title: Classification of metagenomic sequences: methods and challenges.
abstract: Characterizing the taxonomic diversity of microbial communities is one of the primary objectives of metagenomic studies. Taxonomic analysis of microbial communities, a process referred to as binning, is challenging for the following reasons. Primarily, query sequences originating from the genomes of most microbes in an environmental sample lack taxonomically related sequences in existing reference databases. This absence of a taxonomic context makes binning a very challenging task. Limitations of current sequencing platforms, with respect to short read lengths and sequencing errors/artifacts, are also key factors that determine the overall binning efficiency. Furthermore, the sheer volume of metagenomic datasets also demands highly efficient algorithms that can operate within reasonable requirements of compute power. This review discusses the premise, methodologies, advantages, limitations and challenges of various methods available for binning of metagenomic datasets obtained using the shotgun sequencing approach. Various parameters as well as strategies used for evaluating binning efficiency are then reviewed.
authors.affiliation: Bio-Sciences Division, TCS Innovation Labs, India. sharmila@atc.tcs.com
authors.forename: Sharmila S
authors.initials: SS
authors.lastname: Mande
authors.forename: Monzoorul Haque
authors.initials: MH
authors.lastname: Mohammed
authors.forename: Tarini Shankar
authors.initials: TS
authors.lastname: Ghosh
mesh_terms: D000465:Algorithms; D030541:Databases, Genetic; D054892:Metagenome; D056186:Metagenomics; D017422:Sequence Analysis, DNA / Q000379:methods