Binning Metagenomic Sequences

Yu Lin⁰
(0) Research School of Computer Science, Australian National University

Abstract
Metagenomics studies have provided key insights into the composition and structure of microbial communities found in different environments. Among the techniques used to analyze metagenomic data, binning is considered as a crucial step in order to characterize the different species of microorganisms present in microbial communities. We propose two binning methods, GraphBin and MetaBCC-LR, to bin metagenomic sequences. GraphBin makes use of the assembly graph to refine binning results and to enable detecting shared sequences among multiple species. MetaBCC-LR is a new reference-free binning method which directly clusters long reads based on their k-mer coverage histograms and oligonucleotide composition.