Accomplishments and Challenges in High Performance Computing for Computational Biology
We review recent research and development in high performance computing (HPC) for computational biology and discuss the great challenges to both biomedical scientists and IT professionals. During the last decades, research in the fields of molecular biology and biomedicine has provided the scientific community with huge amount of data through sequencing, genome-wide annotation and gene expression profiling projects. The genetic databases have been growing exponentially and sophisticated computer algorithms have been developed to cater for needs of data mining, analysis and simulation. It is clear that development of HPC technologies has become crucial for deployment of the software systems to tackle various bioinformatics problems. The goal of this article is to present the current research and our critical review on construction of parallel and distributed computing systems, design of multi-process algorithms, and development of software systems for biocomputing tasks including sequence alignment, heuristic database searching, phylogenetic analysis gene clustering. We also give a brief introduction to our work in development of highly scalable and reproducible HPC algorithms and indicate the challenging problems in this context.
Keywords: High performance computing, computational biology, sequence analysis, gene clustering, phylogenetic tree, analysis
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