchwork (1 file), SAMtools (1 file), scikit-learn (1 file), Seurat (1 file), Snakemake (1 file), Subread (featureCounts) (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
19 files
At the source: github.com/EpiGN-EPFL/CmeCUT-Tag

Zenodo 19555724

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (8 files), ggplot2 (7 files), pandas (6 files), Matplotlib (5 files), NumPy (5 files), reshape2 (4 files), seaborn (4 files), clusterProfiler (2 files), pheatmap (2 files), patchwork (1 file), SAMtools (1 file), scikit-learn (1 file), Seurat (1 file), Snakemake (1 file), Subread (featureCounts) (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
19 files
At the source:

Code availability

All generated code is available on GitHub: https://github.com/EpiGN-EPFL/CmeCUT-Tag and on Zenodo: 10.5281/zenodo.19555724.

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

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;
  • 36 scripts, each with its path and the digest of its content;
  • 18 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 Statement

The data supporting the findings of this study are available from the corresponding authors upon request. The data generated in this study have been deposited in the GEO database under accession code GSE320203 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE320203). Source data for the figures and Supplementary Figs. are provided as a Source Data file. Previously published data used in this paper include: GSE25970 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE25970]). GSE82022 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE82022). GSE150122 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE150122). GSE158089 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE158089). GSE159071 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE159071). GSE16368 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16368). GSE203377 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE203377). GSE179673 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE179673). GSE35050 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE35050). GSE70847 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70847). For details of the publicly available datasets analyzed in this study, please refer to the “Methods” section. Source data are provided with this paper.

All generated code is available on GitHub: https://github.com/EpiGN-EPFL/CmeCUT-Tag and on Zenodo: 10.5281/zenodo.19555724.

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

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 2 keywords, 8 MeSH terms, 1 funder, 64 references.

Cite

This paper

Hu, H., Simonet, N., Bilgiç, E. N., Murray, H., Reimann, R., Rechsteiner, M., & Zenk, F. (2026). A scalable Tn5-based method for genome-wide DNA methylation profiling in development and disease. Nature communications, 17(1), 6736. https://doi.org/10.1038/s41467-026-73325-4

BibTeX

@article{hu2026scalable,
author = {Hu, Hanrong and Simonet, Nahuel and Bilgiç, Ece Naz and Murray, Heather and Reimann, Regina and Rechsteiner, Markus and Zenk, Fides},
title = {{A scalable Tn5-based method for genome-wide DNA methylation profiling in development and disease}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {6736},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73325-4},
url = {https://doi.org/10.1038/s41467-026-73325-4},
pmid = {42173849},
pmcid = {PMC13385352}
}

RIS

TY - JOUR
AU - Hu, Hanrong
AU - Simonet, Nahuel
AU - Bilgiç, Ece Naz
AU - Murray, Heather
AU - Reimann, Regina
AU - Rechsteiner, Markus
AU - Zenk, Fides
TI - A scalable Tn5-based method for genome-wide DNA methylation profiling in development and disease
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/22
VL - 17
IS - 1
SP - 6736
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73325-4
UR - https://doi.org/10.1038/s41467-026-73325-4
LA - en
ER -

CSL-JSON

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