A novel abundance-based algorithm for binning metagenomic sequences using l-tuples

Yu Wei Wu, Yuzhen Ye

研究成果: 雜誌貢獻文章同行評審

106 引文 斯高帕斯(Scopus)

摘要

Metagenomics is the study of microbial communities sampled directly from their natural environment, without prior culturing. Among the computational tools recently developed for metagenomic sequence analysis, binning tools attempt to classify the sequences in a metagenomic dataset into different bins (i.e., species), based on various DNA composition patterns (e.g., the tetramer frequencies) of various genomes. Composition-based binning methods, however, cannot be used to classify very short fragments, because of the substantial variation of DNA composition patterns within a single genome. We developed a novel approach (AbundanceBin) for metagenomics binning by utilizing the different abundances of species living in the same environment. AbundanceBin is an application of the Lander-Waterman model to metagenomics, which is based on the l-tuple content of the reads. AbundanceBin achieved accurate, unsupervised, clustering of metagenomic sequences into different bins, such that the reads classified in a bin belong to species of identical or very similar abundances in the sample. In addition, AbundanceBin gave accurate estimations of species abundances, as well as their genome sizes-two important parameters for characterizing a microbial community. We also show that AbundanceBin performed well when the sequence lengths are very short (e.g., 75 bp) or have sequencing errors. By combining AbundanceBin and a composition-based method (MetaCluster), we can achieve even higher binning accuracy. Supplementary Material is available at www.liebertonline.com/cmb.
原文英語
頁(從 - 到)523-534
頁數12
期刊Journal of Computational Biology
18
發行號3
DOIs
出版狀態已發佈 - 3月 1 2011
對外發佈

ASJC Scopus subject areas

  • 分子生物學
  • 遺傳學
  • 計算數學
  • 建模與模擬
  • 計算機理論與數學

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