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      LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures

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          Abstract

          For the Library of Integrated Network-based Cellular Signatures (LINCS) project many gene expression signatures using the L1000 technology have been produced. The L1000 technology is a cost-effective method to profile gene expression in large scale. LINCS Canvas Browser (LCB) is an interactive HTML5 web-based software application that facilitates querying, browsing and interrogating many of the currently available LINCS L1000 data. LCB implements two compacted layered canvases, one to visualize clustered L1000 expression data, and the other to display enrichment analysis results using 30 different gene set libraries. Clicking on an experimental condition highlights gene-sets enriched for the differentially expressed genes from the selected experiment. A search interface allows users to input gene lists and query them against over 100 000 conditions to find the top matching experiments. The tool integrates many resources for an unprecedented potential for new discoveries in systems biology and systems pharmacology. The LCB application is available at http://www.maayanlab.net/LINCS/LCB. Customized versions will be made part of the http://lincscloud.org and http://lincs.hms.harvard.edu websites.

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          Most cited references40

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

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            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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              The IL-1 family: regulators of immunity.

              Over recent years it has become increasingly clear that innate immune responses can shape the adaptive immune response. Among the most potent molecules of the innate immune system are the interleukin-1 (IL-1) family members. These evolutionarily ancient cytokines are made by and act on innate immune cells to influence their survival and function. In addition, they act directly on lymphocytes to reinforce certain adaptive immune responses. This Review provides an overview of both the long-established and more recently characterized members of the IL-1 family. In addition to their effects on immune cells, their involvement in human disease and disease models is discussed.
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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
                01 July 2014
                06 June 2014
                06 June 2014
                : 42
                : Web Server issue
                : W449-W460
                Affiliations
                [1 ]Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, Systems Biology Center New York (SBCNY), One Gustave L. Levy Place, New York, NY 10029, USA
                [2 ]Broad Institute, 7 Cambridge Center, Cambridge, MA 02142, USA
                [3 ]Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
                Author notes
                [* ]To whom correspondence should be addressed. Tel: +1 212 659 1739; Fax: +1 212 831 0114; Email: avi.maayan@ 123456mssm.edu
                Article
                10.1093/nar/gku476
                4086130
                24906883
                2e3726f8-3fbe-4f53-9dac-8307d29f44bb
                © The Author(s) 2014. 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/3.0/), 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
                : 13 May 2014
                : 24 April 2014
                : 14 February 2014
                Page count
                Pages: 12
                Categories
                Article
                Custom metadata
                1 July 2014

                Genetics
                Genetics

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