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

Sahlin K, Medvedev P

J. Comput. Biol. 27 (4) 472-484 [2020-04-00; online 2020-03-16]

Long-read sequencing of transcripts with Pacific Biosciences (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 (to scale) and makes use of quality values (to handle variable error rates). We test isONclust on three simulated and five biological data sets, 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 data sets.

Kristoffer Sahlin

SciLifeLab Fellow

PubMed 32181688

DOI 10.1089/cmb.2019.0299

Crossref 10.1089/cmb.2019.0299

pmc: PMC8884114

Publications 9.5.0