A space-efficient algorithm for local similarities.
Existing dynamic-programming algorithms for identifying similar regions of two sequences require time and space proportional to the product of the sequence lengths. Often this space requirement is more limiting than the time requirement. We describe a dynamic-programming local-similarity algorithm that needs only space proportional to the sum of the sequence lengths. The method can also find repeats within a single long sequence. To illustrate the algorithm's potential, we discuss comparison of a 73,360 nucleotide sequence containing the human beta-like globin gene cluster and a corresponding 44,594 nucleotide sequence for rabbit, a problem well beyond the capabilities of other dynamic-programming software.
Huang
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Department of Computer Science, Michigan Technological University, Houghton 49931.
Hardison
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Miller
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D000465:Algorithms; D000818:Animals; D001483:Base Sequence; D006801:Humans; D008969:Molecular Sequence Data; D009696:Nucleic Acids / Q000235:genetics*; D011817:Rabbits; D016415:Sequence Alignment / Q000379:methods*
D009696:Nucleic Acids