An Efficient Algorithm for Finding Similar Short Substrings from Large Scale String Data

Abstract

Finding similar substrings/substructures is a central task in analyzing huge amounts of string data such as genome sequences, web documents, log data, etc. In the sense of complexity theory, the existence of polynomial time algorithms for such problems is usually trivial since the number of substrings is bounded by the square of their lengths. However, straightforward algorithms do not work for practical huge databases because of their computation time of high degree order. This paper addresses the problems of finding pairs of strings with small Hamming distances from huge databases composed of short strings. By solving the problem for all the substrings of fixed length, we can efficiently find candidates of similar non-short substrings. We focus on the practical efficiency of algorithms, and propose an algorithm running in almost linear time of the database size. We prove that the computation time of its variant is bounded by linear of the database size when the length of short strings to be found is constant. Slight modifications of the algorithm adapt to the edit distance and mismatch tolerance computation. Computational experiments for genome sequences show the efficiency of the algorithm. An implementation is available at the author’s homepageKeywordsPolynomial Time AlgorithmEdit DistanceDistance ThresholdSimilar SubstringsDatabase SizeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.