SUMMARY: Taxonomic analysis of microbial communities is well supported at the level of species and strains. However, species can contain significant phenotypic diversity and strains are rarely widely shared across global populations. Stratifying the diversity between species and strains can identify 'subspecies', which are a useful intermediary. High-throughput identification and profiling of subspecies is not yet supported in the microbiome field. Here, we use an operational definition of subspecies based on single nucleotide variant (SNV) patterns within species to identify and profile subspecies in metagenomes, along with their distinctive SNVs and genes. We incorporate this method into metaSNV v2, which extends existing SNV-calling software to support further SNV interpretation for population genetics. These new features support microbiome analyses to link SNV profiles with host phenotype or environment and niche-specificity. We demonstrate subspecies identification in marine and fecal metagenomes. In the latter, we analyze 70 species in 7524 adult and infant subjects, supporting a common subspecies population structure in the human gut microbiome and illustrating some limits in subspecies calling. AVAILABILITY AND IMPLEMENTATION: Source code, documentation, tutorials and test data are available at https://github.com/metasnv-tool/metaSNV and https://metasnv.embl.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: The field of metagenomics (study of genetic material recovered directly from an environment) has grown rapidly, with many bioinformatics analysis methods being developed. To ensure appropriate use of such methods, robust comparative evaluation of their accuracy and features is needed. For taxonomic classification of sequence reads, such evaluation should include use of clade exclusion, which better evaluates a method's accuracy when identical sequences are not present in any reference database, as is common in metagenomic analysis. To date, relatively small evaluations have been performed, with evaluation approaches like clade exclusion limited to assessment of new methods by the authors of the given method. What is needed is a rigorous, independent comparison between multiple major methods, using the same in silico and in vitro test datasets, with and without approaches like clade exclusion, to better characterize accuracy under different conditions. RESULTS: An overview of the features of 38 bioinformatics methods is provided, evaluating accuracy with a focus on 11 programs that have reference databases that can be modified and therefore most robustly evaluated with clade exclusion. Taxonomic classification of sequence reads was evaluated using both in silico and in vitro mock bacterial communities. Clade exclusion was used at taxonomic levels from species to class-identifying how well methods perform in progressively more difficult scenarios. A wide range of variability was found in the sensitivity, precision, overall accuracy, and computational demand for the programs evaluated. In experiments where distilled water was spiked with only 11 bacterial species, frequently dozens to hundreds of species were falsely predicted by the most popular programs. The different features of each method (forces predictions or not, etc.) are summarized, and additional analysis considerations discussed. CONCLUSIONS: The accuracy of shotgun metagenomics classification methods varies widely. No one program clearly outperformed others in all evaluation scenarios; rather, the results illustrate the strengths of different methods for different purposes. Researchers must appreciate method differences, choosing the program best suited for their particular analysis to avoid very misleading results. Use of standardized datasets for method comparisons is encouraged, as is use of mock microbial community controls suitable for a particular metagenomic analysis.

UNLABELLED: Gemma is a database, analysis software system and web site for genomics data re-use and meta-analysis. Currently, Gemma contains analyzed data from over 3300 expression profiling studies, yielding hundreds of millions of differential expression results and coexpression patterns (correlated expression) for retrieval and visualization. With optional registration users can save their own data and securely share it with other users. Web services and integration with third-party resources further increase the scope of the tools, which include a Cytoscape plugin. AVAILABILITY: http://chibi.ubc.ca/Gemma, Apache 2.0 license.

SUMMARY: We present the Sample-based Laboratory Information Management System (SLIMS), a powerful and user-friendly open source web application that provides all members of a laboratory with an interface to view, edit and create sample information. SLIMS aims to simplify common laboratory tasks with tools such as a user-friendly shopping cart for subjects, samples and containers that easily generates reports, shareable lists and plate designs for genotyping. Further key features include customizable data views, database change-logging and dynamically filled pre-formatted reports. Along with being feature-rich, SLIMS' power comes from being able to handle longitudinal data from multiple time-points and biological sources. This type of data is increasingly common from studies searching for susceptibility genes for common complex diseases that collect thousands of samples generating millions of genotypes and overwhelming amounts of data. LIMSs provide an efficient way to deal with this data while increasing accessibility and reducing laboratory errors; however, professional LIMS are often too costly to be practical. SLIMS gives labs a feasible alternative that is easily accessible, user-centrically designed and feature-rich. To facilitate system customization, and utilization for other groups, manuals have been written for users and developers. AVAILABILITY: Documentation, source code and manuals are available at http://genapha.icapture.ubc.ca/SLIMS/index.jsp. SLIMS was developed using Java 1.6.0, JSPs, Hibernate 3.3.1.GA, DB2 and mySQL, Apache Tomcat 6.0.18, NetBeans IDE 6.5, Jasper Reports 3.5.1 and JasperSoft's iReport 3.5.1.

UNLABELLED: As the genome sequences of multiple strains of a given bacterial species are obtained, more generalized bacterial genome databases may be complemented by databases that are focused on providing more information geared for a distinct bacterial phylogenetic group and its associated research community. The Burkholderia Genome Database represents a model for such a database, providing a powerful, user-friendly search and comparative analysis interface that contains features not found in other genome databases. It contains continually updated, curated and tracked information about Burkholderia cepacia complex genome annotations, plus other Burkholderia species genomes for comparison, providing a high-quality resource for its targeted cystic fibrosis research community. AVAILABILITY: http://www.burkholderia.com. Source code: GNU GPL.