000176178 001__ 176178
000176178 005__ 20180913061219.0
000176178 022__ $$a1553-0833
000176178 037__ $$aARTICLE
000176178 245__ $$aA sequence similarity search algorithm based on a probabilistic interpretation of an alignment scoring system
000176178 260__ $$c1996
000176178 269__ $$a1996
000176178 336__ $$aReviews
000176178 520__ $$aWe present a probabilistic interpretation of local sequence alignment methods where the alignment scoring system (ASS) plays the role of a stochastic process defining a probability distribution over all sequence pairs. An explicit algorithms is given to compute the probability of two sequences given and ASS. Based on this definition, a modified version of the Smith-Waterman local similarity search algorithm has been devised, which assesses sequence relationships by log likelihood ratios. When tested on classical examples such as globins or G-protein-coupled receptors, the new method proved to be up to an order of magnitude more sensitive than the native Smith-Waterman algorithm.
000176178 6531_ $$aAlgorithms
000176178 6531_ $$aModels, Molecular
000176178 700__ $$0244404$$g113607$$aBucher, P.
000176178 700__ $$aHofmann, K.
000176178 773__ $$j4$$tProceedings / ... International Conference on Intelligent Systems for Molecular Biology ; ISMB. International Conference on Intelligent Systems for Molecular Biology$$q44-51
000176178 909C0 $$xU11780$$0252244$$pGR-BUCHER
000176178 909CO $$pSV$$preview$$ooai:infoscience.tind.io:176178
000176178 917Z8 $$x182396
000176178 937__ $$aEPFL-REVIEW-176178
000176178 973__ $$rREVIEWED$$sPUBLISHED$$aOTHER
000176178 980__ $$aREVIEW