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

circuitSNPs: Predicting genetic effects using a Neural Network to model regulatory modules of DNase-seq footprints

Alexander G. Shanku, Anthony Findley, Cynthia Kalita, Heejung Shim, View ORCID ProfileFrancesca Luca, View ORCID ProfileRoger Pique-Regi
doi: https://doi.org/10.1101/337774
Alexander G. Shanku
1Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Anthony Findley
1Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Cynthia Kalita
1Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Heejung Shim
3Centre for Systems Genomics, University of Melbourne, Victoria, Australia
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Francesca Luca
1Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, USA
2Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Francesca Luca
Roger Pique-Regi
1Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, USA
2Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Roger Pique-Regi
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Motivation Identifying and characterizing the function of non coding regions in the genome, and the genetic variants disrupting gene regulation, is a challenging question in genetics. Through the use of high throughput experimental assays that provide information about the chromatin state within a cell, coupled with modern computational approaches, much progress has been made towards this goal, yet we still lack a comprehensive characterization of the regulatory grammar. We propose a new method that combines sequence and chromatin accessibility information through a neural network framework with the goal of determining and annotating the effect of genetic variants on regulation of chromatin accessibility and gene transcription. Importantly, our new approach can consider multiple combinations of transcription factors binding at the same location when assessing the functional impact of non-coding genetic variation.

Results Our method, circuitSNPs, generates predictions describing the functional effect of genetic variants on local chromatin accessibility. Further, we demonstrate that circuitSNPs not only performs better than other variant annotation tools, but also retains the causal motifs / transcription factors that drive the predicted regulatory effect.

Contact fluca{at}wayne.edu, rpique{at}wayne.edu

Availability http://github.com/piquelab/circuitSNPs

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-ND 4.0 International license.
Back to top
PreviousNext
Posted June 03, 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.
circuitSNPs: Predicting genetic effects using a Neural Network to model regulatory modules of DNase-seq footprints
(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
circuitSNPs: Predicting genetic effects using a Neural Network to model regulatory modules of DNase-seq footprints
Alexander G. Shanku, Anthony Findley, Cynthia Kalita, Heejung Shim, Francesca Luca, Roger Pique-Regi
bioRxiv 337774; doi: https://doi.org/10.1101/337774
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
circuitSNPs: Predicting genetic effects using a Neural Network to model regulatory modules of DNase-seq footprints
Alexander G. Shanku, Anthony Findley, Cynthia Kalita, Heejung Shim, Francesca Luca, Roger Pique-Regi
bioRxiv 337774; doi: https://doi.org/10.1101/337774

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 (3504)
  • Biochemistry (7346)
  • Bioengineering (5321)
  • Bioinformatics (20259)
  • Biophysics (10013)
  • Cancer Biology (7742)
  • Cell Biology (11298)
  • Clinical Trials (138)
  • Developmental Biology (6437)
  • Ecology (9950)
  • Epidemiology (2065)
  • Evolutionary Biology (13318)
  • Genetics (9360)
  • Genomics (12581)
  • Immunology (7700)
  • Microbiology (19016)
  • Molecular Biology (7439)
  • Neuroscience (41029)
  • Paleontology (300)
  • Pathology (1229)
  • Pharmacology and Toxicology (2135)
  • Physiology (3157)
  • Plant Biology (6860)
  • Scientific Communication and Education (1272)
  • Synthetic Biology (1895)
  • Systems Biology (5311)
  • Zoology (1089)