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De novo clustering of long-read transcriptome data using a greedy, quality-value based algorithm

View ORCID ProfileKristoffer Sahlin, Paul Medvedev
doi: https://doi.org/10.1101/463463
Kristoffer Sahlin
1Department of Computer Science and Engineering, The Pennsylvania State University
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  • For correspondence: kxs624@psu.edu
Paul Medvedev
1Department of Computer Science and Engineering, The Pennsylvania State University
2Department of Biochemistry and Molecular Biology, The Pennsylvania State University
3Center for Computational Biology and Bioinformatics, The Pennsylvania State University
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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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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted November 06, 2018.
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De novo clustering of long-read transcriptome data using a greedy, quality-value based algorithm
Kristoffer Sahlin, Paul Medvedev
bioRxiv 463463; doi: https://doi.org/10.1101/463463
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De novo clustering of long-read transcriptome data using a greedy, quality-value based algorithm
Kristoffer Sahlin, Paul Medvedev
bioRxiv 463463; doi: https://doi.org/10.1101/463463

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