Scalable and Versatile k-mer Indexing for High-Throughput Sequencing Data

Abstract : Philippe et al. (2011) proposed a data structure called Gk ar- rays for indexing and querying large collections of high-throughput sequencing data in main-memory. The data structure supports versa- tile queries for counting, locating, and analysing the coverage profile of k-mers in short-read data. The main drawback of the Gk arrays is its space-consumption, which can easily reach tens of gigabytes of main- memory even for moderate size inputs. We propose a compressed variant of Gk arrays that supports the same set of queries, but in both near-optimal time and space. In practice, the compressed Gk arrays scale up to much larger inputs with highly competitive query times compared to its non-compressed predecessor. The main applica- tions include variant calling, error correction, coverage profiling, and sequence assembly.
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Z. Cai et al. ISBRA'2013: International Symposium on Bioinformatics Research and Applications, May 2013, Charlotte, NC, United States. Spinger-Verlag, 7875 (7875), pp.237-248, 2013, Lecture Notes in Bioinformatics (LNBI). 〈http://www.cvent.com/events/isbra-2013〉
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Niko Välimäki, Eric Rivals. Scalable and Versatile k-mer Indexing for High-Throughput Sequencing Data. Z. Cai et al. ISBRA'2013: International Symposium on Bioinformatics Research and Applications, May 2013, Charlotte, NC, United States. Spinger-Verlag, 7875 (7875), pp.237-248, 2013, Lecture Notes in Bioinformatics (LNBI). 〈http://www.cvent.com/events/isbra-2013〉. 〈lirmm-00806103〉

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