Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

RECAP reveals the true statistical significance of ChIP-seq peak calls

Justin G. Chitpin, Aseel Awdeh, Theodore J. Perkins
doi: https://doi.org/10.1101/260687
Justin G. Chitpin
1Translational and Molecular Medicine Program, University of Ottawa, Ottawa, ON, K1H8M5, Canada
2Regenerative Medicine Program, Ottawa Hospital Research Institute, Ottawa, ON, K1H8L6, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Aseel Awdeh
2Regenerative Medicine Program, Ottawa Hospital Research Institute, Ottawa, ON, K1H8L6, Canada
3School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, K1N6N5, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Theodore J. Perkins
2Regenerative Medicine Program, Ottawa Hospital Research Institute, Ottawa, ON, K1H8L6, Canada
3School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, K1N6N5, Canada
4Department of Biochemistry, Microbiology and Immunology, University of Ottawa, Ottawa, ON, K1H8M5, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: tperkins@ohri.ca
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Motivation ChIP-seq is used extensively to identify sites of transcription factor binding or regions of epigenetic modifications to the genome. The fundamental bioinformatics problem is to take ChIP-seq read data and data representing some kind of control, and determine genomic regions that are enriched in the ChIP-seq versus the control, also called “peak calling.” While many programs have been designed to solve this task, nearly all fall into the statistical trap of using the data twice—once to determine candidate enriched regions, and a second time to assess enrichment by methods of classical statistical hypothesis testing. This double use of the data has the potential to invalidate the statistical significance assigned to enriched regions, or “peaks”, and as a consequence, to invalidate false discovery rate estimates. Thus, the true significance or reliability of peak calls remains unknown.

Results We show, through extensive simulation studies of null hypothesis data, that three well-known peak callers, MACS, SICER and diffReps, output optimistically biased p-values, and therefore optimistic false discovery rate estimates—in some cases, orders of magnitude optimistic. We also propose a new wrapper algorithm called RECAP, that uses resampling of ChIP-seq and control data to estimate and correct for biases built into peak calling algorithms. RECAP also enables for the first time local false discovery rate analysis, so that the likelihood of individual peaks being true positives or false positives can be estimated based on their re-calibrated p-values. RECAP is a powerful new tool for assessing the true statistical significance of ChIP-seq peak calls.

Availability The RECAP software is available at www.perkinslab.ca.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
Back to top
PreviousNext
Posted February 05, 2018.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
RECAP reveals the true statistical significance of ChIP-seq peak calls
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
RECAP reveals the true statistical significance of ChIP-seq peak calls
Justin G. Chitpin, Aseel Awdeh, Theodore J. Perkins
bioRxiv 260687; doi: https://doi.org/10.1101/260687
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
RECAP reveals the true statistical significance of ChIP-seq peak calls
Justin G. Chitpin, Aseel Awdeh, Theodore J. Perkins
bioRxiv 260687; doi: https://doi.org/10.1101/260687

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4093)
  • Biochemistry (8784)
  • Bioengineering (6490)
  • Bioinformatics (23376)
  • Biophysics (11761)
  • Cancer Biology (9163)
  • Cell Biology (13267)
  • Clinical Trials (138)
  • Developmental Biology (7420)
  • Ecology (11379)
  • Epidemiology (2066)
  • Evolutionary Biology (15109)
  • Genetics (10408)
  • Genomics (14017)
  • Immunology (9133)
  • Microbiology (22085)
  • Molecular Biology (8792)
  • Neuroscience (47417)
  • Paleontology (350)
  • Pathology (1421)
  • Pharmacology and Toxicology (2483)
  • Physiology (3709)
  • Plant Biology (8060)
  • Scientific Communication and Education (1433)
  • Synthetic Biology (2213)
  • Systems Biology (6019)
  • Zoology (1251)