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

TL;DR

BayesHammer introduces novel Bayesian clustering algorithms for error correction in single-cell sequencing, significantly improving accuracy and speed over existing tools for both single-cell and multi-cell data.

Contribution

It presents new algorithms based on Hamming graphs and Bayesian subclustering specifically designed for single-cell sequencing error correction.

Findings

Improves error correction accuracy in single-cell sequencing.

Faster processing compared to existing tools on real datasets.

Enhances multi-cell sequencing correction performance.

Abstract

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 BayesHammer. While BayesHammer 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 BayesHammer on both $k$-mer counts and actual assembly results with the SPAdes genome assembler.