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      ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization

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          Abstract

          ReactomePA is an R package providing functional analyses at the gene and sequence levels, with several visualization functions provided.

          Abstract

          Reactome is a manually curated pathway annotation database for unveiling high-order biological pathways from high-throughput data. ReactomePA is an R/Bioconductor package providing enrichment analyses, including hypergeometric test and gene set enrichment analyses. A functional analysis can be applied to the genomic coordination obtained from a sequencing experiment to analyze the functional significance of genomic loci including cis-regulatory elements and non-coding regions. Comparison among different experiments is also supported. Moreover, ReactomePA provides several visualization functions to produce highly customizable, publication-quality figures. The source code and documents of ReactomePA are freely available through Bioconductor (http://www.bioconductor.org/packages/ReactomePA).

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          Most cited references19

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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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            • Record: found
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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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              • Record: found
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              Molecular mechanisms of epithelial-mesenchymal transition.

              The transdifferentiation of epithelial cells into motile mesenchymal cells, a process known as epithelial-mesenchymal transition (EMT), is integral in development, wound healing and stem cell behaviour, and contributes pathologically to fibrosis and cancer progression. This switch in cell differentiation and behaviour is mediated by key transcription factors, including SNAIL, zinc-finger E-box-binding (ZEB) and basic helix-loop-helix transcription factors, the functions of which are finely regulated at the transcriptional, translational and post-translational levels. The reprogramming of gene expression during EMT, as well as non-transcriptional changes, are initiated and controlled by signalling pathways that respond to extracellular cues. Among these, transforming growth factor-β (TGFβ) family signalling has a predominant role; however, the convergence of signalling pathways is essential for EMT.
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                Author and article information

                Journal
                MBOIBW
                Molecular BioSystems
                Mol. BioSyst.
                Royal Society of Chemistry (RSC)
                1742-206X
                1742-2051
                2016
                2016
                : 12
                : 2
                : 477-479
                Affiliations
                [1 ]Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes
                [2 ]Institute of Life and Health Engineering
                [3 ]College of Life Science and Technology
                [4 ]Jinan University
                [5 ]Guangzhou
                Article
                10.1039/C5MB00663E
                26661513
                1014ab16-ab55-49d7-802b-6ca51508a13a
                © 2016
                History

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