AcoSeeD: an ant colony optimization for finding optimal spaced seeds in biological sequence search

Authors:
Dong Do Duc;Huy Q. Dinh;Thanh Hai Dang;Kris Laukens;Xuan Huan Hoang
Affiliations:
Institute of Information Technology, Vietnam National University, Hanoi, Vietnam;Center for Integrative Bioinformatics, Max F Perutz Laboratories, University of Vienna and Medical University, Vienna, Austria;Biomina - Biomedical Informatics Research Center Antwerp, Antwerp University Hospital / University of Antwerp, Edegem, Belgium;Biomina - Biomedical Informatics Research Center Antwerp, Antwerp University Hospital / University of Antwerp, Edegem, Belgium, Advanced Database Research and Modelling (ADReM), University of Antw ...;University of Technology (UET), Vietnam National University, Hanoi, Vietnam
Venue:
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
Year:
2012

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Abstract

Similarity search in biological sequence database is one of the most popular and important bioinformatics tasks. Spaced seeds have been increasingly used to improve the quality and sensitivity of searching, for example, in seeded alignment methods. Finding optimal spaced seeds is a NP-hard problem. In this study we introduce an application of an Ant Colony Optimization (ACO) algorithm to address this problem in a metaheuristics framework. This method, called AcoSeeD, builds optimal spaced seeds in an elegant construction graph that uses the ACO standard framework with a modified pheromone update. Experimental results demonstrate that AcoSeeD brings a significant improvement of sensitivity while demanding the same computational time as other state-of-the-art methods. We also introduces an alternative way of using local search that exerts a fast approximation of the objective function in ACO.