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      Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool.

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          Abstract

          System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement.

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          Gene Ontology: tool for the unification of biology

          Michael Ashburner, Catherine A. Ball, Judith Blake … (2002)
          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            The Pfam protein families database.

            A. Bateman, Lachlan Coin (2004)
            Pfam is a large collection of protein families and domains. Over the past 2 years the number of families in Pfam has doubled and now stands at 6190 (version 10.0). Methodology improvements for searching the Pfam collection locally as well as via the web are described. Other recent innovations include modelling of discontinuous domains allowing Pfam domain definitions to be closer to those found in structure databases. Pfam is available on the web in the UK (http://www.sanger.ac.uk/Software/Pfam/), the USA (http://pfam.wustl.edu/), France (http://pfam.jouy.inra.fr/) and Sweden (http://Pfam.cgb.ki.se/).
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              Prediction of mammalian microRNA targets.

              Benjamin Lewis, I-hung Shih, Matthew W Jones-Rhoades … (2003)
              MicroRNAs (miRNAs) can play important gene regulatory roles in nematodes, insects, and plants by basepairing to mRNAs to specify posttranscriptional repression of these messages. However, the mRNAs regulated by vertebrate miRNAs are all unknown. Here we predict more than 400 regulatory target genes for the conserved vertebrate miRNAs by identifying mRNAs with conserved pairing to the 5' region of the miRNA and evaluating the number and quality of these complementary sites. Rigorous tests using shuffled miRNA controls supported a majority of these predictions, with the fraction of false positives estimated at 31% for targets identified in human, mouse, and rat and 22% for targets identified in pufferfish as well as mammals. Eleven predicted targets (out of 15 tested) were supported experimentally using a HeLa cell reporter system. The predicted regulatory targets of mammalian miRNAs were enriched for genes involved in transcriptional regulation but also encompassed an unexpectedly broad range of other functions.
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                Author and article information

                Journal
                Journal ID (iso-abbrev): BMC Bioinformatics
                Title: BMC bioinformatics
                Publisher: Springer Science and Business Media LLC
                ISSN (Electronic): 1471-2105
                ISSN (Print): 1471-2105
                Publication date (Electronic): Apr 15 2013
                Volume: 14
                Affiliations
                [1 ] Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, One Gustave L, Levy Place, Box 1215, New York, NY 10029, USA.
                Article
                Publisher Item ID: 1471-2105-14-128
                DOI: 10.1186/1471-2105-14-128
                PMC ID: 3637064
                PubMed ID: 23586463
                SO-VID: 1b2ca347-48af-45ef-8374-9995bfa55fbe
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