Using BioAgents for Supporting Manual Annotation on Genome Sequencing Projects

Abstract

Enormous volume of DNA sequences of organisms are continuously being discovered by genome sequencing projects around the world. The task of identifying biological function prediction for the DNA sequences is a key activity in genome projects. This task is done in the annotation phase, which is divided into automatic and manual. The automatic annotation has the objective of finding, for each DNA sequence identified in the project, similar sequences among millions, stored in public databases, by using approximated pattern matching algorithms. The manual annotation is done by the biologists, that use the results produced by the automatic annotation, and their knowledge and experience, to decide the function prediction to each DNA sequence. In this way, the biologists guarantee accuracy and correctness to each sequence function prediction. This work presents a new version of BioAgents, a multiagent system (MAS) for supporting manual annotation. The system simulates the biologists’ knowledge and experience for annotating DNA sequences in genome sequencing projects. The MAS cooperative approach, allows to create different specialized intelligent agents that, working together, suggest proper manual annotation. BioAgents was defined with a three-layer architecture using the JADE framework with a ruler-based engine (JESS). We have done experiments with real data from three different genome sequencing projects: Paracoccidioides brasilienses fungus, Paullinia cupana (guaraná) plant and Anaplasma marginale rickettsia. The produced results were encouraging, which prove the usefulness of BioAgents.