Splice site identification using probabilistic parameters and SVM classification

Background: Recent advances and automation in DNA sequencing technology has created a vast amount of DNA sequence data. This increasing growth of sequence data demands better and efficient analysis methods. Identifying genes in this newly accumulated data is an important issue in bioinformatics, and it requires the prediction of the complete gene structure. Accurate identification of splice sites in DNA sequences plays one of the central roles of gene structural prediction in eukaryotes. Effective detection of splice sites requires the knowledge of characteristics, dependencies, and relationship of nucleotides in the splice site surrounding region. A higher-order Markov model is generally regarded as a useful technique for modeling higher-order dependencies. However, their implementation requires estimating a large number of parameters, which is computationally expensive. Results: The proposed method for splice site detection consists of two stages: a first order Markov model (MM1) is...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: A. K. M. A. Baten, Bill C. H. Chang, Saman K. Halgamuge, Jason Li
• Keywords: BMCBI 2006, Dna Sequences, Sequence Data, Splice Site