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
Hauptverfasser: Jiao, Xiaoli, Imamichi, Hiromi, Sherman, Brad T, Nahar, Rishub, Dewar, Robin L, Lane, H Clifford, Imamichi, Tomozumi, Chang, Weizhong
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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
ISSN:1367-4803
1367-4811
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btac313