Optimizing Smith-Waterman alignments.
Mutual correlation between segments of DNA or protein sequences can be detected by Smith-Waterman local alignments. We present a statistical analysis of alignment of such sequences, based on a recent scaling theory. A new fidelity measure is introduced and shown to capture the significance of the local alignment, i.e., the extent to which the correlated subsequences are correctly identified. It is demonstrated how the fidelity may be optimized in the space of penalty parameters using only the alignment score data of a single sequence pair.
Olsen
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R
Department of Physics, University of California at San Diego, La Jolla 92093-0319, USA. rolf@cezanne.ucsd.edu
Hwa
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Lässig
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D000465:Algorithms; D000595:Amino Acid Sequence; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods; D004247:DNA / Q000737:chemistry*; D008390:Markov Chains; D015233:Models, Statistical; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results; D016415:Sequence Alignment; D017386:Sequence Homology, Amino Acid; D012689:Sequence Homology, Nucleic Acid; D012984:Software
D011506:Proteins; D004247:DNA