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.
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.
Molecular Biologists frequently compute multiple sequence alignments (MSAs) to identify similar regions in protein families. Progressive alignment is a widely used approach to compute MSAs. However, aligning a few hundred sequences by popular progressive alignment tools requires several hours on sequential computers. Due to the rapid growth of biological sequence databases biologists have to compute MSAs in a far shorter time. In this paper we present a new approach to MSA on reconfigurable hardware platforms to gain high performance at low cost. To derive an efficient mapping onto this type of architecture, fine-grained parallel processing elements (PEs) have been designed. Using this PE design as a building block we have constructed a linear systolic array to perform a pairwise sequence distance computation using dynamic programming. This results in an implementation with significant runtime savings on a standard off-the-shelf FPGA.
Summary: Bioinformatics involves the collection, organization and analysis of large amounts of biological data, using networks of computers and databases. Developing countries in the Asia-Pacific region are just moving into this new field of information-based biotechnology. However, the computational infrastructure and network bandwidths available in these countries are still at a basic level compared to that in developed countries. In this study, we assessed the utility of a BitTorrent-based Peer-to-Peer (btP2P) file distribution model for automatic synchronization and distribution of large amounts of biological data among developing countries. The initial country-level nodes in the Asia-Pacific region comprised Thailand, Korea and Singapore. The results showed a significant improvement in download performance using btP2P-three times faster overall download performance than conventional File Transfer Protocol (FTP). This study demonstrated the reliability of btP2P in the dissemination of continuously growing multi-gigabyte biological databases across the three Asia-Pacific countries. The download performance for btP2P can be further improved by including more nodes from other countries into the network. This suggests that the btP2P technology is appropriate for automatic synchronization and distribution of biological databases and software over low-bandwidth networks among developing countries in the AsiaPacific region.
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