Abstract
Current technologies allow the sequencing of microbial communities directly from the environment without prior culturing. One of the major problems when analyzing a microbial sample is to taxonomically annotate its reads to identify the species it contains. Taxonomic analysis of microbial communities requires reads clustering, a process referred to as binning. The major problems of metagenomics reads binning are the lack of taxonomically related genomes in existing reference databases, the uneven abundance ratio of species, and sequencing errors.
In this paper we present MetaProb 2 an unsupervised binning method based on reads assembly and probabilistic k-mers statistics. The novelties of MetaProb 2 are the use of minimizers to efficiently assemble reads into unitigs and a community detection algorithm based on graph modularity to cluster unitigs and to detect representative unitigs. The effectiveness of MetaProb 2 is demonstrated in both simulated and synthetic datasets in comparison with state-of-art binning tools such as MetaProb, AbundanceBin, Bimeta and MetaCluster.
Available at: https://github.com/frankandreace/metaprob2.
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Andreace, F., Pizzi, C., Comin, M. (2021). MetaProb 2: Improving Unsupervised Metagenomic Binning with Efficient Reads Assembly Using Minimizers. In: Jha, S.K., Măndoiu, I., Rajasekaran, S., Skums, P., Zelikovsky, A. (eds) Computational Advances in Bio and Medical Sciences. ICCABS 2020. Lecture Notes in Computer Science(), vol 12686. Springer, Cham. https://doi.org/10.1007/978-3-030-79290-9_2
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