Gnare: Automated System For High-Throughput Genome Analysis With Grid Computational Backend

Sulakhe, Dinanath; Rodríguez, Álex; D’Souza, Mark; Wilde, Michael; Nefedova, Veronika; Foster, Ian; Maltsev, Natalia

doi:10.1007/s10877-005-3463-y

Cited by 20 publications

(11 citation statements)

References 27 publications

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“…Grid technology has been noticed as an issue to solve large-scale bioinformatics related problems and improves data accuracy and processing speed on multiple computation platforms with distributed bioDATA sets. Grid technologies such as Globus, Condor, and the Gryphyn Virtual Data System were applied in the development of GNARE (Genome Analysis Research Environment) (Dinanath et al 2005). The use of GNARE was essential for the development of the applications in evolutionary analysis of genomes (PUMA2, the SEED), bio-defense research (PathosDB), structural biology (TarGet DB) and bioremediation (MetaGenome) that are dependent on availability of up-to-date annotations and rely on comparative analysis of large sets of phylogenetically diverse organisms.…”

Section: Bioinformaticsmentioning

confidence: 99%

Systems genetics, bioinformatics and eQTL mapping

Deng

2010

Genetica

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Jansen and Nap (Trends Genet 17(7):388-391, 2001) and Jansen (Nat Rev Genet 4:145-151, 2003) first proposed the concept of genetical genomics, or genome-wide genetic analysis of gene expression data, which is also called transcriptome mapping. In this approach, microarrays are used for measuring gene expression levels across genetic mapping populations. These gene expression patterns have been used for genome-wide association analysis, an analysis referred to as expression QTL (eQTL) mapping. Recent progress in genomics and experimental biology has brought exponential growth of the biological information available for computational analysis in public genomics databases. Bioinformatics is essential to genome-wide analysis of gene expression data and used as an effective tool for eQTL mapping. The use of Plabsoft database, EcoTILLING, GNARE and FastMap allowed for dramatic reduction of time in genome analysis. Some web-based tools (e.g., Lirnet, eQTL Viewer) provide efficient and intuitive ways for biologists to explore transcriptional regulation patterns, and to generate hypotheses on the genetic basis of transcriptional regulations. Expression quantitative trait loci (eQTL) mapping concerns finding genomic variation to elucidate variation of expression traits. This problem poses significant challenges due to high dimensionality of both the gene expression and the genomic marker data. The core challenges in understanding and explaining eQTL associations are the fine mapping and the lack of mechanistic explanation. But with the development of genetical genomics and computer technology, many new approaches for eQTL mapping will emerge. The statistical methods used for the analysis of expression QTL will become mature in the future.

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Section: Bioinformaticsmentioning

confidence: 99%

Systems genetics, bioinformatics and eQTL mapping

Deng

2010

Genetica

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“…The integrated database [12] warehouses sequence data and annotations from the monitored public databases as well as the results of data analyses using GADU.…”

Section: Gadu the Genome Analysis Servermentioning

confidence: 99%

“…GADU has implemented a site selector, presented in the paper on GNARE [12], that can referred for a detailed understanding of the site selection mechanism. The site selector automatically picks the best possible site for a given batch job at a given time.…”

Section: Site Selection Across Gridsmentioning

confidence: 99%

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Using multiple grid resources for bioinformatics applications in GADU

Sulakhe

Rodríguez

Wilde

et al. 2006

Sixth IEEE International Symposium on Cluster Computing and the Grid (CCGRID'06)

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During the past decade, the scientific community has witnessed the rapid accumulation of gene sequence data and data related to physiology and biochemistry of organisms. Bioinformatics tools used for efficient and computationally intensive analysis of genetic sequences require large-scale computational resources to accommodate the growing data. Grid computational resources such as the Open Science Grid and TeraGrid have proved useful for scientific discovery. GADU is a high-throughput computational system developed to automate the steps involved in accessing the Grid resources for running bioinformatics applications. This paper describes the requirements for building an automated scalable system such as GADU that can run a job simultaneously on different Grids. The paper describes the resource-independent configuration of GADU using the Pegasus-based Virtual Data System that helps in using heterogeneous Grid resources. The paper also highlights the features implemented to make GADU a gateway to computationally intensive bioinformatics applications on the Grid.

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“…Such capability will allow us to leverage an enormous expert knowledge accumulated in the scientific community for annotation of information in the Sentra database. All computationally intensive operations in Sentra are performed using the Grid technology-based engine GADU (23) being developed by the Bioinformatics group at Argonne.…”

Section: Introductionmentioning

confidence: 99%

Sentra: a database of signal transduction proteins for comparative genome analysis

D’Souza¹,

Glass²,

Syed³

et al. 2007

Nucleic Acids Research

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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.

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Gnare: Automated System For High-Throughput Genome Analysis With Grid Computational Backend

Abstract: Abstract

Cited by 20 publications

References 27 publications

Systems genetics, bioinformatics and eQTL mapping

Systems genetics, bioinformatics and eQTL mapping

Using multiple grid resources for bioinformatics applications in GADU

Sentra: a database of signal transduction proteins for comparative genome analysis

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