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

BEHST: genomic set enrichment analysis enhanced through integration of chromatin long-range interactions

View ORCID ProfileDavide Chicco, View ORCID ProfileHaixin Sarah Bi, Jüri Reimand, View ORCID ProfileMichael M. Hoffman
doi: https://doi.org/10.1101/168427
Davide Chicco
aPrincess Margaret Cancer Centre
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Davide Chicco
Haixin Sarah Bi
aPrincess Margaret Cancer Centre
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Haixin Sarah Bi
Jüri Reimand
bOntario Institute for Cancer Research & University of Toronto
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Michael M. Hoffman
cPrincess Margaret Cancer Centre & University of Toronto & Vector Institute
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Michael M. Hoffman
  • For correspondence: michael.hoffman@utoronto.ca
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Transforming data from genome-scale assays into knowledge of affected molecular functions and pathways is a key challenge in biomedical research. Using vocabularies of functional terms and databases annotating genes with these terms, pathway enrichment methods can identify terms enriched in a gene list. With data that can refer to intergenic regions, however, one must first connect the regions to the terms, which are usually annotated only to genes. To make these connections, existing pathway enrichment approaches apply unwarranted assumptions such as annotating non-coding regions with the terms from adjacent genes. We developed a computational method that instead links genomic regions to annotations using data on long-range chromatin interactions. Our method, Biological Enrichment of Hidden Sequence Targets (BEHST), finds Gene Ontology (GO) terms enriched in genomic regions more precisely and accurately than existing methods. We demonstrate BEHST’s ability to retrieve more pertinent and less ambiguous GO terms associated with results of in vivo mouse enhancer screens or enhancer RNA assays for multiple tissue types. BEHST will accelerate the discovery of affected pathways mediated through long-range interactions that explain non-coding hits in genome-wide association study (GWAS) or genome editing screens. BEHST is free software with a command-line interface for Linux or macOS and a web interface (http://behst.hoffmanlab.org/).

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted January 15, 2019.
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.
BEHST: genomic set enrichment analysis enhanced through integration of chromatin long-range interactions
(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
BEHST: genomic set enrichment analysis enhanced through integration of chromatin long-range interactions
Davide Chicco, Haixin Sarah Bi, Jüri Reimand, Michael M. Hoffman
bioRxiv 168427; doi: https://doi.org/10.1101/168427
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
BEHST: genomic set enrichment analysis enhanced through integration of chromatin long-range interactions
Davide Chicco, Haixin Sarah Bi, Jüri Reimand, Michael M. Hoffman
bioRxiv 168427; doi: https://doi.org/10.1101/168427

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 (3482)
  • Biochemistry (7329)
  • Bioengineering (5301)
  • Bioinformatics (20212)
  • Biophysics (9985)
  • Cancer Biology (7706)
  • Cell Biology (11273)
  • Clinical Trials (138)
  • Developmental Biology (6425)
  • Ecology (9923)
  • Epidemiology (2065)
  • Evolutionary Biology (13292)
  • Genetics (9353)
  • Genomics (12559)
  • Immunology (7681)
  • Microbiology (18964)
  • Molecular Biology (7421)
  • Neuroscience (40915)
  • Paleontology (298)
  • Pathology (1226)
  • Pharmacology and Toxicology (2130)
  • Physiology (3145)
  • Plant Biology (6842)
  • Scientific Communication and Education (1271)
  • Synthetic Biology (1893)
  • Systems Biology (5299)
  • Zoology (1086)