Haplotype-aware pantranscriptome analyses using spliced pangenome graphs

Abstract

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 novel toolchain can construct spliced pangenome graphs, map RNA-seq data to these graphs, and perform haplotype-aware expression quantification of transcripts in a pantranscriptome. This workflow improves accuracy over state-of-the-art RNA-seq mapping methods, and it can efficiently quantify haplotype-specific transcript expression without needing to characterize a sample's haplotypes beforehand.

Computational graph pangenomics: a tutorial on data structures and their applications

“…These indexes are becoming extremely useful in many applications, since haplotypes are able to distinguish specific SNPs that are relevant in personalized medicine. Sibbesen et al (2021) used the GBWT to represent a pangenome graph for haplotypes that is annotated with the additional information of a splicing graph. Then quantification of transcripts from RNA-seq data is obtained by taking into account the haplotype information and then by implementing an RNA-seq aligner to the pangenome graph.…”

“…The main idea of Sibbesen et al (2021) is to represent the exons of a splicing graph directly in a pangenome graph by mapping exons to haplotype sequences of the pangenome graph. In this way, they propose a tool for mapping RNA-seq data that is able to take into account haplotype variations in the analysis of transcripts.…”

Pangenomics in crop improvement—from coding structural variations to finding regulatory variants with pangenome graphs

“…The peaks found were generally more motif enriched, suggesting higher‐quality calls (Grytten et al., 2019). Recently a VG‐based pantranscriptome pipeline became available, which allows construction of spliced pangenome graphs, mapping of RNA sequencing data, and haplotype‐aware expression quantification (Sibbesen et al., 2021). The pangenome graph also provides a convenient framework for the genotyping of SVs across a large number of individuals, for example, for use in SV GWAS studies (Liu et al., 2020; Ruperao et al., 2021; Song et al., 2020; Zhao et al., 2020).…”

“…Pangenome graph-based workflow improves accuracy over state-of-the-art RNA-seq mapping methods (Sibbesen et al, 2021). It allows quantification of haplotype-specific transcript expression.…”

ODGI: understanding pangenome graphs

“…These flexible models let us encode any kind of variation, allowing the generation of comprehensive data systems that builds the basis for the analyses of genome evolution. Although these data structures are of utility to researchers (Consortium, 2018;Garrison et al, 2018;Baaijens et al, 2019;Hickey et al, 2020;Sibbesen et al, 2021), the scientific community still lacks a toolset specifically focused on graph manipulation and interrogation.…”