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      VarCon: An R Package for Retrieving Neighboring Nucleotides of an SNV

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          Abstract

          Reporting of a single nucleotide variant (SNV) follows the Sequence Variant Nomenclature ( http://varnomen.hgvs.org/), using an unambiguous numbering scheme specific for coding and noncoding DNA. However, the corresponding sequence neighborhood of a given SNV, which is required to assess its impact on splicing regulation, is not easily accessible from this nomenclature. Providing fast and easy access to this neighborhood just from a given SNV reference, the novel tool VarCon combines information of the Ensembl human reference genome and the corresponding transcript table for accurate retrieval. VarCon also displays splice site scores (HBond and MaxEnt scores) and HEXplorer profiles of an SNV neighborhood, reflecting position-dependent splice enhancing and silencing properties.

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          Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals.

          We propose a framework for modeling sequence motifs based on the maximum entropy principle (MEP). We recommend approximating short sequence motif distributions with the maximum entropy distribution (MED) consistent with low-order marginal constraints estimated from available data, which may include dependencies between nonadjacent as well as adjacent positions. Many maximum entropy models (MEMs) are specified by simply changing the set of constraints. Such models can be utilized to discriminate between signals and decoys. Classification performance using different MEMs gives insight into the relative importance of dependencies between different positions. We apply our framework to large datasets of RNA splicing signals. Our best models out-perform previous probabilistic models in the discrimination of human 5' (donor) and 3' (acceptor) splice sites from decoys. Finally, we discuss mechanistically motivated ways of comparing models.
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            Potential etiologic and functional implications of genome-wide association loci for human diseases and traits.

            We have developed an online catalog of SNP-trait associations from published genome-wide association studies for use in investigating genomic characteristics of trait/disease-associated SNPs (TASs). Reported TASs were common [median risk allele frequency 36%, interquartile range (IQR) 21%-53%] and were associated with modest effect sizes [median odds ratio (OR) 1.33, IQR 1.20-1.61]. Among 20 genomic annotation sets, reported TASs were significantly overrepresented only in nonsynonymous sites [OR = 3.9 (2.2-7.0), p = 3.5 x 10(-7)] and 5kb-promoter regions [OR = 2.3 (1.5-3.6), p = 3 x 10(-4)] compared to SNPs randomly selected from genotyping arrays. Although 88% of TASs were intronic (45%) or intergenic (43%), TASs were not overrepresented in introns and were significantly depleted in intergenic regions [OR = 0.44 (0.34-0.58), p = 2.0 x 10(-9)]. Only slightly more TASs than expected by chance were predicted to be in regions under positive selection [OR = 1.3 (0.8-2.1), p = 0.2]. This new online resource, together with bioinformatic predictions of the underlying functionality at trait/disease-associated loci, is well-suited to guide future investigations of the role of common variants in complex disease etiology.
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              An overview of Ensembl.

              Ensembl (http://www.ensembl.org/) is a bioinformatics project to organize biological information around the sequences of large genomes. It is a comprehensive source of stable automatic annotation of individual genomes, and of the synteny and orthology relationships between them. It is also a framework for integration of any biological data that can be mapped onto features derived from the genomic sequence. Ensembl is available as an interactive Web site, a set of flat files, and as a complete, portable open source software system for handling genomes. All data are provided without restriction, and code is freely available. Ensembl's aims are to continue to "widen" this biological integration to include other model organisms relevant to understanding human biology as they become available; to "deepen" this integration to provide an ever more seamless linkage between equivalent components in different species; and to provide further classification of functional elements in the genome that have been previously elusive.
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                Author and article information

                Journal
                Cancer Inform
                Cancer Inform
                CIX
                spcix
                Cancer Informatics
                SAGE Publications (Sage UK: London, England )
                1176-9351
                24 November 2020
                2020
                : 19
                : 1176935120976399
                Affiliations
                [1-1176935120976399]Institute of Virology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
                Author notes
                [*]Stephan Theiss, Institute of Virology, Medical Faculty, Heinrich Heine University Düsseldorf, D-40225 Düsseldorf, Germany. Email: theiss@ 123456uni-duesseldorf.de
                [*]Heiner Schaal, Institute of Virology, Medical Faculty, Heinrich Heine University Düsseldorf, D-40225 Düsseldorf, Germany. Email: schaal@ 123456uni-duesseldorf.de
                Author information
                https://orcid.org/0000-0002-0322-5649
                https://orcid.org/0000-0002-1636-4365
                Article
                10.1177_1176935120976399
                10.1177/1176935120976399
                7691889
                af2cd912-a9d1-4df4-8114-9f3b787561a5
                © The Author(s) 2020

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 1 September 2020
                : 1 November 2020
                Categories
                Technical Advances
                Custom metadata
                January-December 2020
                ts1

                Oncology & Radiotherapy
                snps,alternative splicing,r package,sequence retrieval,hexplorer score,hbond score

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