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A variant selection framework for genome graphs

Jain, C and Tavakoli, N and Aluru, S (2021) A variant selection framework for genome graphs. In: Bioinformatics, 37 . I460-I467.

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Official URL: https://doi.org/10.1093/bioinformatics/btab302


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 δdifferences. 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 δparameter 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. © 2021 The Author(s). Published by Oxford University Press.

Item Type: Journal Article
Publication: Bioinformatics
Publisher: Oxford University Press
Additional Information: The copyright for this article belongs to Authors
Keywords: algorithm; DNA sequence; genome; human; human genome; single nucleotide polymorphism; software, Algorithms; Genome; Genome, Human; Humans; Polymorphism, Single Nucleotide; Sequence Analysis, DNA; Software
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 20 Nov 2021 11:33
Last Modified: 20 Nov 2021 11:33
URI: http://eprints.iisc.ac.in/id/eprint/69884

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