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F Meyer,
D Paarmann,
M D'Souza,
R Olson,
E M Glass,
M Kubal,
T Paczian, A Rodriguez,
R Stevens,
A Wilke,
J Wilkening,
R A Edwards
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ABSTRACT: 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 next-generation sequencing has provided access to metagenomics to a wide range of researchers.
A high-throughput pipeline has been constructed to provide high-performance computing to all researchers interested in using metagenomics. The pipeline produces automated functional assignments of sequences in the metagenome by comparing both protein and nucleotide databases. Phylogenetic and functional summaries of the metagenomes are generated, and tools for comparative metagenomics are incorporated into the standard views. User access is controlled to ensure data privacy, but the collaborative environment underpinning the service provides a framework for sharing datasets between multiple users. In the metagenomics RAST, all users retain full control of their data, and everything is available for download in a variety of formats.
The open-source metagenomics RAST service provides a new paradigm for the annotation and analysis of metagenomes. With built-in support for multiple data sources and a back end that houses abstract data types, the metagenomics RAST is stable, extensible, and freely available to all researchers. This service has removed one of the primary bottlenecks in metagenome sequence analysis - the availability of high-performance computing for annotating the data. http://metagenomics.nmpdr.org.
BMC Bioinformatics 10/2008; 9:386. · 2.75 Impact Factor
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ABSTRACT: Sentra (http://compbio.mcs.anl.gov/sentra), a database of signal transduction proteins encoded in completely sequenced prokaryotic genomes, has been updated to reflect recent advances in understanding signal transduction events on a whole-genome scale. Sentra consists of two principal components, a manually curated list of signal transduction proteins in 202 completely sequenced prokaryotic genomes and an automatically generated listing of predicted signaling proteins in 235 sequenced genomes that are awaiting manual curation. In addition to two-component histidine kinases and response regulators, the database now lists manually curated Ser/Thr/Tyr protein kinases and protein phosphatases, as well as adenylate and diguanylate cyclases and c-di-GMP phosphodiesterases, as defined in several recent reviews. All entries in Sentra are extensively annotated with relevant information from public databases (e.g. UniProt, KEGG, PDB and NCBI). Sentra's infrastructure was redesigned to support interactive cross-genome comparisons of signal transduction capabilities of prokaryotic organisms from a taxonomic and phenotypic perspective and in the framework of signal transduction pathways from KEGG. Sentra leverages the PUMA2 system to support interactive analysis and annotation of signal transduction proteins by the users.
Nucleic Acids Research 02/2007; 35(Database issue):D271-3. · 8.03 Impact Factor
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ABSTRACT: GeNome Analysis Research Environment (GNARE) is a bioinformatics server that supports both auto-mated and interactive expert-driven analysis of user-submitted genomes and metagenomes. These analyses include gene function prediction and development of organism-specific metabolic reconstructions from sequence data. GNARE provides a framework for comparative and evolu-tionary analysis as well as annotation of genomes and metabolic networks in the context of phenoty-pic and taxonomic information. Results of analyses and metabolic models are visualized and extensively annotated with information from public databases. GNARE uses automated workflows and a Grid-based computational backend to perform high-throughput analysis of genomes. This use of distributed computing allows the analysis of an average-sized prokaryotic genome in less than 5 h.
Nucleic Acids Research 01/2007; · 8.03 Impact Factor
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ABSTRACT: The PUMA2 system (available at http://compbio.mcs.anl.gov/puma2) is an interactive, integrated bioinformatics environment for high-throughput genetic sequence analysis and metabolic reconstructions from sequence data. PUMA2 provides a framework for comparative and evolutionary analysis of genomic data and metabolic networks in the context of taxonomic and phenotypic information. Grid infrastructure is used to perform computationally intensive tasks. PUMA2 currently contains precomputed analysis of 213 prokaryotic, 22 eukaryotic, 650 mitochondrial and 1493 viral genomes and automated metabolic reconstructions for >200 organisms. Genomic data is annotated with information integrated from >20 sequence, structural and metabolic databases and ontologies. PUMA2 supports both automated and interactive expert-driven annotation of genomes, using a variety of publicly available bioinformatics tools. It also contains a suite of unique PUMA2 tools for automated assignment of gene function, evolutionary analysis of protein families and comparative analysis of metabolic pathways. PUMA2 allows users to submit batch sequence data for automated functional analysis and construction of metabolic models. The results of these analyses are made available to the users in the PUMA2 environment for further interactive sequence analysis and annotation.
Nucleic Acids Research 01/2006; 34(Database issue):D369-72. · 8.03 Impact Factor
