Long-read sequencing transcriptome quantification with lr-kallisto

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.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

* We added Table 2 with the IGVF accessions for the processed data for Figure 1 for both lr-kallisto's pseudobulk and single-cell processing. In revisions of single-cell processing pipeline, we mistakenly omitted the addition of an author in our previous revision. Therefore, we are now adding A Sina Booeshaghi as an author on this manuscript.

* https://github.com/pachterlab/LSRRSRLFKOTWMWMP_2024

* https://registry.opendata.aws/sg-nex-data

* https://zenodo.org/records/11201284

* https://zenodo.org/records/13733737