Versioned online books with Bioconductor Author(s): Jacques Serizay Affiliation(s): Institut Pasteur There seems to be a need for a more rigorous approach to authoring/publishing books related to bioinformatics. Currently, some of the major pitfalls of authoring/publishing are: Automation of the publishing process (i.e. not building locally) Ensuring reproducibility when assembling books (i.e. documenting dependencies used in rendering) Enable book versioning in sync with Bioconductor releases Allow the end user to reuse resources assembled during book compilation Few points which could be covered during the session: Existing books, why they work/limitations (testimonials welcome!
Shiny app deployment using ShinyProxy Author(s): Tobia De Koninck Affiliation(s): Open Analytics ShinyProxy (https://shinyproxy.io/) is a 100% open source tool to deploy Shiny (and other) apps or web-based IDEs (like RStudio). Many bioinformatics and computational biology departments across the world use it to share their work as web applications. The purpose of this BoF is to share experiences in using ShinyProxy. You can 'bring your own package' to learn and see how to deploy it whether it is to deploy it within your research group or to present your work to the world.
Interoperability in the single-cell field Author(s): Davide Risso, Helena Crowell Affiliation(s): Department of Statistical Sciences, University of Padova, Italy Python and R each have their distinct strengths and modern data analysis approaches benefit from leveraging them using cross-language interoperable workflows. In particular, Bioconductor and scverse provide classes, foundational tools, and analysis packages that implement cutting-edge methods for the comprehension and analysis of single-cell omics data. R has a long tradition of interoperability with other languages and Python is no exception, thanks to e.
Comparison of deployment methods for Shiny Apps Author(s): Ivo Kwee, Mauro Masiero Affiliation(s): BigOmics Analytics There are quite a few proposed deployment methods for Shiny apps such as: R package (CRAN/BioC,GitHub) Running a Shiny-server Publishing on Shinyapp.io Posit Connect ShinyProxy Docker swarm / Kubernetes Serverless (AWS Fargate/Lambda, Google Cloud Run, etc.) The proposed plan is to compare and exchange experiences between these methods and see perhaps what other methods the people in the community are using.
Bioconductor 4.0: Aspirational anatomy of a computational ecosystem for genomic data science Author(s): Vince Carey, Marcel Ramos Affiliation(s): Channing Division of Network Medicine, Mass General Brigham, Harvard Medical School Rapid evolution of methods in biotechnology and scientific computing dictates that analysts and toolmakers must be willing to unshackle themselves from past approaches, however successful those approaches may have been. This talk presents thoughts on the anatomy of Bioconductor 3.x to help conceptualize emerging changes.