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BMC Genomics

, 14:S7

First Online: 21 January 2013DOI: 10.1186-1471-2164-14-S1-S7

Cite this article as: Nikolenko, S.I., Korobeynikov, A.I. & Alekseyev, M.A. BMC Genomics 2013 14Suppl 1: S7. doi:10.1186-1471-2164-14-S1-S7


Error correction of sequenced reads remains a difficult task, especially in single-cell sequencing projects with extremely non-uniform coverage. While existing error correction tools designed for standard multi-cell sequencing data usually come up short in single-cell sequencing projects, algorithms actually used for single-cell error correction have been so far very simplistic.

We introduce several novel algorithms based on Hamming graphs and Bayesian subclustering in our new error correction tool BAYES HAMMER. While BAYES HAMMER was designed for single-cell sequencing, we demonstrate that it also improves on existing error correction tools for multi-cell sequencing data while working much faster on real-life datasets. We benchmark BAYES HAMMER on both k-mer counts and actual assembly results with the SPADES genome assembler.

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Autor: Sergey I Nikolenko - Anton I Korobeynikov - Max A Alekseyev


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