Meta-analysis of ChIP-seq Datasets through the Rank Aggregation Approach

Semyon K. Kolmykov, Yury V. Kondrakhin, Ruslan N. Sharipov, Ivan S. Yevshi, Anna S. Ryabova, Fedor A. Kolpakov

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

Understanding the basic mechanisms of transcription regulation is a major challenge in modern biology. Regulation of transcription is a complex process in which transcription factors (TFs) play a key role. Chromatin immunoprecipitation followed by high throughput sequencing is a widely and intensively used experimental technology for the identification of TF binding sites (TFBSs). Nowadays, there are tens or hundreds of ChIP-seq datasets measured for the same transcription factor. Meta-processing of such datasets into an integrated dataset is relevant. We have developed a novel method for creating these integrated datasets of TFBSs. This method consists of a three-stage application of the Rank Aggregation approach. The identified TFBSs can be sorted to further select the most reliable TFBSs. We have found a high saturation of site motifs in the most reliable TFBSs. We have also demonstrated that the most reliable TFBSs prefer to be located in open chromatin regions.

Original languageEnglish
Title of host publicationProceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages180-184
Number of pages5
ISBN (Electronic)9781728195971
DOIs
Publication statusPublished - Jul 2020
Event2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020 - Novosibirsk, Russian Federation
Duration: 6 Jul 202010 Jul 2020

Publication series

NameProceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020

Conference

Conference2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020
CountryRussian Federation
CityNovosibirsk
Period06.07.202010.07.2020

Keywords

  • ChIP-seq
  • GTRD database
  • Meta-analysis
  • normal mixture
  • rank aggregation approach
  • transcription factor binding sites

OECD FOS+WOS

  • 1.02 COMPUTER AND INFORMATION SCIENCES
  • 1.06 BIOLOGICAL SCIENCES
  • 3.03 HEALTH SCIENCES
  • 5.09 OTHER SOCIAL SCIENCES

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