Correspondence should be addressed to:
Lab. of Medical Informatics, PO BOX 323, 54124, University of Thessaloniki, Greece
While biological processes underlying gene expression are still under experimental research, computational gene prediction techniques have reached high level of sophistication with the employment of efficient intrinsic and extrinsic methods that identify proteincoding regions within query genomic sequences. Their ability though to delineate the exact exon boundaries is characterized by a trade off between sensitivity and specificity and still is prone to alternations in gene regulation during transcription and splicing and to inherent complexities introduced by the implemented methodology. Evaluation studies have shown that combinatorial approaches exhibit improved accuracy levels through the integration of evidence data from multiple resources that are further assessed in order to end up with the most probable gene assembly. In this work, we present an integration and information handling architecture that exploits evidence derived from multiple gene finding resources, in order to generate machinereadable representations of optimal/suboptimal gene structure predictions, signal features identification and high scoring similarity matches. Unlike most combinatorial techniques, which end up with the most probable gene assembly, the objective of this architecture is to support advanced information handling mechanisms that may give more in depth insights on the underlying gene expression machinery and the alternations that may occur. Technically, XML was adopted to build and interchange structured data among the architectures components together with relevant technologies offering graphical representations and queries formulation/execution over single/multiple information sources.