QuasiSeq: profiling viral quasispecies via self-tuning spectral clustering with PacBio long sequencing reads
Abstract Motivation The existence of quasispecies in the viral population causes difficulties for disease prevention and treatment. High-throughput sequencing provides opportunity to determine rare quasispecies and long sequencing reads covering full genomes reduce quasispecies determination to a cl...
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Veröffentlicht in: | Bioinformatics 2022-06, Vol.38 (12), p.3192-3199 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Abstract
Motivation
The existence of quasispecies in the viral population causes difficulties for disease prevention and treatment. High-throughput sequencing provides opportunity to determine rare quasispecies and long sequencing reads covering full genomes reduce quasispecies determination to a clustering problem. The challenge is high similarity of quasispecies and high error rate of long sequencing reads.
Results
We developed QuasiSeq using a novel signature-based self-tuning clustering method, SigClust, to profile viral mixtures with high accuracy and sensitivity. QuasiSeq can correctly identify quasispecies even using low-quality sequencing reads (accuracy |
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ISSN: | 1367-4803 1367-4811 1460-2059 1367-4811 |
DOI: | 10.1093/bioinformatics/btac313 |