Evidence map›Paper›PMID 31842753›Full record

ArticleBMC bioinformatics2019

Deconvoluting the diversity of within-host pathogen strains in a multi-locus sequence typing framework.

Guo Liang Gan, Elijah Willie, Cedric Chauve, Leonid Chindelevitch

Abstract read
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

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2 · The registry

The trial behind it

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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

4 authors.

Guo Liang GanSchool of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby (BC), V5A 1S6, Canada.
Elijah WillieSchool of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby (BC), V5A 1S6, Canada.
Cedric ChauveDepartment of Mathematics, Simon Fraser University, 8888 University Drive, Burnaby (BC), V5A 1S6, Canada.
Leonid ChindelevitchSchool of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby (BC), V5A 1S6, Canada. leonid@sfu.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBacterial pathogens exhibit an impressive amount of genomic diversity. This diversity can be informative of evolutionary adaptations, host-pathogen interactions, and disease transmission patterns. However, capturing this diversity directly from biological samples is challenging.

resultsWe introduce a framework for understanding the within-host diversity of a pathogen using multi-locus sequence types (MLST) from whole-genome sequencing (WGS) data. Our approach consists of two stages. First we process each sample individually by assigning it, for each locus in the MLST scheme, a set of alleles and a proportion for each allele. Next, we associate to each sample a set of strain types using the alleles and the strain proportions obtained in the first step. We achieve this by using the smallest possible number of previously unobserved strains across all samples, while using those unobserved strains which are as close to the observed ones as possible, at the same time respecting the allele proportions as closely as possible. We solve both problems using mixed integer linear programming (MILP). Our method performs accurately on simulated data and generates results on a real data set of Borrelia burgdorferi genomes suggesting a high level of diversity for this pathogen.

conclusionsOur approach can apply to any bacterial pathogen with an MLST scheme, even though we developed it with Borrelia burgdorferi, the etiological agent of Lyme disease, in mind. Our work paves the way for robust strain typing in the presence of within-host heterogeneity, overcoming an essential challenge currently not addressed by any existing methodology for pathogen genomics.

Indexed as

Genetic VariationMultilocus Sequence TypingAllelesBorrelia burgdorferiComputer SimulationDatabases, GeneticGenetic LociHost-Pathogen InteractionsModels, BiologicalBacterial diversityInteger Linear ProgrammingMulti-Locus Sequence Typing

Identifiers

PMID31842753
PMCPMC6915855

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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.