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      REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms

      1 , 2 , * , 1 , 1 , 1

      PLoS ONE

      Public Library of Science

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          Abstract

          Outcomes of high-throughput biological experiments are typically interpreted by statistical testing for enriched gene functional categories defined by the Gene Ontology (GO). The resulting lists of GO terms may be large and highly redundant, and thus difficult to interpret.

          REVIGO is a Web server that summarizes long, unintelligible lists of GO terms by finding a representative subset of the terms using a simple clustering algorithm that relies on semantic similarity measures. Furthermore, REVIGO visualizes this non-redundant GO term set in multiple ways to assist in interpretation: multidimensional scaling and graph-based visualizations accurately render the subdivisions and the semantic relationships in the data, while treemaps and tag clouds are also offered as alternative views. REVIGO is freely available at http://revigo.irb.hr/.

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          Most cited references 26

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          Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.

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            The neighbor-joining method: a new method for reconstructing phylogenetic trees.

            A new method called the neighbor-joining method is proposed for reconstructing phylogenetic trees from evolutionary distance data. The principle of this method is to find pairs of operational taxonomic units (OTUs [= neighbors]) that minimize the total branch length at each stage of clustering of OTUs starting with a starlike tree. The branch lengths as well as the topology of a parsimonious tree can quickly be obtained by using this method. Using computer simulation, we studied the efficiency of this method in obtaining the correct unrooted tree in comparison with that of five other tree-making methods: the unweighted pair group method of analysis, Farris's method, Sattath and Tversky's method, Li's method, and Tateno et al.'s modified Farris method. The new, neighbor-joining method and Sattath and Tversky's method are shown to be generally better than the other methods.
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              Gene expression profiling predicts clinical outcome of breast cancer.

              Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumours according to their clinical behaviour. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70-80% of patients receiving this treatment would have survived without it. None of the signatures of breast cancer gene expression reported to date allow for patient-tailored therapy strategies. Here we used DNA microarray analysis on primary breast tumours of 117 young patients, and applied supervised classification to identify a gene expression signature strongly predictive of a short interval to distant metastases ('poor prognosis' signature) in patients without tumour cells in local lymph nodes at diagnosis (lymph node negative). In addition, we established a signature that identifies tumours of BRCA1 carriers. The poor prognosis signature consists of genes regulating cell cycle, invasion, metastasis and angiogenesis. This gene expression profile will outperform all currently used clinical parameters in predicting disease outcome. Our findings provide a strategy to select patients who would benefit from adjuvant therapy.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2011
                18 July 2011
                : 6
                : 7
                Affiliations
                [1 ]Division of Electronics, Rudjer Boskovic Institute, Zagreb, Croatia
                [2 ]Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG) and UPF, Barcelona, Spain
                University of North Carolina at Charlotte, United States of America
                Author notes

                Conceived and designed the experiments: FS TS. Performed the experiments: FS MB. Analyzed the data: FS MB NS. Wrote the paper: FS NS.

                PONE-D-11-04111
                10.1371/journal.pone.0021800
                3138752
                21789182
                Supek et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                Counts
                Pages: 9
                Categories
                Research Article
                Biology
                Genetics
                Gene Function
                Genomics
                Genome Analysis Tools
                Gene Ontologies
                Functional Genomics
                Genome Expression Analysis
                Computer Science
                Computer Applications
                Web-Based Applications

                Uncategorized

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