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

  • Kristoffer SahlinEmail author
  • Paul Medvedev
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Notes

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringPennsylvania State UniversityState CollegeUSA
  2. 2.Department of Biochemistry and Molecular BiologyPennsylvania State UniversityState CollegeUSA
  3. 3.Center for Computational Biology and BioinformaticsPennsylvania State UniversityState CollegeUSA

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