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2019 | OriginalPaper | Buchkapitel

De Novo Clustering of Long-Read Transcriptome Data Using a Greedy, Quality-Value Based Algorithm

verfasst von : Kristoffer Sahlin, Paul Medvedev

Erschienen in: Research in Computational Molecular Biology

Verlag: Springer International Publishing

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Abstract

Long-read sequencing of transcripts with PacBio Iso-Seq and Oxford Nanopore Technologies has proven to be central to the study of complex isoform landscapes in many organisms. However, current de novo transcript reconstruction algorithms from long-read data are limited, leaving the potential of these technologies unfulfilled. A common bottleneck is the dearth of scalable and accurate algorithms for clustering long reads according to their gene family of origin. To address this challenge, we develop isONclust, a clustering algorithm that is greedy (in order to scale) and makes use of quality values (in order to handle variable error rates). We test isONclust on three simulated and five biological datasets, across a breadth of organisms, technologies, and read depths. Our results demonstrate that isONclust is a substantial improvement over previous approaches, both in terms of overall accuracy and/or scalability to large datasets. Our tool is available at https://github.com/ksahlin/isONclust.

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Vorheriges Kapitel Fast Approximation of Frequent k-mers and Applications to Metagenomics

Nächstes Kapitel A Sticky Multinomial Mixture Model of Strand-Coordinated Mutational Processes in Cancer

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Titel: De Novo Clustering of Long-Read Transcriptome Data Using a Greedy, Quality-Value Based Algorithm
verfasst von: Kristoffer Sahlin
Paul Medvedev
Verlag: Springer International Publishing
Buch: Research in Computational Molecular Biology
Print ISBN: 978-3-030-17082-0

Electronic ISBN: 978-3-030-17083-7

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-17083-7_14

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