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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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Contributor : Eric Rivals <>
Submitted on : Friday, March 29, 2013 - 2:34:19 PM
Last modification on : Tuesday, March 17, 2020 - 1:48:01 AM
Long-term archiving on: : Sunday, June 30, 2013 - 4:02:06 AM


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  • HAL Id : lirmm-00806103, version 1



Niko Välimäki, Eric Rivals. Scalable and Versatile k-mer Indexing for High-Throughput Sequencing Data. ISBRA'2013: International Symposium on Bioinformatics Research and Applications, May 2013, Charlotte, NC, United States. pp.237-248. ⟨lirmm-00806103⟩



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