476
views
0
recommends
+1 Recommend
0 collections
    4
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool

      product-review

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          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.

          Results

          Here, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. We applied Enrichr to analyze nine cancer cell lines by comparing their enrichment signatures to the enrichment signatures of matched normal tissues. We observed a common pattern of up regulation of the polycomb group PRC2 and enrichment for the histone mark H3K27me3 in many cancer cell lines, as well as alterations in Toll-like receptor and interlukin signaling in K562 cells when compared with normal myeloid CD33+ cells. Such analyses provide global visualization of critical differences between normal tissues and cancer cell lines but can be applied to many other scenarios.

          Conclusions

          Enrichr is an easy to use intuitive enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr.

          Related collections

          Most cited references24

          • Record: found
          • Abstract: found
          • Article: not found

          Gene Ontology: tool for the unification of biology

          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.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The Pfam protein families database.

            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/).
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Prediction of mammalian microRNA targets.

              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.
                Bookmark

                Author and article information

                Contributors
                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central
                1471-2105
                2013
                15 April 2013
                : 14
                : 128
                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
                [2 ]Laboratório Nacional de Biociências (LNBio), Centro Nacional de Pesquisa em Energia e Materiais (CNPEM), Rua Giuseppe Máximo Scolfaro, Campinas, São Paulo, Brazil
                Article
                1471-2105-14-128
                10.1186/1471-2105-14-128
                3637064
                23586463
                1b2ca347-48af-45ef-8374-9995bfa55fbe
                Copyright © 2013 Chen et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 17 January 2013
                : 28 March 2013
                Categories
                Software

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

                Comments

                Comment on this article