Biomedical subjects

J Quinqueton

Publications and source records attributed to J Quinqueton.

5 recordsLinked to original sources

PubMed1999

A multi-agent system simulating human splice site recognition.

The present paper describes a method detecting splice sites automatically on the basis of sequence data and models of site/signal recognition supported by experimental evidences. The method is designed to simulate splicing and while doing so, track prediction failures, missing information and possibly test correcting hypotheses. Correlations between nucleotides in the splice site regions and the various elements of the acceptor region are evaluated and combined to assess compensating interactions between elements of the splicing machinery. A scanning model of the acceptor region and a model of interaction between the splicing complexes (exon definition model) are also incorporated in the detection process. Subsets of sites presenting deficiencies of several splice site elements could be identified. Further examination of these sites helps to determine lacking elements and refine models.

Computer Simulation↗

PubMed1996

Exon prediction in eucaryotic genomes.

Two independent computer systems, NetPlantGene and AMELIE, dedicated to the identification of splice sites in plant and human genomes, respectively, are introduced here. Both methods were designed in relation to experimental work; they rely on automatically generated rules involving the nucleotide content of sequences regardless of the coding properties of exons. The specificity of plant sequences as considered in NetPlantGene is shown to enhance the quality of detection as opposed to general methods such as GRAIL. A scanning model of the acceptor site recognition is being simulated by AMELIE leading to a relatively accurate selection process of sites.

Arabidopsis↗

PubMed1994

High speed pattern matching in genetic data base with reconfigurable hardware.

Homology detection in large data bases is probably the most time consuming operation in molecular genetic computing systems. Moreover, the progresses made all around the world concerning the mapping and sequencing of the genome of Homo Sapiens and other species have increased the size of data bases exponentially. Therefore even the best workstation would not be able to reach the scanning speed required. In order to answer this need we propose an algorithm, A2R2, and its implementation on a massively parallel system. Basically, two kinds of algorithms are used to search in molecular genetic data bases. The first kind is based on dynamic programming and the second on word processing, A2R2 belongs to the second kind. The structure of the motif (pattern) searched by A2R2 can support those from FAST, BLAST and FLASH algorithms. After a short presentation of the reconfigurable hardware concept and technology used in our massively parallel accelerator we present the A2R2 implementation. This parallel implementation outperforms any kind of previously published genetic data base scanning hardware or algorithms. We report up to 25 million nucleotides per scanning seconds as our best results.

Algorithms↗

PubMed1985

OURCIN: a tool to build expert systems.

OURCIN is a tool to build expert systems, which was developed by a joint team from INRIA and SEMA. We emphasized developing on this system the ergonomic features which make Expert Systems a comfortable and interesting approach for knowledge engineering.

Computers↗

PubMed1985

Application of learning techniques to splicing site recognition.

Most genes of eukaryotic genomes are disrupted by introns. The application of a learning technique which uses both statistic and syntactic analysis lead to the establishment of logical rules enabling the recognition of intron/exon junctions between uncoding and coding sequences. The rules were tested on rat actin gene sequences containing some or all of the introns and 50 exon nucleotides on either side of the intron. The results show good recognition of the excision site. This recognition is more ambiguous when the sequence is short; for the acceptor sequence it presents a good selection. The learning achieved with both the donor and acceptor sequence does not lead to recognition. This result indicates that it is not the relationship between donor and acceptor sites in the same intron which determines sequence selection or the splicing mechanism.

Base Sequence↗