The application of alternative splicing graphs in quantitative analysis of alternative splicing form from EST database

Alternative splicing of a single pre-mRNA can give rise to different mRNA transcripts. Alternative splicing of pre-messenger RNA is an important layer of gene expression regulation in eukaryotic cell. Consequently, alternative splicing is an important mechanism for generating protein diversity from a single gene. Although alternative splicing is an important biological process, standard molecular biology techniques have only identified several hundred alternative splicing variants and create a bottleneck in terms of experimental validation. In this paper, we propose methods of obtaining models of weighted alternative splicing graphs and ways of generating all alternative splicing forms from a weighted alternative splicing graph and formulate linear programming models and use the popular linear programming solver to obtain the quantitative distributions of various alternative splicing forms. Basically, the method uses the UniGene clusters of human expressed sequence tags (ESTs) to identify alternative splicing sites. Furthermore, we propose linear time algorithms that correctly produce all possible alternative splicing variants with their corresponding probabilities. Using these methods, we infer several sets of putative alternative splicing forms; these results are then compared with methods proposed by others. Then by aligning sequences of EST database to the genomic data, we identify locations of exons as well as the alternative splicing sites. To quantify these putative alternative splicing forms, we choose segments in genome to count the EST number, and combine the information of EST and alternative splicing form by constructing the suitable linear programming model.