On Construction of Cluster and Grid Computing Platforms for Parallel Bioinformatics Applications

1. Introduction

Bioinformatics is a combination of biology and information technology and includes any computational tools and methods for managing, analyzing and manipulating large sets of biology data. Thus, computing technologies are vital for bioinformatics applications (Konishi et al., 2002; Trelles et al., 1998). For example, biology problems often require repeating the same task millions of times such as when searching for sequence similarities in existing databases or comparing groups of sequences to determine evolutionary relationships. In such cases, the high-performance computers to process this information are indispensable. Biological information is stored on many computers around the world. The easiest way to access this information is to join these computers together through networking. Such activities require high-performance computing infrastructures (Prodan & Fahringer, 2002) with access to huge databases of information.

The major advances in computer technology and computer science over the past 30 years have dramatically changed much of our society. Currently, many parallel versions of bioinformatics applications can be used to conduct computing tasks on Linux PC Cluster or Grid systems, including, HMMer (http://www.rcsb.org/pdb/index.html), and many of these genome websites distribute large datasets as flat files (e.g., tab-delimited files). Flat files are text files lacking any form of markup language. The tasks of automating the processes of information retrieval and integration of heterogeneous biological data are difficult due to their unstructured formats. The data involved may range from nucleic acid and protein sequences, to three-dimensional protein structures, and relationships among various metabolic pathways. Furthermore, different approaches are used for data modeling, storage, analysis, and querying purposes. Therefore, Molecular Biology databases have only a few widely accepted schemas.