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.

A Multi-Agent System for Exon Prediction in Human Sequences.

Given the problem of identifying exons in new genomic DNA, the sketch of a resolution process was drawn using sequence data and models of site/signal recognition. A multi-agent architecture is used to validate these models and test hypotheses on the chronology of events involved in gene splicing. Information is channelled through a hierarchy of agents. Each type of agent is the result of a successful step in the resolution process. The system does not rely on the compositional bias of coding sequences which is a key feature of current computer methods.

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.