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Viral coinfection analysis using a MinHash toolkit
Eric T Dawson1,2, Sarah Wagner3, David Roberson3
1Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA.
BMC Bioinformatics
|July 14, 2019
Summary
A new computational tool, rkmh, can now distinguish between highly similar human papillomavirus (HPV) sublineages in coinfections. This advancement improves the analysis of complex viral infections and cancer risk assessment.
Area of Science:
- Virology
- Computational Biology
- Genomics
Background:
- Human papillomavirus (HPV) is a common sexually transmitted infection linked to cervical cancer.
- HPV coinfections often involve multiple types and subtypes, complicating analysis.
- Current typing methods cannot differentiate highly similar HPV sublineages with varying cancer risks.
Purpose of the Study:
- To develop an efficient computational tool for analyzing complex viral coinfections.
- To accurately classify human papillomavirus (HPV) reads by type, lineage, and sublineage.
Main Methods:
- Development of the rkmh computational tool utilizing MinHash similarity measures.
- Implementation of utilities for host DNA removal and read classification.
- Application of rkmh to sequence data from HPV coinfections.
Main Results:
- The rkmh tool efficiently analyzes complex mixed viral infections using sequence data.
- rkmh accurately classifies HPV reads to type, lineage, and sublineage, including HPV16.
- Demonstrated capability with multiple sequencing technologies.
Conclusions:
- Accurate read classification by rkmh allows for precise estimation of mixed lineage or sublineage composition.
- The rkmh tool is effective for analyzing HPV coinfections and applicable to other related sequence mixtures.
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