bioconductor-submission

name	bioconductor-submission
description	Bioconductor-specific package submission requirements, BiocCheck validation, version numbering, and review process beyond standard CRAN requirements

Bioconductor Submission Guide

This skill covers submitting packages to Bioconductor, which has additional requirements beyond CRAN. Bioconductor is the primary repository for computational biology and bioinformatics R packages.

Rules

1. Version scheme: Use x.y.z where y is even for release, odd for devel
2. biocViews required: Must include appropriate biocViews terms in DESCRIPTION
3. BiocCheck must pass: Run BiocCheck::BiocCheck() and fix all errors/warnings
4. Vignettes must run: No eval=FALSE for all chunks; use real data
5. S4 classes preferred: Use S4 for complex data structures
6. BiocStyle for vignettes: Use BiocStyle package for consistent formatting
7. Use BiocManager::install(): Not install.packages() in documentation
8. Submit via GitHub issue: To Bioconductor/Contributions repository
9. Active maintenance required: Must respond to build reports and issues
10. Follow Bioconductor guidelines: Read package guidelines thoroughly

Bioconductor vs CRAN

When to Submit to Bioconductor

Submit to Bioconductor if your package:

• Analyzes genomic data (sequences, annotations, variants)
• Analyzes high-throughput biological data (microarrays, RNA-seq, proteomics)
• Provides infrastructure for biological data types
• Integrates with existing Bioconductor packages
• Uses Bioconductor data structures (SummarizedExperiment, GenomicRanges, etc.)

When to Submit to CRAN

Submit to CRAN if your package:

• Provides general statistical methods
• Doesn't specifically work with biological data
• Doesn't depend on Bioconductor packages
• Is a general-purpose tool that happens to be useful for biology

Note: Packages can be on both, but start with one.

Version Numbering Scheme

Bioconductor uses a specific version numbering system:

Version Format: x.y.z

• x (major): Rarely changes; major redesign
• y (minor): Even for release, odd for devel
• z (patch): Bug fixes and minor updates

Examples

# Initial development
0.99.0  -> Start here for new packages
0.99.1  -> Bug fixes during review
0.99.2  -> More fixes

# First release (Bioconductor assigns this)
1.0.0   -> First Bioc release version

# Development continues
1.1.0   -> Devel version after 1.0.0 release

# Next release cycle
1.2.0   -> Next Bioc release (even y)
1.3.0   -> Devel version after 1.2.0

# Bug fixes
1.2.1   -> Bug fix for release
1.3.1   -> Bug fix for devel

Version Management

# Start new package
Version: 0.99.0

# During review, bump z for fixes
Version: 0.99.1
Version: 0.99.2

# After acceptance, Bioconductor core sets
Version: 1.0.0  # For next release

# You continue development
Version: 1.1.0  # Devel version

Critical: Don't manually set version to 1.0.0 - Bioconductor does this.

DESCRIPTION File Requirements

Complete Bioconductor DESCRIPTION

Package: MyBiocPackage
Title: Analysis of Single-Cell RNA Sequencing Data
Version: 0.99.0
Authors@R: c(
    person("First", "Last",
           email = "[e-mail]",
           role = c("aut", "cre"),
           comment = c(ORCID = "0000-0001-2345-6789")),
    person("Second", "Author",
           role = "aut",
           comment = c(ORCID = "0000-0001-2345-6780"))
    )
Description: Provides methods for analyzing single-cell RNA sequencing
    data. Implements novel clustering algorithms and visualization
    techniques. Integrates with Bioconductor infrastructure including
    SingleCellExperiment and other core data structures.
License: Artistic-2.0
Encoding: UTF-8
LazyData: false
Depends:
    R (>= 4.4.0)
Imports:
    BiocGenerics,
    S4Vectors,
    SummarizedExperiment,
    SingleCellExperiment,
    methods,
    stats,
    graphics
Suggests:
    BiocStyle,
    knitr,
    rmarkdown,
    testthat (>= 3.0.0),
    scRNAseq
biocViews: Software, SingleCell, RNASeq, Clustering, Visualization,
    DimensionReduction, GeneExpression
VignetteBuilder: knitr
RoxygenNote: 7.3.0
Roxygen: list(markdown = TRUE)
URL: https://github.com/username/MyBiocPackage
BugReports: https://github.com/username/MyBiocPackage/issues

biocViews Requirements

Every Bioconductor package must have appropriate biocViews terms:

# Find appropriate terms
BiocManager::install("biocViews")
library(biocViews)

# Browse available terms
data(biocViewsVocab)
biocViewsVocab

# Common top-level categories
# Software - computational tools
# AnnotationData - annotation packages
# ExperimentData - example/experiment data packages
# Workflow - workflow packages

Common biocViews Terms

Software packages:

biocViews: Software, RNASeq, GeneExpression, Transcriptomics,
    DifferentialExpression, Sequencing, Coverage, Alignment

By technology:

• RNASeq, ChIPSeq, ATACSeq, DNASeq, MethylSeq
• Microarray, Proteomics, Metabolomics
• SingleCell, SpatialData, MultiChannel

By analysis type:

• DifferentialExpression, Clustering, Classification
• Normalization, Preprocessing, QualityControl
• Visualization, Annotation, GenomeAnnotation

By data type:

• GeneExpression, Epigenetics, StructuralVariation
• CopyNumberVariation, SNP, Transcriptomics

License Requirements

Bioconductor prefers open-source licenses:

• Artistic-2.0 (recommended for Bioconductor)
• GPL (>= 2)
• LGPL
• MIT
• BSD

# Set license
usethis::use_mit_license()
# Or manually in DESCRIPTION
License: Artistic-2.0

BiocCheck Validation

Installing BiocCheck

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("BiocCheck")

Running BiocCheck

# Basic check
BiocCheck::BiocCheck()

# More detailed
BiocCheck::BiocCheck(".", new.package = TRUE)

# For package updates
BiocCheck::BiocCheck(".", new.package = FALSE)

Understanding BiocCheck Output

BiocCheck reports three levels:

• ERROR: Must fix (will prevent acceptance)
• WARNING: Should fix (reviewers will ask)
• NOTE: Consider fixing (good practice)

Common BiocCheck Issues and Fixes

1. Version Number Issues

Problem:

ERROR: Version number must be 0.99.0 for new packages

Fix:

# In DESCRIPTION
Version: 0.99.0

2. biocViews Missing

Problem:

ERROR: Package must have biocViews

Fix:

# Add to DESCRIPTION
biocViews: Software, Sequencing, RNASeq

3. Vignette eval=FALSE

Problem:

WARNING: Vignette chunks should not use eval=FALSE

Fix: Make vignettes actually run:

# Bad
```{r, eval=FALSE}
result <- analyze_data(data)

Good - provide real example data

data("example_dataset")
result <- analyze_data(example_dataset)

#### 4. Non-BiocStyle Vignette

**Problem**:

NOTE: Consider using BiocStyle package

**Fix**:
```yaml
---
title: "Package Vignette"
author: "Your Name"
output: BiocStyle::html_document
vignette: >
  %\VignetteIndexEntry{Package Vignette}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r style, echo=FALSE, results='asis'}
BiocStyle::markdown()

#### 5. install.packages() in Documentation

**Problem**:

WARNING: Use BiocManager::install() not install.packages()

**Fix**:
```r
# Bad
#' Install with: install.packages("MyPackage")

# Good
#' Install with: BiocManager::install("MyPackage")

6. Long Line Lengths

Problem:

NOTE: Lines should be <= 80 characters

Fix: Break long lines:

# Use styler
styler::style_pkg()

# Or manually
result <- my_function(
    argument1 = value1,
    argument2 = value2,
    argument3 = value3
)

7. Missing NEWS File

Problem:

NOTE: Consider adding NEWS file

Fix:

usethis::use_news_md()

Content:

# MyBiocPackage 0.99.0

* Initial Bioconductor submission
* Implements core functionality for X
* Includes vignette demonstrating Y

8. Package Size Too Large

Problem:

WARNING: Package size is X MB

Fix:

• Remove large example data
• Create separate data package
• Compress data files
• Use external data with ExperimentHub

9. Using .Rbuildignore Incorrectly

Problem:

WARNING: Don't use .Rbuildignore excessively

Fix: Only ignore truly unnecessary files:

^.*\.Rproj$
^\.Rproj\.user$
^\.github$
^_pkgdown\.yml$
^docs$
^pkgdown$

10. Missing runnable examples

Problem:

ERROR: All exported functions must have runnable examples

Fix:

#' My Function
#'
#' @examples
#' # Load example data
#' data("example_data")
#'
#' # Run analysis
#' result <- my_function(example_data)
#'

#' @export
my_function <- function(x) {
    # implementation
}

S4 Classes and Methods

Bioconductor strongly encourages S4 for complex data structures:

Defining S4 Classes

#' MyData Class
#'
#' @slot counts matrix of counts
#' @slot metadata data.frame of sample metadata
#' @slot features data.frame of feature metadata
#'
#' @export
setClass("MyData",
    slots = c(
        counts = "matrix",
        metadata = "data.frame",
        features = "data.frame"
    )
)

S4 Constructor

#' Create MyData Object
#'
#' @param counts matrix of counts
#' @param metadata data.frame of metadata
#' @param features data.frame of features
#'
#' @return MyData object
#'
#' @examples
#' counts <- matrix(rpois(100, 10), nrow=10)
#' metadata <- data.frame(sample=paste0("S", 1:10))
#' features <- data.frame(gene=paste0("G", 1:10))
#' obj <- MyData(counts, metadata, features)
#'

#' @export
MyData <- function(counts, metadata, features) {
    new("MyData",
        counts = counts,
        metadata = metadata,
        features = features
    )
}

S4 Methods

#' @export
setGeneric("getCounts", function(x) standardGeneric("getCounts"))

#' @export
setMethod("getCounts", "MyData", function(x) x@counts)

#' @export
setMethod("show", "MyData", function(object) {
    cat("MyData object\n")
    cat("  Samples:", ncol(object@counts), "\n")
    cat("  Features:", nrow(object@counts), "\n")
})

Vignette Requirements

BiocStyle Vignette Template

---
title: "Introduction to MyBiocPackage"
author:
  - name: Your Name
    affiliation: Institution
    email: [e-mail]
date: "`r Sys.Date()`"
output:
  BiocStyle::html_document:
    toc: true
    toc_depth: 2
vignette: >
  %\VignetteIndexEntry{Introduction to MyBiocPackage}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)

BiocStyle::markdown()