VILOCA: Sequencing quality-aware haplotype reconstruction and mutation calling for short- and long-read data Article Swipe
Lara Fuhrmann
,
Benjamin Langer
,
Ivan Topolsky
,
Niko Beerenwinkel
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.06.06.597712
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.06.06.597712
RNA viruses exist in large heterogeneous populations within their host. The structure and diversity of virus populations affects disease progression and treatment outcomes. Next-generation sequencing allows detailed viral population analysis, but inferring diversity from error-prone reads is challenging. Here, we present VILOCA, a method for mutation calling and reconstruction of local haplotypes from short- and long-read viral sequencing data. Local haplotypes refer to genomic regions that have approximately the length of the input reads. VILOCA recovers local haplotypes by using a Dirichlet process mixture model to cluster reads around their unobserved haplotypes and leveraging quality scores of the sequencing reads. We assessed the performance of VILOCA in terms of mutation calling and haplotype reconstruction accuracy on simulated and experimental Illumina, PacBio, and Oxford Nanopore data. On simulated and experimental Illumina data, VILOCA performed better or similar to existing methods. On the simulated long-read data, VILOCA is able to recover on average 82% of the ground truth mutations with perfect precision compared to only 64% recall and 90% precision of the second-best method. In summary, VILOCA provides significantly improved accuracy in mutation and haplotype calling, especially for long-read sequencing data, and therefore facilitates the comprehensive characterization of heterogeneous within-host viral populations.
Related Topics
Concepts
Haplotype
DNA sequencing
Population
Mutation
Genetics
Deep sequencing
Computational biology
Biology
Nanopore sequencing
Computer science
Genome
Genotype
Gene
Demography
Sociology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.06.06.597712
- https://www.biorxiv.org/content/biorxiv/early/2024/06/09/2024.06.06.597712.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399506191
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399506191Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2024.06.06.597712Digital Object Identifier
- Title
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VILOCA: Sequencing quality-aware haplotype reconstruction and mutation calling for short- and long-read dataWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-06-09Full publication date if available
- Authors
-
Lara Fuhrmann, Benjamin Langer, Ivan Topolsky, Niko BeerenwinkelList of authors in order
- Landing page
-
https://doi.org/10.1101/2024.06.06.597712Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2024/06/09/2024.06.06.597712.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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-
greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2024/06/09/2024.06.06.597712.full.pdfDirect OA link when available
- Concepts
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Haplotype, DNA sequencing, Population, Mutation, Genetics, Deep sequencing, Computational biology, Biology, Nanopore sequencing, Computer science, Genome, Genotype, Gene, Demography, SociologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
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41Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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