The prevalence of species and strains in the human microbiome: A resource for experimental efforts

Experimental efforts to characterize the human microbiota often use bacterial strains that were chosen for historical rather than biological reasons. Here, we report an analysis of 380 whole-genome shotgun samples from 100 subjects from the NIH Human Microbiome Project. By mapping their reads to 1,751 reference genome sequences and analyzing the resulting relative strain abundance in each sample we present metrics and visualizations that can help identify strains of interest for experimentalists. We also show that approximately 14 strains of 10 species account for 80% of the mapped reads from a typical stool sample, indicating that the function of a community may not be irreducibly complex. Some of these strains account for >20% of the sequence reads in a subset of samples but are absent in others, a dichotomy that could underlie biological differences among subjects. These data should serve as an important strain selection resource for the community of researchers who take experimental approaches to studying the human microbiota

Nematode.net update 2011: addition of data sets and tools featuring next-generation sequencing data

Nematode.net (http://nematode.net) has been a publicly available resource for studying nematodes for over a decade. In the past 3 years, we reorganized Nematode.net to provide more user-friendly navigation through the site, a necessity due to the explosion of data from next-generation sequencing platforms. Organism-centric portals containing dynamically generated data are available for over 56 different nematode species. Next-generation data has been added to the various data-mining portals hosted, including NemaBLAST and NemaBrowse. The NemaPath metabolic pathway viewer builds associations using KOs, rather than ECs to provide more accurate and fine-grained descriptions of proteins. Two new features for data analysis and comparative genomics have been added to the site. NemaSNP enables the user to perform population genetics studies in various nematode populations using next-generation sequencing data. HelmCoP (Helminth Control and Prevention) as an independent component of Nematode.net provides an integrated resource for storage, annotation and comparative genomics of helminth genomes to aid in learning more about nematode genomes, as well as drug, pesticide, vaccine and drug target discovery. With this update, Nematode.net will continue to realize its original goal to disseminate diverse bioinformatic data sets and provide analysis tools to the broad scientific community in a useful and user-friendly manner

Nematode.net update 2008: improvements enabling more efficient data mining and comparative nematode genomics

Nematode.net (http://nematode.net) is a publicly available resource dedicated to the study of parasitic nematodes. In 2000, the Genome Center at Washington University (GC) joined a consortium including the Nematode Genomics group in Edinburgh, and the Pathogen Sequencing Unit of the Sanger Institute to generate expressed sequence tags (ESTs) as an inexpensive and efficient solution for gene discovery in parasitic nematodes. As of 2008 the GC, sampling key parasites of humans, animals and plants, has generated over 500 000 ESTs and 1.2 million genome survey sequences from more than 30 non-Caenorhabditis elegans nematodes. Nematode.net was implemented to offer user-friendly access to data produced by this project. In addition to sequence data, the site hosts: assembled NemaGene clusters in GBrowse views characterizing composition and protein homology, functional Gene Ontology annotations presented via the AmiGO browser, KEGG-based graphical display of NemaGene clusters mapped to metabolic pathways, codon usage tables, NemFam protein families which represent conserved nematode-restricted coding sequences not found in public protein databases, a web-based WU-BLAST search tool that allows complex querying and other assorted resources. The primary aim of Nematode.net is the dissemination of this diverse collection of information to the broader scientific community in a way that is useful, consistent, centralized and enduring

Species-level functional profiling of metagenomes and metatranscriptomes.

Functional profiles of microbial communities are typically generated using comprehensive metagenomic or metatranscriptomic sequence read searches, which are time-consuming, prone to spurious mapping, and often limited to community-level quantification. We developed HUMAnN2, a tiered search strategy that enables fast, accurate, and species-resolved functional profiling of host-associated and environmental communities. HUMAnN2 identifies a community's known species, aligns reads to their pangenomes, performs translated search on unclassified reads, and finally quantifies gene families and pathways. Relative to pure translated search, HUMAnN2 is faster and produces more accurate gene family profiles. We applied HUMAnN2 to study clinal variation in marine metabolism, ecological contribution patterns among human microbiome pathways, variation in species' genomic versus transcriptional contributions, and strain profiling. Further, we introduce 'contributional diversity' to explain patterns of ecological assembly across different microbial community types

The draft genome of the parasitic nematode Trichinella spiralis

Genome evolution studies for the phylum Nematoda have been limited by focusing on comparisons involving Caenorhabditis elegans. We report a draft genome sequence of Trichinella spiralis, a food-borne zoonotic parasite, which is the most common cause of human trichinellosis. This parasitic nematode is an extant member of a clade that diverged early in the evolution of the phylum, enabling identification of archetypical genes and molecular signatures exclusive to nematodes. We sequenced the 64-Mb nuclear genome,which is estimated to contain 15,808 protein-coding genes,at ~35-fold coverage using whole-genome shotgun and hierarchal map–assisted sequencing. Comparative genome analyses support intrachromosomal rearrangements across the phylum, disproportionate numbers of protein family deaths over births in parasitic compared to a non-parasitic nematode and a preponderance of gene-loss and -gain events in nematodes relative to Drosophila melanogaster. This genome sequence and the identified pan-phylum characteristics will contribute to genome evolution studies of Nematoda as well as strategies to combat global parasites of humans, food animals and crops

A practical, bioinformatic workflow system for large data sets generated by next-generation sequencing

Transcriptomics (at the level of single cells, tissues and/or whole organisms) underpins many fields of biomedical science, from understanding the basic cellular function in model organisms, to the elucidation of the biological events that govern the development and progression of human diseases, and the exploration of the mechanisms of survival, drug-resistance and virulence of pathogens. Next-generation sequencing (NGS) technologies are contributing to a massive expansion of transcriptomics in all fields and are reducing the cost, time and performance barriers presented by conventional approaches. However, bioinformatic tools for the analysis of the sequence data sets produced by these technologies can be daunting to researchers with limited or no expertise in bioinformatics. Here, we constructed a semi-automated, bioinformatic workflow system, and critically evaluated it for the analysis and annotation of large-scale sequence data sets generated by NGS. We demonstrated its utility for the exploration of differences in the transcriptomes among various stages and both sexes of an economically important parasitic worm (Oesophagostomum dentatum) as well as the prediction and prioritization of essential molecules (including GTPases, protein kinases and phosphatases) as novel drug target candidates. This workflow system provides a practical tool for the assembly, annotation and analysis of NGS data sets, also to researchers with a limited bioinformatic expertise. The custom-written Perl, Python and Unix shell computer scripts used can be readily modified or adapted to suit many different applications. This system is now utilized routinely for the analysis of data sets from pathogens of major socio-economic importance and can, in principle, be applied to transcriptomics data sets from any organism

A practical, bioinformatic workflow system for large data sets generated by next-generation sequencing

Transcriptomics (at the level of single cells, tissues and/or whole organisms) underpins many fields of biomedical science, from understanding the basic cellular function in model organisms, to the elucidation of the biological events that govern the development and progression of human diseases, and the exploration of the mechanisms of survival, drug-resistance and virulence of pathogens. Next-generation sequencing (NGS) technologies are contributing to a massive expansion of transcriptomics in all fields and are reducing the cost, time and performance barriers presented by conventional approaches. However, bioinformatic tools for the analysis of the sequence data sets produced by these technologies can be daunting to researchers with limited or no expertise in bioinformatics. Here, we constructed a semi-automated, bioinformatic workflow system, and critically evaluated it for the analysis and annotation of large-scale sequence data sets generated by NGS. We demonstrated its utility for the exploration of differences in the transcriptomes among various stages and both sexes of an economically important parasitic worm (Oesophagostomum dentatum) as well as the prediction and prioritization of essential molecules (including GTPases, protein kinases and phosphatases) as novel drug target candidates. This workflow system provides a practical tool for the assembly, annotation and analysis of NGS data sets, also to researchers with a limited bioinformatic expertise. The custom-written Perl, Python and Unix shell computer scripts used can be readily modified or adapted to suit many different applications. This system is now utilized routinely for the analysis of data sets from pathogens of major socio-economic importance and can, in principle, be applied to transcriptomics data sets from any organism