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      From protein-protein to isoform-isoform interactions: the toolkit to map alternative splicing to interactome

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      conference-abstract
      1 , , 1 , 2 , 1 , 2 , 3 ,   1 , 4 , 5 , 6 , 2
      ScienceOpen
      Genetoberfest 2023
      16-18 October 2023
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            Abstract

            Alternative splicing (AS) impacts protein function and structure and lead to protein-protein interaction (PPI) rewiring. However, available PPI networks neglect alternative splicing isoforms. Since it is not feasible to validate all isoform-isoform interactions experimentally, we present a set of tools to investigate AS impact on a network level: DIGGER to map splicing to the PPI network, as well as NEASE and Spycone to evaluate the functional consequences of network rewiring. DIGGER (https://exbio.wzw.tum.de/digger) integrates PPIs, domain-domain, and residue-level interactions - the structures that might be spliced in or out and result in interaction gain or loss. Users can explore possible rewiring for an isoform or exon of interest and extract relevant subnetworks. NEASE (https://github.com/louadi/NEASE) identifies pathways that are significantly affected by network rewiring. NEASE extends classic gene set enrichment analysis by considering isoform-specific interactions affecting pathways. Spycone (https://github.com/yollct/spycone) addresses the time-course changes in AS. It searches for isoforms demonstrating similar temporal splicing patterns and reflecting the splicing co-regulation. Spycone further integrates gene set, network, and splicing-aware NEASE enrichment. Overall, we offer a splicing-focused network analysis toolkit that allows for studying the mechanistic consequences of AS.

            Author and article information

            Conference
            ScienceOpen
            9 October 2023
            Affiliations
            [1 ] Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany;
            [2 ] Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany;
            [3 ] Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Saarbrücken, Germany;
            [4 ] Institute of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark;
            [5 ] Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany;
            [6 ] Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany;
            Author information
            https://orcid.org/0000-0002-7592-2080
            https://orcid.org/0000-0003-4763-0264
            https://orcid.org/0000-0003-2297-831X
            https://orcid.org/0000-0002-9445-477X
            https://orcid.org/0000-0002-0282-0462
            https://orcid.org/0000-0002-5393-2413
            https://orcid.org/0000-0002-0941-4168
            Article
            10.14293/GOF.23.20
            13f516a0-5d9b-4fab-b3fc-0f1cedb0bcf5

            Published under Creative Commons Attribution 4.0 International ( CC BY 4.0). Users are allowed to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material for any purpose, even commercially), as long as the authors and the publisher are explicitly identified and properly acknowledged as the original source.

            Genetoberfest 2023
            16-18 October 2023
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            ScienceOpen


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