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      SplicePort—An interactive splice-site analysis tool

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          Abstract

          SplicePort is a web-based tool for splice-site analysis that allows the user to make splice-site predictions for submitted sequences. In addition, the user can also browse the rich catalog of features that underlies these predictions, and which we have found capable of providing high classification accuracy on human splice sites. Feature selection is optimized for human splice sites, but the selected features are likely to be predictive for other mammals as well. With our interactive feature browsing and visualization tool, the user can view and explore subsets of features used in splice-site prediction (either the features that account for the classification of a specific input sequence or the complete collection of features). Selected feature sets can be searched, ranked or displayed easily. The user can group features into clusters and frequency plot WebLogos can be generated for each cluster. The user can browse the identified clusters and their contributing elements, looking for new interesting signals, or can validate previously observed signals. The SplicePort web server can be accessed at http://www.cs.umd.edu/projects/SplicePort and http://www.spliceport.org.

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          Systematic identification and analysis of exonic splicing silencers.

          Zefeng Wang, Michael E Rolish, Gene Yeo … (2004)
          Exonic splicing silencers (ESSs) are cis-regulatory elements that inhibit the use of adjacent splice sites, often contributing to alternative splicing (AS). To systematically identify ESSs, an in vivo splicing reporter system was developed to screen a library of random decanucleotides. The screen yielded 141 ESS decamers, 133 of which were unique. The silencer activity of over a dozen of these sequences was also confirmed in a heterologous exon/intron context and in a second cell type. Of the unique ESS decamers, most could be clustered into groups to yield seven putative ESS motifs, some resembling known motifs bound by hnRNPs H and A1. Potential roles of ESSs in constitutive splicing were explored using an algorithm, ExonScan, which simulates splicing based on known or putative splicing-related motifs. ExonScan and related bioinformatic analyses suggest that these ESS motifs play important roles in suppression of pseudoexons, in splice site definition, and in AS.
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            Prediction of human mRNA donor and acceptor sites from the DNA sequence.

            S Brunak, J Engelbrecht, S. KNUDSEN (1991)
            Artificial neural networks have been applied to the prediction of splice site location in human pre-mRNA. A joint prediction scheme where prediction of transition regions between introns and exons regulates a cutoff level for splice site assignment was able to predict splice site locations with confidence levels far better than previously reported in the literature. The problem of predicting donor and acceptor sites in human genes is hampered by the presence of numerous amounts of false positives: here, the distribution of these false splice sites is examined and linked to a possible scenario for the splicing mechanism in vivo. When the presented method detects 95% of the true donor and acceptor sites, it makes less than 0.1% false donor site assignments and less than 0.4% false acceptor site assignments. For the large data set used in this study, this means that on average there are one and a half false donor sites per true donor site and six false acceptor sites per true acceptor site. With the joint assignment method, more than a fifth of the true donor sites and around one fourth of the true acceptor sites could be detected without accompaniment of any false positive predictions. Highly confident splice sites could not be isolated with a widely used weight matrix method or by separate splice site networks. A complementary relation between the confidence levels of the coding/non-coding and the separate splice site networks was observed, with many weak splice sites having sharp transitions in the coding/non-coding signal and many stronger splice sites having more ill-defined transitions between coding and non-coding.
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              Comparative analysis identifies exonic splicing regulatory sequences--The complex definition of enhancers and silencers.

              Hadas Keren‐Shaul, Oren Ram, Amir Goren … (2006)
              Exonic splicing regulatory sequences (ESRs) are cis-acting factor binding sites that regulate constitutive and alternative splicing. A computational method based on the conservation level of wobble positions and the overabundance of sequence motifs between 46,103 human and mouse orthologous exons was developed, identifying 285 putative ESRs. Alternatively spliced exons that are either short in length or contain weak splice sites show the highest conservation level of those ESRs, especially toward the edges of exons. ESRs that are abundant in those subgroups show a different distribution between constitutively and alternatively spliced exons. Representatives of these ESRs and two SR protein binding sites were shown, experimentally, to display variable regulatory effects on alternative splicing, depending on their relative locations in the exon. This finding signifies the delicate positional effect of ESRs on alternative splicing regulation.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Nucleic Acids Res
                Journal ID (iso-abbrev): Nucleic Acids Res
                Journal ID (publisher-id): nar
                Journal ID (hwp): nar
                Title: Nucleic Acids Research
                Publisher: Oxford University Press
                ISSN (Print): 0305-1048
                ISSN (Electronic): 1362-4962
                Publication date (Print): July 2007
                Publication date (Electronic): 18 June 2007
                Publication date PMC-release: 18 June 2007
                Volume: 35
                Issue: Web Server issue
                Pages: W285-W291
                Affiliations
                1Computer Science Department, University of Maryland, College Park, Maryland, 20742, 2National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, 20894, 3Department of Cell Biology and Molecular Genetics and 4Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland 20742, USA
                Author notes
                *To whom correspondence should be addressed. (301) 405 2717 rezarta@ 123456cs.umd.edu
                Article
                DOI: 10.1093/nar/gkm407
                PMC ID: 1933122
                PubMed ID: 17576680
                SO-VID: 8c67b231-f2b6-477e-bacc-52a90912eceb
                Copyright © © 2007 The Author(s)
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 31 January 2007
                Date revision received : 18 April 2007
                Date accepted : 3 May 2007
                Categories
                Subject: Articles

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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