Katerina Perdikuri

Computation of repetitions and regularities of biologically weighted sequences.

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.

Efficient Algorithms for Handling Molecular 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.

Motif Extraction from Weighted Sequences

We present in this paper three algorithms. The first extracts repeated motifs from a weighted sequence. The motifs correspond to words which occur at least q times and with hamming distance e in a weighted sequence with probability ≥ 1/k each time, where k is a small constant. The second algorithm extracts common motifs from a set of N ≥ 2 weighted sequences with hamming distance e. In the second case, the motifs must occur twice with probability ≥ 1/k, in 1 ≤ q ≤ N distinct sequences of the set. The third algorithm extracts maximal pairs from a weighted sequence. A pair in a sequence is the occurrence of the same substring twice. In addition, the algorithms presented in this paper improve slightly on previous work on these problems.

Algorithms for extracting motifs from biological weighted sequences

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=

Motif extraction from biological sequences: trends and contributions to other scientific fields

In this paper we present algorithms for the localization and extraction of interesting motifs from biological sequences. We are especially interested in weighted sequences, which are extensively used in molecular biology as profiles for protein families and for the representation of binding sites. It is our belief that these algorithms can also be applied to other information technology applications such as network management, data mining and signal processing.

Discovering regularities in biosequences: challenges and applications

Computational methods on molecular sequence data (strings) are at the heart of computational molecular biology. A DNA molecule can be thought of as a string over an alphabet of four characters {a,c,g,t} (nucleotides), while a protein can be thought of as a string over an alphabet of twenty characters (amino acids). A gene, which is physically embedded in a DNA molecule, typically encodes the amino acid sequence for a particular protein. Existing and emerging algorithms for string computation provide a significant intersection between computer science and molecular biology.