Use of simulated data sets to evaluate the fidelity of metagenomic processing methods

Metagenomics is a rapidly emerging field of research for studying microbial communities. To evaluate methods presently used to process metagenomic sequences, we constructed three simulated data sets of varying complexity by combining sequencing reads randomly selected from 113 isolate genomes. These data sets were designed to model real metagenomes in terms of complexity and phylogenetic composition. We assembled sampled reads using three commonly used genome assemblers (Phrap, Arachne and JAZZ), and predicted genes using two popular gene-finding pipelines (fgenesb and CRITICA/GLIMMER). The phylogenetic origins of the assembled contigs were predicted using one sequence similarity-based (blast hit distribution) and two sequence composition-based (PhyloPythia, oligonucleotide frequencies) binning methods. We explored the effects of the simulated community structure and method combinations on the fidelity of each processing step by comparison to the corresponding isolate genomes. The simulated data sets are available online to facilitate standardized benchmarking of tools for metagenomic analysis.     

Accurate phylogenetic classification of variable-length DNA fragments

Metagenome studies have retrieved vast amounts of sequence data from a variety of environments leading to new discoveries and insights into the uncultured microbial world. Except for very simple communities, the encountered diversity has made fragment assembly and the subsequent analysis a challenging problem. A taxonomic characterization of metagenomic fragments is required for a deeper understanding of shotgun-sequenced microbial communities, but success has mostly been limited to sequences containing phylogenetic marker genes. Here we present PhyloPythia, a composition-based classifier that combines higher-level generic clades from a set of 340 completed genomes with sample-derived population models. Extensive analyses on synthetic and real metagenome data sets showed that PhyloPythia allows the accurate classification of most sequence fragments across all considered taxonomic ranks, even for unknown organisms. The method requires no more than 100 kb of training sequence for the creation of accurate models of sample-specific populations and can assign fragments >or=1 kb with high specificity.     

Dictionary-driven prokaryotic gene finding

Gene identification, also known as gene finding or gene recognition, is among the important problems of molecular biology that have been receiving increasing attention with the advent of large scale sequencing projects. Previous strategies for solving this problem can be categorized into essentially two schools of thought: one school employs sequence composition statistics, whereas the other relies on database similarity searches. In this paper, we propose a new gene identification scheme that combines the best characteristics from each of these two schools. In particular, our method determines gene candidates among the ORFs that can be identified in a given DNA strand through the use of the Bio-Dictionary, a database of patterns that covers essentially all of the currently available sample of the natural protein sequence space. Our approach relies entirely on the use of redundant patterns as the agents on which the presence or absence of genes is predicated and does not employ any additional evidence, e.g. ribosome-binding site signals. The Bio-Dictionary Gene Finder (BDGF), the algorithm’s implementation, is a single computational engine able to handle the gene identification task across distinct archaeal and bacterial genomes. The engine exhibits performance that is characterized by simultaneous very high values of sensitivity and specificity, and a high percentage of correctly predicted start sites. Using a collection of patterns derived from an old (June 2000) release of the Swiss-Prot/TrEMBL database that contained 451 602 proteins and fragments, we demonstrate our method’s generality and capabilities through an extensive analysis of 17 complete archaeal and bacterial genomes. Examples of previously unreported genes are also shown and discussed in detail.     

Genomic analysis of immunity in a Urochordate and the emergence of the vertebrate immune system: “waiting for Godot”

Genome-wide sequence analysis in the invertebrate chordate, Ciona intestinalis, has provided a comprehensive picture of immune-related genes in an organism that occupies a key phylogenetic position in vertebrate evolution. The pivotal genes for adaptive immunity, such as the major histocompatibility complex (MHC) class I and II genes, T-cell receptors, or dimeric immunoglobulin molecules, have not been identified in the Ciona genome. Many genes involved in innate immunity have been identified, including complement components, Toll-like receptors, and the genes involved in intracellular signal transduction of immune responses, and show both expansion and unexpected diversity in comparison with the vertebrates. In addition, a number of genes were identified which predicted integral membrane proteins with extracellular C-type lectin or immunoglobulin domains and intracellular immunoreceptor tyrosine-based inhibitory motifs (ITIMs) and immunoreceptor tyrosine-based activation motifs (ITAMs) (plus their associated signal transduction molecules), suggesting that activating and inhibitory receptors have an MHC-independent function and an early evolutionary origin. A crucial component of vertebrate adaptive immunity is somatic diversification, and the recombination activating genes (RAG) and activation-induced cytidine deaminase (AID) genes responsible for the Generation of diversity are not present in Ciona. However, there are key V regions, the essential feature of an immunoglobulin superfamily VC1-like core, and possible proto-MHC regions scattered throughout the genome waiting for Godot.