Emmanuel Dimont1, Oliver Hofmann1, Shannan J Ho Sui1, Alistair R R Forrest2, Hideya Kawaji3, Winston Hide1. 1. Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA, RIKEN Omics Science Center, Yokohama, Kanagawa 230-0045 Japan, Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Kanagawa 230-0045, Japan and RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Saitama 351-0198, Japan. 2. Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA, RIKEN Omics Science Center, Yokohama, Kanagawa 230-0045 Japan, Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Kanagawa 230-0045, Japan and RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Saitama 351-0198, Japan Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA, RIKEN Omics Science Center, Yokohama, Kanagawa 230-0045 Japan, Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Kanagawa 230-0045, Japan and RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Saitama 351-0198, Japan. 3. Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA, RIKEN Omics Science Center, Yokohama, Kanagawa 230-0045 Japan, Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Kanagawa 230-0045, Japan and RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Saitama 351-0198, Japan Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA, RIKEN Omics Science Center, Yokohama, Kanagawa 230-0045 Japan, Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Kanagawa 230-0045, Japan and RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Saitama 351-0198, Japan Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA, RIKEN Omics Science Center, Yokohama, Kanagawa 230-0045 Japan, Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Kanagawa 230-0045, Japan and RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Saitama 351-0198, Japan.
Abstract
Alternate promoter usage is an important molecular mechanism for generating RNA and protein diversity. Cap Analysis Gene Expression (CAGE) is a powerful approach for revealing the multiplicity of transcription start site (TSS) events across experiments and conditions. An understanding of the dynamics of TSS choice across these conditions requires both sensitive quantification and comparative visualization. We have developed CAGExploreR, an R package to detect and visualize changes in the use of specific TSS in wider promoter regions in the context of changes in overall gene expression when comparing different CAGE samples. These changes provide insight into the modification of transcript isoform generation and regulatory network alterations associated with cell types and conditions. CAGExploreR is based on the FANTOM5 and MPromDb promoter set definitions but can also work with user-supplied regions. The package compares multiple CAGE libraries simultaneously. Supplementary Materials describe methods in detail, and a vignette demonstrates a workflow with a real data example. AVAILABILITY AND IMPLEMENTATION: The package is freely available under the MIT license from CRAN (http://cran.r-project.org/web/packages/CAGExploreR). CONTACT: edimont@mail.harvard.edu Supplementary information: Supplementary data are available at Bioinformatics online.
Alternate promoter usage is an important molecular mechanism for generating RNA and protein diversity. Cap Analysis Gene Expression (CAGE) is a powerful approach for revealing the multiplicity of transcription start site (TSS) events across experiments and conditions. An understanding of the dynamics of TSS choice across these conditions requires both sensitive quantification and comparative visualization. We have developed CAGExploreR, an R package to detect and visualize changes in the use of specific TSS in wider promoter regions in the context of changes in overall gene expression when comparing different CAGE samples. These changes provide insight into the modification of transcript isoform generation and regulatory network alterations associated with cell types and conditions. CAGExploreR is based on the FANTOM5 and MPromDb promoter set definitions but can also work with user-supplied regions. The package compares multiple CAGE libraries simultaneously. Supplementary Materials describe methods in detail, and a vignette demonstrates a workflow with a real data example. AVAILABILITY AND IMPLEMENTATION: The package is freely available under the MIT license from CRAN (http://cran.r-project.org/web/packages/CAGExploreR). CONTACT: edimont@mail.harvard.edu Supplementary information: Supplementary data are available at Bioinformatics online.