Understanding the molecular mechanisms responsible for the regulation of the transcriptome present in eukaryotic cells is/none of the most challenging tasks in the postgenomic era. In this regard, alternative splicing (AS) is a key phenomenon/ncontributing to the production of different mature transcripts from the same primary RNA sequence. As a plethora of/ndifferent transcript forms is available in databases, a first step to uncover the biology that drives AS is to identify the/ndifferent types of reflected splicing variation. In this work, we present a general definition of the AS event along with a/nnotation system that involves the relative positions of the splice sites. This nomenclature univocally and dynamically assigns/na specific ‘‘AS code’’ to every possible pattern of splicing variation. On the basis of this definition and the corresponding/ncodes, we have developed a computational tool (AStalavista) that automatically characterizes the complete landscape of AS/nevents in a given transcript annotation of a genome, thus providing a platform to investigate the transcriptome diversity/nacross genes, chromosomes, and species. Our analysis reveals that a substantial part—in human more than a quarter—of/nthe observed splicing variations are ignored in common classification pipelines. We have used AStalavista to investigate and/nto compare the AS landscape of different reference annotation sets in human and in other metazoan species and found that/nproportions of AS events change substantially depending on the annotation protocol, species-specific attributes, and/ncoding constraints acting on the transcripts. The AStalavista system therefore provides a general framework to conduct/nspecific studies investigating the occurrence, impact, and regulation of AS.

This work has been funded by a DAAD (German Academic Exchange Service) postdoctoral fellowship to MS. Further support has been provided by grants from the NHGRI Encode project, the European Union ATD project, and the Spanish Plan Nacional de I+D.