Ultrafast clustering algorithms for metagenomic sequence analysis

The rapid advances of high-throughput sequencing technologies dramatically prompted metagenomic studies of microbial communities that exist at various environments. Fundamental questions in metagenomics include the identities, composition and dynamics of microbial populations and their functions and...

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Vydáno v:Briefings in bioinformatics Ročník 13; číslo 6; s. 656 - 668
Hlavní autoři: Li, W., Fu, L., Niu, B., Wu, S., Wooley, J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Oxford Publishing Limited (England) 01.11.2012
Oxford University Press
Témata:
Algorithms
Bioinformatics
Cluster Analysis
Data analysis
Errors
Genomics
Metagenome
Metagenomics
Microbial activity
Microbiology
Sequence Analysis, DNA
ISSN:1467-5463, 1477-4054, 1477-4054
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Shrnutí:The rapid advances of high-throughput sequencing technologies dramatically prompted metagenomic studies of microbial communities that exist at various environments. Fundamental questions in metagenomics include the identities, composition and dynamics of microbial populations and their functions and interactions. However, the massive quantity and the comprehensive complexity of these sequence data pose tremendous challenges in data analysis. These challenges include but are not limited to ever-increasing computational demand, biased sequence sampling, sequence errors, sequence artifacts and novel sequences. Sequence clustering methods can directly answer many of the fundamental questions by grouping similar sequences into families. In addition, clustering analysis also addresses the challenges in metagenomics. Thus, a large redundant data set can be represented with a small non-redundant set, where each cluster can be represented by a single entry or a consensus. Artifacts can be rapidly detected through clustering. Errors can be identified, filtered or corrected by using consensus from sequences within clusters.
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ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbs035