Biomedical subjects

M F Sagot

Publications and source records attributed to M F Sagot.

6 recordsLinked to original sources

PubMed2000

Inferring regulatory elements from a whole genome. An analysis of Helicobacter pylori sigma(80) family of promoter signals.

Helicobacter pylori is adapted to life in a unique niche, the gastric epithelium of primates. Its promoters may therefore be different from those of other bacteria. Here, we determine motifs possibly involved in the recognition of such promoter sequences by the RNA polymerase using a new motif identification method. An important feature of this method is that the motifs are sought with the least possible assumptions about what they may look like. The method starts by considering the whole genome of H. pylori and attempts to infer directly from it a description for a family of promoters. Thus, this approach differs from searching for such promoters with a previously established description. The two algorithms are based on the idea of inferring motifs by flexibly comparing words in the sequences with an external object, instead of between themselves. The first algorithm infers single motifs, the second a combination of two motifs separated from one another by strictly defined, sterically constrained distances. Besides independently finding motifs known to be present in other bacteria, such as the Shine-Dalgarno sequence and the TATA-box, this approach suggests the existence in H. pylori of a new, combined motif, TTAAGC, followed optimally 21 bp downstream by TATAAT. Between these two motifs, there is in some cases another, TTTTAA or, less frequently, a repetition of TTAAGC separated optimally from the TATA-box by 12 bp. The combined motif TTAAGCx(21+/-2)TATAAT is present with no errors immediately upstream from the only two copies of the ribosomal 23 S-5 S RNA genes in H. pylori, and with one error upstream from the only two copies of the ribosomal 16 S RNA genes. The operons of both ribosomal RNA molecules are strongly expressed, representing an encouraging sign of the pertinence of the motifs found by the algorithms. In 25 cases out of a possible 30, the combined motif is found with no more than three substitutions immediately upstream from ribosomal proteins, or operons containing a ribosomal protein. This is roughly the same frequency of occurrence as for TTGACAx(15-19)TATAAT (with the same maximum number of substitutions allowed) described as being the sigma(70 )promoter sequence consensus in Bacillus subtilis and Escherichia coli. The frequency of occurrence of the new motif obtained, TTAAGCx(19-23)TATAAT, remains high when all protein genes in H. pylori are considered, as is the case for the TTGACAx(15-19)TATAAT motif in B. subtilis but not in E. coli.

Algorithms↗

PubMed2000

Algorithms for extracting structured motifs using a suffix tree with an application to promoter and regulatory site consensus identification.

This paper introduces two exact algorithms for extracting conserved structured motifs from a set of DNA sequences. Structured motifs may be described as an ordered collection of p > or = 1 "boxes" (each box corresponding to one part of the structured motif), p substitution rates (one for each box) and p - 1 intervals of distance (one for each pair of successive boxes in the collection). The contents of the boxes--that is, the motifs themselves--are unknown at the start of the algorithm. This is precisely what the algorithms are meant to find. A suffix tree is used for finding such motifs. The algorithms are efficient enough to be able to infer site consensi, such as, for instance, promoter sequences or regulatory sites, from a set of unaligned sequences corresponding to the noncoding regions upstream from all genes of a genome. In particular, both algorithms time complexity scales linearly with N2n where n is the average length of the sequences and N their number. An application to the identification of promoter and regulatory consensus sequences in bacterial genomes is shown.

Algorithms↗

PubMed1998

Identifying satellites and periodic repetitions in biological sequences.

We present in this paper an algorithm for identifying satellites in DNA sequences. Satellites (simple, micro, or mini) are repeats in number between 30 and as many as 1,000,000 whose lengths vary between 2 and hundreds of base pairs and that appear, with some mutations, in tandem along the sequence. We concentrate here on short to moderately long (up to 30-40 base pairs) approximate tandem repeats where copies may differ up to epsilon = 15-20% from a consensus model of the repeating unit (implying individual units may vary by 2 epsilon from each other). The algorithm is composed of two parts. The first one consists of a filter that basically eliminates all regions whose probability of containing a satellite is less than one in 10(4) when epsilon = 10%. The second part realizes an exhaustive exploration of the space of all possible models for the repeating units present in the sequence. It therefore has the advantage over previous work of being able to report a consensus model, say m, of the repeated unit as well as the span of the satellite. The first phase was designed for efficiency and takes only O (n) time where n is the length of the sequence. The second phase was designed for sensitivity and takes time O (n . N (e, k)) in the worst case where k is the length of the repeating unit m, e = [epsilon k] is the number of differences allowed between each repeat unit and the model m, and N (e, k) is the maximum number of words that are not more than e differences from another word of length k. That is, N (e, k) is the maximum size of an e-neighborhood of a string of length k. Experiments reveal the second phase to be considerably faster in practice than the worst-case complexity bound suggests. Finally, the present algorithm is easily adapted to finding tandem repeats in protein sequences, as well as extended to identifying mixed direct-inverse tandem repeats.

Algorithms↗

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↗

PubMedDate not supplied

Promoter sequences and algorithmical methods for identifying them.

This paper presents a survey of currently available mathematical models and algorithmical methods for trying to identify promoter sequences. The methods concern both searching in a genome for a previously defined consensus and extracting a consensus from a set of sequences. Such methods were often tailored for either eukaryotes or prokaryotes although this does not preclude use of the same method for both types of organisms. The survey therefore covers all methods; however, emphasis is placed on prokaryotic promoter sequence identification. Illustrative applications of the main extracting algorithms are given for three bacteria.

Algorithms↗