class: center, middle, inverse, title-slide .title[ # Bioconductor ] .author[ ### Mikhail Dozmorov ] .institute[ ### Virginia Commonwealth University ] .date[ ### 2025-09-15 ] --- <!-- HTML style block --> <style> .large { font-size: 130%; } .small { font-size: 70%; } .tiny { font-size: 40%; } </style> ## Bioconductor Project - Launched in 2001 to support open-source software for genomics and computational biology. - **Goal**: Enable reproducible and consistent analysis of high-throughput biological data. - Core team based at the Fred Hutchinson Cancer Center. - New versions released twice a year, aligned with R updates. - Thousands of community-contributed software packages, plus annotation and experiment data resources. .small[https://bioconductor.org/] --- ## Distinctive Features of the Bioconductor Project - Comprehensive **documentation** accompanies each package. - Packages include **vignettes** with reproducible examples and workflows. - Provides tools to access and integrate public databases and metadata (e.g., Ensembl, GEO). - Strong emphasis on **reproducibility**, interoperability, and community-driven development. --- ## Vignettes - Bioconductor uses **vignettes** as a core documentation format. - A vignette is an executable document combining text, code, and results. - Provides dynamic, integrated, and reproducible analyses that update with data or code changes. - Typically created with **Sweave**, **knitr**, or **rmarkdown**, often alongside **roxygen2** for function documentation. .small[ https://bioconductor.org/packages/HiCcompare/ ] --- ## Bioconductor Packages for RNA-seq - Bioconductor provides R add-on packages for high-throughput genomics analysis. - An R package is a structured collection of code, documentation, and data for specific analyses. - Key RNA-seq packages: - **DESeq2**, **edgeR** – differential expression analysis - **tximport** – import and summarize transcript-level estimates - **sva** – batch effect correction - **biomaRt**, **AnnotationDbi** – functional annotation --- class: middle, center # Visualization --- ## Gviz R package - Visualize genomic data and annotations along genomic coordinates. - Track-based plotting system for flexible genome graphics. - Supports multiple data types: gene models, genomic signals, ideograms, and more. <img src="img/gviz.png" width="650px" style="display: block; margin: auto;" /> .small[ https://bioconductor.org/packages/Gviz/ ] <!-- ## epivizR R package - D3-based interactive visualization tool for functional genomics data. - Multiple visualizations using scatterplots, heatmaps and other user-supplied visualizations. - Includes data from the Gene Expression Barcode project for transcriptome visualization. \tiny http://epiviz.cbcb.umd.edu/ https://epiviz.github.io/ --> --- ## ggbio R package - Grammar of graphics framework for genomic data visualization. - Integrates with Bioconductor data structures (e.g., GRanges, SummarizedExperiment). - Supports track-based, ideogram, and circular genome plots. - Enables flexible, publication-quality genome graphics. <img src="img/ggbio-show-mutation.png" width="350px" style="display: block; margin: auto;" /> .small[ https://bioconductor.org/packages/ggbio/ http://www.sthda.com/english/wiki/ggbio-visualize-genomic-data ] --- ## karyoploteR R package - Create customizable, publication-quality karyotype plots. - Plot genomic data and annotations directly on chromosomes. - Supports multiple data types and flexible track layouts. - Ideal for visualizing large-scale genomic patterns and structural variations. <img src="img/karyoploter.png" width="650px" style="display: block; margin: auto;" /> .small[ Gel, Bernat, and Eduard Serra. “KaryoploteR: An R/Bioconductor Package to Plot Customizable Genomes Displaying Arbitrary Data.” Bioinformatics 33, no. 19 (October 1, 2017): 3088–90. https://doi.org/10.1093/bioinformatics/btx346. https://bioconductor.org/packages/karyoploteR/ https://bernatgel.github.io/karyoploter_tutorial/ ] --- ## More visualization tools - Review of omics data visualization tools, summary table: Schroeder, Michael P., Abel Gonzalez-Perez, and Nuria Lopez-Bigas. “Visualizing Multidimensional Cancer Genomics Data.” Genome Medicine 5, no. 1 (2013): 9. https://doi.org/10.1186/gm413.