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.

"rhinotypeR" is designed to automate the comparison of sequence data against prototype strains, streamlining the genotype assignment process. By implementing predefined pairwise distance thresholds, this package makes genotype assignment accessible to researchers and public health professionals. This tool enhances our epidemiological toolkit by enabling more efficient surveillance and analysis of rhinoviruses (RVs) and other viral pathogens with complex genomic landscapes. Additionally, "rhinotypeR" supports comprehensive visualization and analysis of single nucleotide polymorphisms (SNPs) and amino acid substitutions, facilitating in-depth genetic and evolutionary studies.

Generate HTML or PDF reports to explore a set of regions such as the results from annotation-agnostic expression analysis of RNA-seq data at base-pair resolution performed by derfinder. You can also create reports for DESeq2 or edgeR results.

Agilent Rat annotation data (chip rgug4130a) assembled using data from public repositories.

ramr is an R package for detection of epimutations (i.e., infrequent aberrant DNA methylation events) in large data sets obtained by methylation profiling using array or high-throughput methylation sequencing. In addition, package provides functions to visualize found aberrantly methylated regions (AMRs), to generate sets of all possible regions to be used as reference sets for enrichment analysis, and to generate biologically relevant test data sets for performance evaluation of AMR/DMR search algorithms.

raerdata is an ExperimentHub package that provides a collection of files useful for demostrating functionality in the raer package. Datasets include 10x genomics scRNA-seq, bulk RNA-seq, and paired whole-genome and RNA-seq data. Additionally databases of human and mouse RNA editing sites are provided.

This package provides SummarizedExperiment-like containers for storing and manipulating dimensionally-reduced assay data. The ReducedExperiment classes allow users to simultaneously manipulate their original dataset and their decomposed data, in addition to other method-specific outputs like feature loadings. Implements utilities and specialised classes for the application of stabilised independent component analysis (sICA) and weighted gene correlation network analysis (WGCNA).

This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was RaGene-1\_0-st-v1\_probe\_tab.

rTRM identifies transcriptional regulatory modules (TRMs) from protein-protein interaction networks.

Affymetrix rta10 annotation data (chip rta10probeset) assembled using data from public repositories.

Package that allows to explore the exposome and to perform association analyses between exposures and health outcomes.

recoup calculates and plots signal profiles created from short sequence reads derived from Next Generation Sequencing technologies. The profiles provided are either sumarized curve profiles or heatmap profiles. Currently, recoup supports genomic profile plots for reads derived from ChIP-Seq and RNA-Seq experiments. The package uses ggplot2 and ComplexHeatmap graphics facilities for curve and heatmap coverage profiles respectively.

Companion data sets to showcase the functionality of the ReactomeGSA package. This package contains proteomics and RNA-seq data of the melanoma B-cell induction study by Griss et al. and scRNA-seq data from Jerby-Arnon et al.

Package provides clinical datasets from The Cancer Genome Atlas Project for all cohorts types from http://gdac.broadinstitute.org/. Clinical data format is explained here https://wiki.nci.nih.gov/display/TCGA/Clinical+Data+Overview. Data from 2015-11-01 snapshot.

This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was Rice\_probe\_tab.

Package provides clinical, expression, cnv and mutation data from Genome Cancer Browser.

This package provides a package containing an environment representing the RAE230A.CDF file.

Workflow to process tandem MS files and build MassBank records. Functions include automated extraction of tandem MS spectra, formula assignment to tandem MS fragments, recalibration of tandem MS spectra with assigned fragments, spectrum cleanup, automated retrieval of compound information from Internet databases, and export to MassBank records.

Parses BioPAX files and represents them in R, at the moment BioPAX level 2 and level 3 are supported.

ReUseData is an _R/Bioconductor_ software tool to provide a systematic and versatile approach for standardized and reproducible data management. ReUseData facilitates transformation of shell or other ad hoc scripts for data preprocessing into workflow-based data recipes. Evaluation of data recipes generate curated data files in their generic formats (e.g., VCF, bed). Both recipes and data are cached using database infrastructure for easy data management and reuse. Prebuilt data recipes are available through ReUseData portal ("https://rcwl.org/dataRecipes/") with full annotation and user instructions. Pregenerated data are available through ReUseData cloud bucket that is directly downloadable through "getCloudData()".

Base annotation databases for rhesus, intended ONLY to be used by AnnotationDbi to produce regular annotation packages.

This package provides a collection of Bioinformatics tools and pipelines based on R and the Common Workflow Language.

RedeR is an R-based package combined with a stand-alone Java application for interactive visualization and manipulation of nested networks. Graph, node, and edge attributes can be configured using either graphical or command-line methods, following igraph syntax rules.

Agilent Chips that use Agilent design number 028282 annotation data (chip RnAgilentDesign028282) assembled using data from public repositories.