Estimating and evaluating the statistics of gapped local-alignment scores


Autoria(s): Bailey, T. L.; Gribskov, M.
Contribuinte(s)

M.S. Waterman

S. Istrail

Data(s)

01/01/2002

Resumo

We present a novel maximum-likelihood-based algorithm for estimating the distribution of alignment scores from the scores of unrelated sequences in a database search. Using a new method for measuring the accuracy of p-values, we show that our maximum-likelihood-based algorithm is more accurate than existing regression-based and lookup table methods. We explore a more sophisticated way of modeling and estimating the score distributions (using a two-component mixture model and expectation maximization), but conclude that this does not improve significantly over simply ignoring scores with small E-values during estimation. Finally, we measure the classification accuracy of p-values estimated in different ways and observe that inaccurate p-values can, somewhat paradoxically, lead to higher classification accuracy. We explain this paradox and argue that statistical accuracy, not classification accuracy, should be the primary criterion in comparisons of similarity search methods that return p-values that adjust for target sequence length.

Identificador

http://espace.library.uq.edu.au/view/UQ:62160

Idioma(s)

eng

Publicador

Mary Ann Liebert, Inc. Publishers

Palavras-Chave #Mathematics, Interdisciplinary Applications #Biochemical Research Methods #Biotechnology & Applied Microbiology #Computer Science, Interdisciplinary Applications #Statistics & Probability #Statistics #Sequence Alignment #Homology Search #Evaluation #Sequence #Database #C1 #780101 Mathematical sciences
Tipo

Journal Article