Abstract
Biological sequence alignment is an essential tool used in molecular biology and biomedical applications. The growing volume of genetic data and the complexity of sequence alignment present a challenge in obtaining alignment results in a timely manner. Known methods to accelerate alignment on reconfigurable hardware only address sequence comparison, limit the sequence length, or exhibit memory and I/O bottlenecks. A space‐efficient, global sequence alignment algorithm and architecture is presented that accelerates the forward scan and traceback in hardware without memory and I/O limitations. With 256 processing elements in FPGA technology, a performance gain over 300 times that of a desktop computer is demonstrated on sequence lengths of 16000. For greater performance, the architecture is scalable to more processing elements.
Highlights
Searching and comparing biological sequences in genomic databases are essential processes in molecular biology
Three global alignment implementations are tested in the evaluation: (1) as a baseline, a software-only version of the algorithm presented in this paper; (2) a version accelerated by the FPGA; and (3) an implementation of the Myers-Miller global alignment algorithm for an additional point of reference
With the presented algorithm and architecture, long sequences are globally aligned with supercomputing performance on reconfigurable hardware
Summary
Searching and comparing biological sequences in genomic databases are essential processes in molecular biology. The collection of genetic sequence data is increasing exponentially each year and consists mostly of nucleotide (DNA/RNA) and amino acid (protein) symbols. Given the large volume of data, sequence comparison applications require efficient computing methods to produce timely results. Biologists and other researchers use sequence alignment as a fundamental comparison method to find common patterns between sequences, predict protein structure, identify important genetic regions, and facilitate drug design. Sequence alignment consists of matching characters between two or more sequences and positioning them together in a column. A count of the matching characters results in a measure of similarity between the sequences. Pairwise alignment involves two sequences (see Figure 1) and multiple alignment considers three or more sequences. Multiple alignment algorithms [3, 4] often compute a pairwise alignment between all the sequences
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