MetaProb: Accurate metagenomic reads binning based on probabilistic sequence signatures

Abstract 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 [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online. </jats:sec

Costruzione efficiente di overlap graph mediante FM-index per il metagenomic binning

Il metagenomic binnig indica il raggruppamento delle read metagenomiche di specie differenti in cluster prima della ricostruzione del genoma originale. In questa tesi è stato modificato un software per realizzare tale raggruppamento studiando i k-mer condivisi tra le read. In particolare, si utilizzeranno come strumenti di ricerca su stringhe il suffix array, il longest common prefixes, l'FM-index e la Burrows Wheeler transform per ridurre il consumo di memoria RAM.ope