AlphaMissense Data for R / Bioconductor

The AlphaMissense publication outlines how a variant of AlphaFold / DeepMind was used to predict missense variant pathogenicity. Supporting data on Zenodo include, for instance 70+M variants across hg19 and hg38 genome builds. The AlphaMissense package allows ready access to the data, downloading individual files to DuckDB databases for ready exploration and integration into R and Bioconductor worksflows.

Installation

Install the package from Bioconductor or GitHub, ensuring correct Bioconductor dependencies.

When the package is available on Bioconductor, use

if (!"BiocManager" %in% rownames(installed.packages()))
    install.packages("BiocManager", repos = "https://cloud.R-project.org")
if (BiocManager::version() >= "3.19") {
    BiocManager::install("AlphaMissenseR")
} else {
    stop(
        "'AlphaMissenseR' requires Bioconductor version 3.19 or later, ",
        "install from GitHub?"
    )
}

Use the pre-release or devel version with

if (!"remotes" %in% rownames(installed.packages()))
    install.packages("remotes", repos = "https://cloud.R-project.org")
remotes::install_github(
    "mtmorgan/AlphaMissenseR",
    repos = BiocManager::repositories()
)

Load the library.

library(AlphaMissenseR)

Next steps

• Visit the Introduction to learn more about accessing AlphaMissense data in R.
• The AlphaFold Integration article shows how missense effects can be plotted on AlphaFold (or other) protein structures.
• Use ClinVar Integration to compare AlphaMissense and ClinVar classifications.
• See Benchmarking with ProteinGym for assessing AlphaMissense predictions.
• Troubleshoot common problems with Issues & Solutions.