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      Available Software for Meta-analyses of Genome-wide Expression Studies

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          Abstract

          Advances in transcriptomic methods have led to a large number of published Genome-Wide Expression Studies (GWES), in humans and model organisms. For several years, GWES involved the use of microarray platforms to compare genome-expression data for two or more groups of samples of interest. Meta-analysis of GWES is a powerful approach for the identification of differentially expressed genes in biological topics or diseases of interest, combining information from multiple primary studies. In this article, the main features of available software for carrying out meta-analysis of GWES have been reviewed and seven packages from the Bioconductor platform and five packages from the CRAN platform have been described. In addition, nine previously described programs and four online programs are reviewed. Finally, advantages and disadvantages of these available programs and proposed key points for future developments have been discussed.

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          Most cited references49

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          Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences

          Increased reliance on computational approaches in the life sciences has revealed grave concerns about how accessible and reproducible computation-reliant results truly are. Galaxy http://usegalaxy.org, an open web-based platform for genomic research, addresses these problems. Galaxy automatically tracks and manages data provenance and provides support for capturing the context and intent of computational methods. Galaxy Pages are interactive, web-based documents that provide users with a medium to communicate a complete computational analysis.
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            Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments.

            One of the main objectives in the analysis of microarray experiments is the identification of genes that are differentially expressed under two experimental conditions. This task is complicated by the noisiness of the data and the large number of genes that are examined simultaneously. Here, we present a novel technique for identifying differentially expressed genes that does not originate from a sophisticated statistical model but rather from an analysis of biological reasoning. The new technique, which is based on calculating rank products (RP) from replicate experiments, is fast and simple. At the same time, it provides a straightforward and statistically stringent way to determine the significance level for each gene and allows for the flexible control of the false-detection rate and familywise error rate in the multiple testing situation of a microarray experiment. We use the RP technique on three biological data sets and show that in each case it performs more reliably and consistently than the non-parametric t-test variant implemented in Tusher et al.'s significance analysis of microarrays (SAM). We also show that the RP results are reliable in highly noisy data. An analysis of the physiological function of the identified genes indicates that the RP approach is powerful for identifying biologically relevant expression changes. In addition, using RP can lead to a sharp reduction in the number of replicate experiments needed to obtain reproducible results.
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              RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis.

              While meta-analysis provides a powerful tool for analyzing microarray experiments by combining data from multiple studies, it presents unique computational challenges. The Bioconductor package RankProd provides a new and intuitive tool for this purpose in detecting differentially expressed genes under two experimental conditions. The package modifies and extends the rank product method proposed by Breitling et al., [(2004) FEBS Lett., 573, 83-92] to integrate multiple microarray studies from different laboratories and/or platforms. It offers several advantages over t-test based methods and accepts pre-processed expression datasets produced from a wide variety of platforms. The significance of the detection is assessed by a non-parametric permutation test, and the associated P-value and false discovery rate (FDR) are included in the output alongside the genes that are detected by user-defined criteria. A visualization plot is provided to view actual expression levels for each gene with estimated significance measurements. RankProd is available at Bioconductor http://www.bioconductor.org. A web-based interface will soon be available at http://cactus.salk.edu/RankProd
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                Author and article information

                Journal
                Curr Genomics
                Curr. Genomics
                CG
                Current Genomics
                Bentham Science Publishers
                1389-2029
                1875-5488
                August 2019
                August 2019
                : 20
                : 5
                : 325-331
                Affiliations
                [1 ]PhD Program in Health Sciences, School of Medicine, Universidad Antonio Nariño , Bogotá, , Colombia;
                [2 ]Laboratory of NeuroPsychiatric Genetics, Biomedical Sciences Research Group, School of Medicine, Universidad Antonio Nariño , Bogotá, , Colombia
                Author notes
                [* ]Address correspondence to this author at the Laboratory of NeuroPsychiatric Genetics, Biomedical Sciences Research Group, School of Medicine, Universidad Antonio Nariño, Bogotá, Colombia; Tel: + 57 313 2610427; E-mail: diego.forero@ 123456uan.edu.co
                Article
                CG-20-325
                10.2174/1389202920666190822113912
                7235394
                c1a27d22-5474-4e8c-8e26-75d27f866769
                © 2019 Bentham Science Publishers

                This is an open access article licensed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 International Public License (CC BY-NC 4.0) ( https://creativecommons.org/licenses/by-nc/4.0/legalcode), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

                History
                : 24 June 2019
                : 24 July 2019
                : 08 August 2019
                Categories
                Genomics

                Genetics
                genomics,transcriptomics,bioinformatics,meta-analysis,genome-wide expression,microarray experiment

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