Deriving enzymatic and taxonomic signatures of metagenomes from short read data

Uri Weingart,Uri Gophna,Erez Persi,David Horn

doi:10.1186/1471-2105-11-390

Abstract

BackgroundWe propose a method for deriving enzymatic signatures from short read metagenomic data of unknown species. The short read data are converted to six pseudo-peptide candidates. We search for occurrences of Specific Peptides (SPs) on the latter. SPs are peptides that are indicative of enzymatic function as defined by the Enzyme Commission (EC) nomenclature. The number of SP hits on an ensemble of short reads is counted and then converted to estimates of numbers of enzymatic genes associated with different EC categories in the studied metagenome. Relative amounts of different EC categories define the enzymatic spectrum, without the need to perform genomic assemblies of short reads.ResultsThe method is developed and tested on 22 bacteria for which there exist many EC annotations in Uniprot. Enzymatic signatures are derived for 3 metagenomes, and their functional profiles are explored.We extend the SP methodology to taxon-specific SPs (TSPs), allowing us to estimate taxonomic features of metagenomic data from short reads. Using recent Swiss-Prot data we obtain TSPs for different phyla of bacteria, and different classes of proteobacteria. These allow us to analyze the major taxonomic content of 4 different metagenomic data-sets.ConclusionsThe SP methodology can be successfully extended to applications on short read genomic and metagenomic data. This leads to direct derivation of enzymatic signatures from raw short reads. Furthermore, by employing TSPs, one obtains valuable taxonomic information.

Highlights

We propose a method for deriving enzymatic signatures from short read metagenomic data of unknown species
Using the errors determined by the training procedure, we quote the quality of fits by using the chi-square test, which is expected to be of the order of the number of degrees of freedom, E[(X-μ)2/s2] = N
The poor chi-square values reflect the fact that metagenomic averages smooth-out differences

Summary

Introduction

We propose a method for deriving enzymatic signatures from short read metagenomic data of unknown species. Characterizing complex microbial ecosystems remains a challenge for metagenomics Environments such as soil, containing many thousands of species require massive sequencing power to obtain a reasonable coverage of the microbial community. The so called “deep sequencing” technologies offer hope due to their tremendously high-throughput - the Illumina Genome analyzer and the SOLiD 3 (Life Technologies) can currently produce over 10 Gb, and up to 40 Gb of high quality reads, respectively. These fantastic capacities come with a price - a short read length that currently stands at 100 bases or lower for both these technologies. For a recent review of experimental and computational achievements and challenges in metagenomics see Wooley et al [2]

Methods

Results

Conclusion