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

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Book cover Research in Computational Molecular Biology (RECOMB 2019)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 11467))

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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Acknowledgements

This work has been supported in part by NSF awards DBI-1356529, CCF-551439057, IIS-1453527, and IIS-1421908 to PM.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Pennsylvania State University, State College, USA

    Kristoffer Sahlin & Paul Medvedev

  2. Department of Biochemistry and Molecular Biology, Pennsylvania State University, State College, USA

    Paul Medvedev

  3. Center for Computational Biology and Bioinformatics, Pennsylvania State University, State College, USA

    Paul Medvedev

Authors
  1. Kristoffer Sahlin
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  2. Paul Medvedev
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Correspondence to Kristoffer Sahlin .

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Editors and Affiliations

  1. Tufts University, Cambridge, MA, USA

    Lenore J. Cowen

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Sahlin, K., Medvedev, P. (2019). De Novo Clustering of Long-Read Transcriptome Data Using a Greedy, Quality-Value Based Algorithm. In: Cowen, L. (eds) Research in Computational Molecular Biology. RECOMB 2019. Lecture Notes in Computer Science(), vol 11467. Springer, Cham. https://doi.org/10.1007/978-3-030-17083-7_14

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  • DOI: https://doi.org/10.1007/978-3-030-17083-7_14

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-17083-7

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