Deconvoluting the diversity of within-host pathogen strains in a multi-locus sequence typing framework.
Guo Liang Gan1, Elijah Willie1, Cedric Chauve2,3
1School of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby (BC), V5A 1S6, Canada.
BMC Bioinformatics
|December 18, 2019
Summary
This study introduces a new computational framework to analyze bacterial pathogen diversity within hosts using whole-genome sequencing (WGS) and multi-locus sequence typing (MLST). The method accurately identifies diverse pathogen strains, improving genomic analysis.
Area of Science:
- Genomics
- Computational Biology
- Microbial Evolution
Background:
- Bacterial pathogens display significant genomic diversity, crucial for understanding evolution and disease.
- Accurately characterizing this within-host diversity from biological samples presents a major challenge in pathogen genomics.
Purpose of the Study:
- To develop a novel computational framework for analyzing within-host bacterial pathogen diversity.
- To leverage whole-genome sequencing (WGS) and multi-locus sequence typing (MLST) data for robust strain identification.
Main Methods:
- A two-stage approach was developed, processing individual samples to determine allele proportions for each MLST locus.
- Mixed integer linear programming (MILP) was employed to associate samples with strain types, minimizing novel strains while respecting allele proportions.
Main Results:
- The framework demonstrated high accuracy on simulated data.
- Application to Borrelia burgdorferi (Lyme disease agent) revealed substantial within-host diversity.
Conclusions:
- The developed method enables robust strain typing for bacterial pathogens with an MLST scheme, even with within-host heterogeneity.
- This overcomes a critical limitation in current pathogen genomics methodologies.
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