Biological weighted sequences are used extensively in molecular biology as profiles for protein families, in the representation of binding sites and often for the representation of sequences produced by a shotgun sequencing strategy. In this paper, we address three fundamental problems in the area of biologically weighted sequences: (i) computation of repetitions, (ii) pattern matching, and (iii) computation of regularities. Our algorithms can be used as basic building blocks for more sophisticated algorithms applied on weighted sequences.
In this paper we introduce the Weighted Suffix Tree‚ an efficient data structure for computing string regularities in weighted sequences of molecular data. Molecular Weighted Sequences can model important biological processes such as the DNA Assembly Process or the DNA-Protein Binding Process. Thus pattern matching or identification of repeated patterns‚ in biological weighted sequences is a very important procedure in the translation of gene expression and regulation. We present time and space efficient algorithms for constructing the weighted suffix tree and some applications of the proposed data structure to problems taken from the Molecular Biology area such as pattern matching‚ repeats discovery‚ discovery of the longest common subsequence of two weighted sequences and computation of covers.
In this paper we present three algorithms for the Motif Identification Problem in Biological Weighted Sequences. The first algorithm extracts repeated motifs from a biological weighted sequence. The motifs correspond to repetitive words which are approximately equal, under a Hamming distance, with probability of occurrence 1/k, where k is a small constant. The second algorithm extracts common motifs from a set of N 2 weighted sequences. In this case, the motifs consists of words that must occur with probability 1/k, in 1 q N distinct sequences of the set. The third algorithm extracts maximal pairs from a biological weighted sequence. A pair in a sequence is the occurrence of the same word twice. In addition, the algorithms presented in this paper improve previous work on these problems.
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