Isocall enables scalable transcript identification from long-read RNA-sequencing data
- 1PacBio
Abstract
Long-read RNA sequencing directly resolves the full structures of RNA transcripts. Advances in throughput now enable the generation of deeply sequenced cohorts of hundreds of samples, making joint transcript discovery across large datasets possible. However, existing transcript identification methods were designed for small datasets, which limits their applicability at this scale. Here, we present Isocall, a scalable and deterministic computational method for jointly calling transcripts from multiple PacBio long-read RNA sequencing samples. Isocall converts aligned full-length non-concatemer reads into compact per-sample transcript profiles, merges these profiles across samples, and jointly identifies known and novel transcripts supported by reads in the analyzed dataset. Filtering is tunable: presets provide coarse control and individual parameters, including relative abundance and internal priming thresholds, provide fine control. Isocall demonstrated high precision in our accuracy benchmarks, including the WTC11 SIRV spike-in controls, for which Isocall reported 0-2 false-positive transcripts per sample across the three SIRV mixes at default settings. To demonstrate scalability, we applied Isocall to 206 samples, totalling 3.5 billion raw reads, from the Human Pangenome Reference Consortium. After parallelized pbmm2 alignment and Isocall profile, the call step performed joint calling across the entire dataset in 25 minutes, using 1.3 GB of peak memory and 8 threads. Finally, in Genome in a Bottle samples with matched SNP genotypes, splice-site polymorphisms provide an additional measure of call accuracy: Isocall recovered 337 polymorphic splice sites, including a de novo donor site in BTN3A1 that corresponds to a complete isoform switch on the mutant allele.
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BibTeX
@misc{dolzhenko2026isocall,
title = {{Isocall enables scalable transcript identification from long-read RNA-sequencing data}},
author = {E. Dolzhenko and M. Schertzer and R. Gossart and T. Mokveld and J. R. Belyeu and A. Varabyou and X. Zheng and E. Tseng and Z. Kronenberg and M. Chaisson and G. M. Sheynkman and F. J. Sedlazeck and Y. Z. Kurmangaliyev and J. Bruand},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.08.749180},
url = {https://www.biorxiv.org/content/10.64898/2026.09.08.749180v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Isocall enables scalable transcript identification from long-read RNA-sequencing data
AU - E. Dolzhenko
AU - M. Schertzer
AU - R. Gossart
AU - T. Mokveld
AU - J. R. Belyeu
AU - A. Varabyou
AU - X. Zheng
AU - E. Tseng
AU - Z. Kronenberg
AU - M. Chaisson
AU - G. M. Sheynkman
AU - F. J. Sedlazeck
AU - Y. Z. Kurmangaliyev
AU - J. Bruand
PY - 2026
DA - 2026/09/08
PB - bioRxiv
DO - 10.64898/2026.09.08.749180
UR - https://www.biorxiv.org/content/10.64898/2026.09.08.749180v1
AB - Long-read RNA sequencing directly resolves the full structures of RNA transcripts. Advances in throughput now enable the generation of deeply sequenced cohorts of hundreds of samples, making joint transcript discovery across large datasets possible. However, existing transcript identification methods were designed for small datasets, which limits their applicability at this scale. Here, we present Isocall, a scalable and deterministic computational method for jointly calling transcripts from multiple PacBio long-read RNA sequencing samples. Isocall converts aligned full-length non-concatemer reads into compact per-sample transcript profiles, merges these profiles across samples, and jointly identifies known and novel transcripts supported by reads in the analyzed dataset. Filtering is tunable: presets provide coarse control and individual parameters, including relative abundance and internal priming thresholds, provide fine control. Isocall demonstrated high precision in our accuracy benchmarks, including the WTC11 SIRV spike-in controls, for which Isocall reported 0-2 false-positive transcripts per sample across the three SIRV mixes at default settings. To demonstrate scalability, we applied Isocall to 206 samples, totalling 3.5 billion raw reads, from the Human Pangenome Reference Consortium. After parallelized pbmm2 alignment and Isocall profile, the call step performed joint calling across the entire dataset in 25 minutes, using 1.3 GB of peak memory and 8 threads. Finally, in Genome in a Bottle samples with matched SNP genotypes, splice-site polymorphisms provide an additional measure of call accuracy: Isocall recovered 337 polymorphic splice sites, including a de novo donor site in BTN3A1 that corresponds to a complete isoform switch on the mutant allele.
ER -