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

A comprehensive evaluation of long read error correction methods

View ORCID ProfileHaowen Zhang, Chirag Jain, Srinivas Aluru
doi: https://doi.org/10.1101/519330
Haowen Zhang
1School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Haowen Zhang
Chirag Jain
1School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Srinivas Aluru
1School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
2Institute for Data Engineering and Science, Georgia Institute of Technology, Atlanta, GA 30332, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: aluru@cc.gatech.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Background Third-generation single molecule sequencing technologies can sequence long reads, which is advancing the frontiers of genomics research. However, their high error rates prohibit accurate and efficient downstream analysis. This difficulty has motivated the development of many long read error correction tools, which tackle this problem through sampling redundancy and/or leveraging accurate short reads of the same biological samples. Existing studies to asses these tools use simulated data sets, and are not sufficiently comprehensive in the range of software covered or diversity of evaluation measures used.

Results In this paper, we present a categorization and review of long read error correction methods, and provide a comprehensive evaluation of the corresponding long read error correction tools. Leveraging recent real sequencing data, we establish benchmark data sets and set up evaluation criteria for a comparative assessment which includes quality of error correction as well as run-time and memory usage. We study how trimming and long read sequencing depth affect error correction in terms of length distribution and genome coverage post-correction, and the impact of error correction performance on an important application of long reads, genome assembly. We provide guidelines for practitioners for choosing among the available error correction tools and identify directions for future research.

Conclusions Despite the high error rate of long reads, the state-of-the-art correction tools can achieve high correction quality. When short reads are available, the best hybrid methods outperform non-hybrid methods in terms of correction quality and computing resource usage. When choosing tools for use, practitioners are suggested to be careful with a few correction tools that discard reads, and check the effect of error correction tools on downstream analysis. Our evaluation code is available as open-source at https://github.com/haowenz/LRECE.

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 4.0 International license.
Back to top
PreviousNext
Posted May 29, 2019.
Download PDF

Supplementary Material

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.
A comprehensive evaluation of long read error correction methods
(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
A comprehensive evaluation of long read error correction methods
Haowen Zhang, Chirag Jain, Srinivas Aluru
bioRxiv 519330; doi: https://doi.org/10.1101/519330
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
A comprehensive evaluation of long read error correction methods
Haowen Zhang, Chirag Jain, Srinivas Aluru
bioRxiv 519330; doi: https://doi.org/10.1101/519330

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 (4078)
  • Biochemistry (8750)
  • Bioengineering (6467)
  • Bioinformatics (23314)
  • Biophysics (11718)
  • Cancer Biology (9133)
  • Cell Biology (13227)
  • Clinical Trials (138)
  • Developmental Biology (7403)
  • Ecology (11359)
  • Epidemiology (2066)
  • Evolutionary Biology (15076)
  • Genetics (10390)
  • Genomics (14000)
  • Immunology (9109)
  • Microbiology (22025)
  • Molecular Biology (8772)
  • Neuroscience (47312)
  • Paleontology (350)
  • Pathology (1418)
  • Pharmacology and Toxicology (2480)
  • Physiology (3701)
  • Plant Biology (8043)
  • Scientific Communication and Education (1427)
  • Synthetic Biology (2206)
  • Systems Biology (6009)
  • Zoology (1247)