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

Removing Contaminants from Metagenomic Databases

Jennifer Lu, View ORCID ProfileSteven L. Salzberg
doi: https://doi.org/10.1101/261859
Jennifer Lu
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
2Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Steven L. Salzberg
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
2Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine
3Departments of Computer Science and Biostatistics, Johns Hopkins University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Steven L. Salzberg
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Metagenomic sequencing of patient samples is a very promising method for the diagnosis of human infections. Sequencing has the ability to capture all the DNA or RNA from pathogenic organisms in a human sample. However, complete and accurate characterization of the sequence, including identification of any pathogens, depends on the availability and quality of genomes for comparison. Thousands of genomes are now available, and as these numbers grow, the power of metagenomic sequencing for diagnosis should increase. However, recent studies have exposed the presence of contamination in published genomes, which when used for diagnosis increases the risk of falsely identifying the wrong pathogen.

To address this problem, we have developed a bioinformatics system for eliminating contamination as well as low-complexity genomic sequences in the draft genomes of eukaryotic pathogens. We applied this software to identify and remove human, bacterial, archaeal, and viral sequences present in a comprehensive database of all sequenced eukaryotic pathogen genomes. We also removed low-complexity genomic sequences, another source of false positives. Using this pipeline, we have produced a database of “clean” eukaryotic pathogen genomes for use with bioinformatics classification and analysis tools. We demonstrate that when attempting to find eukaryotic pathogens in metagenomic samples, the new database provides better sensitivity than one using the original genomes while offering a dramatic reduction in false positives.

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 4.0 International license.
Back to top
PreviousNext
Posted February 08, 2018.
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.
Removing Contaminants from Metagenomic Databases
(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
Removing Contaminants from Metagenomic Databases
Jennifer Lu, Steven L. Salzberg
bioRxiv 261859; doi: https://doi.org/10.1101/261859
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Removing Contaminants from Metagenomic Databases
Jennifer Lu, Steven L. Salzberg
bioRxiv 261859; doi: https://doi.org/10.1101/261859

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 (3609)
  • Biochemistry (7585)
  • Bioengineering (5533)
  • Bioinformatics (20825)
  • Biophysics (10344)
  • Cancer Biology (7995)
  • Cell Biology (11653)
  • Clinical Trials (138)
  • Developmental Biology (6617)
  • Ecology (10224)
  • Epidemiology (2065)
  • Evolutionary Biology (13639)
  • Genetics (9557)
  • Genomics (12856)
  • Immunology (7930)
  • Microbiology (19568)
  • Molecular Biology (7675)
  • Neuroscience (42182)
  • Paleontology (308)
  • Pathology (1259)
  • Pharmacology and Toxicology (2208)
  • Physiology (3271)
  • Plant Biology (7058)
  • Scientific Communication and Education (1295)
  • Synthetic Biology (1953)
  • Systems Biology (5433)
  • Zoology (1119)