Human immunogenetic variation in the form of HLA and KIR types has been shown to be strongly associated with a multitude of immune-related phenotypes. We present MiDAS, an R package enabling statistical association analysis and using immunogenetic data transformation functions for HLA amino acid fine mapping, analysis of HLA evolutionary divergence as well as HLA-KIR interactions. MiDAS closes the gap between inference of immunogenetic variation and its efficient utilization to make meaningful discoveries.
Installation
# Install from Bioconductor
BiocManager::install("midasHLA")
# Install development version from GitHub
devtools::install_github("Genentech/MiDAS")Usage
A user tutorial is available here: https://genentech.github.io/midasHLA/articles/MiDAS_tutorial.html
Developers notes
External data
The package is shipped together with external data such as alignment files or allele frequencies. With time, it is possible that those resources will become outdated. Here follows a brief description of scripts that can be used for updating those data. It should be noted that data storage details, at the external sources, may be changed. In such circumstances, the scripts might become obsolete. Nevertheless, they should be a good starting point to update those sources in next package iterations.
inst/scripts/download_extdata.R script is used to download HLA alignments files. Those are used for translating HLA alleles to amino acid level. The alignments are downloaded from EBI’s IPD-IMGT/HLA database. In some cases alignment files contain sequences for multiple genes, those will be split into separate files (eg. DRB genes)
inst/scripts/parse_alignments.R script pre-parses alignments files for package use. Purpose of using pre-parsed files is to speed up allele to amino acid sequence translation. Resulting .Rdata files should be then placed in inst/extdata replacing the old alignment files.
data-raw/allele_frequencies.R script fetches HLA allele frequencies (for genes A, B, C, DQA1, DQB1, DPA1, DPB1, DRB1, DRB3, DRB4, DRB5) from www.allelefrequencies.net database and save it in a usable format in data directory. This data is then available under the allele_frequencies variable.
data-raw/kir_frequencies.R script fetches KIR genes frequencies (for genes 3DL3, 2DS2, 2DL2, 2DL3, 2DP1, 2DL1, 3DP1, 2DL4, 3DL1, 3DS1, 2DL5, 2DS3, 2DS5, 2DS4, 2DS1, 3DL2) from www.allelefrequencies.net database and save it in a usable format in data directory. This data is then available under the kir_frequencies variable.
Package performance
Package performance can be checked using scripts located in inst/benchmark. Briefly, we used GNU’s time (/usr/bin/time -v) to measure time and memory consumption of data transformation and 2 workflows. All tests include loading package and reading in input files steps. Look into inst/benchmark/* for more details.
Citing
Migdal M, Ruan DF, Forrest WF, Horowitz A, Hammer C (2021) MiDAS—Meaningful Immunogenetic Data at Scale. PLOS Computational Biology 17(7): e1009131. https://doi.org/10.1371/journal.pcbi.1009131