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

In-silico read normalization using set multicover optimization

Dilip A Durai, Marcel H Schulz
doi: https://doi.org/10.1101/133579
Dilip A Durai
1Cluster of Excellence on Multimodal Computing and Interaction, Saarland University 66123
2Department of Computational Biology & Applied Algorithmics, Max Planck Institute for Informatics, 66123 Saarbrücken
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Marcel H Schulz
1Cluster of Excellence on Multimodal Computing and Interaction, Saarland University 66123
2Department of Computational Biology & Applied Algorithmics, Max Planck Institute for Informatics, 66123 Saarbrücken
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Motivation Advances in high-throughput sequencing technologies has resulted in the generation of high coverage sequencing datasets. Assembling such datasets is challenging for current assemblers in terms of computational resources. It has been shown previously that a large part of these datasets is redundant and can be removed, without affecting assembly quality. But determining which reads to eliminate is a challenge as it might have an impact on the quality of the assembly produced. A de Bruijn graph (DBG), which is the base for many de novo assemblers, uses k-mers from reads as nodes and generate assemblies by traversing the nodes. Hence, removal of reads might result in loss of k-mers forming connections between genomic regions.

Results This work presents ORNA, a set multicover based read normalization algorithm. It focuses on the fact that each connection in the DBG is k + 1-mer connecting two k-mers. It selects the minimum number of reads which is required to retain all the k + 1-mers from the original dataset and hence retains all the connections from the original graph. Different datasets were normalized using ORNA and assembled using different DBG based assemblers. ORNA was also compared against well established read normalization algorithms was mostly found to be better.

Conclusion ORNA allows read normalization without losing connectivity information of the original DBG. It is fast and memory efficient. Together with available error correction approaches, OR able to provide high quality assembly. This is a step forward in making the assembly of high coverage dataset easier and computationally inexpensive.

ORNA is available at https://github.com/SchulzLab/ORNA

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 03, 2017.
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.
In-silico read normalization using set multicover optimization
(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
In-silico read normalization using set multicover optimization
Dilip A Durai, Marcel H Schulz
bioRxiv 133579; doi: https://doi.org/10.1101/133579
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
In-silico read normalization using set multicover optimization
Dilip A Durai, Marcel H Schulz
bioRxiv 133579; doi: https://doi.org/10.1101/133579

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 (4682)
  • Biochemistry (10357)
  • Bioengineering (7670)
  • Bioinformatics (26332)
  • Biophysics (13523)
  • Cancer Biology (10683)
  • Cell Biology (15438)
  • Clinical Trials (138)
  • Developmental Biology (8497)
  • Ecology (12821)
  • Epidemiology (2067)
  • Evolutionary Biology (16853)
  • Genetics (11399)
  • Genomics (15478)
  • Immunology (10616)
  • Microbiology (25208)
  • Molecular Biology (10220)
  • Neuroscience (54465)
  • Paleontology (401)
  • Pathology (1668)
  • Pharmacology and Toxicology (2897)
  • Physiology (4342)
  • Plant Biology (9245)
  • Scientific Communication and Education (1586)
  • Synthetic Biology (2557)
  • Systems Biology (6780)
  • Zoology (1466)