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      IMP: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks

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          Abstract

          Integrative multi-species prediction (IMP) is an interactive web server that enables molecular biologists to interpret experimental results and to generate hypotheses in the context of a large cross-organism compendium of functional predictions and networks. The system provides a framework for biologists to analyze their candidate gene sets in the context of functional networks, as they expand or focus these sets by mining functional relationships predicted from integrated high-throughput data. IMP integrates prior knowledge and data collections from multiple organisms in its analyses. Through flexible and interactive visualizations, researchers can compare functional contexts and interpret the behavior of their gene sets across organisms. Additionally, IMP identifies homologs with conserved functional roles for knowledge transfer, allowing for accurate function predictions even for biological processes that have very few experimental annotations in a given organism. IMP currently supports seven organisms ( Homo sapiens, Mus musculus, Rattus novegicus, Drosophila melanogaster, Danio rerio, Caenorhabditis elegans and Saccharomyces cerevisiae), does not require any registration or installation and is freely available for use at http://imp.princeton.edu.

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          Most cited references16

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            DNA double-strand breaks: signaling, repair and the cancer connection.

            To ensure the high-fidelity transmission of genetic information, cells have evolved mechanisms to monitor genome integrity. Cells respond to DNA damage by activating a complex DNA-damage-response pathway that includes cell-cycle arrest, the transcriptional and post-transcriptional activation of a subset of genes including those associated with DNA repair, and, under some circumstances, the triggering of programmed cell death. An inability to respond properly to, or to repair, DNA damage leads to genetic instability, which in turn may enhance the rate of cancer development. Indeed, it is becoming increasingly clear that deficiencies in DNA-damage signaling and repair pathways are fundamental to the etiology of most, if not all, human cancers. Here we describe recent progress in our understanding of how cells detect and signal the presence and repair of one particularly important form of DNA damage induced by ionizing radiation-the DNA double-strand break (DSB). Moreover, we discuss how tumor suppressor proteins such as p53, ATM, Brca1 and Brca2 have been linked to such pathways, and how accumulating evidence is connecting deficiencies in cellular responses to DNA DSBs with tumorigenesis.
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              The IntAct molecular interaction database in 2012

              IntAct is an open-source, open data molecular interaction database populated by data either curated from the literature or from direct data depositions. Two levels of curation are now available within the database, with both IMEx-level annotation and less detailed MIMIx-compatible entries currently supported. As from September 2011, IntAct contains approximately 275 000 curated binary interaction evidences from over 5000 publications. The IntAct website has been improved to enhance the search process and in particular the graphical display of the results. New data download formats are also available, which will facilitate the inclusion of IntAct's data in the Semantic Web. IntAct is an active contributor to the IMEx consortium (http://www.imexconsortium.org). IntAct source code and data are freely available at http://www.ebi.ac.uk/intact.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                July 2012
                July 2012
                7 June 2012
                7 June 2012
                : 40
                : Web Server issue
                : W484-W490
                Affiliations
                1Department of Computer Science, 2Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA, 3Department of Computer Science, University of Tromsø, N-9037 Tromsø, Norway, 4Department of Computational Medicine and Bioinformatics and 5Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
                Author notes
                *To whom correspondence should be addressed. Tel: +1 609 258 1749; Fax: +1 609 258 1771; Email: ogt@ 123456genomics.princeton.edu

                The authors wish it to be known that, in their opinion, the first three authors should be regarded as joint First Authors.

                Article
                gks458
                10.1093/nar/gks458
                3394282
                22684505
                1308d2ce-5be2-477a-bf8a-3b9211c5359e
                © The Author(s) 2012. 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/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 31 January 2012
                : 20 April 2012
                : 30 April 2012
                Page count
                Pages: 7
                Categories
                Articles

                Genetics
                Genetics

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