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bioRxiv · 10.1101/682799

Validating paired-end read alignments in sequence graphs

Jain, C.·Zhang, H.·Dilthey, A.·Aluru, S.
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Abstract

Graph based non-linear reference structures such as variation graphs and colored de Bruijn graphs enable incorporation of full genomic diversity within a population. However, transitioning from a simple string-based reference to graphs requires addressing many computational challenges, one of which concerns accurately mapping sequencing read sets to graphs. Paired-end Illumina sequencing is a commonly used sequencing platform in genomics, where the paired-end distance constraints allow disambiguation of repeats. Many recent works have explored provably good index-based and alignment-based strategies for mapping individual reads to graphs. However, validating distance constraints efficiently over graphs is not trivial, and existing sequence to graph mappers rely on heuristics. We introduce a mathematical formulation of the problem, and provide a new algorithm to solve it exactly. We take advantage of the high sparsity of reference graphs, and use sparse matrix-matrix multiplications (SpGEMM) to build an index which can be queried efficiently by a mapping algorithm for validating the distance constraints. Effectiveness of the algorithm is demonstrated using real reference graphs, including a human MHC variation graph, and a pan-genome de-Bruijn graph built using genomes of 20 B. anthracis strains. While the one-time indexing time can vary from a few minutes to a few hours using our algorithm, answering a million distance queries takes less than a second.\n\n2012 ACM Subject ClassificationMathematics of computing [->] Paths and connectivity problems; Applied computing [->] Computational genomics

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Jain, C., Zhang, H., Dilthey, A., Aluru, S.. 2019-06-26. Validating paired-end read alignments in sequence graphs. https://doi.org/10.1101/682799

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