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      Babelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling

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          Abstract

          Babelomics is a response to the growing necessity of integrating and analyzing different types of genomic data in an environment that allows an easy functional interpretation of the results. Babelomics includes a complete suite of methods for the analysis of gene expression data that include normalization (covering most commercial platforms), pre-processing, differential gene expression (case-controls, multiclass, survival or continuous values), predictors, clustering; large-scale genotyping assays (case controls and TDTs, and allows population stratification analysis and correction). All these genomic data analysis facilities are integrated and connected to multiple options for the functional interpretation of the experiments. Different methods of functional enrichment or gene set enrichment can be used to understand the functional basis of the experiment analyzed. Many sources of biological information, which include functional (GO, KEGG, Biocarta, Reactome, etc.), regulatory (Transfac, Jaspar, ORegAnno, miRNAs, etc.), text-mining or protein–protein interaction modules can be used for this purpose. Finally a tool for the de novo functional annotation of sequences has been included in the system. This provides support for the functional analysis of non-model species. Mirrors of Babelomics or command line execution of their individual components are now possible. Babelomics is available at http://www.babelomics.org.

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          Most cited references22

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

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            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.
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              Genome-wide association studies for complex traits: consensus, uncertainty and challenges.

              The past year has witnessed substantial advances in understanding the genetic basis of many common phenotypes of biomedical importance. These advances have been the result of systematic, well-powered, genome-wide surveys exploring the relationships between common sequence variation and disease predisposition. This approach has revealed over 50 disease-susceptibility loci and has provided insights into the allelic architecture of multifactorial traits. At the same time, much has been learned about the successful prosecution of association studies on such a scale. This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of interest and to translate the information gathered into improvements in clinical management.
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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
                1 July 2010
                16 May 2010
                15 May 2010
                : 38
                : Web Server issue
                : W210-W213
                Affiliations
                1Bioinformatics Department, Centro de Investigación Príncipe Felipe (CIPF), Autopista del Saler 16, 2CIBER de Enfermedades Raras (CIBERER), Valencia, Spain, 3Knowledge Discovery and Bioinformatics (KDBIO) group, INESC-ID, Lisbon, Portugal, 4Instituto Superior Técnico, Technical University of Lisbon, Lisbon, Portugal, 5Functional Genomics Node, INB, CIPF, Valencia, Spain and 6National Center for Biotechnology-CSIC, Madrid, Spain
                Author notes
                *To whom correspondence should be addressed. Tel: +34 96 328 96 80; Fax: +34 96 328 97 01; Email: jdopazo@ 123456cipf.es
                Article
                gkq388
                10.1093/nar/gkq388
                2896184
                20478823
                fb3caa53-0a29-47d6-8531-df443647980f
                © The Author(s) 2010. 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/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 21 February 2010
                : 16 April 2010
                : 24 April 2010
                Categories
                Articles

                Genetics
                Genetics

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