REMP
Repetitive Element Methylation Prediction
Bioconductor version: 3.23 · Package version: 1.36.0
Machine learning-based tools to predict DNA methylation of locus-specific repetitive elements (RE) by learning surrounding genetic and epigenetic information. These tools provide genomewide and single-base resolution of DNA methylation prediction on RE that are difficult to measure using array-based or sequencing-based platforms, which enables epigenome-wide association study (EWAS) and differentially methylated region (DMR) analysis on RE.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("REMP") Details
| Maintainer | Yinan Zheng <y-zheng@northwestern.edu> |
| Author | Yinan Zheng [aut, cre], Lei Liu [aut], Wei Zhang [aut], Warren Kibbe [aut], Lifang Hou [aut, cph] |
| License | GPL-3 |
| URL | https://github.com/YinanZheng/REMP |
| Bug Reports | https://github.com/YinanZheng/REMP/issues |
| Downloads rank | 544 |
| Source branch | RELEASE_3_23 |
| biocViews | DNAMethylation, DataImport, DifferentialMethylation, Epigenetics, GenomeWideAssociation, MethylationArray, Microarray, MultiChannel, Preprocessing, QualityControl, Sequencing, Software, TwoChannel |
Documentation
Download
Follow the installation instructions to use this package in your R session.
| Source package | REMP_1.36.0.tar.gz |
| Windows binary (x86_64) | REMP_1.36.0.zip |
| macOS binary (arm64) | REMP_1.36.0.tgz |
| macOS binary (x86_64) | REMP_1.36.0.tgz |
Dependencies
Depends: R (>= 3.6), SummarizedExperiment (>= 1.1.6), minfi (>= 1.22.0)
Imports: readr, rtracklayer, graphics, stats, utils, methods, settings, BiocGenerics, S4Vectors, Biostrings, GenomicRanges, IRanges, Seqinfo, BiocParallel, doParallel, parallel, foreach, caret, kernlab, ranger, BSgenome, AnnotationHub, org.Hs.eg.db, impute, iterators
Suggests: IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, minfiDataEPIC