An Accelerated Computational Approach in Proteomics

The advent of new technologies and research in the field of computational bioinformatics has revolutionized the rate of biological data generation. As a result, the contribution of data from proteomics and genomics has increased by many folds, doubling every 18 months. Thereby, the operations involved in proteomics study have become significantly compute intensive. Protein identification, a fundamental process in proteomics study, requires identification of one or more proteins from a large database of proteins. It is rigorously used for disease diagnosis and prognosis by assisting in biomarker identification and discovery for the futuristic medical prescription. Now a days, mass spectrometry is a widely used analytical tool in proteomics studies which includes peak detection and database searching as essential steps. To cope up with the ever increasing growth of biological data in the domain of proteomics, protein identification requires accelerated and efficient solutions. This chapter mainly focuses on the review of various hardware accelerated methodologies for peak detection in mass spectrometry data and database searching for strings from an algorithmic and architectural perspective in the context of protein identification.