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Bioinformatics Advance Access originally published online on April 3, 2008
Bioinformatics 2008 24(10):1300-1304; doi:10.1093/bioinformatics/btn113
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

ASPicDB: A database resource for alternative splicing analysis

T. Castrignanò 1, M. D’Antonio 1, A. Anselmo 2, D. Carrabino 1, A. D’Onorio De Meo 1, A. M. D’Erchia 3, F. Licciulli 4, M. Mangiulli 3, F. Mignone 2, G. Pavesi 2, E. Picardi 3, A. Riva 3,5, R. Rizzi 6, P. Bonizzoni 6 and G. Pesole 3,4,*

1Consorzio Interuniversitario per le Applicazioni di Supercalcolo per Università e Ricerca, CASPUR, Rome, 2University of Milan, Dipartimento di Scienze Biomolecolari e Biotecnologie, via Celoria 26, Milan 20133, 3University of Bari, Dipartimento di Biochimica e Biologia Molecolare, via Orabona, 4, Bari 70126, 4Istituto Tecnologie Biomediche del Consiglio Nazionale delle Ricerche, via Amendola 122/D, Bari 70126, Italy, 5Department of Molecular Genetics and Microbiology, University of Florida, PO Box 103610, Gainesville, FL 32610-3610, USA and 6DISCo, University of Milan Bicocca, via Bicocca degli Arcimboldi, 8, Milan, 20135, Italy

*To whom correspondence should be addressed.


  Abstract

Motivation: Alternative splicing has recently emerged as a key mechanism responsible for the expansion of transcriptome and proteome complexity in human and other organisms. Although several online resources devoted to alternative splicing analysis are available they may suffer from limitations related both to the computational methodologies adopted and to the extent of the annotations they provide that prevent the full exploitation of the available data. Furthermore, current resources provide limited query and download facilities.

Results: ASPicDB is a database designed to provide access to reliable annotations of the alternative splicing pattern of human genes and to the functional annotation of predicted splicing isoforms. Splice-site detection and full-length transcript modeling have been carried out by a genome-wide application of the ASPic algorithm, based on the multiple alignments of gene-related transcripts (typically a Unigene cluster) to the genomic sequence, a strategy that greatly improves prediction accuracy compared to methods based on independent and progressive alignments.

Enhanced query and download facilities for annotations and sequences allow users to select and extract specific sets of data related to genes, transcripts and introns fulfilling a combination of user-defined criteria. Several tabular and graphical views of the results are presented, providing a comprehensive assessment of the functional implication of alternative splicing in the gene set under investigation.

ASPicDB, which is regularly updated on a monthly basis, also includes information on tissue-specific splicing patterns of normal and cancer cells, based on available EST sequences and their library source annotation.

Availability: www.caspur.it/ASPicDB

Contact: graziano.pesole{at}biologia.uniba.it

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Alfonso Valencia

Received on November 30, 2007; revised on February 26, 2008; accepted on March 28, 2008

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