期刊
JOURNAL OF COMPUTATIONAL BIOLOGY
卷 7, 期 1-2, 页码 115-141出版社
MARY ANN LIEBERT, INC
DOI: 10.1089/10665270050081414
关键词
sequence comparison, alignment algorithm, homology; evolution model, longest common subsequence
We study the problem of similarity detection by sequence alignment with gaps, using a recently established theoretical framework based on the morphology of alignment paths, Alignments of sequences without mutual correlations are found to have scale-invariant statistics. This is the basis for a scaling theory of alignments of correlated sequences. Using a simple Markov model of evolution, we generate sequences with well-defined mutual correlations and quantify the fidelity of an alignment in an unambiguous way. The scaling theory predicts the dependence of the fidelity on the alignment parameters and on the statistical evolution parameters characterizing the sequence correlations. Specific criteria for the optimal choice of alignment parameters emerge from this theory. The results are verified by extensive numerical simulations.
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