Sophisticated tools are needed to annotate this amount of data. Given the nature of genomic sequence in humans, where large introns are known to exist, we recognize the need for highly specific gene finding algorithms. Existing gene finding methods rely on a heuristic mix of content-statistics, specific signal sequences, with or without homology information. The goal is always to get the complete and exact sequence of a particular gene. Homology hits or content statistics may give us a strong hint that a particular sequence has coding potential but we will always need to refine these predictions using signal sequences if we are to find the exact structure of a gene.
Splice sites are the key signal sequences that determine the boundaries of the exons.
Therefore, we need to find an optimal splice site recognition method. Such a method should ideally be based on a thorough understanding of the complex eukaryotic splicing process. The newest available genomic data can be used to shed some light on this process. We analyze the structure of donor and acceptor sites using a separate neural network recognizer for each site. These two neural network recognizers were developed as described in Brunak et al., 1991.