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Viral coinfection analysis using a MinHash toolkit

  1. Author:
    Dawson, Eric T
    Wagner,Sarah
    Roberson, David
    Yeager,Meredith
    Boland,Joseph
    Garrison, Erik
    Chanock, Stephen
    Schiffman, Mark
    Raine-Bennett, Tina
    Lorey, Thomas
    Castle, Phillip E
    Mirabello, Lisa
    Durbin, Richard [ORCID]

  2. Author Address

    Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA., Department of Genetics, University of Cambridge, Cambridge, UK., Cancer Genomics Research Laboratory, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA., Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK., Women 39;s Health Research Institute, Kaiser Permanente Northern California, Oakland, California, USA., Regional Laboratory, Kaiser Permanente Northern California, Oakland, California, USA., Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA., Department of Genetics, University of Cambridge, Cambridge, UK. rd109@cam.ac.uk., Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK. rd109@cam.ac.uk.,
    1. Year: 2019
    2. Date: Jul 12
    3. Epub Date: 2019 07 12
  2. Journal: BMC bioinformatics
    1. 20
    2. 1
    3. Pages: 389
  4. Type of Article: Article
  5. Article Number: 389
  6. ISSN: 1471-2105
  1. Abstract:

    Background: Human papillomavirus (HPV) is a common sexually transmitted infection associated with cervical cancer that frequently occurs as a coinfection of types and subtypes. Highly similar sublineages that show over 100-fold differences in cancer risk are not distinguishable in coinfections with current typing methods. Results: We describe an efficient set of computational tools, rkmh, for analyzing complex mixed infections of related viruses based on sequence data. rkmh makes extensive use of MinHash similarity measures, and includes utilities for removing host DNA and classifying reads by type, lineage, and sublineage. We show that rkmh is capable of assigning reads to their HPV type as well as HPV16 lineage and sublineages. Conclusions: Accurate read classification enables estimates of percent composition when there are multiple infecting lineages or sublineages. While we demonstrate rkmh for HPV with multiple sequencing technologies, it is also applicable to other mixtures of related sequences.

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External Sources

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  2. DOI: 10.1186/s12859-019-2918-y
  3. PMID: 31299914
  4. View record in Web of Science®
  5. WOS: 000475529000002
  6. PII : 10.1186/s12859-019-2918-y

Library Notes

  1. Fiscal Year: FY2018-2019
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