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

Improving long-read consensus sequencing accuracy with deep learning

View ORCID ProfileAvantika Lal, Michael Brown, Rahul Mohan, Joyjit Daw, James Drake, Johnny Israeli
doi: https://doi.org/10.1101/2021.06.28.450238
Avantika Lal
1NVIDIA Corporation, Santa Clara, CA 95051, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Avantika Lal
Michael Brown
2Pacific Biosciences, Menlo Park, CA 94025, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Rahul Mohan
1NVIDIA Corporation, Santa Clara, CA 95051, USA
3Department of Computer Science, University of Southern California, Los Angeles, CA 90089, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Joyjit Daw
1NVIDIA Corporation, Santa Clara, CA 95051, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
James Drake
2Pacific Biosciences, Menlo Park, CA 94025, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Johnny Israeli
1NVIDIA Corporation, Santa Clara, CA 95051, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: jisraeli@nvidia.com
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Data/Code
  • Preview PDF
Loading

Abstract

The PacBio HiFi sequencing technology combines less accurate, multi-read passes from the same molecule (subreads) to yield consensus sequencing reads that are both long (averaging 10-25 kb) and highly accurate. However, these reads can retain residual sequencing error, predominantly insertions or deletions at homopolymeric regions. Here, we train deep learning models to polish HiFi reads by recognizing and correcting sequencing errors. We show that our models are effective at reducing these errors by 25-40% in HiFi reads from human as well as E. coli genomes.

Competing Interest Statement

Avantika Lal, Joyjit Daw, and Johnny Israeli are employees of NVIDIA Corporation. Michael Brown and James Drake are employees of Pacific Biosciences.

Footnotes

  • https://nv-pacbio.s3.us-east-2.amazonaws.com

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-ND 4.0 International license.
Back to top
PreviousNext
Posted June 30, 2021.
Download PDF

Supplementary Material

Data/Code
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.
Improving long-read consensus sequencing accuracy with deep learning
(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
Improving long-read consensus sequencing accuracy with deep learning
Avantika Lal, Michael Brown, Rahul Mohan, Joyjit Daw, James Drake, Johnny Israeli
bioRxiv 2021.06.28.450238; doi: https://doi.org/10.1101/2021.06.28.450238
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Improving long-read consensus sequencing accuracy with deep learning
Avantika Lal, Michael Brown, Rahul Mohan, Joyjit Daw, James Drake, Johnny Israeli
bioRxiv 2021.06.28.450238; doi: https://doi.org/10.1101/2021.06.28.450238

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

  • Genomics
Subject Areas
All Articles
  • Animal Behavior and Cognition (3607)
  • Biochemistry (7581)
  • Bioengineering (5529)
  • Bioinformatics (20809)
  • Biophysics (10338)
  • Cancer Biology (7988)
  • Cell Biology (11647)
  • Clinical Trials (138)
  • Developmental Biology (6611)
  • Ecology (10217)
  • Epidemiology (2065)
  • Evolutionary Biology (13630)
  • Genetics (9550)
  • Genomics (12854)
  • Immunology (7925)
  • Microbiology (19555)
  • Molecular Biology (7668)
  • Neuroscience (42147)
  • Paleontology (308)
  • Pathology (1258)
  • Pharmacology and Toxicology (2203)
  • Physiology (3269)
  • Plant Biology (7051)
  • Scientific Communication and Education (1294)
  • Synthetic Biology (1952)
  • Systems Biology (5429)
  • Zoology (1119)