Data bank homology search algorithm with linear computation complexity.

V B Strelets; A A Ptitsyn; L Milanesi; H A Lim

doi:10.1093/bioinformatics/10.3.319

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Data bank homology search algorithm with linear computation complexity.

V B Strelets·A A Ptitsyn·L Milanesi·H A Lim

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Abstract

A new algorithm for data bank homology search is proposed. The principal advantages of the new algorithm are: (i) linear computation complexity; (ii) low memory requirements; and (iii) high sensitivity to the presence of local region homology. The algorithm first calculates indicative matrices of k-tuple 'realization' in the query sequence and then searches for an appropriate number of matching k-tuples within a narrow range in database sequences. It does not require k-tuple coordinates tabulation and in-memory placement for database sequences. The algorithm is implemented in a program for execution on PC-compatible computers and tested on PIR and GenBank databases with good results. A few modifications designed to improve the selectivity are also discussed. As an application example, the search for homology of the mouse homeotic protein HOX 3.1 is given.

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Algorithms Amino Acid Sequence Animals Databases, Factual Gene Library Genes, Homeobox Information Storage and Retrieval Linear Models Mathematical Computing Mice Sensitivity and Specificity Sequence Homology Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

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V B Strelets, A A Ptitsyn, L Milanesi, H A Lim. 1994. Data bank homology search algorithm with linear computation complexity.. https://doi.org/10.1093/bioinformatics%2F10.3.319

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