MetaComBin: combining abundances and overlaps for binning metagenomics reads

Francesco Tomasella ¹ Cinzia Pizzi ^1,2*

• ¹ Department of Information Engineering, School of Engineering, University of Padua, Padua, Veneto, Italy
• ² University of Padua, Padua, Italy

Metagenomics is the discipline that studies heterogeneous microbial samples extracted directly from their natural environment, for example from soil, water, or the human body. The detection and quantification of species that populate microbial communities have been the subject of many recent studies based on classification and clustering, motivated by being the first step in more complex pipelines (e.g. for functional analysis, de-novo assembly or comparison of metagenomes). Metagenomics has impact on both environmental studies and precision medicine, thus it is crucial to be able to improve the quality of species identification through computational tools.In this paper we explore the idea of improving the overall quality of metagenomics binning at reads-level by proposing a computational framework that sequentially combine two complementary read binning approaches: one based on species abundances determination, and another one relying on reads overlap in order to cluster reads together. We called this approach MetaComBin (Metagenomics Combined Binning).The results of our experiments with the MetaComBin approach showed that the combination of two tools, based on different approaches, can lead to the improvement of the clustering quality in realistic conditions where the number of species is not known beforehand.