期刊
JOURNAL OF COMPUTATIONAL BIOLOGY
卷 16, 期 1, 页码 1-18出版社
MARY ANN LIEBERT, INC
DOI: 10.1089/cmb.2008.0137
关键词
algorithms; computational molecular biology; statistics; alignment; dynamic programming; genomics
类别
资金
- National Institutes of Health/National Human Genome Research Institute [K25 HG003291, R01 HG001257]
- U. S. Department of Energy [DE-FG02-04ER63924]
- NATIONAL HUMAN GENOME RESEARCH INSTITUTE [R01HG001257, K25HG003291] Funding Source: NIH RePORTER
Computational biology is replete with high-dimensional discrete prediction and inference problems. Dynamic programming recursions can be applied to several of the most important of these, including sequence alignment, RNA secondary-structure prediction, phylogenetic inference, and motif finding. In these problems, attention is frequently focused on some scalar quantity of interest, a score, such as an alignment score or the free energy of an RNA secondary structure. In many cases, score is naturally defined on integers, such as a count of the number of pairing differences between two sequence alignments, or else an integer score has been adopted for computational reasons, such as in the test of significance of motif scores. The probability distribution of the score under an appropriate probabilistic model is of interest, such as in tests of significance of motif scores, or in calculation of Bayesian confidence limits around an alignment. Here we present three algorithms for calculating the exact distribution of a score of this type; then, in the context of pairwise local sequence alignments, we apply the approach so as to find the alignment score distribution and Bayesian confidence limits.
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