Curated Papers > Highly Cited Authoritative Literature on "Single-Cell Sequencing"

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

Literature Information

DOI	10.1186/1471-2164-14-S1-S7
PMID	23368723
Journal	BMC genomics
Impact Factor	3.7
JCR Quartile	Q2
Publication Year	2013
Times Cited	233
Keywords	Bayesian clustering, single-cell sequencing, error correction, Hamming graphs, algorithms
Literature Type	Journal Article, Research Support, Non-U.S. Gov't
ISSN	1471-2164
Pages	S7
Issue	14 Suppl 1()
Authors	Sergey I Nikolenko, Anton I Korobeynikov, Max A Alekseyev

TL;DR

This study addresses the challenge of error correction in single-cell sequencing, where traditional tools fall short due to non-uniform coverage. The authors introduce BAYESHAMMER, a novel algorithm leveraging Hamming graphs and Bayesian subclustering, which not only enhances error correction in single-cell projects but also outperforms existing methods for multi-cell sequencing, demonstrating improved speed and accuracy on real-life datasets.

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Bayesian clustering · single-cell sequencing · error correction · Hamming graphs · algorithms

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.

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Primary Questions Addressed

1. What are the specific limitations of existing error correction tools for single-cell sequencing that BayesHammer addresses?
2. How does the performance of BayesHammer compare to traditional error correction methods in terms of speed and accuracy?
3. Can the algorithms used in BayesHammer be adapted for other types of sequencing technologies beyond single-cell and multi-cell sequencing?
4. What are the potential implications of improved error correction in single-cell sequencing for downstream analyses, such as gene expression profiling?
5. How does the integration of Hamming graphs and Bayesian subclustering enhance the error correction process in BayesHammer?

Key Findings

Background and Objectives

Single-cell sequencing has revolutionized genomic studies, particularly for uncultivated bacteria. However, error correction in single-cell sequencing is challenging due to the non-uniform coverage of datasets. Existing tools often fail to adequately correct errors in such scenarios. This study introduces BAYESHAMMER, a novel error correction tool that utilizes Bayesian clustering and Hamming graphs to improve error correction for both single-cell and multi-cell sequencing datasets.

Main Methods/Materials/Experimental Design

BAYESHAMMER's workflow consists of several key steps, detailed below:

1. Counting k-mers: K-mers are extracted from the input reads, with statistics on their counts and quality calculated.
2. Constructing Hamming Graph: A graph is created where k-mers are vertices, and edges connect k-mers with a Hamming distance below a threshold.
3. Bayesian Subclustering: Connected components in the Hamming graph are further refined into clusters using Bayesian methods, considering k-mer quality.
4. Selecting Solid k-mers: Clusters with high-quality k-mers are identified as "solid," serving as reliable references for correction.
5. Iterative Expansion: The solid k-mers are iteratively expanded to include k-mers that are covered by them.
6. Reads Correction: The final step involves correcting the original reads based on the solid k-mers, using a consensus approach.

Key Results and Findings

• BAYESHAMMER significantly reduces the error rate in both single-cell and multi-cell datasets compared to existing tools.
• In benchmarks, BAYESHAMMER outperformed HAMMER, QUAKE, EULER-SR, and CAMEL in terms of both speed and accuracy.
• The tool was tested on three datasets: single-cell E. coli, single-cell S. aureus, and multi-cell E. coli, showing improvements in k-mer statistics and assembly results.

Main Conclusions/Significance/Innovation

BAYESHAMMER addresses the unique challenges posed by single-cell sequencing data through its innovative use of Bayesian clustering and Hamming graphs. The tool not only improves error correction but also enhances the overall efficiency of genome assembly, making it a significant advancement in the field of bioinformatics.

Research Limitations and Future Directions

• While BAYESHAMMER demonstrates improved performance, the authors note the need for further refinements, such as better modeling of error distributions and handling contamination from other DNA sources.
• Future work will explore the application of minimizers to reduce memory usage and enhance the handling of paired information in sequencing datasets.

Summary Table of Key Metrics

Metric	BAYESHAMMER	QUAKE	EULER-SR	CAMEL
Running Time	Faster	Moderate	Moderate	Slow
Error Rate Reduction	Significant	Moderate	Moderate	Low
Assembly Improvement	Yes	No	Yes	Yes
Dataset Types	Single-cell, Multi-cell	Multi-cell	Single-cell	Single-cell

This structured summary encapsulates the key aspects of the BAYESHAMMER study, emphasizing its methodology, results, and contributions to the field of genomic error correction.

References

1. Short read fragment assembly of bacterial genomes. - Mark J Chaisson;Pavel A Pevzner - Genome research (2008)
2. Microbial community profiling for human microbiome projects: Tools, techniques, and challenges. - Micah Hamady;Rob Knight - Genome research (2009)
3. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. - Anton Bankevich;Sergey Nurk;Dmitry Antipov;Alexey A Gurevich;Mikhail Dvorkin;Alexander S Kulikov;Valery M Lesin;Sergey I Nikolenko;Son Pham;Andrey D Prjibelski;Alexey V Pyshkin;Alexander V Sirotkin;Nikolay Vyahhi;Glenn Tesler;Max A Alekseyev;Pavel A Pevzner - Journal of computational biology : a journal of computational molecular cell biology (2012)
4. QUAST: quality assessment tool for genome assemblies. - Alexey Gurevich;Vladislav Saveliev;Nikolay Vyahhi;Glenn Tesler - Bioinformatics (Oxford, England) (2013)
5. Single cell genome amplification accelerates identification of the apratoxin biosynthetic pathway from a complex microbial assemblage. - Rashel V Grindberg;Thomas Ishoey;Dumitru Brinza;Eduardo Esquenazi;R Cameron Coates;Wei-ting Liu;Lena Gerwick;Pieter C Dorrestein;Pavel Pevzner;Roger Lasken;William H Gerwick - PloS one (2011)
6. Reducing storage requirements for biological sequence comparison. - Michael Roberts;Wayne Hayes;Brian R Hunt;Stephen M Mount;James A Yorke - Bioinformatics (Oxford, England) (2004)
7. Metagenomic analysis of the human distal gut microbiome. - Steven R Gill;Mihai Pop;Robert T Deboy;Paul B Eckburg;Peter J Turnbaugh;Buck S Samuel;Jeffrey I Gordon;David A Relman;Claire M Fraser-Liggett;Karen E Nelson - Science (New York, N.Y.) (2006)
8. The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. - Peter J A Cock;Christopher J Fields;Naohisa Goto;Michael L Heuer;Peter M Rice - Nucleic acids research (2010)
9. Efficient de novo assembly of single-cell bacterial genomes from short-read data sets. - Hamidreza Chitsaz;Joyclyn L Yee-Greenbaum;Glenn Tesler;Mary-Jane Lombardo;Christopher L Dupont;Jonathan H Badger;Mark Novotny;Douglas B Rusch;Louise J Fraser;Niall A Gormley;Ole Schulz-Trieglaff;Geoffrey P Smith;Dirk J Evers;Pavel A Pevzner;Roger S Lasken - Nature biotechnology (2011)
10. Genomic sequencing of single microbial cells from environmental samples. - Thomas Ishoey;Tanja Woyke;Ramunas Stepanauskas;Mark Novotny;Roger S Lasken - Current opinion in microbiology (2008)

Literatures Citing This Work

1. The draft assembly of the radically organized Stylonychia lemnae macronuclear genome. - Samuel H Aeschlimann;Franziska Jönsson;Jan Postberg;Nicholas A Stover;Robert L Petera;Hans-Joachim Lipps;Mariusz Nowacki;Estienne C Swart - Genome biology and evolution (2014)
2. Draft Genome Sequence of Cronobacter sakazakii Clonal Complex 45 Strain HPB5174, Isolated from a Powdered Infant Formula Facility in Ireland. - Arthur W Pightling;Franco Pagotto - Genome announcements (2014)
3. Hidden diversity in honey bee gut symbionts detected by single-cell genomics. - Philipp Engel;Ramunas Stepanauskas;Nancy A Moran - PLoS genetics (2014)
4. Genome Sequence of Pectobacterium atrosepticum Strain 21A. - Yevgeny Nikolaichik;Vladimir Gorshkov;Yuri Gogolev;Leonid Valentovich;Anatoli Evtushenkov - Genome announcements (2014)
5. Draft Genome Sequences of Two Clostridium botulinum Group II (Nonproteolytic) Type B Strains (DB-2 and KAPB-3). - Nicholas Petronella;Robyn Kenwell;Franco Pagotto;Arthur W Pightling - Genome announcements (2014)
6. HyDA-Vista: towards optimal guided selection of k-mer size for sequence assembly. - Basir Shariat;Narjes Sadat Movahedi;Hamidreza Chitsaz;Christina Boucher - BMC genomics (2014)
7. Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform. - Melanie Schirmer;Umer Z Ijaz;Rosalinda D'Amore;Neil Hall;William T Sloan;Christopher Quince - Nucleic acids research (2015)
8. Genome sequence of Coxiella burnetii strain Namibia. - Mathias C Walter;Caroline Öhrman;Kerstin Myrtennäs;Andreas Sjödin;Mona Byström;Pär Larsson;Anna Macellaro;Mats Forsman;Dimitrios Frangoulidis - Standards in genomic sciences (2014)
9. Evaluation of methods to purify virus-like particles for metagenomic sequencing of intestinal viromes. - Manuel Kleiner;Lora V Hooper;Breck A Duerkop - BMC genomics (2015)
10. Draft Genome Sequences of Four Vibrio parahaemolyticus Isolates from Clinical Cases in Canada. - Swapan Banerjee;Nicholas Petronella;Courtney Chew Leung;Jeffrey Farber - Genome announcements (2015)

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