A sequence similarity search algorithm based on a probabilistic interpretation of an alignment scoring system

We present a probabilistic interpretation of local sequence alignment methods where the alignment scoring system (ASS) plays the role of a stochastic process defining a probability distribution over all sequence pairs. An explicit algorithms is given to compute the probability of two sequences given and ASS. Based on this definition, a modified version of the Smith-Waterman local similarity search algorithm has been devised, which assesses sequence relationships by log likelihood ratios. When tested on classical examples such as globins or G-protein-coupled receptors, the new method proved to be up to an order of magnitude more sensitive than the native Smith-Waterman algorithm.


Published in:
Proceedings / ... International Conference on Intelligent Systems for Molecular Biology ; ISMB. International Conference on Intelligent Systems for Molecular Biology, 4, 44-51
Year:
1996
ISSN:
1553-0833
Keywords:
Laboratories:




 Record created 2012-04-13, last modified 2018-03-17


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