A variant selection framework for genome graphs

• Published in: Bioinformatics (Oxford, England) Vol. 37; no. Supplement_1; pp. i460 - i467
• Main Authors: Jain, Chirag, Tavakoli, Neda, Aluru, Srinivas
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 12.07.2021; Oxford Publishing Limited (England)
• Subjects: Algorithms; Availability; BASIC BIOLOGICAL SCIENCES; Bias; Bioinformatics; Chromosome 1; Chromosomes; Gene mapping; General Computational Biology; Genetic diversity; Genetic variance; Genomes; Genomic analysis; Graph representations; Graphical representations; Graphs; Mapping; Mathematics; Nucleotides; Parameters; Population genetics; Single-nucleotide polymorphism; Size reduction
• DOI: 10.1093/bioinformatics/btab302
• ISSN: 1367-4803; 1367-4811

Abstract Motivation Variation graph representations are projected to either replace or supplement conventional single genome references due to their ability to capture population genetic diversity and reduce reference bias. Vast catalogues of genetic variants for many species now exist, and it is natural to ask which among these are crucial to circumvent reference bias during read mapping. Results In this work, we propose a novel mathematical framework for variant selection, by casting it in terms of minimizing variation graph size subject to preserving paths of length α with at most δ differences. This framework leads to a rich set of problems based on the types of variants [e.g. single nucleotide polymorphisms (SNPs), indels or structural variants (SVs)], and whether the goal is to minimize the number of positions at which variants are listed or to minimize the total number of variants listed. We classify the computational complexity of these problems and provide efficient algorithms along with their software implementation when feasible. We empirically evaluate the magnitude of graph reduction achieved in human chromosome variation graphs using multiple α and δ parameter values corresponding to short and long-read resequencing characteristics. When our algorithm is run with parameter settings amenable to long-read mapping (α = 10 kbp, δ = 1000), 99.99% SNPs and 73% SVs can be safely excluded from human chromosome 1 variation graph. The graph size reduction can benefit downstream pan-genome analysis. Availability and implementation https://github.com/AT-CG/VF. Supplementary information Supplementary data are available at Bioinformatics online.