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This package provides the headers and static library of Protocol buffers for other R packages to compile and link against.

Functions, workflow, and a Shiny application for visualizing sequence conservation and designing degenerate primers, probes, and (RT)-(q/d)PCR assays from a multiple DNA sequence alignment. The results can be presented in data frame format and visualized as dashboard-like plots. For more information, please see the package vignette.

Probabilistic analysis of probe reliability and differential gene expression on short oligonucleotide arrays.

Calculates the Reproducibility-Optimized Test Statistic (ROTS) for differential testing in omics data.

ROSeq - A rank based approach to modeling gene expression with filtered and normalized read count matrix. ROSeq takes filtered and normalized read matrix and cell-annotation/condition as input and determines the differentially expressed genes between the contrasting groups of single cells. One of the input parameters is the number of cores to be used.

Latent variable modeling with Principal Component Analysis (PCA) and Partial Least Squares (PLS) are powerful methods for visualization, regression, classification, and feature selection of omics data where the number of variables exceeds the number of samples and with multicollinearity among variables. Orthogonal Partial Least Squares (OPLS) enables to separately model the variation correlated (predictive) to the factor of interest and the uncorrelated (orthogonal) variation. While performing similarly to PLS, OPLS facilitates interpretation. Successful applications of these chemometrics techniques include spectroscopic data such as Raman spectroscopy, nuclear magnetic resonance (NMR), mass spectrometry (MS) in metabolomics and proteomics, but also transcriptomics data. In addition to scores, loadings and weights plots, the package provides metrics and graphics to determine the optimal number of components (e.g. with the R2 and Q2 coefficients), check the validity of the model by permutation testing, detect outliers, and perform feature selection (e.g. with Variable Importance in Projection or regression coefficients). The package can be accessed via a user interface on the Workflow4Metabolomics.org online resource for computational metabolomics (built upon the Galaxy environment).

Suite of tools for functional analysis.

The rols package is an interface to the Ontology Lookup Service (OLS) to access and query hundred of ontolgies directly from R.

RolDE detects longitudinal differential expression between two conditions in noisy high-troughput data. Suitable even for data with a moderate amount of missing values.RolDE is a composite method, consisting of three independent modules with different approaches to detecting longitudinal differential expression. The combination of these diverse modules allows RolDE to robustly detect varying differences in longitudinal trends and expression levels in diverse data types and experimental settings.

The package analyzes the Curve ROC, identificates it among different types of Curve ROC and calculates the area under de curve through the method that is most accuracy. This package is able to standarizate proper and improper pAUC.

Provide utilities for ROC, with microarray focus.

This package implements a variety of functions useful for gene set analysis using rotations to approximate the null distribution. It contributes with the implementation of seven test statistic scores that can be used with different goals and interpretations. Several functions are available to complement the statistical results with graphical representations.

Identify preferential usage of APA sites, comparing two biological conditions, starting from known alternative sites and alignments obtained from standard RNA-seq experiments.

R/Bioconductor package for normalization, curve registration and inference in time course gene expression data.

RnBeads facilitates comprehensive analysis of various types of DNA methylation data at the genome scale.

RNAshapeQC provides coverage-shape-based quality control (QC) metrics for mRNA-seq and total RNA-seq data. It supports per-gene pileup construction from BAM files as well as toy datasets for quick-start examples. The package implements protocol-specific metrics, including decay rate (DR), degradation score (DS), mean coverage depth (MCD), window coefficient of variation (wCV), area under the curve (AUC), and shape-based sample-level indices. RNAshapeQC also includes HPC-friendly functions for per-gene batch processing and cross-study pileup generation. This package enables interpretable, protocol-specific QC assessments for diverse RNA-seq workflows.

RnaSeqSampleSize package provides a sample size calculation method based on negative binomial model and the exact test for assessing differential expression analysis of RNA-seq data. It controls FDR for multiple testing and utilizes the average read count and dispersion distributions from real data to estimate a more reliable sample size. It is also equipped with several unique features, including estimation for interested genes or pathway, power curve visualization, and parameter optimization.

RNA-seq, sample size

The RNAseqCovarImpute package makes linear model analysis for RNA sequencing read counts compatible with multiple imputation (MI) of missing covariates. A major problem with implementing MI in RNA sequencing studies is that the outcome data must be included in the imputation prediction models to avoid bias. This is difficult in omics studies with high-dimensional data. The first method we developed in the RNAseqCovarImpute package surmounts the problem of high-dimensional outcome data by binning genes into smaller groups to analyze pseudo-independently. This method implements covariate MI in gene expression studies by 1) randomly binning genes into smaller groups, 2) creating M imputed datasets separately within each bin, where the imputation predictor matrix includes all covariates and the log counts per million (CPM) for the genes within each bin, 3) estimating gene expression changes using `limma::voom` followed by `limma::lmFit` functions, separately on each M imputed dataset within each gene bin, 4) un-binning the gene sets and stacking the M sets of model results before applying the `limma::squeezeVar` function to apply a variance shrinking Bayesian procedure to each M set of model results, 5) pooling the results with Rubins’ rules to produce combined coefficients, standard errors, and P-values, and 6) adjusting P-values for multiplicity to account for false discovery rate (FDR). A faster method uses principal component analysis (PCA) to avoid binning genes while still retaining outcome information in the MI models. Binning genes into smaller groups requires that the MI and limma-voom analysis is run many times (typically hundreds). The more computationally efficient MI PCA method implements covariate MI in gene expression studies by 1) performing PCA on the log CPM values for all genes using the Bioconductor `PCAtools` package, 2) creating M imputed datasets where the imputation predictor matrix includes all covariates and the optimum number of PCs to retain (e.g., based on Horn’s parallel analysis or the number of PCs that account for >80% explained variation), 3) conducting the standard limma-voom pipeline with the `voom` followed by `lmFit` followed by `eBayes` functions on each M imputed dataset, 4) pooling the results with Rubins’ rules to produce combined coefficients, standard errors, and P-values, and 5) adjusting P-values for multiplicity to account for false discovery rate (FDR).

Several quantitative and visualized benchmarks for RNA-seq quantification pipelines. Two-condition quantifications for genes, transcripts, junctions or exons by each pipeline with necessary meta information should be organized into numeric matrices in order to proceed the evaluation.

RNA-sense tool compares RNA-seq time curves in two experimental conditions, i.e. wild-type and mutant, and works in three steps. At Step 1, it builds expression profile for each transcript in one condition (i.e. wild-type) and tests if the transcript abundance grows or decays significantly. Dynamic transcripts are then sorted to non-overlapping groups (time profiles) by the time point of switch up or down. At Step 2, RNA-sense outputs the groups of differentially expressed transcripts, which are up- or downregulated in the mutant compared to the wild-type at each time point. At Step 3, Correlations (Fisher's exact test) between the outputs of Step 1 (switch up- and switch down- time profile groups) and the outputs of Step2 (differentially expressed transcript groups) are calculated. The results of the correlation analysis are printed as two-dimensional color plot, with time profiles and differential expression groups at y- and x-axis, respectively, and facilitates the biological interpretation of the data.

RNAmodR.RiboMethSeq implements the detection of 2'-O methylations on RNA from experimental data generated with the RiboMethSeq protocol. The package builds on the core functionality of the RNAmodR package to detect specific patterns of the modifications in high throughput sequencing data.

RNAmodR.ML extend the functionality of the RNAmodR package and classical detection strategies towards detection through machine learning models. RNAmodR.ML provides classes, functions and an example workflow to establish a detection stratedy, which can be packaged.

RNAmodR.AlkAnilineSeq implements the detection of m7G, m3C and D modifications on RNA from experimental data generated with the AlkAnilineSeq protocol. The package builds on the core functionality of the RNAmodR package to detect specific patterns of the modifications in high throughput sequencing data.

RNAmodR provides classes and workflows for loading/aggregation data from high througput sequencing aimed at detecting post-transcriptional modifications through analysis of specific patterns. In addition, utilities are provided to validate and visualize the results. The RNAmodR package provides a core functionality from which specific analysis strategies can be easily implemented as a seperate package.

RNAeditr analyzes site-specific RNA editing events, as well as hyper-editing regions. The editing frequencies can be tested against binary, continuous or survival outcomes. Multiple covariate variables as well as interaction effects can also be incorporated in the statistical models.

RNA degradation is monitored through measurement of RNA abundance after inhibiting RNA synthesis. This package has functions and example scripts to facilitate (1) data normalization, (2) data modeling using constant decay rate or time-dependent decay rate models, (3) the evaluation of treatment or genotype effects, and (4) plotting of the data and models. Data Normalization: functions and scripts make easy the normalization to the initial (T0) RNA abundance, as well as a method to correct for artificial inflation of Reads per Million (RPM) abundance in global assessments as the total size of the RNA pool decreases. Modeling: Normalized data is then modeled using maximum likelihood to fit parameters. For making treatment or genotype comparisons (up to four), the modeling step models all possible treatment effects on each gene by repeating the modeling with constraints on the model parameters (i.e., the decay rate of treatments A and B are modeled once with them being equal and again allowing them to both vary independently). Model Selection: The AICc value is calculated for each model, and the model with the lowest AICc is chosen. Modeling results of selected models are then compiled into a single data frame. Graphical Plotting: functions are provided to easily visualize decay data model, or half-life distributions using ggplot2 package functions.

It has been shown that both DNA methylation and RNA transcription are linked to chronological age and age related diseases. Several estimators have been developed to predict human aging from DNA level and RNA level. Most of the human transcriptional age predictor are based on microarray data and limited to only a few tissues. To date, transcriptional studies on aging using RNASeq data from different human tissues is limited. The aim of this package is to provide a tool for across-tissue and tissue-specific transcriptional age calculation based on GTEx RNASeq data.

The rmspc package runs MSPC (Multiple Sample Peak Calling) software using R. The analysis of ChIP-seq samples outputs a number of enriched regions (commonly known as "peaks"), each indicating a protein-DNA interaction or a specific chromatin modification. When replicate samples are analyzed, overlapping peaks are expected. This repeated evidence can therefore be used to locally lower the minimum significance required to accept a peak. MSPC uses combined evidence from replicated experiments to evaluate peak calling output, rescuing peaks, and reduce false positives. It takes any number of replicates as input and improves sensitivity and specificity of peak calling on each, and identifies consensus regions between the input samples.

RNA-Seq is currently used routinely, and it provides accurate information on gene transcription. However, the method cannot accurately estimate duplicated genes expression. Several strategies have been previously used, but all of them provide biased results. With Rmmquant, if a read maps at different positions, the tool detects that the corresponding genes are duplicated; it merges the genes and creates a merged gene. The counts of ambiguous reads is then based on the input genes and the merged genes. Rmmquant is a drop-in replacement of the widely used tools findOverlaps and featureCounts that handles multi-mapping reads in an unabiased way.

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.

Microarray Classification is designed for both biologists and statisticians. It offers the ability to train a classifier on a labelled microarray dataset and to then use that classifier to predict the class of new observations. A range of modern classifiers are available, including support vector machines (SVMs), nearest shrunken centroids (NSCs)... Advanced methods are provided to estimate the predictive error rate and to report the subset of genes which appear essential in discriminating between classes.

A classification algorithm, based on a multi-chip, multi-SNP approach for Affymetrix SNP arrays. Using a large training sample where the genotype labels are known, this aglorithm will obtain more accurate classification results on new data. RLMM is based on a robust, linear model and uses the Mahalanobis distance for classification. The chip-to-chip non-biological variation is removed through normalization. This model-based algorithm captures the similarities across genotype groups and probes, as well as thousands other SNPs for accurate classification. NOTE: 100K-Xba only at for now.

RLassoCox is a package that implements the RLasso-Cox model proposed by Wei Liu. The RLasso-Cox model integrates gene interaction information into the Lasso-Cox model for accurate survival prediction and survival biomarker discovery. It is based on the hypothesis that topologically important genes in the gene interaction network tend to have stable expression changes. The RLasso-Cox model uses random walk to evaluate the topological weight of genes, and then highlights topologically important genes to improve the generalization ability of the Lasso-Cox model. The RLasso-Cox model has the advantage of identifying small gene sets with high prognostic performance on independent datasets, which may play an important role in identifying robust survival biomarkers for various cancer types.

This package does nucleosome positioning using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling.

An implementation of a probabilistic modeling framework that jointly analyzes personal genome and transcriptome data to estimate the probability that a variant has regulatory impact in that individual. It is based on a generative model that assumes that genomic annotations, such as the location of a variant with respect to regulatory elements, determine the prior probability that variant is a functional regulatory variant, which is an unobserved variable. The functional regulatory variant status then influences whether nearby genes are likely to display outlier levels of gene expression in that person. See the RIVER website for more information, documentation and examples.

Tools for comprehensive gene set enrichment and extraction of multi-resource high confidence subnetworks. RITAN facilitates bioinformatic tasks for enabling network biology research.

The RImmPort package simplifies access to ImmPort data for analysis in the R environment. It provides a standards-based interface to the ImmPort study data that is in a proprietary format.

The IGVF Catalog provides data on the impact of genomic variants on function. The `rigvf` package provides an interface to the IGVF Catalog, allowing easy integration with Bioconductor resources.

Vendors the igraph C source code and builds it into a static library. Other Bioconductor packages can link to libigraph.a in their own C/C++ code. This is intended for packages wrapping C/C++ libraries that depend on the igraph C library and cannot be easily adapted to use the igraph R package.

'rifiComparative' is a continuation of rifi package. It compares two conditions output of rifi using half-life and mRNA at time 0 segments. As an input for the segmentation, the difference between half-life of both condtions and log2FC of the mRNA at time 0 are used. The package provides segmentation, statistics, summary table, fragments visualization and some additional useful plots for further anaylsis.

'rifi' analyses data from rifampicin time series created by microarray or RNAseq. 'rifi' is a transcriptome data analysis tool for the holistic identification of transcription and decay associated processes. The decay constants and the delay of the onset of decay is fitted for each probe/bin. Subsequently, probes/bins of equal properties are combined into segments by dynamic programming, independent of a existing genome annotation. This allows to detect transcript segments of different stability or transcriptional events within one annotated gene. In addition to the classic decay constant/half-life analysis, 'rifi' detects processing sites, transcription pausing sites, internal transcription start sites in operons, sites of partial transcription termination in operons, identifies areas of likely transcriptional interference by the collision mechanism and gives an estimate of the transcription velocity. All data are integrated to give an estimate of continous transcriptional units, i.e. operons. Comprehensive output tables and visualizations of the full genome result and the individual fits for all probes/bins are produced.

Ribo-Seq (also named ribosome profiling or footprinting) measures translatome (unlike RNA-Seq, which sequences the transcriptome) by direct quantification of the ribosome-protected fragments (RPFs). This package provides the tools for quality assessment of ribosome profiling. In addition, it can preprocess Ribo-Seq data for subsequent differential analysis.

Plotting functions, frameshift detection and parsing of sequencing data from ribosome profiling experiments.

The ribor package provides an R Interface for .ribo files. It provides functionality to read the .ribo file, which is of HDF5 format, and performs common analyses on its contents.

This package performs differential pattern analysis for Ribo-seq data. It identifies genes with significantly different patterns in the ribosome footprint between two conditions. RiboDiPA contains five major components including bam file processing, P-site mapping, data binning, differential pattern analysis and footprint visualization.

This package provides version 1.18 of the 'HTSlib' C library for high-throughput sequence analysis. The package is primarily useful to developers of other R packages who wish to make use of HTSlib. Motivation and instructions for use of this package are in the vignette, vignette(package="Rhtslib", "Rhtslib").

An R interface to the HISAT2 spliced short-read aligner by Kim et al. (2015). The package contains wrapper functions to create a genome index and to perform the read alignment to the generated index.

"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.

Provides C and C++ hdf5 libraries.