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

By Sergey I. Nikolenko, Anton I. Korobeynikov, Max A. Alekseyev. BMC Genomics, 2012

447 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/03218468106d5797d07c33dc74c6d397491efecd

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Citations in the corpus

Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform (2015, background): “We compared different error removal techniques including trimming the start and end of the reads, trimming based on quality scores with Sickle (https://github.com/najoshi/ sickle), error correction with BayesHammer (13) and over-lapping reads with PEAR (v0.”
Improvements to PATRIC, the all-bacterial Bioinformatics Database and Analysis Resource Center (2016, methodology): “PATRIC provides several assembly strategies (Figure 3) that include algorithms for base calling correction [BayesHammer (12) and Kmer-genie (13)], and assemblers [Velvet (14), IDBA (15), Spades (16), MEGAHIT (17) and MiniASM (18)].”
metaSPAdes: a new versatile metagenomic assembler. (2017, methodology): “We also parallelized the most time-consuming procedures for transforming the de Bruijn graph into the assembly graph (e.g., processing of bulges) and optimized the BayesHammer error-correction module of SPAdes (Nikolenko et al. 2013).”
rnaSPAdes: a de novo transcriptome assembler and its application to RNA-Seq data (2018, unknown): “Although the 8 datasets used in this article may not represent all kinds of transcriptomic data, they are sufficient for comparing different assembly tools and detecting their strengths and disadvantages.”
Using SPAdes De Novo Assembler (2020, methodology): “The SPAdes package includes two modules for read-error correction: BayesHam-mer (Nikolenko, Korobeynikov, & Alekseyev, 2013) for correcting Illumina reads and Ion-Hammer (Ershov et al., 2019) for correcting IonTorrent data.”