Journal Article

BlindCall: ultra-fast base-calling of high-throughput sequencing data by blind deconvolution

Chengxi Ye, Chiaowen Hsiao and Héctor Corrada Bravo

in Bioinformatics

Volume 30, issue 9, pages 1214-1219
Published in print May 2014 | ISSN: 1367-4803
Published online January 2014 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btu010

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Motivation: Base-calling of sequencing data produced by high-throughput sequencing platforms is a fundamental process in current bioinformatics analysis. However, existing third-party probabilistic or machine-learning methods that significantly improve the accuracy of base-calls on these platforms are impractical for production use due to their computational inefficiency.

Results: We directly formulate base-calling as a blind deconvolution problem and implemented BlindCall as an efficient solver to this inverse problem. BlindCall produced base-calls at accuracy comparable to state-of-the-art probabilistic methods while processing data at rates 10 times faster in most cases. The computational complexity of BlindCall scales linearly with read length making it better suited for new long-read sequencing technologies.

Availability and Implementation: BlindCall is implemented as a set of Matlab scripts available for download at http://cbcb.umd.edu/∼hcorrada/secgen.

Contact: hcorrada@umiacs.umd.edu

Journal Article.  4061 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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