Publications
bioRxiv : the preprint server for biologyJul 2024 DOI:
10.1101/2024.07.19.604364

Long-read sequencing transcriptome quantification with lr-kallisto

Loving, Rebekah; Sullivan, Delaney K; Reese, Fairlie; Rebboah, Elisabeth; Sakr, Jasmine; Rezaie, Narges; Liang, Heidi Y; Filimban, Ghassan; Kawauchi, Shimako; Oakes, Conrad; Trout, Diane; Williams, Brian A; MacGregor, Grant; Wold, Barbara J; Mortazavi, Ali; Pachter, Lior
Product Used
Variant Libraries
Abstract
RNA abundance quantification has become routine and affordable thanks to high-throughput short-read technologies that provide accurate molecule counts at the gene level. Similarly accurate and affordable quantification of definitive full-length, transcript isoforms has remained a stubborn challenge, despite its obvious biological significance across a wide range of problems. Long-read sequencing platforms now produce data-types that can, in principle, drive routine definitive isoform quantification. However some particulars of contemporary long-read datatypes, together with isoform complexity and genetic variation, present bioinformatic challenges. We show here, using ONT data, that fast and accurate quantification of long-read data is possible and that it is improved by exome capture. To perform quantifications we developed lr-kallisto, which adapts the kallisto bulk and single-cell RNA-seq quantification methods for long-read technologies.
Product Used
Variant Libraries

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