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      Molecular Interaction Search Tool (MIST): an integrated resource for mining gene and protein interaction data

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          Abstract

          Model organism and human databases are rich with information about genetic and physical interactions. These data can be used to interpret and guide the analysis of results from new studies and develop new hypotheses. Here, we report the development of the Molecular Interaction Search Tool (MIST; http://fgrtools.hms.harvard.edu/MIST/). The MIST database integrates biological interaction data from yeast, nematode, fly, zebrafish, frog, rat and mouse model systems, as well as human. For individual or short gene lists, the MIST user interface can be used to identify interacting partners based on protein–protein and genetic interaction (GI) data from the species of interest as well as inferred interactions, known as interologs, and to view a corresponding network. The data, interologs and search tools at MIST are also useful for analyzing ‘omics datasets. In addition to describing the integrated database, we also demonstrate how MIST can be used to identify an appropriate cut-off value that balances false positive and negative discovery, and present use-cases for additional types of analysis. Altogether, the MIST database and search tools support visualization and navigation of existing protein and GI data, as well as comparison of new and existing data.

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          Most cited references18

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          The BioGRID interaction database: 2017 update

          The Biological General Repository for Interaction Datasets (BioGRID: https://thebiogrid.org) is an open access database dedicated to the annotation and archival of protein, genetic and chemical interactions for all major model organism species and humans. As of September 2016 (build 3.4.140), the BioGRID contains 1 072 173 genetic and protein interactions, and 38 559 post-translational modifications, as manually annotated from 48 114 publications. This dataset represents interaction records for 66 model organisms and represents a 30% increase compared to the previous 2015 BioGRID update. BioGRID curates the biomedical literature for major model organism species, including humans, with a recent emphasis on central biological processes and specific human diseases. To facilitate network-based approaches to drug discovery, BioGRID now incorporates 27 501 chemical–protein interactions for human drug targets, as drawn from the DrugBank database. A new dynamic interaction network viewer allows the easy navigation and filtering of all genetic and protein interaction data, as well as for bioactive compounds and their established targets. BioGRID data are directly downloadable without restriction in a variety of standardized formats and are freely distributed through partner model organism databases and meta-databases.
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            Cytoscape.js: a graph theory library for visualisation and analysis

            Summary: Cytoscape.js is an open-source JavaScript-based graph library. Its most common use case is as a visualization software component, so it can be used to render interactive graphs in a web browser. It also can be used in a headless manner, useful for graph operations on a server, such as Node.js. Availability and implementation: Cytoscape.js is implemented in JavaScript. Documentation, downloads and source code are available at http://js.cytoscape.org. Contact: gary.bader@utoronto.ca
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              SAINT: Probabilistic Scoring of Affinity Purification - Mass Spectrometry Data

              We present SAINT (Significance Analysis of INTeractome), a computational tool that assigns confidence scores to protein-protein interaction data generated using affinity-purification coupled to mass spectrometry (AP-MS). The method utilizes label-free quantitative data and constructs separate distributions for true and false interactions to derive the probability of a bona fide protein-protein interaction. We demonstrate that SAINT is applicable to data of different scales and protein connectivity and allows for the transparent analysis of AP-MS data.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                04 January 2018
                16 November 2017
                16 November 2017
                : 46
                : Database issue , Database issue
                : D567-D574
                Affiliations
                Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
                Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
                Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
                Howard Hughes Medical Institute, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
                Author notes
                To whom correspondence should be addressed. Tel: +1 617 432 7672; Fax: +1 617 432 7688; Email: perrimon@ 123456receptor.med.harvard.edu

                These authors contributed equally to the paper as first authors.

                Article
                gkx1116
                10.1093/nar/gkx1116
                5753374
                29155944
                8c01db79-b304-4700-bd14-b621bbd10c18
                © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                : 25 October 2017
                : 20 October 2017
                : 14 August 2017
                Page count
                Pages: 8
                Categories
                Database Issue

                Genetics
                Genetics

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