Long-read sequencing transcriptome quantification with lr-kallisto

Loving, R. K.; Sullivan, D. K.; Reese, F.; Rebboah, E.; Sakr, J.; Rezaie, N.; Liang, H. Y.; Filimban, G.; Kawauchi, S.; Oakes, C.; Trout, D.; Williams, B. A.; MacGregor, G.; Wold, B.; Mortazavi, A.; Pachter, L.

doi:10.1101/2024.07.19.604364

bioRxiv · 10.1101/2024.07.19.604364

Long-read sequencing transcriptome quantification with lr-kallisto

Loving, R. K.·Sullivan, D. K.·Reese, F.·Rebboah, E.·Sakr, J.·Rezaie, N.·Liang, H. Y.·Filimban, G.·Kawauchi, S.·Oakes, C.·Trout, D.·Williams, B. A.·MacGregor, G.·Wold, B.·Mortazavi, A.·Pachter, L.

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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 fulllength, 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.

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Loving, R. K., Sullivan, D. K., Reese, F., Rebboah, E., Sakr, J., Rezaie, N., Liang, H. Y., Filimban, G., Kawauchi, S., Oakes, C., Trout, D., Williams, B. A., MacGregor, G., Wold, B., Mortazavi, A., Pachter, L.. 2024-07-19. Long-read sequencing transcriptome quantification with lr-kallisto. https://doi.org/10.1101/2024.07.19.604364

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