Bioconductor Notes, Autumn 2022

We discuss the release of Bioconductor 3.16, along with educational activities and general project news.

Bioconductor Core Developer Team (Dana-Farber Cancer Institute, Roswell Park Comprehensive Cancer Center, City University of New York, Fred Hutchinson Cancer Research Center, Mass General Brigham)

1 Introduction

Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data. The project has entered its twentieth year, with funding for core development and infrastructure maintenance secured through 2025 (NIH NHGRI 2U24HG004059). Additional support is provided by NIH NCI, Chan-Zuckerberg Initiative, National Science Foundation, Microsoft, and Amazon. In this news report, we give some details about the software and data resource collection, infrastructure for building, checking, and distributing resources, core team activities, and some new initiatives.

2 Software

Bioconductor 3.16 was released on 2 November, 2022. It is compatible with R 4.2 and consists of 2183 software packages, 416 experiment data packages, 909 up-to-date annotation packages, 28 workflows, and 3 books. are built regularly from source and therefore fully reproducible; an example is the community-developed Orchestrating Single-Cell Analysis with Bioconductor. The Bioconductor 3.16 release announcement includes descriptions of 71 new software packages, 9 new data experiment packages, 2 new annotation packages, and updates to NEWS files for many additional packages.

3 Core team updates

4 Educational activities and resources

Engagement with The Carpentries

In August 2022, Bioconductor joined The Carpentries. Details and opportunities for receiving training on teaching are discussed in this blog post. We are currently inviting applications to become a Bioconductor Carpentries instructor through this form and particularly encourage people who could teach underserved communities in their local languages to apply.

Three lessons are under development in the Carpentries incubator: Introduction to data analysis with R and Bioconductor, RNA-seq analysis with Bioconductor and The Bioconductor project. We welcome any contributions, feedback or testing of the material.

Anyone is welcome to join the #education-and-training channel in Bioconductor Slack or the monthly Bioconductor Teaching Committee meetings to learn more.


The Dana-Farber/Harvard Cancer Center Young Empowered Scientists program included a module on cancer data science for Summer 2022 participants. Materials presented are assembled at a pkgdown site; contact Vince Carey for information on an interactive deployment of these materials.

5 Using Bioconductor

Start using Bioconductor by installing the most recent version of R and evaluating the commands

  if (!requireNamespace("BiocManager", quietly = TRUE))

Install additional packages and dependencies, e.g., SingleCellExperiment, with


Docker images provides a very effective on-ramp for power users to rapidly obtain access to standardized and scalable computing environments. Key resources include:

Upcoming and recently completed conferences are browsable at our events page.

The Technical and and Community Advisory Boards provide guidance to ensure that the project addresses leading-edge biological problems with advanced technical approaches, and adopts practices (such as a project-wide Code of Conduct that encourages all to participate. We look forward to welcoming you!


Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".


For attribution, please cite this work as

Team, "The R Journal: Bioconductor Notes, Autumn 2022", The R Journal, 2022

BibTeX citation

  author = {Team, Bioconductor Core Developer},
  title = {The R Journal: Bioconductor Notes, Autumn 2022},
  journal = {The R Journal},
  year = {2022},
  note = {},
  volume = {14},
  issue = {3},
  issn = {2073-4859},
  pages = {303-304}