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      iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data

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          Abstract

          Background

          RNA-seq is widely used for transcriptomic profiling, but the bioinformatics analysis of resultant data can be time-consuming and challenging, especially for biologists. We aim to streamline the bioinformatic analyses of gene-level data by developing a user-friendly, interactive web application for exploratory data analysis, differential expression, and pathway analysis.

          Results

          iDEP (integrated Differential Expression and Pathway analysis) seamlessly connects 63 R/Bioconductor packages, 2 web services, and comprehensive annotation and pathway databases for 220 plant and animal species. The workflow can be reproduced by downloading customized R code and related pathway files. As an example, we analyzed an RNA-Seq dataset of lung fibroblasts with Hoxa1 knockdown and revealed the possible roles of SP1 and E2F1 and their target genes, including microRNAs, in blocking G1/S transition. In another example, our analysis shows that in mouse B cells without functional p53, ionizing radiation activates the MYC pathway and its downstream genes involved in cell proliferation, ribosome biogenesis, and non-coding RNA metabolism. In wildtype B cells, radiation induces p53-mediated apoptosis and DNA repair while suppressing the target genes of MYC and E2F1, and leads to growth and cell cycle arrest. iDEP helps unveil the multifaceted functions of p53 and the possible involvement of several microRNAs such as miR-92a, miR-504, and miR-30a. In both examples, we validated known molecular pathways and generated novel, testable hypotheses.

          Conclusions

          Combining comprehensive analytic functionalities with massive annotation databases, iDEP ( http://ge-lab.org/idep/) enables biologists to easily translate transcriptomic and proteomic data into actionable insights.

          Electronic supplementary material

          The online version of this article (10.1186/s12859-018-2486-6) contains supplementary material, which is available to authorized users.

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          Most cited references80

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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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            miRDB: an online resource for microRNA target prediction and functional annotations

            MicroRNAs (miRNAs) are small non-coding RNAs that are extensively involved in many physiological and disease processes. One major challenge in miRNA studies is the identification of genes regulated by miRNAs. To this end, we have developed an online resource, miRDB (http://mirdb.org), for miRNA target prediction and functional annotations. Here, we describe recently updated features of miRDB, including 2.1 million predicted gene targets regulated by 6709 miRNAs. In addition to presenting precompiled prediction data, a new feature is the web server interface that allows submission of user-provided sequences for miRNA target prediction. In this way, users have the flexibility to study any custom miRNAs or target genes of interest. Another major update of miRDB is related to functional miRNA annotations. Although thousands of miRNAs have been identified, many of the reported miRNAs are not likely to play active functional roles or may even have been falsely identified as miRNAs from high-throughput studies. To address this issue, we have performed combined computational analyses and literature mining, and identified 568 and 452 functional miRNAs in humans and mice, respectively. These miRNAs, as well as associated functional annotations, are presented in the FuncMir Collection in miRDB.
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              Pathview: an R/Bioconductor package for pathway-based data integration and visualization

              Summary: Pathview is a novel tool set for pathway-based data integration and visualization. It maps and renders user data on relevant pathway graphs. Users only need to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps and integrates user data onto the pathway and renders pathway graphs with the mapped data. Although built as a stand-alone program, Pathview may seamlessly integrate with pathway and functional analysis tools for large-scale and fully automated analysis pipelines. Availability: The package is freely available under the GPLv3 license through Bioconductor and R-Forge. It is available at http://bioconductor.org/packages/release/bioc/html/pathview.html and at http://Pathview.r-forge.r-project.org/. Contact: luo_weijun@yahoo.com Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                001-605-688-5845 , gexijin@gmail.com
                kruny1001@gmail.com
                runan.yao@sdstate.edu
                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                19 December 2018
                19 December 2018
                2018
                : 19
                : 534
                Affiliations
                ISNI 0000 0001 2167 853X, GRID grid.263791.8, Department of Mathematics and Statistics, , South Dakota State University, ; Box 2225, Brookings, SD 57007 USA
                Author information
                http://orcid.org/0000-0001-7406-3782
                Article
                2486
                10.1186/s12859-018-2486-6
                6299935
                30567491
                9b026686-0cc7-42db-814f-5a0c93f859d7
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 17 May 2018
                : 12 November 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: IIA-1355423
                Award ID: ACI-1548562
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: GM083226
                Award Recipient :
                Categories
                Methodology Article
                Custom metadata
                © The Author(s) 2018

                Bioinformatics & Computational biology
                rna-seq,bioinformatics,web application,differential gene expression, pathway analysis

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