ATUCG — AN AGENT–BASED ENVIRONMENT FOR AUTOMATIC ANNOTATION OF GENOMES

This work reports on the ATUCG environment (Agent-based environmenT for aUtomatiC annotation of Genomes). It consists of three layers, each having several agents in charge of performing repetitive and time-consuming tasks. Layer I aims at automating the tasks behind the process of finding ORFs (Open Reading Frames). Layer II (the core of our approach) is associated with three main tasks: extraction and formatting of data, automatic annotation of data regarding profiles or families of proteins, and generation and validation of rules to automatically annotate the Keywords field in the SWISS-PROT database. Layer III permits the user to check the correctness of the automatic annotation. This environment is being designed having the sequencing of the Mycoplasma hyopneumoniae in mind. Thus examples are presented using data of organisms of the Mycoplasmataceae family. We have concentrated the developments in layer II because this is the most general one and because it focusses on machine learning algorithms, a characteristic which is not usual in annotation systems. Results regarding this layer show that with learning (individual or colaborative), agents are able to generate rules for annotation which achieve better results than those reported in the literature.