Exon prediction in eucaryotic genomes

Protein sequences classification by means of feature extraction with substitution matrices

BACKGROUND

This paper deals with the preprocessing of protein sequences for supervised classification. Motif extraction is one way to address that task. It has been largely used to encode biological sequences into feature vectors to enable using well-known machine-learning classifiers which require this format. However, designing a suitable feature space, for a set of proteins, is not a trivial task. For this purpose, we propose a novel encoding method that uses amino-acid substitution matrices to define similarity between motifs during the extraction step.

RESULTS

In order to demonstrate the efficiency of such approach, we compare several encoding methods using some machine learning classifiers. The experimental results showed that our encoding method outperforms other ones in terms of classification accuracy and number of generated attributes. We also compared the classifiers in term of accuracy. Results indicated that SVM generally outperforms the other classifiers with any encoding method. We showed that SVM, coupled with our encoding method, can be an efficient protein classification system. In addition, we studied the effect of the substitution matrices variation on the quality of our method and hence on the classification quality. We noticed that our method enables good classification accuracies with all the substitution matrices and that the variances of the obtained accuracies using various substitution matrices are slight. However, the number of generated features varies from a substitution matrix to another. Furthermore, the use of already published datasets allowed us to carry out a comparison with several related works.

CONCLUSIONS

The outcomes of our comparative experiments confirm the efficiency of our encoding method to represent protein sequences in classification tasks.

An approach of encoding for prediction of splice sites using SVM

In splice sites prediction, the accuracy is lower than 90% though the sequences adjacent to the splice sites have a high conservation. In order to improve the prediction accuracy, much attention has been paid to the improvement of the performance of the algorithms used, and few used for solving the fundamental issues, namely, nucleotide encoding. In this paper, a predictor is constructed to predict the true and false splice sites for higher eukaryotes based on support vector machines (SVM). Four types of encoding, which were mono-nucleotide (MN) encoding, MN with frequency difference between the true sites and false sites (FDTF) encoding, Pair-wise nucleotides (PN) encoding and PN with FDTF encoding, were applied to generate the input for the SVM. The results showed that PN with FDTF encoding as input to SVM led to the most reliable recognition of splice sites and the accuracy for the prediction of true donor sites and false sites were 96.3%, 93.7%, respectively, and the accuracy for predicting of true acceptor sites and false sites were 94.0%, 93.2%, respectively.

Genome analysis with gene-indexing databases

The recent release of the draft sequence and the eventual completion of the human genome present the scientific community with a rich source of data to mine. Yet, these data are content poor in the absence of additional correlative information. Expressed sequence tag (EST) datasets and their associated gene indices have existed for many years, and represent the first attempt at understanding the complexity of the genome. These datasets remain extremely important as information sources and, in particular, as tools for analyzing the completed genomes. Here, we discuss the nature of ESTs and their associated tools and gene-indexing databases. In particular, we will compare three EST gene indices (UNIGENE, Merck Gene Index Version 2.0 and Doubletwist CAT), discuss how these gene indices are applied for both genome analysis and drug discovery, and demonstrate their importance as a complementary dataset to the annotated human genome.

A multi-agent system simulating human splice site recognition

The present paper describes a method detecting splice sites automatically on the basis of sequence data and models of site/signal recognition supported by experimental evidences. The method is designed to simulate splicing and while doing so, track prediction failures, missing information and possibly test correcting hypotheses. Correlations between nucleotides in the splice site regions and the various elements of the acceptor region are evaluated and combined to assess compensating interactions between elements of the splicing machinery. A scanning model of the acceptor region and a model of interaction between the splicing complexes (exon definition model) are also incorporated in the detection process. Subsets of sites presenting deficiencies of several splice site elements could be identified. Further examination of these sites helps to determine lacking elements and refine models.