title: Computational methods for alternative splicing prediction.
abstract: The fact that a large majority of mammalian genes are subject to alternative splicing indicates that this phenomenon represents a major mechanism for increasing proteome complexity. Here, we provide an overview of current methods for the computational prediction of alternative splicing based on the alignment of genome and transcript sequences. Specific features and limitations of different approaches and software are discussed, particularly those affecting prediction accuracy and assembly of alternative transcripts.
authors.affiliation: Dipartimento di Biochimica e Biologia Molecolare, Universita di Bari, Via Orabona 4, 70126 Bari, Italy. graziano.pesole@biologia.uniba.it.
authors.forename: Paola
authors.initials: P
authors.lastname: Bonizzoni
authors.forename: Raffaella
authors.initials: R
authors.lastname: Rizzi
authors.forename: Graziano
authors.initials: G
authors.lastname: Pesole
mesh_terms: D000465:Algorithms; D017398:Alternative Splicing; D000818:Animals; D020224:Expressed Sequence Tags; D016678:Genome; D015894:Genome, Human; D006801:Humans; D011237:Predictive Value of Tests; D016415:Sequence Alignment; D017423:Sequence Analysis, RNA; D012984:Software