M F Sagot | Search PubMed

PubMed1995

Finding flexible patterns in a text: an application to three-dimensional molecular matching.

Finding certain regularities in a text is an important problem in many areas, e.g. in the analysis of biological molecules such as nucleic acids or proteins. In the latter case, the text may be sequences of amino acids or a linear coding of three-dimensional structures, and the regularities then correspond to lexical or structural motifs common to two, or more, proteins. We first recall an earlier algorithm that found these regularities in a flexible way. Then we introduce a generalized version of this algorithm designed for the particular case of protein three-dimensional structures, since these structures present a few peculiarities that make them computationally harder to process. Finally, we give some applications of our new algorithm on concrete examples.

Algorithms↗

PubMed1995

A distance-based block searching algorithm.

We present in this paper an algorithm for the multiple comparison of a set of protein sequences. Our approach is that of peptide matching and consists in looking for all the words that occur approximatively in at least q of the sequences in the set, where q is a parameter. Words are compared by using a reference object called a model, that is itself a word over the alphabet of the amino acids, and the comparison between a model and a word is based on w-length words instead of single symbols. This idea is similar to the one used in the Blast program in the case of pairwise comparisons. Two w-length words are considered to be related if an alignment without gaps of the two using a similarity matrix has a score greater than a certain threshold value t. In our case, we say that a k-length word u is an occurrence of a model m of the same length if every w-length subword of u is related to the corresponding subword of m in the sense given above. If a model m has occurrences in at least q of the sequences of the set, m is said to occur in the set. In percentage terms, the value of q may correspond to something as small as 5% of the sequences (search for recurrent words in a set of non homologous proteins) or as high as 70-100% (establishment of a list of all similar words as a first step in a multiple alignment program). The algorithm presented here is an efficient and exact way of looking for all the models, of a fixed length k or of the greatest possible length kmax, that occur in a set of sequences. It can work with any kind of scoring matrix and an extension of the algorithm allows for the introduction of gaps between a model and its occurrences.

Algorithms↗