Background: Random community genomes (metagenomes) are now commonly used to study microbes in different environments. Over the past few years, the major challenge associated with metagenomics shifted from generating to analyzing sequences. High-throughput, low-cost nextgeneration sequencing has provided access to metagenomics to a wide range of researchers.
BackgroundComputing of sequence similarity results is becoming a limiting factor in metagenome analysis. Sequence similarity search results encoded in an open, exchangeable format have the potential to limit the needs for computational reanalysis of these data sets. A prerequisite for sharing of similarity results is a common reference.DescriptionWe introduce a mechanism for automatically maintaining a comprehensive, non-redundant protein database and for creating a quarterly release of this resource. In addition, we present tools for translating similarity searches into many annotation namespaces, e.g. KEGG or NCBI's GenBank.ConclusionsThe data and tools we present allow the creation of multiple result sets using a single computation, permitting computational results to be shared between groups for large sequence data sets.
INTRODUCTIONShotgun metagenomics creates millions of fragments of short DNA reads, which are meaningless unless analyzed appropriately. The Metagenomics RAST server (MG-RAST) is a web-based, open source system that offers a unique suite of tools for analyzing these data sets. After de-replication and quality control, fragments are mapped against a comprehensive nonredundant database (NR). Phylogenetic and metabolic reconstructions are computed from the set of hits against the NR. The resulting data are made available for browsing, download, and most importantly, comparison against a comprehensive collection of public metagenomes. A submitted metagenome is visible only to the user, unless the user makes it public or shares with other registered users. Public metagenomes are available to all.
Metagenomic sequencing has produced significant amounts of data in recent years. For example, as of summer 2013, MG-RAST has been used to annotate over 110,000 data sets totaling over 43 Terabases. With metagenomic sequencing finding even wider adoption in the scientific community, the existing web-based analysis tools and infrastructure in MG-RAST provide limited capability for data retrieval and analysis, such as comparative analysis between multiple data sets. Moreover, although the system provides many analysis tools, it is not comprehensive. By opening MG-RAST up via a web services API (application programmers interface) we have greatly expanded access to MG-RAST data, as well as provided a mechanism for the use of third-party analysis tools with MG-RAST data. This RESTful API makes all data and data objects created by the MG-RAST pipeline accessible as JSON objects. As part of the DOE Systems Biology Knowledgebase project (KBase, http://kbase.us) we have implemented a web services API for MG-RAST. This API complements the existing MG-RAST web interface and constitutes the basis of KBase's microbial community capabilities. In addition, the API exposes a comprehensive collection of data to programmers. This API, which uses a RESTful (Representational State Transfer) implementation, is compatible with most programming environments and should be easy to use for end users and third parties. It provides comprehensive access to sequence data, quality control results, annotations, and many other data types. Where feasible, we have used standards to expose data and metadata. Code examples are provided in a number of languages both to show the versatility of the API and to provide a starting point for users. We present an API that exposes the data in MG-RAST for consumption by our users, greatly enhancing the utility of the MG-RAST service.
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