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Read Mapping on Genome Variation Graphs

Authors Kavya Vaddadi, Rajgopal Srinivasan, Naveen Sivadasan

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Author Details

Kavya Vaddadi
  • TCS Research, Hyderabad, India
Rajgopal Srinivasan
  • TCS Research, Hyderabad, India
Naveen Sivadasan
  • TCS Research, Hyderabad, India

Acknowledgements

Authors would like to thank the anonymous reviewers for their valuable comments. Authors would also like to acknowledge Kshitij Tayal for the initial implementation of the algorithm.

Cite AsGet BibTex

Kavya Vaddadi, Rajgopal Srinivasan, and Naveen Sivadasan. Read Mapping on Genome Variation Graphs. In 19th International Workshop on Algorithms in Bioinformatics (WABI 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 143, pp. 7:1-7:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/LIPIcs.WABI.2019.7

Abstract

Genome variation graphs are natural candidates to represent a pangenome collection. In such graphs, common subsequences are encoded as vertices and the genomic variations are captured by introducing additional labeled vertices and directed edges. Unlike a linear reference, a reference graph allows a rich representation of the genomic diversities and avoids reference bias. We address the fundamental problem of mapping reads to genome variation graphs. We give a novel mapping algorithm V-MAP for efficient identification of small subgraph of the genome graph for optimal gapped alignment of the read. V-MAP creates space efficient index using locality sensitive minimizer signatures computed using a novel graph winnowing and graph embedding onto metric space for fast and accurate mapping. Experiments involving graph constructed from the 1000 Genomes data and using both real and simulated reads show that V-MAP is fast, memory efficient and can map short reads, as well as PacBio/Nanopore long reads with high accuracy. V-MAP performance was significantly better than the state-of-the-art, especially for long reads.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Combinatorial algorithms
  • Applied computing → Computational genomics
Keywords
  • read mapping
  • pangenome
  • genome variation graphs
  • locality sensitive hashing

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