Approaches to metagenomic classification and assembly

Abstract

Microbiome is an ecological community of commensal, symbiotic, and pathogenic microorganisms that share the same environment. The study of microbiome, i.e., genetic material sampled directly from environmental samples is called metagenomics. In recent years, genome sequencing methods have dramatically improved and the number and variety of sequenced genomes has rapidly increased. New technology has significantly increased the variety and complexity of the microbiome research and ever-larger datasets present new challenges in analysis of metagenomic data. Two main tasks in metagenomic analysis are classification of sequenced metagenomic data into taxonomic group of any rank, such as a species, family, or class, and assembly of the data into longer contiguous sequences. The final aim of both tasks is to correctly identify species presented in the metagenomic sample. This has various applications in medicine (e.g. infectious disease diagnosis), development of biofuels, biotechnology, agriculture, and many other areas. In this paper, we present a description of common procedures and methods for metagenomic data analysis and challenges facing these procedures. We give an overview of existing software tools and a review of public genome databases used for metagenomic analysis. Finally, we explore possible improvements to the existing methods for metagenomic classification and assembly.