Using multiple grid resources for bioinformatics applications in GADU

Abstract

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.

Trends in life science grid: from computing grid to knowledge grid

“…The Genome Analysis and Database Update system (GADU) provides an automated, scalable, high-throughput computational workflow engine that executes bioinformatics tools (BLAST, BLOCKS, PFam, Chisel and InterPro) with public databases (NCBI RefSeq, PIR, InterPro and KEGG) on multiple Grids of different architectures and environment, a collective member of more than 18,000 CPUs contributed by more than 60 institutions [31]. …”

Retraction: Changing from computing grid to knowledge grid in life‐science grid

“…Many GRID BLAST implementations have been developed and reported [25][26][27][28][29][30]. The characteristics of Grid Blast are summarized as follows: (i) prestag-ing of sequence databases to minimize the runtime overhead of transferal of large sequence databases, which often reach several Gigabytes in size, (ii) databases update, which keeps data consistency on the data grid, (iii) dynamic load balancing of query sequences to avoid unexpected slow responses, especially when dealing with thousands of query sequences in heterogeneous computation pools including PC-clusters and desktop computers, and (iv) assembling of the results from distributed jobs.…”