Using substitution probabilities to improve position-specific scoring matrices

@article{citeulike:2535239, abstract = {Each column of amino acids in a multiple alignment of protein sequences can be represented as a vector of 20 amino acid counts. For alignment and searching applications, the count vector is an imperfect representation of a position, because the observed sequences are an incomplete sample of the full set of related sequences. One general solution to this problem is to model unobserved sequences by adding artificial pseudo-counts' to the observed counts. We introduce a simple method for computing pseudo-counts that combines the diversity observed in each alignment position with amino acid substitution probabilities. In extensive empirical tests, this position-based method out-performed other pseudo-count methods and was a substantial improvement over the traditional average score method used for constructing profiles. 10.1093/bioinformatics/12.2.135}, author = {Henikoff, Jorja G. and Henikoff, Steven }, citeulike-article-id = {2535239}, doi = {10.1093/bioinformatics/12.2.135}, journal = {Comput. Appl. Biosci.}, keywords = {pssm}, month = {April}, number = {2}, pages = {135--143}, posted-at = {2008-03-15 01:22:39}, priority = {2}, title = {Using substitution probabilities to improve position-specific scoring matrices}, url = {http://dx.doi.org/10.1093/bioinformatics/12.2.135}, volume = {12}, year = {1996} }

TY - JOUR ID - citeulike:2535239 L3 - citeulike-article-id:2535239 TI - Using substitution probabilities to improve position-specific scoring matrices JF - Comput. Appl. Biosci. VL - 12 IS - 2 SP - 135 EP - 143 N2 - Each column of amino acids in a multiple alignment of protein sequences can be represented as a vector of 20 amino acid counts. For alignment and searching applications, the count vector is an imperfect representation of a position, because the observed sequences are an incomplete sample of the full set of related sequences. One general solution to this problem is to model unobserved sequences by adding artificial pseudo-counts' to the observed counts. We introduce a simple method for computing pseudo-counts that combines the diversity observed in each alignment position with amino acid substitution probabilities. In extensive empirical tests, this position-based method out-performed other pseudo-count methods and was a substantial improvement over the traditional average score method used for constructing profiles. 10.1093/bioinformatics/12.2.135 KW - pssm AU - Henikoff, Jorja AU - Henikoff, Steven PY - 1996/04/01/ UR - http://dx.doi.org/10.1093/bioinformatics/12.2.135 ER -