Identification, improved modeling and integration of signals to predict constitutive and altering splicing

Abstract

(cont.) manipulation of intronic elements that enables fish genes to be spliced properly in mammalian cells; (iii) A computational analysis using EST data, genome sequence data, and microarray expression data of tissue- specific alternative splicing is conducted, which distinguishes human brain, testis and liver as having unusually high levels of AS, highlights differences in the types of AS occurring commonly in different tissues, and identifies candidate cis-regulatory elements and trans-factors likely to play important roles in tissue-specific AS in human cells; (iv) The identification of a set of discriminatory sequence features and their integration into a statistical machine-learning algorithm, ACEScan, which distinguishes exons subject to evolutionarily conserved alternative splicing from constitutively spliced or lineage-specifically-spliced exons is described; (v) The genome-wide search for and experimental validation of exon-skipping events using the combination of two silencing cis-elements, UAGG and GGGG.

The regulation of pre-messenger RNA splicing by the spliceosomal machinery via interactions between cis-regulatory elements and splicing trans-factors to generate a specific mRNA i.e. constitutive splicing, or sometimes many distinct mRNA isoforms i.e. alternative splicing, is still a poorly understood process. Progress into illuminating this process is further exacerbated by the variation of splicing in the multitude of tissues and cell types present, as well as the variation of cis and trans elements in different organisms, and the possibility that some alternative splicing events present in expressed sequence tag (EST) databases may constitute biochemical 'noise' or transient evolutionary fluctuations. Several studies, mainly computational in nature, addressing different questions regarding constitutive and alternative splicing are described here, ranging from improved modeling of splicing signals, studying the variation of alternative splicing in various tissues, analyzing evolutionary differences of cis and trans elements of splicing in various vertebrates, and utilizing attributes indicative of alternative splicing events conserved in human and mouse to identify novel alternatively spliced exons. In particular: (i) A general approach for improved modeling of short sequence motifs, based on the Maximum Entropy principle, that incorporates local adjacent and non-adjacent position dependencies is introduced, and applied to understanding splice site signals. The splice site recognition algorithm, MaxENTScan, performs better than previous models that utilize as input similar length sequences; (ii) The first large-scale bioinformatics study is conducted that identifies similarities and differences in candidate cis-regulatory elements and trans-acting splicing