Title: Statistical approach of functional profiling for a microbial community.

Authors: An, Lingling; Pookhao, Nauromal; Jiang, Hongmei; Xu, Jiannong

Published In PLoS One, (2014)

Abstract: Metagenomics is a relatively new but fast growing field within environmental biology and medical sciences. It enables researchers to understand the diversity of microbes, their functions, cooperation, and evolution in a particular ecosystem. Traditional methods in genomics and microbiology are not efficient in capturing the structure of the microbial community in an environment. Nowadays, high-throughput next-generation sequencing technologies are powerfully driving the metagenomic studies. However, there is an urgent need to develop efficient statistical methods and computational algorithms to rapidly analyze the massive metagenomic short sequencing data and to accurately detect the features/functions present in the microbial community. Although several issues about functions of metagenomes at pathways or subsystems level have been investigated, there is a lack of studies focusing on functional analysis at a low level of a hierarchical functional tree, such as SEED subsystem tree.A two-step statistical procedure (metaFunction) is proposed to detect all possible functional roles at the low level from a metagenomic sample/community. In the first step a statistical mixture model is proposed at the base of gene codons to estimate the abundances for the candidate functional roles, with sequencing error being considered. As a gene could be involved in multiple biological processes the functional assignment is therefore adjusted by utilizing an error distribution in the second step. The performance of the proposed procedure is evaluated through comprehensive simulation studies. Compared with other existing methods in metagenomic functional analysis the new approach is more accurate in assigning reads to functional roles, and therefore at more general levels. The method is also employed to analyze two real data sets.metaFunction is a powerful tool in accurate profiling functions in a metagenomic sample.

PubMed ID: 25198674

MeSH Terms: Humans; Metagenomics*