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

BMC Genomics

Proceedings

Sergey I Nikolenko¹  Max A Alekseyev²  Anton I Korobeynikov³ 

[1] Algorithmic Biology Laboratory, Academic University, St. Petersburg, Russia;Algorithmic Biology Laboratory, Academic University, St. Petersburg, Russia;Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, USA;Algorithmic Biology Laboratory, Academic University, St. Petersburg, Russia;St. Petersburg State University, Russia;

关键词: Multiple Displacement Amplification;    Uniform Coverage;    Assembly Result;    Solid Center;    Consensus String;   

DOI  :  10.1186/1471-2164-14-S1-S7

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© Nikolenko et al.; licensee BioMed Central Ltd. 2013