CoChat
Sign Up
Cited by 154Genomics and Chromatin DynamicsChromatin Remodeling and CancerEpigenetics and DNA Methylation

Occupancy maps of 208 chromatin-associated proteins in one human cell type

Open access
Partridge, E. Christopher, Chhetri, Surya B., Prokop, Jeremy W., Ramaker, Ryne C., Jansen, Camden S., Goh, Say-Tar, Mackiewicz, Mark, Newberry, Kimberly M. +12 more·2020·Nature
DOI 10.1038/s41586-020-2023-4PMC PMC7398277
Open this paper in CoChat — free account

Abstract

Transcription factors are DNA-binding proteins that have key roles in gene regulation 1,2 . Genome-wide occupancy maps of transcriptional regulators are important for understanding gene regulation and its effects on diverse biological processes 3–6 . However, only a minority of the more than 1,600 transcription factors encoded in the human genome has been assayed. Here we present, as part of the ENCODE (Encyclopedia of DNA Elements) project, data and analyses from chromatin immunoprecipitation followed by high-throughput sequencing (ChIP–seq) experiments using the human HepG2 cell line for 208 chromatin-associated proteins (CAPs). These comprise 171 transcription factors and 37 transcriptional cofactors and chromatin regulator proteins, and represent nearly one-quarter of CAPs expressed in HepG2 cells. The binding profiles of these CAPs form major groups associated predominantly with promoters or enhancers, or with both. We confirm and expand the current catalogue of DNA sequence motifs for transcription factors, and describe motifs that correspond to other transcription factors that are co-enriched with the primary ChIP target. For example, FOX family motifs are enriched in ChIP–seq peaks of 37 other CAPs. We show that motif content and occupancy patterns can distinguish between promoters and enhancers. This catalogue reveals high-occupancy target regions at which many CAPs associate, although each contains motifs for only a minority of the numerous associated transcription factors. These analyses provide a more complete overview of the gene regulatory networks that define this cell type, and demonstrate the usefulness of the large-scale production efforts of the ENCODE Consortium.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41586-020-2023-4

Cites

11 of 11 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Using CRISPR epitope tagging of DNMT3B revealed DNMT3B signals decreased along the gene body, indicating a loss of the repressive epigenetic marks along the gene body.

    “Using CRISPR epitope tagging (through insertion) of DNMT3B60 revealed DNMT3B signals decreased along the gene body (Supplementary Fig. 5c), indicating a loss of the repressive epigenetic marks along the gene body.”

    From Alternative promoters in CpG depleted regions are prevalently associated with epigenetic misregulation of liver cancer transcriptomes (2023) · cites this paper as [60]

    The Methods (ChIP–seq and CETCh–seq; page unavailable) describe experiments with epitope-tagged chromatin-associated proteins, supporting the tagging-method portion of the sentence. The passages found do not identify DNMT3B as a tagged protein or report decreasing DNMT3B signal along gene bodies or a loss of repressive marks; the citing sentence points to its own Supplementary Fig. 5c for that observation. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 65

  1. [2]Lambert, S. A. et al. The human transcription factors. Cell 172, 650–665 (2018). 29425488 PMC12908702
  2. [4]Busskamp, V. et al. Rapid neurogenesis through transcriptional activation in human stem cells. Mol. Syst. Biol. 10, 760 (2014). 25403753 PMC4299601
  3. [5]Chen, X. et al. Integration of external signaling pathways with the core transcriptional network in embryonic stem cells. Cell 133, 1106–1117 (2008). 18555785
  4. [6]Iwafuchi-Doi, M. & Zaret, K. S. Pioneer transcription factors in cell reprogramming. Genes Dev. 28, 2679–2692 (2014). 25512556 PMC4265672
  5. [7]Wingender, E., Schoeps, T. & Dönitz, J. TFClass: an expandable hierarchical classification of human transcription factors. Nucleic Acids Res. 41, D165–D170 (2013). 23180794 PMC3531165
  6. [8]Weirauch, M. T. et al. Determination and inference of eukaryotic transcription factor sequence specificity. Cell 158, 1431–1443 (2014). 25215497 PMC4163041
  7. [9]Cowper-Sal-lari, R. et al. Breast cancer risk-associated SNPs modulate the affinity of chromatin for FOXA1 and alter gene expression. Nat. Genet. 44, 1191–1198 (2012). 23001124 PMC3483423
  8. [10]Dror, I., Golan, T., Levy, C., Rohs, R. & Mandel-Gutfreund, Y. A widespread role of the motif environment in transcription factor binding across diverse protein families. Genome Res. 25, 1268–1280 (2015). 26160164 PMC4561487
  9. [11]Dasen, J. S., Tice, B. C., Brenner-Morton, S. & Jessell, T. M. A Hox regulatory network establishes motor neuron pool identity and target-muscle connectivity. Cell 123, 477–491 (2005). 16269338
  10. [12]Black, J. B. et al. Targeted epigenetic remodeling of endogenous loci by CRISPR/Cas9-based transcriptional activators directly converts fibroblasts to neuronal cells. Cell Stem Cell 19, 406–414 (2016). 27524438 PMC5010447
  11. [13]Visel, A. et al. ChIP–seq accurately predicts tissue-specific activity of enhancers. Nature 457, 854–858 (2009). 19212405 PMC2745234
  12. [14]Johnson, D. S., Mortazavi, A., Myers, R. M. & Wold, B. Genome-wide mapping of in vivo protein-DNA interactions. Science 316, 1497–1502 (2007). 17540862
  13. [15]Mikkelsen, T. S. et al. Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature 448, 553–560 (2007). 17603471 PMC2921165
  14. [16]Robertson, G. et al. Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nat. Methods 4, 651–657 (2007). 17558387
  15. [17]Savic, D. et al. CETCh–seq: CRISPR epitope tagging ChIP–seq of DNA-binding proteins. Genome Res. 25, 1581–1589 (2015). 26355004 PMC4579343
  16. [18]Partridge, E. C., Watkins, T. A. & Mendenhall, E. M. Every transcription factor deserves its map: scaling up epitope tagging of proteins to bypass antibody problems. BioEssays 38, 801–811 (2016). 27311628
  17. [19]Zhang, Y., An, L., Yue, F. & Hardison, R. C. Jointly characterizing epigenetic dynamics across multiple human cell types. Nucleic Acids Res. 44, 6721–6731 (2016). 27095202 PMC5772166
  18. [20]Gerstein, M. B. et al. Architecture of the human regulatory network derived from ENCODE data. Nature 489, 91–100 (2012). 22955619 PMC4154057
  19. [21]Mendenhall, E. M. et al. GC-rich sequence elements recruit PRC2 in mammalian ES cells. PLoS Genet. 6, e1001244 (2010). 21170310 PMC3000368
  20. [23]Dao, L. T. M. et al. Genome-wide characterization of mammalian promoters with distal enhancer functions. Nat. Genet. 49, 1073–1081 (2017). 28581502
  21. [24]Andersson, R., Sandelin, A. & Danko, C. G. A unified architecture of transcriptional regulatory elements. Trends Genet. 31, 426–433 (2015). 26073855
  22. [25]Sandelin, A., Alkema, W., Engström, P., Wasserman, W. W. & Lenhard, B. JASPAR: an open-access database for eukaryotic transcription factor binding profiles. Nucleic Acids Res. 32, D91–D94 (2004). 14681366 PMC308747
  23. [26]Mathelier, A. et al. JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 44, D110–D115 (2016). 26531826 PMC4702842
  24. [27]Oliphant, A. R., Brandl, C. J. & Struhl, K. Defining the sequence specificity of DNA-binding proteins by selecting binding sites from random-sequence oligonucleotides: analysis of yeast GCN4 protein. Mol. Cell. Biol. 9, 2944–2949 (1989). 2674675 PMC362762
  25. [28]Worsley Hunt, R. & Wasserman, W. W. Non-targeted transcription factors motifs are a systemic component of ChIP–seq datasets. Genome Biol. 15, 412 (2014). 25070602 PMC4165360
  26. [29]Jolma, A. et al. DNA-binding specificities of human transcription factors. Cell 152, 327–339 (2013). 23332764
  27. [30]Morgunova, E. & Taipale, J. Structural perspective of cooperative transcription factor binding. Curr. Opin. Struct. Biol. 47, 1–8 (2017). 28349863
  28. [31]Wei, B. et al. A protein activity assay to measure global transcription factor activity reveals determinants of chromatin accessibility. Nat. Biotechnol. 36, 521–529 (2018). 29786094
  29. [32]Mortazavi, A. et al. Integrating and mining the chromatin landscape of cell-type specificity using self-organizing maps. Genome Res. 23, 2136–2148 (2013). 24170599 PMC3847782
  30. [33]Longabaugh, W. J. R. et al. Bcl11b and combinatorial resolution of cell fate in the T-cell gene regulatory network. Proc. Natl Acad. Sci. USA 114, 5800–5807 (2017). 28584128 PMC5468679
  31. [34]Whyte, W. A. et al. Enhancer decommissioning by LSD1 during embryonic stem cell differentiation. Nature 482, 221–225 (2012). 22297846 PMC4144424
  32. [35]Liang, Z. et al. A high-resolution map of transcriptional repression. eLife 6, e22767 (2017). 28318487 PMC5373822
  33. [36]Zhang, Y. et al. Analysis of the NuRD subunits reveals a histone deacetylase core complex and a connection with DNA methylation. Genes Dev. 13, 1924–1935 (1999). 10444591 PMC316920
  34. [37]Ramírez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016). 27079975 PMC4987876 · DOI · Paper page
  35. [39]Faherty, N. et al. Negative autoregulation of BMP dependent transcription by SIN3B splicing reveals a role for RBM39. Sci. Rep. 6, 28210 (2016). 27324164 PMC4914931
  36. [40]Choi, W. I. et al. Promyelocytic leukemia zinc finger-retinoic acid receptor α (PLZF-RARα), an oncogenic transcriptional repressor of cyclin-dependent kinase inhibitor 1A (p21WAF/CDKN1A) and tumor protein p53 (TP53) genes. J. Biol. Chem. 289, 18641–18656 (2014). 24821728 PMC4081909
  37. [42]Günther, K. et al. Differential roles for MBD2 and MBD3 at methylated CpG islands, active promoters and binding to exon sequences. Nucleic Acids Res. 41, 3010–3021 (2013). 23361464 PMC3597697
  38. [45]Hervouet, E., Vallette, F. M. & Cartron, P. F. Dnmt3/transcription factor interactions as crucial players in targeted DNA methylation. Epigenetics 4, 487–499 (2009). 19786833
  39. [46]Boyle, A. P. et al. Comparative analysis of regulatory information and circuits across distant species. Nature 512, 453–456 (2014). 25164757 PMC4336544
  40. [47]Gerstein, M. B. et al. Integrative analysis of the Caenorhabditis elegans genome by the modENCODE project. Science 330, 1775–1787 (2010). 21177976 PMC3142569
  41. [48]Moorman, C. et al. Hotspots of transcription factor colocalization in the genome of Drosophila melanogaster. Proc. Natl Acad. Sci. USA 103, 12027–12032 (2006). 16880385 PMC1567692
  42. [49]Wreczycka, K. et al. HOT or not: examining the basis of high-occupancy target regions. Nucleic Acids Res. 47, 5735–5745 (2019). 31114922 PMC6582337
  43. [50]Shin, H., Liu, T., Duan, X., Zhang, Y. & Liu, X. S. Computational methodology for ChIP–seq analysis. Quant. Biol. 1, 54–70 (2013). 25741452 PMC4346130
  44. [51]Teytelman, L., Thurtle, D. M., Rine, J. & van Oudenaarden, A. Highly expressed loci are vulnerable to misleading ChIP localization of multiple unrelated proteins. Proc. Natl Acad. Sci. USA 110, 18602–18607 (2013). 24173036 PMC3831989
  45. [52]Sherwood, R. I. et al. Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape. Nat. Biotechnol. 32, 171–178 (2014). 24441470 PMC3951735
  46. [53]Panne, D., Maniatis, T. & Harrison, S. C. An atomic model of the interferon-β enhanceosome. Cell 129, 1111–1123 (2007). 17574024 PMC2020837
  47. [54]Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009). 19451168 PMC2705234
  48. [55]Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009). 19505943 PMC2723002
  49. [56]Landt, S. G. et al. ChIP–seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 22, 1813–1831 (2012). 22955991 PMC3431496
  50. [57]Kharchenko, P. V., Tolstorukov, M. Y. & Park, P. J. Design and analysis of ChIP–seq experiments for DNA-binding proteins. Nat. Biotechnol. 26, 1351–1359 (2008). 19029915 PMC2597701
  51. [58]Worsley Hunt, R., Mathelier, A., Del Peso, L. & Wasserman, W. W. Improving analysis of transcription factor binding sites within ChIP–seq data based on topological motif enrichment. BMC Genomics 15, 472 (2014). 24927817 PMC4082612
  52. [59]Teng, M. & Irizarry, R. A. Accounting for GC-content bias reduces systematic errors and batch effects in ChIP–seq data. Genome Res. 27, 1930–1938 (2017). 29025895 PMC5668949
  53. [60]Machanick, P. & Bailey, T. L. MEME-ChIP: motif analysis of large DNA datasets. Bioinformatics 27, 1696–1697 (2011). 21486936 PMC3106185
  54. [61]Ma, W., Noble, W. S. & Bailey, T. L. Motif-based analysis of large nucleotide data sets using MEME-ChIP. Nat. Protocols 9, 1428–1450 (2014). 24853928 PMC4175909
  55. [62]Bailey, T. L. & Machanick, P. Inferring direct DNA binding from ChIP–seq. Nucleic Acids Res. 40, e128 (2012). 22610855 PMC3458523
  56. [63]Gupta, S., Stamatoyannopoulos, J. A., Bailey, T. L. & Noble, W. S. Quantifying similarity between motifs. Genome Biol. 8, R24 (2007). 17324271 PMC1852410
  57. [64]Uhlén, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015). 25613900
  58. [65]Dale, R. K., Pedersen, B. S. & Quinlan, A. R. Pybedtools: a flexible Python library for manipulating genomic datasets and annotations. Bioinformatics 27, 3423–3424 (2011). 21949271 PMC3232365
  59. [66]Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010). 20110278 PMC2832824
  60. [67]Fletez-Brant, C., Lee, D., McCallion, A. S. & Beer, M. A. kmer-SVM: a web server for identifying predictive regulatory sequence features in genomic data sets. Nucleic Acids Res. 41, W544–W556 (2013). 23771147 PMC3692045
  61. [71]Quinlan, A. R. BEDTools: the swiss-army tool for genome feature analysis. Curr. Protoc. Bioinformatics 47, 11.12.11–11.12.34 (2014). PMC4213956 25199790
  62. [72]Ghandi, M. et al. gkmSVM: an R package for gapped-kmer SVM. Bioinformatics 32, 2205–2207 (2016). 27153639 PMC4937197
  63. [73]Pedregosa, F. et al. Scikit-learn: machine learning in Python. JMLR 12, 2825–2830 (2011).
  64. [75]Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016). 27207943
  65. [77]Mellacheruvu, D. et al. The CRAPome: a contaminant repository for affinity purification-mass spectrometry data. Nat. Methods 10, 730–736 (2013). 23921808 PMC3773500

Cited by

Sorted by citations

Authors
Partridge, E. C., Chhetri, S. B., Prokop, J. W., Ramaker, R. C., Jansen, C. S., Goh, S.-T., Mackiewicz, M., Newberry, K. M., Brandsmeier, L. A., Meadows, S. K., Messer, C. L., Hardigan, A. A., Coppola, C. J., Dean, E. C., Jiang, S., Savic, D., Mortazavi, A., Wold, B. J., Myers, R. M., & Mendenhall, E. M.
Affiliations
  • HudsonAlpha Institute for Biotechnology (US)
  • Johns Hopkins University (US)
  • University of Alabama at Birmingham (US)
  • University of California, Irvine (US)
  • California Institute of Technology (US)
  • University of Alabama in Huntsville (US)
  • St. Jude Children's Research Hospital (US)
Corresponding author
  • Myers, Richard M.
  • Mendenhall, Eric M.
ORCID
Venue
Nature
Publisher
Springer Science and Business Media LLC
Published
2020-07-29
Volume
583
Issue
7818
Pages
720-728
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
DOI
10.1038/s41586-020-2023-4
PMC
PMC7398277
OpenAlex
W3045747015
Cited by
154
References
11
Citations by year
2026: 11 · 2025: 23 · 2024: 42 · 2023: 25 · 2022: 23 · 2021: 22 · 2020: 8
FWCI
7.45
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • CaltechAUTHORS (California Institute of Technology) (submittedVersion, cc-by-nc-nd)
  • RePEc: Research Papers in Economics (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
Funders
National Human Genome Research Institute (UM1HG009411, U54HG006998)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
Keywords
chromatin proteins, transcription factors, transcriptional cofactors, chromatin regulators, protein-DNA binding sites, DNA sequence motifs, promoter binding, enhancer binding, HepG2 cells, gene regulatory networks, ENCODE
MeSH terms
Chromatin Immunoprecipitation Sequencing, Chromatin, DNA-Binding Proteins, Enhancer Elements, Genetic, Humans, Promoter Regions, Genetic, Protein Binding, Regulatory Sequences, Nucleic Acid, Transcription Factors, Hep G2 Cells, Molecular Sequence Annotation, Nucleotide Motifs, Datasets as Topic

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review