Abstract
Recognition of the functional sites of genes, such as translation initiation sites, donor and acceptor splice sites and stop codons, is a relevant part of many current problems in bioinformatics. Recognition of the functional sites of genes is also a fundamental step in gene structure predictions in the most powerful programs. The best approaches to this type of recognition use sophisticated classifiers, such as support vector machines. However, with the rapid accumulation of sequence data, methods for combining many sources of evidence are necessary as it is unlikely that a single classifier can solve this type of problem with the best possible performance.
A major issue is that the number of possible models to combine is large and the use of all of these models is impractical. In this paper, we present a framework that is based on floating search for combining as many classifiers as needed for the recognition of any functional sites of a gene. The methodology can be used for the recognition of translation initiation sites, donor and acceptor splice sites and stop codons. Furthermore, we can combine any number of classifiers that are trained on any species. The method is also scalable to large datasets, as is shown in experiments in which the whole human genome is used. The method is also applicable to other recognition tasks.
We present experiments on the recognition of these four functional sites in the human genome, which is used as the target genome, and use another 20 species as sources of evidence. The proposed methodology shows significant improvement over state-of-the-art methods for use in a thorough evaluation process. The proposed method is also able to improve heuristic selection of species to be used as sources of evidence as the search finds the most useful datasets.
Author summary In this paper we present a methodology for combining many sources of information to recognize some of the most important functional sites in a genomic sequence. The functional sites of the sequences, such as, translation start sites, translation initiation sites, acceptor and donor splice sites and stop codons, play a very relevant role in many Bioinformatics tasks. Their accurate recognition is an important task by itself and also as part of gene structure prediction programs.
Our approach uses a methodology usually termed in Computer Science as “floating search”. This is a powerful heuristics applicable when the cost of evaluating each possible solution is high. The methodology is applied to the recognition of four different functional sites in the human genome using as additional sources of evidence the annotated genomes of other twenty different species.
The results show an advantage of the proposed method and also challenge the standard assumption of using only genomes not very close and not very far from the human to improve the recognition of functional sites in the human genome.
