Bioconductor vs devpi
Bioconductor
bioconductor.orgdevpi
devpi.netSide by side
| Figure | Bioconductor | devpi |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | Yes | — |
| Web | Yes | Yes |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 0 |
| Spec score · Package Registries | 9.1 (#1) | 7.4 (#21) |
| Integrations | SourceForge lists integrations with R and Docker. | The project documentation demonstrates a Jenkins build trigger setting on an index. |
| Intended users | The project serves researchers and data scientists working with biological and genomic data. | The project describes user and team indexes for Python package workflows. |
| Purpose | Bioconductor develops and shares open-source software for rigorous, reproducible analysis of biological data. | devpi is a Python package index server and packaging, testing, and release tool. |
| Support | SourceForge lists online support, documentation and videos as available support resources. | The official site lists support contracts and paid help via [email protected]. |
Plans
In their own words
Bioconductor
Bioconductor develops and shares free, open-source software for rigorous and reproducible analysis of biological data. It serves researchers and data scientists working with biological and genomic datasets. The project uses R, with most components distributed as R packages. Those packages support analysis of DNA microarray, sequence, flow, SNP, and other genetic or genomic data. Tools can associate genomic data with biological metadata from sources such as GenBank, Entrez genes, and PubMed. Each package includes one or more task-oriented vignettes, and reproducible research is a stated project goal. Bioconductor maintains a stable release updated twice a year and a development version for new features and packages. Each release is designed for a specific R version; the current release page lists Bioconductor 3.23 with R 4.6.0. Docker containers with preinstalled R and Bioconductor versions support reproducible environments. The project documents container deployment with Amazon ECS, Google Kubernetes Engine, Microsoft Azure Container Instances, and Docker Desktop or Docker Engine. Help is available through its Support site and community chat. The project began in 2001 and is overseen by a core team.