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Profiling the quantitative occupancy of myriad transcription factors across conditions by modeling chromatin accessibility data.

Publication ,  Journal Article
Luo, K; Zhong, J; Safi, A; Hong, LK; Tewari, AK; Song, L; Reddy, TE; Ma, L; Crawford, GE; Hartemink, AJ
Published in: Genome Res
June 2022

Over a thousand different transcription factors (TFs) bind with varying occupancy across the human genome. Chromatin immunoprecipitation (ChIP) can assay occupancy genome-wide, but only one TF at a time, limiting our ability to comprehensively observe the TF occupancy landscape, let alone quantify how it changes across conditions. We developed TF occupancy profiler (TOP), a Bayesian hierarchical regression framework, to profile genome-wide quantitative occupancy of numerous TFs using data from a single chromatin accessibility experiment (DNase- or ATAC-seq). TOP is supervised, and its hierarchical structure allows it to predict the occupancy of any sequence-specific TF, even those never assayed with ChIP. We used TOP to profile the quantitative occupancy of hundreds of sequence-specific TFs at sites throughout the genome and examined how their occupancies changed in multiple contexts: in approximately 200 human cell types, through 12 h of exposure to different hormones, and across the genetic backgrounds of 70 individuals. TOP enables cost-effective exploration of quantitative changes in the landscape of TF binding.

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Published In

Genome Res

DOI

EISSN

1549-5469

Publication Date

June 2022

Volume

32

Issue

6

Start / End Page

1183 / 1198

Location

United States

Related Subject Headings

  • Transcription Factors
  • Protein Binding
  • Humans
  • Genome, Human
  • Chromatin
  • Bioinformatics
  • Binding Sites
  • Bayes Theorem
  • 3105 Genetics
  • 11 Medical and Health Sciences
 

Citation

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MLA
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Luo, K., Zhong, J., Safi, A., Hong, L. K., Tewari, A. K., Song, L., … Hartemink, A. J. (2022). Profiling the quantitative occupancy of myriad transcription factors across conditions by modeling chromatin accessibility data. Genome Res, 32(6), 1183–1198. https://doi.org/10.1101/gr.272203.120
Luo, Kaixuan, Jianling Zhong, Alexias Safi, Linda K. Hong, Alok K. Tewari, Lingyun Song, Timothy E. Reddy, Li Ma, Gregory E. Crawford, and Alexander J. Hartemink. “Profiling the quantitative occupancy of myriad transcription factors across conditions by modeling chromatin accessibility data.Genome Res 32, no. 6 (June 2022): 1183–98. https://doi.org/10.1101/gr.272203.120.
Luo K, Zhong J, Safi A, Hong LK, Tewari AK, Song L, et al. Profiling the quantitative occupancy of myriad transcription factors across conditions by modeling chromatin accessibility data. Genome Res. 2022 Jun;32(6):1183–98.
Luo, Kaixuan, et al. “Profiling the quantitative occupancy of myriad transcription factors across conditions by modeling chromatin accessibility data.Genome Res, vol. 32, no. 6, June 2022, pp. 1183–98. Pubmed, doi:10.1101/gr.272203.120.
Luo K, Zhong J, Safi A, Hong LK, Tewari AK, Song L, Reddy TE, Ma L, Crawford GE, Hartemink AJ. Profiling the quantitative occupancy of myriad transcription factors across conditions by modeling chromatin accessibility data. Genome Res. 2022 Jun;32(6):1183–1198.

Published In

Genome Res

DOI

EISSN

1549-5469

Publication Date

June 2022

Volume

32

Issue

6

Start / End Page

1183 / 1198

Location

United States

Related Subject Headings

  • Transcription Factors
  • Protein Binding
  • Humans
  • Genome, Human
  • Chromatin
  • Bioinformatics
  • Binding Sites
  • Bayes Theorem
  • 3105 Genetics
  • 11 Medical and Health Sciences