Opportunities from the use of FPGAs as platforms for bioinformatics algorithms

This paper presents an in-depth look of how FPGA computing can offer substantial speedups in the execution of bioinformatics algorithms, with specific results achieved to date for a broad range of algorithms. Examples and case studies are presented for sequence comparison (BLAST, CAST), multiple sequence alignment (MAFFT, T-Coffee), RNA and protein secondary structure prediction (Zuker, Predator), gene prediction (Glimmer/GlimmerHMM) and phylogenetic tree computation (RAxML), running on mainstream FPGA technologies as well as high-end FPGA-based systems (Convey HC1, BeeCube). This work also presents technological and other obstacles that need to be overcome in order for FPGA computing to become a mainstream technology in Bioinformatics.