Classifying proteins by family using the product of correlated p-values

An important goal in bioinformatics is determining the homology and function of proteins from their sequences. Pairwise sequence similarity algorithms are often employed for this purpose. This paper describes a method for improving the accuracy of such algorithms using knowledge about families of proteins. The method requires a library of protein families against which to compare query sequences. A standard pairwise similarity search algorithm is used to search the library with the query, and a new variant of the Family Pairwise Search (FPS) algorithm converts the results into a list sorted by the E-values of the matches between the query and the families. The E-value of each query-family match is calculated using a statistical distribution introduced here that describes the behavior of the product of the p-values of correlated random variables. We also describe an algorithm (ESIZE) for estimating the single parameter of this distribution. This parameter summarizes the amount of corre...

• Type: Conference
• Year: 1999
• Where: RECOMB
• Authors: Timothy L. Bailey, William Noble Grundy
• Keywords: Algorithm, Computational Biology, Pairwise Similarity Search, RECOMB 1999, Sequence Similarity Algorithms