MetaCluster 5.0: a two-round binning approach for metagenomic data for low-abundance species in a noisy sample

Yi, Wang; Leung, Ho-fung; Yiu, Siu Ming; Chin, Francis Y. L.

doi:10.1093/bioinformatics/bts397

Cited by 120 publications

(101 citation statements)

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“…We compared MBMC with two taxonomy-independent methods, AbundanceBin (Wu and Ye, 2011) and MetaCluster 5.1 (Wang et al, 2012). We ran MBMC without the information of the number of species present.…”

Section: Comparisons With Other Methodsmentioning

confidence: 99%

“…Two HMP real datasets, one human gut dataset, and HiSeq/MiSeq datasets were used because such datasets were widely used in assessing other metagenomic tools (Alneberg et al, 2014;Kultima et al, 2012;Namiki et al, 2012;Wang et al, 2012;Wood and Salzberg, 2014). The HMP mock dataset was used because it was different from other three datasets and contained only single-end read.…”

Section: Mbmc Was Superior To Other Methods On Experimental Datasetsmentioning

confidence: 99%

“…For each species, Illumina paired-end reads were simulated using MetaSim (Richter et al, 2008) with the read length as 75 base pairs and with the empirical error model in MetaSim. The 75 base pair long reads were used because many benchmark datasets contained reads of such a length, and it was much more challenging to bin shorter reads than to bin longer reads (Wang et al, 2012;Wu and Ye, 2011). We also simulated datasets with longer reads, and in general, MBMC worked even better on these datasets.…”

Section: Simulated and Experimental Datasetsmentioning

confidence: 99%

“…One category is the taxonomy-dependent methods (Brady and Salzberg, 2009;Haque et al, 2009;Huson et al, 2007;Krause et al, 2008;Matsen et al, 2010;McHardy et al, 2007;Meyer et al, 2008;Mohammed et al, 2011;Stark et al, 2010;Wood and Salzberg, 2014;Wu and Eisen, 2008), which compare reads with sequences in public databases or models inferred from public databases to group reads and determine which known species are present. The other category is the taxonomy-independent methods (Chatterji et al, 2008;Diaz et al, 2009;Wang et al, 2012Wang et al, , 2015Wu and Ye, 2011), which employ the difference of GC content (guanine-cytosine content), k-mer (a k base pairs long DNA segment) frequencies, etc., of different microbes in the same samples to bin reads.…”

mentioning

confidence: 99%

“…The difficulty stems from the fact that the majority of microbial species are still not sequenced, therefore making read binning from these species problematic by the developed taxonomy-dependent approaches (Albertsen et al, 2013;Mande et al, 2012). The difficulty also commonly arises when the genome coverage of different microbial species is similar (<2 fold difference), which renders it unlikely to separate reads from species with similar abundance by taxonomy-independent methods such as AbundanceBin (Wu and Ye, 2011) and MetaCluster (Wang et al, 2012). In this study, the genome coverage (or sequence depth) of a genome in a dataset under consideration is defined as the sum of the length of all reads from this genome in this dataset divided by the length of the genome.…”

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confidence: 99%

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MBMC: An Effective Markov Chain Approach for Binning Metagenomic Reads from Environmental Shotgun Sequencing Projects

Wang

2016

OMICS: A Journal of Integrative Biology

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Metagenomics is a next-generation omics field currently impacting postgenomic life sciences and medicine. Binning metagenomic reads is essential for the understanding of microbial function, compositions, and interactions in given environments. Despite the existence of dozens of computational methods for metagenomic read binning, it is still very challenging to bin reads. This is especially true for reads from unknown species, from species with similar abundance, and/or from low-abundance species in environmental samples. In this study, we developed a novel taxonomy-dependent and alignment-free approach called MBMC (Metagenomic Binning by Markov Chains). Different from all existing methods, MBMC bins reads by measuring the similarity of reads to the trained Markov chains for different taxa instead of directly comparing reads with known genomic sequences. By testing on more than 24 simulated and experimental datasets with species of similar abundance, species of low abundance, and/or unknown species, we report here that MBMC reliably grouped reads from different species into separate bins. Compared with four existing approaches, we demonstrated that the performance of MBMC was comparable with existing approaches when binning reads from sequenced species, and superior to existing approaches when binning reads from unknown species. MBMC is a pivotal tool for binning metagenomic reads in the current era of Big Data and postgenomic integrative biology. The MBMC software can be freely downloaded at http://hulab.ucf.edu/research/projects/ metagenomics/MBMC.html.

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Section: Comparisons With Other Methodsmentioning

confidence: 99%