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Validating Paired-End Read Alignments in Sequence Graphs

Authors Chirag Jain, Haowen Zhang, Alexander Dilthey, Srinivas Aluru

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Author Details

Chirag Jain
  • School of Computational Science and Engineering, Georgia Institute of Technology, USA
Haowen Zhang
  • School of Computational Science and Engineering, Georgia Institute of Technology, USA
Alexander Dilthey
  • Institute of Medical Microbiology, University Hospital of Düsseldorf, Germany
Srinivas Aluru
  • School of Computational Science and Engineering, Georgia Institute of Technology, USA

Funding

This work is supported in part by the US National Science Foundation under CCF-1816027.

Acknowledgements

The authors thank Abdurrahman Yasar, Siva Rajamanickam and Srinivas Eswar for sharing their insights on sparse matrix manipulations.

Cite As Get BibTex

Chirag Jain, Haowen Zhang, Alexander Dilthey, and Srinivas Aluru. Validating Paired-End Read Alignments in Sequence Graphs. In 19th International Workshop on Algorithms in Bioinformatics (WABI 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 143, pp. 17:1-17:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019) https://doi.org/10.4230/LIPIcs.WABI.2019.17

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.

Subject Classification

ACM Subject Classification
  • Mathematics of computing → Paths and connectivity problems
  • Applied computing → Computational genomics
Keywords
  • Sequence graphs
  • read mapping
  • index
  • sparse matrix-matrix multiplication

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