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      ASPicDB: a database of annotated transcript and protein variants generated by alternative splicing

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          Abstract

          Alternative splicing is emerging as a major mechanism for the expansion of the transcriptome and proteome diversity, particularly in human and other vertebrates. However, the proportion of alternative transcripts and proteins actually endowed with functional activity is currently highly debated. We present here a new release of ASPicDB which now provides a unique annotation resource of human protein variants generated by alternative splicing. A total of 256 939 protein variants from 17 191 multi-exon genes have been extensively annotated through state of the art machine learning tools providing information of the protein type (globular and transmembrane), localization, presence of PFAM domains, signal peptides, GPI-anchor propeptides, transmembrane and coiled-coil segments. Furthermore, full-length variants can be now specifically selected based on the annotation of CAGE-tags and polyA signal and/or polyA sites, marking transcription initiation and termination sites, respectively. The retrieval can be carried out at gene, transcript, exon, protein or splice site level allowing the selection of data sets fulfilling one or more features settled by the user. The retrieval interface also enables the selection of protein variants showing specific differences in the annotated features. ASPicDB is available at http://www.caspur.it/ASPicDB/.

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          Most cited references33

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          A new generation of homology search tools based on probabilistic inference.

          Many theoretical advances have been made in applying probabilistic inference methods to improve the power of sequence homology searches, yet the BLAST suite of programs is still the workhorse for most of the field. The main reason for this is practical: BLAST's programs are about 100-fold faster than the fastest competing implementations of probabilistic inference methods. I describe recent work on the HMMER software suite for protein sequence analysis, which implements probabilistic inference using profile hidden Markov models. Our aim in HMMER3 is to achieve BLAST's speed while further improving the power of probabilistic inference based methods. HMMER3 implements a new probabilistic model of local sequence alignment and a new heuristic acceleration algorithm. Combined with efficient vector-parallel implementations on modern processors, these improvements synergize. HMMER3 uses more powerful log-odds likelihood scores (scores summed over alignment uncertainty, rather than scoring a single optimal alignment); it calculates accurate expectation values (E-values) for those scores without simulation using a generalization of Karlin/Altschul theory; it computes posterior distributions over the ensemble of possible alignments and returns posterior probabilities (confidences) in each aligned residue; and it does all this at an overall speed comparable to BLAST. The HMMER project aims to usher in a new generation of more powerful homology search tools based on probabilistic inference methods.
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            Understanding alternative splicing: towards a cellular code.

            In violation of the 'one gene, one polypeptide' rule, alternative splicing allows individual genes to produce multiple protein isoforms - thereby playing a central part in generating complex proteomes. Alternative splicing also has a largely hidden function in quantitative gene control, by targeting RNAs for nonsense-mediated decay. Traditional gene-by-gene investigations of alternative splicing mechanisms are now being complemented by global approaches. These promise to reveal details of the nature and operation of cellular codes that are constituted by combinations of regulatory elements in pre-mRNA substrates and by cellular complements of splicing regulators, which together determine regulated splicing pathways.
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              Splicing in disease: disruption of the splicing code and the decoding machinery.

              Human genes contain a dense array of diverse cis-acting elements that make up a code required for the expression of correctly spliced mRNAs. Alternative splicing generates a highly dynamic human proteome through networks of coordinated splicing events. Cis- and trans-acting mutations that disrupt the splicing code or the machinery required for splicing and its regulation have roles in various diseases, and recent studies have provided new insights into the mechanisms by which these effects occur. An unexpectedly large fraction of exonic mutations exhibit a primary pathogenic effect on splicing. Furthermore, normal genetic variation significantly contributes to disease severity and susceptibility by affecting splicing efficiency.
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                Author and article information

                Journal
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                January 2011
                January 2011
                4 November 2010
                4 November 2010
                : 39
                : Database issue , Database issue
                : D80-D85
                Affiliations
                1Biocomputing Group, University of Bologna, Bologna 40126, 2Consorzio Interuniversitario per le Applicazioni di Supercalcolo per Università e Ricerca (CASPUR), Rome 00185, 3DISCo, University of Milan-Bicocca, Milan, 20135, 4Dipartimento di Biochimica e Biologia Molecolare, University of Bari, Bari 70126, 5BioDec srl, Casalecchio di Reno, Bologna 40033, 6Istituto Tecnologie Biomediche, Consiglio Nazionale delle Ricerche, Bari 70126, 7Dipartimento di Chimica Strutturale e Stereochimica Inorganica, University of Milan, 8Dipartimento di Scienze Biomolecolari e Biotecnologie, University of Milan, Milan 20133 and 9Istituto Biomembrane e Bioenergetica, Consiglio Nazionale delle Ricerche, Bari 70125, Italy
                Author notes
                *To whom correspondence should be addressed. Tel: +39 080 5443588; Fax: +39 080 5443317; Email: graziano.pesole@ 123456biologia.uniba.it
                Correspondence may also be addressed to Rita Casadio. Tel: +39 0512094005; Fax: +39 051242576; Email: casadio@ 123456alma.unibo.it

                The authors wish it to be known that in their opinion, the first two authors are the joint First Authors.

                Article
                gkq1073
                10.1093/nar/gkq1073
                3013677
                21051348
                ca7043e5-db75-4214-80cb-1d0510edfe52
                © The Author(s) 2010. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 9 August 2010
                : 14 October 2010
                : 14 October 2010
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                Genetics
                Genetics

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