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README.md
# Fast, epiallele-aware methylation reporter <img align="right" src="inst/epialleleR_logo.svg"> [![](https://github.com/BBCG/epialleleR/workflows/R-CMD-check-bioc/badge.svg)](https://github.com/BBCG/epialleleR/actions) [![](https://codecov.io/gh/BBCG/epialleleR/branch/master/graph/badge.svg)](https://codecov.io/gh/BBCG/epialleleR) [![](https://bioconductor.org/shields/years-in-bioc/epialleleR.svg)](https://bioconductor.org/packages/release/bioc/html/epialleleR.html) ## Introduction DISCLAIMER: This is a work in progress, however the package is already usable, and the obtained experimental results has been published. Main methods (*`preprocessBam`*, *`generateCytosineReport`*, *`generateBedReport`*) won't change. The *`generateVcfReport`* method might at some point be improved to include variable-length sequence variations, while *`generateBedEcdf`* should be considered somewhat experimental and may undergo significant changes or be substituted with some other method in the future. *`epialleleR`* is an R package for calling hypermethylated variant epiallele frequencies (VEF) at the level of genomic regions or individual cytosines in next-generation sequencing data using binary alignment map (BAM) files as an input. Other functionality includes extracting methylation patterns, computing the empirical cumulative distribution function for per-read beta values, and testing the significance of the association between epiallele methylation status and base frequencies at particular genomic positions (SNPs). ### Current Features * conventional reporting of cytosine methylation (*`generateCytosineReport`*) * calling the hypermethylated variant epiallele frequency (VEF) at the level of genomic regions (*`generate[Bed|Amplicon|Capture]Report`*) or individual cytosines (*`generateCytosineReport`*) * extracting methylation patterns for genomic region of interest (*`extractPatterns`*) * assessing the distribution of per-read beta values for genomic regions of interest (*`generateBedEcdf`*) * testing for the association between epiallele methylation status and sequence variations (*`generateVcfReport`*) ### Recent improvements ##### v1.4 * significant speed-up * method to visualize methylation patterns ##### v1.2 * even faster and more memory-efficient BAM loading (by means of HTSlib) * min.baseq parameter to reduce the effect of low quality bases on methylation or SNV calling (in v1.0 the output of *`generateVcfReport`* was equivalent to the one of `samtools mpileup -Q 0 ...`) check out NEWS for more! ------- ## Installation ### install via Bioconductor ```r if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("epialleleR") ``` ### Install the latest version via install_github ```r library(devtools) install_github("BBCG/epialleleR", build_vignettes=FALSE, repos=BiocManager::repositories(), dependencies=TRUE, type="source") ``` ------- ## Using the package Please read *`epialleleR`* vignettes at [GitHub pages](https://bbcg.github.io/epialleleR/articles/epialleleR.html) or within the R environment: `vignette("epialleleR", package="epialleleR")`, or consult the function's help pages for the extensive information on usage, parameters and output values. Very brief synopsis: ```r library(epialleleR) # external files amplicon.bam <- system.file("extdata", "amplicon010meth.bam", package="epialleleR") amplicon.bed <- system.file("extdata", "amplicon.bed", package="epialleleR") amplicon.vcf <- system.file("extdata", "amplicon.vcf.gz", package="epialleleR") # preloading the data bam.data <- preprocessBam(amplicon.bam) # methylation patterns, check vignettes or method description for plotting them patterns <- extractPatterns(bam=amplicon.bam, bed=amplicon.bed, bed.row=3) # CpG VEF report for individual bases cg.vef.report <- generateCytosineReport(bam.data) # BED-guided VEF report for genomic ranges bed.report <- generateBedReport(bam=amplicon.bam, bed=amplicon.bed, bed.type="capture") # VCF report vcf.report <- generateVcfReport(bam=amplicon.bam, bed=amplicon.bed, vcf=amplicon.vcf, vcf.style="NCBI") ``` ------- ### Citing the *`epialleleR`* package Oleksii Nikolaienko, Per Eystein L√łnning, Stian Knappskog, *epialleleR*: an R/BioC package for sensitive allele-specific methylation analysis in NGS data. bioRxiv 2022.06.30.498213, [https://www.biorxiv.org/content/10.1101/2022.06.30.498213v1](https://www.biorxiv.org/content/10.1101/2022.06.30.498213v1) ### The experimental data analysed using the package Per Eystein Lonning, Oleksii Nikolaienko, Kathy Pan, Allison W. Kurian, Hans Petter Petter Eikesdal, Mary Pettinger, Garnet L Anderson, Ross L Prentice, Rowan T. Chlebowski, and Stian Knappskog. Constitutional *BRCA1* methylation and risk of incident triple-negative breast cancer and high-grade serous ovarian cancer. JAMA Oncology 2022. [https://doi.org/10.1001/jamaoncol.2022.3846](https://doi.org/10.1001/jamaoncol.2022.3846) ### *`epialleleR`* at Bioconductor [release](https://bioconductor.org/packages/release/bioc/html/epialleleR.html), [development version](https://bioconductor.org/packages/devel/bioc/html/epialleleR.html) ------- License --------- Artistic License 2.0