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      VIPER: Visualization Pipeline for RNA-seq, a Snakemake workflow for efficient and complete RNA-seq analysis

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          Abstract

          Background

          RNA sequencing has become a ubiquitous technology used throughout life sciences as an effective method of measuring RNA abundance quantitatively in tissues and cells. The increase in use of RNA-seq technology has led to the continuous development of new tools for every step of analysis from alignment to downstream pathway analysis. However, effectively using these analysis tools in a scalable and reproducible way can be challenging, especially for non-experts.

          Results

          Using the workflow management system Snakemake we have developed a user friendly, fast, efficient, and comprehensive pipeline for RNA-seq analysis. VIPER (Visualization Pipeline for RNA-seq analysis) is an analysis workflow that combines some of the most popular tools to take RNA-seq analysis from raw sequencing data, through alignment and quality control, into downstream differential expression and pathway analysis. VIPER has been created in a modular fashion to allow for the rapid incorporation of new tools to expand the capabilities. This capacity has already been exploited to include very recently developed tools that explore immune infiltrate and T-cell CDR (Complementarity-Determining Regions) reconstruction abilities. The pipeline has been conveniently packaged such that minimal computational skills are required to download and install the dozens of software packages that VIPER uses.

          Conclusions

          VIPER is a comprehensive solution that performs most standard RNA-seq analyses quickly and effectively with a built-in capacity for customization and expansion.

          Electronic supplementary material

          The online version of this article (10.1186/s12859-018-2139-9) contains supplementary material, which is available to authorized users.

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          Most cited references16

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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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            Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data

            A large number of computational methods have been developed for analyzing differential gene expression in RNA-seq data. We describe a comprehensive evaluation of common methods using the SEQC benchmark dataset and ENCODE data. We consider a number of key features, including normalization, accuracy of differential expression detection and differential expression analysis when one condition has no detectable expression. We find significant differences among the methods, but note that array-based methods adapted to RNA-seq data perform comparably to methods designed for RNA-seq. Our results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth.
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              Transcriptome Sequencing to Detect Gene Fusions in Cancer

              Recurrent gene fusions, typically associated with hematological malignancies and rare bone and soft tissue tumors1, have been recently described in common solid tumors2–9. Here we employ an integrative analysis of high-throughput long and short read transcriptome sequencing of cancer cells to discover novel gene fusions. As a proof of concept we successfully utilized integrative transcriptome sequencing to “re-discover” the BCR-ABL1 10 gene fusion in a chronic myelogenous leukemia cell line and the TMPRSS2-ERG 2,3 gene fusion in a prostate cancer cell line and tissues. Additionally, we nominated, and experimentally validated, novel gene fusions resulting in chimeric transcripts in cancer cell lines and tumors. Taken together, this study establishes a robust pipeline for the discovery of novel gene chimeras using high throughput sequencing, opening up an important class of cancer-related mutations for comprehensive characterization.
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                Author and article information

                Contributors
                mcornwell1957@gmail.com
                vangalamaheshh@gmail.com
                Len.taing@gmail.com
                Zachary_herbert@dfci.harvard.edu
                johannes.koester@protonmail.com
                bli@jimmy.harvard.edu
                hfsun.tju@gmail.com
                litaiwen@scu.edu.cn
                jzhang10@foxmail.com
                Xintao_Qiu@dfci.harvard.edu
                matthew_pun@alumni.duke.edu
                rinath_jeselsohn@dfci.harvard.edu
                myles_brown@dfci.harvard.edu
                xsliu@jimmy.harvard.edu
                henry_long@dfci.harvard.edu
                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                12 April 2018
                12 April 2018
                2018
                : 19
                : 135
                Affiliations
                [1 ]ISNI 0000 0001 2106 9910, GRID grid.65499.37, Department of Medical Oncology, , Dana-Farber Cancer Institute, ; Boston, MA 02215 USA
                [2 ]ISNI 0000 0001 2106 9910, GRID grid.65499.37, Center for Functional Cancer Epigenetics, , Dana-Farber Cancer Institute, ; Boston, MA 02215 USA
                [3 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Biostatistics and Computational Biology, , Dana-Farber Cancer Institute and Harvard School of Public Health, ; Boston, MA 02215 USA
                [4 ]ISNI 0000 0001 2187 5445, GRID grid.5718.b, Institute of Human Genetics, University of Duisburg-Essen, ; Essen, Germany
                [5 ]ISNI 0000 0001 0742 0364, GRID grid.168645.8, University of Massachusetts Medical School, ; Worcester, MA 01655 USA
                [6 ]ISNI 0000 0001 2106 9910, GRID grid.65499.37, Molecular Biology Core Facilities, , Dana-Farber Cancer Institute, ; Boston, MA 02215 USA
                [7 ]ISNI 0000000123704535, GRID grid.24516.34, Department of Bioinformatics, School of Life Sciences, , Tongji University, ; Shanghai, 200092 China
                [8 ]ISNI 0000 0001 0807 1581, GRID grid.13291.38, State Key Laboratory of Oral Diseases, West China Hospital of Stomatology, ; Sichuan University, Chengdu, China
                [9 ]ISNI 0000 0004 0632 3409, GRID grid.410318.f, Beijing Institute of Basic Medical Sciences, ; Beijing, China
                Article
                2139
                10.1186/s12859-018-2139-9
                5897949
                29649993
                d05d78c2-0930-49a3-a537-e03dd1281286
                © 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 August 2017
                : 26 March 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: U01 CA180980
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 31329003
                Award Recipient :
                Categories
                Software
                Custom metadata
                © The Author(s) 2018

                Bioinformatics & Computational biology
                rna-seq,analysis,pipeline,snakemake,gene fusion,immunological infiltrate

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