General Information
| Description | Accurate metagenomic reads binning based on probabilistic sequence signatures |
| Homepage | https://bitbucket.org/samu661/metaprob |
Publications
MetaProb: accurate metagenomic reads binning based on probabilistic sequence signatures
Motivation
: Sequencing technologies allow the sequencing of microbial communities directly from the environment without prior culturing. Taxonomic analysis of microbial communities, a process referred to as binning, is one of the most challenging tasks when analyzing metagenomic reads data. The major problems are the lack of taxonomically related genomes in existing reference databases, the uneven abundance ratio of species and the limitations due to short read lengths and sequencing errors.
Results
: MetaProb is a novel assembly-assisted tool for unsupervised metagenomic binning. The novelty of MetaProb derives from solving a few important problems: how to divide reads into groups of independent reads, so that k -mer frequencies are not overestimated; how to convert k -mer counts into probabilistic sequence signatures, that will correct for variable distribution of k -mers, and for unbalanced groups of reads, in order to produce better estimates of the underlying genome statistic; how to estimate the number of species in a dataset. We show that MetaProb is more accurate and efficient than other state-of-the-art tools in binning both short reads datasets ( F -measure 0.87) and long reads datasets ( F -measure 0.97) for various abundance ratios. Also, the estimation of the number of species is more accurate than MetaCluster. On a real human stool dataset MetaProb identifies the most predominant species, in line with previous human gut studies.
Availability and Implementation:
https://bitbucket.org/samu661/metaprob
Contacts:
cinzia.pizzi@dei.unipd.it or comin@dei.unipd.it
Supplementary information:
Supplementary data are available at Bioinformatics online.
Related Tools
MTR
Published December 2010
Taxonomic annotation of short metagenomic reads using clustering at multiple taxonomic ranks
MetaCluster 5.0
Published September 2012
A two-round binning approach for metagenomic data for low-abundance species in a noisy sample
ISRNA
Published December 2013
An integrative online toolkit for short reads from high-throughput sequencing data
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