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

Model-based differential sequencing analysis

View ORCID ProfileAkosua Busia, View ORCID ProfileJennifer Listgarten
doi: https://doi.org/10.1101/2023.03.29.534803
Akosua Busia
1Department of Electrical Engineering & Computer Science, University of California, Berkeley, Berkeley, 94720, CA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Akosua Busia
  • For correspondence: akosua@berkeley.edu jennl@berkeley.edu
Jennifer Listgarten
1Department of Electrical Engineering & Computer Science, University of California, Berkeley, Berkeley, 94720, CA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Jennifer Listgarten
  • For correspondence: akosua@berkeley.edu jennl@berkeley.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Characterizing differences in biological sequences between two conditions using high-throughput sequencing data is a prevalent problem wherein we seek to (i) quantify how sequence abundances change between conditions, and (ii) build predictive models to estimate such differences for unobserved sequences. A key shortcoming of current approaches is their extremely limited ability to share information across related but non-identical reads. Consequently, they cannot make effective use of sequencing data, nor can they be directly applied in many settings of interest. We introduce model-based enrichment (MBE) to overcome this shortcoming. MBE is based on sound theoretical principles, is easy to implement, and can trivially make use of advances in modernday machine learning classification architectures or related innovations. We extensively evaluate MBE empirically, both in simulation and on real data. Overall, we find that our new approach improves accuracy compared to current ways of performing such differential analyses.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Supplementary Figures 2 and 4 revised; introductory text updated to clarify variable-length classifiers

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 April 07, 2023.
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.
Model-based differential sequencing analysis
(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
Model-based differential sequencing analysis
Akosua Busia, Jennifer Listgarten
bioRxiv 2023.03.29.534803; doi: https://doi.org/10.1101/2023.03.29.534803
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Model-based differential sequencing analysis
Akosua Busia, Jennifer Listgarten
bioRxiv 2023.03.29.534803; doi: https://doi.org/10.1101/2023.03.29.534803

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 (4687)
  • Biochemistry (10371)
  • Bioengineering (7689)
  • Bioinformatics (26358)
  • Biophysics (13544)
  • Cancer Biology (10713)
  • Cell Biology (15452)
  • Clinical Trials (138)
  • Developmental Biology (8508)
  • Ecology (12831)
  • Epidemiology (2067)
  • Evolutionary Biology (16877)
  • Genetics (11407)
  • Genomics (15489)
  • Immunology (10632)
  • Microbiology (25242)
  • Molecular Biology (10233)
  • Neuroscience (54565)
  • Paleontology (402)
  • Pathology (1670)
  • Pharmacology and Toxicology (2898)
  • Physiology (4350)
  • Plant Biology (9263)
  • Scientific Communication and Education (1587)
  • Synthetic Biology (2558)
  • Systems Biology (6786)
  • Zoology (1470)