2-AIN-506, 2-AIN-252: Seminar in Bioinformatics (2), (4)
Summer 2024

Jonas A. Sibbesen, Jordan M. Eizenga, Adam M. Novak, Jouni Siren, Xian Chang, Erik Garrison, Benedict Paten. Haplotype-aware pantranscriptome analyses using spliced pangenome graphs. Nature methods, 20(2):239-247. 2023.

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Download from publisher: https://doi.org/10.1038/s41592-022-01731-9 PubMed

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Pangenomics is emerging as a powerful computational paradigm in bioinformatics. 
This field uses population-level genome reference structures, typically 
consisting of a sequence graph, to mitigate reference bias and facilitate 
analyses that were challenging with previous reference-based methods. In this 
work, we extend these methods into transcriptomics to analyze sequencing data 
using the pantranscriptome: a population-level transcriptomic reference. Our 
toolchain, which consists of additions to the VG toolkit and a standalone tool, 
RPVG, can construct spliced pangenome graphs, map RNA sequencing data to these 
graphs, and perform haplotype-aware expression quantification of transcripts in a 
pantranscriptome. We show that this workflow improves accuracy over 
state-of-the-art RNA sequencing mapping methods, and that it can efficiently 
quantify haplotype-specific transcript expression without needing to characterize 
the haplotypes of a sample beforehand.