BayesHammer: Bayesian clustering for error correction in single-cell sequencing

• Published in: BMC genomics Vol. 14; no. Suppl 1; p. S7
• Main Authors: Nikolenko, Sergey I, Korobeynikov, Anton I, Alekseyev, Max A
• Format: Journal Article
• Language: English
• Published: London BioMed Central 2013; Springer Nature B.V
• Subjects: Algorithms; Animal Genetics and Genomics; Bacteria; Bayes Theorem; Biomedical and Life Sciences; Cluster Analysis; E coli; Error correction & detection; Escherichia coli - genetics; Genomes; Life Sciences; Microarrays; Microbial Genetics and Genomics; Plant Genetics and Genomics; Proceedings; Proteomics; Sequence Analysis, DNA; Single-Cell Analysis; Studies; Assembly Result; Uniform Coverage; Consensus String; Multiple Displacement Amplification; Solid Center
• ISSN: 1471-2164; 1471-2164
• 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 B AYES H AMMER . While B AYES H AMMER 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 B AYES H AMMER on both k -mer counts and actual assembly results with the SPA DES genome assembler.