METAGENOGRIDS

Algorithmics for Metagenomics on Grids

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 Obiettivo del progetto (Objective)

'Numerous critical problems in bioinformatics and computational biology are computationally intensive and could benefit from being solved on a computational Grid, that is a network of geographically distributed computers. Most works that combine bioinformatics and Grids focus on biological problems that can be trivially adapted to Grids, staying clear of more complex problems. In this fellowship we propose to address, in an interdisciplinary research, the resolution on Grids of two complex and demanding problems: metagenomics assembly and metagenomics annotation. Metagenomics is the study of the genomes of an entire community of microbes; metagenomics assembly is the problem of assembling the genome of each of the microbial species in the studied community. The applicant, a Grid computing expert, proposes to work with experts in bioinformatics and experts in Grid computing to design new algorithmic solutions to solve these two metagenomics problems. In this proposal, the applicant would simultaneously visit for a year, and work with, Prof. Guylaine Poisson's Bioinformatics Lab (BiL) and Prof. Henri Casanova's Concurrent Research Group (CoRG), both located at the University of Hawai`i at Manoa, USA.'

Introduzione (Teaser)

Bioinformatics uses computer applications and statistical concepts to make sense of data retrieved from studies in biological processes at the molecular level. To do this, researchers rely on databases to solve problems related to managing and analysing this information.

Descrizione progetto (Article)

Problems presenting in the field of bioinformatics and computational biology are often related to intricate, time-consuming and 'loaded' computational processes. Researchers believe many of these operational difficulties can be resolved by using a computational grid, a network of computers in various locations.

The bioinformatics field of metagenomics involves the study of genomes of a community of microbes. Problems presenting here have to do with annotation (identifying the key features) of these genomes as well as their assembly in various environments. Annotation gives biological meaning to raw datasets of information.

The 'Algorithmics for metagenomics on grids' (Metagenogrids) project aimed to discover algorithmic solutions to these metagenomics problems, to be resolved on grid computing platforms. However, researchers decided to refocus the study when efforts failed to form a solid collaboration for study on metagenomics assembly. The new purpose was to come up with more efficient batch scheduling so as to improve the allocation and management of resources across computational clusters. An added challenge was that of ensuring the quality of service for users of these platforms.

The EU-funded Metagenogrids team first tackled the issue of allocating resources for applications with unabated resource consumption rates and either never-ending execution times or unknown execution times. Several job-scheduling algorithms were developed, and results for high-performance computing (HPC) workloads were presented. In comparison to standard batch scheduling algorithms, results favoured using the newly proposed algorithms for vastly improved performance in the majority of experimental scenarios. This represented a step forward for the potential of combining virtualisation technology with lightweight scheduling strategies.

The project's results have advanced efforts with a view to the future design of resource management systems for enhanced use of platforms, improved outcomes and quality of service. The foundations laid in this study appear strong enough to support further study that aims to extend results to applications whose resource consumption rates change during an application's execution.