We present metaSNV, a tool for single nucleotide variant (SNV) analysis in metagenomic samples, capable of comparing populations of thousands of bacterial and archaeal species. The tool uses as input nucleotide sequence alignments to reference genomes in standard SAM/BAM format, performs SNV calling for individual samples and across the whole data set, and generates various statistics for individual species including allele frequencies and nucleotide diversity per sample as well as distances and fixation indices across samples. Using published data from 676 metagenomic samples of different sites in the oral cavity, we show that the results of metaSNV are comparable to those of MIDAS, an alternative implementation for metagenomic SNV analysis, while data processing is faster and has a smaller storage footprint. Moreover, we implement a set of distance measures that allow the comparison of genomic variation across metagenomic samples and delineate sample-specific variants to enable the tracking of specific strain populations over time. The implementation of metaSNV is available at: http://metasnv.embl.de/.

Accumulating evidence indicates that the gut microbiota affects colorectal cancer development, but previous studies have varied in population, technical methods, and associations with cancer. Understanding these variations is needed for comparisons and for potential pooling across studies. Therefore, we performed whole-genome shotgun sequencing on fecal samples from 52 pre-treatment colorectal cancer cases and 52 matched controls from Washington, DC. We compared findings from a previously published 16S rRNA study to the metagenomics-derived taxonomy within the same population. In addition, metagenome-predicted genes, modules, and pathways in the Washington, DC cases and controls were compared to cases and controls recruited in France whose specimens were processed using the same platform. Associations between the presence of fecal Fusobacteria, Fusobacterium, and Porphyromonas with colorectal cancer detected by 16S rRNA were reproduced by metagenomics, whereas higher relative abundance of Clostridia in cancer cases based on 16S rRNA was merely borderline based on metagenomics. This demonstrated that within the same sample set, most, but not all taxonomic associations were seen with both methods. Considering significant cancer associations with the relative abundance of genes, modules, and pathways in a recently published French metagenomics dataset, statistically significant associations in the Washington, DC population were detected for four out of 10 genes, three out of nine modules, and seven out of 17 pathways. In total, colorectal cancer status in the Washington, DC study was associated with 39% of the metagenome-predicted genes, modules, and pathways identified in the French study. More within and between population comparisons are needed to identify sources of variation and disease associations that can be reproduced despite these variations. Future studies should have larger sample sizes or pool data across studies to have sufficient power to detect associations that are reproducible and significant after correction for multiple testing.

MOCAT is a highly configurable, modular pipeline for fast, standardized processing of single or paired-end sequencing data generated by the Illumina platform. The pipeline uses state-of-the-art programs to quality control, map, and assemble reads from metagenomic samples sequenced at a depth of several billion base pairs, and predict protein-coding genes on assembled metagenomes. Mapping against reference databases allows for read extraction or removal, as well as abundance calculations. Relevant statistics for each processing step can be summarized into multi-sheet Excel documents and queryable SQL databases. MOCAT runs on UNIX machines and integrates seamlessly with the SGE and PBS queuing systems, commonly used to process large datasets. The open source code and modular architecture allow users to modify or exchange the programs that are utilized in the various processing steps. Individual processing steps and parameters were benchmarked and tested on artificial, real, and simulated metagenomes resulting in an improvement of selected quality metrics. MOCAT can be freely downloaded at http://www.bork.embl.de/mocat/.

Due to the complexity of the protocols and a limited knowledge of the nature of microbial communities, simulating metagenomic sequences plays an important role in testing the performance of existing tools and data analysis methods with metagenomic data. We developed metagenomic read simulators with platform-specific (Sanger, pyrosequencing, Illumina) base-error models, and simulated metagenomes of differing community complexities. We first evaluated the effect of rigorous quality control on Illumina data. Although quality filtering removed a large proportion of the data, it greatly improved the accuracy and contig lengths of resulting assemblies. We then compared the quality-trimmed Illumina assemblies to those from Sanger and pyrosequencing. For the simple community (10 genomes) all sequencing technologies assembled a similar amount and accurately represented the expected functional composition. For the more complex community (100 genomes) Illumina produced the best assemblies and more correctly resembled the expected functional composition. For the most complex community (400 genomes) there was very little assembly of reads from any sequencing technology. However, due to the longer read length the Sanger reads still represented the overall functional composition reasonably well. We further examined the effect of scaffolding of contigs using paired-end Illumina reads. It dramatically increased contig lengths of the simple community and yielded minor improvements to the more complex communities. Although the increase in contig length was accompanied by increased chimericity, it resulted in more complete genes and a better characterization of the functional repertoire. The metagenomic simulators developed for this research are freely available.

BACKGROUND: Corals worldwide are in decline due to climate change effects (e.g., rising seawater temperatures), pollution, and exploitation. The ability of corals to cope with these stressors in the long run depends on the evolvability of the underlying genetic networks and proteins, which remain largely unknown. A genome-wide scan for positively selected genes between related coral species can help to narrow down the search space considerably. METHODOLOGY/PRINCIPAL FINDINGS: We screened a set of 2,604 putative orthologs from EST-based sequence datasets of the coral species Acropora millepora and Acropora palmata to determine the fraction and identity of proteins that may experience adaptive evolution. 7% of the orthologs show elevated rates of evolution. Taxonomically-restricted (i.e. lineage-specific) genes show a positive selection signature more frequently than genes that are found across many animal phyla. The class of proteins that displayed elevated evolutionary rates was significantly enriched for proteins involved in immunity and defense, reproduction, and sensory perception. We also found elevated rates of evolution in several other functional groups such as management of membrane vesicles, transmembrane transport of ions and organic molecules, cell adhesion, and oxidative stress response. Proteins in these processes might be related to the endosymbiotic relationship corals maintain with dinoflagellates in the genus Symbiodinium. CONCLUSION/RELEVANCE: This study provides a birds-eye view of the processes potentially underlying coral adaptation, which will serve as a foundation for future work to elucidate the rates, patterns, and mechanisms of corals' evolutionary response to global climate change.

The amount of genomic sequence information continues to grow at an exponential rate, while the identification and characterization of genes without known homologs remains a major challenge. For non-model organisms with limited resources for manipulative studies, high-throughput transcriptomic data combined with bioinformatics methods provide a powerful approach to obtain initial insights into the function of unknown genes. In this study, we report the identification and characterization of a novel family of putatively secreted, small, cysteine-rich proteins herein named Small Cysteine-Rich Proteins (SCRiPs). Their discovery in expressed sequence tag (EST) libraries from the coral Montastraea faveolata required the performance of an iterative search strategy based on BLAST and Hidden-Markov-Model algorithms. While a discernible homolog could neither be identified in the genome of the sea anemone Nematostella vectensis, nor in a large EST dataset from the symbiotic sea anemone Aiptasia pallida, we identified SCRiP sequences in multiple scleractinian coral species. Therefore, we postulate that this gene family is an example of lineage-specific gene expansion in reef-building corals. Previously published gene expression microarray data suggest that a sub-group of SCRiPs is highly responsive to thermal stress. Furthermore, data from microarray experiments investigating developmental gene expression in the coral Acropora millepora suggest that different SCRiPs may play distinct roles in the development of corals. The function of these proteins remains to be elucidated, but our results from in silico, transcriptomic, and phylogenetic analyses provide initial insights into the evolution of SCRiPs, a novel, taxonomically restricted gene family that may be responsible for a lineage-specific trait in scleractinian corals.