Shiny-Seq: advanced guided transcriptome analysis
Abstract Objective A comprehensive analysis of RNA-Seq data uses a wide range of different tools and algorithms, which are normally limited to R users only. While several tools and advanced analysis pipelines are available, some require programming skills and others lack the support for many importa...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2019-07-01
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| Series: | BMC Research Notes |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13104-019-4471-1 |
| _version_ | 1818151115676450816 |
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| author | Zenitha Sundararajan Rainer Knoll Peter Hombach Matthias Becker Joachim L. Schultze Thomas Ulas |
| author_facet | Zenitha Sundararajan Rainer Knoll Peter Hombach Matthias Becker Joachim L. Schultze Thomas Ulas |
| author_sort | Zenitha Sundararajan |
| collection | DOAJ |
| description | Abstract Objective A comprehensive analysis of RNA-Seq data uses a wide range of different tools and algorithms, which are normally limited to R users only. While several tools and advanced analysis pipelines are available, some require programming skills and others lack the support for many important features that enable a more comprehensive data analysis. There is thus, a need for a guided and easy to use comprehensive RNA-Seq data platform, which integrates the state of the art analysis workflow. Results We present the tool Shiny-Seq, which provides a guided and easy to use comprehensive RNA-Seq data analysis pipeline. It has many features such as batch effect estimation and removal, quality check with several visualization options, enrichment analysis with multiple biological databases, identification of patterns using advanced methods such as weighted gene co-expression network analysis, summarizing analysis as power point presentation and all results as tables via a one-click feature. The source code is published on GitHub (https://github.com/schultzelab/Shiny-Seq) and licensed under GPLv3. Shiny-Seq is written in R using the Shiny framework. In addition, the application is hosted on a public website hosted by the shinyapps.io server (https://schultzelab.shinyapps.io/Shiny-Seq/) and as a Docker image https://hub.docker.com/r/makaho/shiny-seq. |
| first_indexed | 2024-12-11T13:33:42Z |
| format | Article |
| id | doaj.art-72063060ffd64809b0aa25d75266cb92 |
| institution | Directory Open Access Journal |
| issn | 1756-0500 |
| language | English |
| last_indexed | 2024-12-11T13:33:42Z |
| publishDate | 2019-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Research Notes |
| spelling | doaj.art-72063060ffd64809b0aa25d75266cb922022-12-22T01:05:10ZengBMCBMC Research Notes1756-05002019-07-011211510.1186/s13104-019-4471-1Shiny-Seq: advanced guided transcriptome analysisZenitha Sundararajan0Rainer Knoll1Peter Hombach2Matthias Becker3Joachim L. Schultze4Thomas Ulas5Genomics and Immunoregulation, LIMES Institute, University of BonnGenomics and Immunoregulation, LIMES Institute, University of BonnGenomics and Immunoregulation, LIMES Institute, University of BonnPlatform for Single Cell Genomics and Epigenomics (PRECISE) at the German Center for Neurodegenerative Diseases and the University of BonnGenomics and Immunoregulation, LIMES Institute, University of BonnGenomics and Immunoregulation, LIMES Institute, University of BonnAbstract Objective A comprehensive analysis of RNA-Seq data uses a wide range of different tools and algorithms, which are normally limited to R users only. While several tools and advanced analysis pipelines are available, some require programming skills and others lack the support for many important features that enable a more comprehensive data analysis. There is thus, a need for a guided and easy to use comprehensive RNA-Seq data platform, which integrates the state of the art analysis workflow. Results We present the tool Shiny-Seq, which provides a guided and easy to use comprehensive RNA-Seq data analysis pipeline. It has many features such as batch effect estimation and removal, quality check with several visualization options, enrichment analysis with multiple biological databases, identification of patterns using advanced methods such as weighted gene co-expression network analysis, summarizing analysis as power point presentation and all results as tables via a one-click feature. The source code is published on GitHub (https://github.com/schultzelab/Shiny-Seq) and licensed under GPLv3. Shiny-Seq is written in R using the Shiny framework. In addition, the application is hosted on a public website hosted by the shinyapps.io server (https://schultzelab.shinyapps.io/Shiny-Seq/) and as a Docker image https://hub.docker.com/r/makaho/shiny-seq.http://link.springer.com/article/10.1186/s13104-019-4471-1RNA-SeqBioinformaticsAnalysisShinyDeSeq2Functional prediction |
| spellingShingle | Zenitha Sundararajan Rainer Knoll Peter Hombach Matthias Becker Joachim L. Schultze Thomas Ulas Shiny-Seq: advanced guided transcriptome analysis BMC Research Notes RNA-Seq Bioinformatics Analysis Shiny DeSeq2 Functional prediction |
| title | Shiny-Seq: advanced guided transcriptome analysis |
| title_full | Shiny-Seq: advanced guided transcriptome analysis |
| title_fullStr | Shiny-Seq: advanced guided transcriptome analysis |
| title_full_unstemmed | Shiny-Seq: advanced guided transcriptome analysis |
| title_short | Shiny-Seq: advanced guided transcriptome analysis |
| title_sort | shiny seq advanced guided transcriptome analysis |
| topic | RNA-Seq Bioinformatics Analysis Shiny DeSeq2 Functional prediction |
| url | http://link.springer.com/article/10.1186/s13104-019-4471-1 |
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