Aligning hundreds of sequences using progressive alignment tools such as ClustalW requires several hours on state-of-the-art workstations. We present a new approach to compute multiple sequence alignments in far shorter time using reconfigurable hardware. This results in an implementation of ClustalW with significant runtime savings on a standard off-the-shelf FPGA.
Abstract. Profile Hidden Markov Models (PHMMs) are used as a popular tool in bioinformatics for probabilistic sequence database searching. The search operation consists of computing the Viterbi score for each sequence in the database with respect to a given query PHMM. Because of the rapid growth of biological sequence databases, finding fast solutions is of highest importance to research in this area. Unfortunately, the required scan times of currently available sequential software implementations are very high. In this paper we show how reconfigurable hardware can be used as a computational platform to accelerate this application by two orders of magnitude.
HMMER, based on the profile Hidden Markov Model (HMM) is one of the most widely used sequence database searching tools, allowing researchers to compare HMMs to sequence databases or sequences to HMM databases. Such searches often take many hours and consume a great number of CPU cycles on modern computers. We present a cluster-enabled hardware/software-accelerated implementation of the HMMER search tool hmmsearch. Our results show that combining the parallel efficiency of a cluster with one or more high-speed hardware accelerators (FPGAs) can significantly improve performance for even the most time consuming searches, often reducing search times from several hours to minutes.
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