Evidence map›Paper›PMID 31299914›Full record

ArticleBMC bioinformatics2019

Viral coinfection analysis using a MinHash toolkit.

Eric T Dawson, Sarah Wagner, David Roberson, Meredith Yeager, Joseph Boland, Erik Garrison, Stephen Chanock, Mark Schiffman, Tina Raine-Bennett, Thomas Lorey and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Eric T DawsonDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA.
Sarah WagnerCancer Genomics Research Laboratory, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA.
David RobersonCancer Genomics Research Laboratory, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA.
Meredith YeagerCancer Genomics Research Laboratory, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA.
Joseph BolandCancer Genomics Research Laboratory, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA.
Erik GarrisonWellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.
Stephen ChanockDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA.
Mark SchiffmanDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA.
Tina Raine-BennettWomen's Health Research Institute, Kaiser Permanente Northern California, Oakland, California, USA.
Thomas LoreyRegional Laboratory, Kaiser Permanente Northern California, Oakland, California, USA.
Phillip E CastleDepartment of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA.
Lisa MirabelloDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA.
Richard DurbinDepartment of Genetics, University of Cambridge, Cambridge, UK. rd109@cam.ac.uk.ORCID http://orcid.org/0000-0002-9130-1006

Funding

CCR NIH HHS HHSN261200800001CNCI NIH HHS HHSN261200800001ENCI NIH HHS intramural research program of the Division of Cancer Epidemiology and GeneticsNIH HHS HHSN261200800001EWellcome Trust 207492/Z/17/ZWellcome Trust WT206194Wellcome Trust WT207492
6 · The paper itself

Abstract

backgroundHuman 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.

resultsWe 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.

conclusionsAccurate 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.

Indexed as

SoftwareCoinfectionComputational BiologyDNA, ViralHuman papillomavirus 16HumansPapillomavirus InfectionsPhylogenySequence Analysis, DNATime FactorsDNA, ViralBioinformaticsCoinfectionHPVHuman papillomavirusKmersMinHash

Identifiers

PMID31299914
PMCPMC6626348

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.