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NanoMethViz is a toolkit for visualising methylation data from Oxford Nanopore sequencing. It can be used to explore methylation patterns from reads derived from Oxford Nanopore direct DNA sequencing with methylation called by callers including nanopolish, f5c and megalodon. The plots in this package allow the visualisation of methylation profiles aggregated over experimental groups and across classes of genomic features.

GBScleanR is a package for quality check, filtering, and error correction of genotype data derived from next generation sequcener (NGS) based genotyping platforms. GBScleanR takes Variant Call Format (VCF) file as input. The main function of this package is `estGeno()` which estimates the true genotypes of samples from given read counts for genotype markers using a hidden Markov model with incorporating uneven observation ratio of allelic reads. This implementation gives robust genotype estimation even in noisy genotype data usually observed in Genotyping-By-Sequnencing (GBS) and similar methods, e.g. RADseq. The current implementation accepts genotype data of a diploid population at any generation of multi-parental cross, e.g. biparental F2 from inbred parents, biparental F2 from outbred parents, and 8-way recombinant inbred lines (8-way RILs) which can be refered to as MAGIC population.

cytofQC is a package for initial cleaning of CyTOF data. It uses a semi-supervised approach for labeling cells with their most likely data type (bead, doublet, debris, dead) and the probability that they belong to each label type. This package does not remove data from the dataset, but provides labels and information to aid the data user in cleaning their data. Our algorithm is able to distinguish between doublets and large cells.

The MsQuality provides functionality to calculate quality metrics for mass spectrometry-derived, spectral data at the per-sample level. MsQuality relies on the mzQC framework of quality metrics defined by the Human Proteom Organization-Proteomics Standards Initiative (HUPO-PSI). These metrics quantify the quality of spectral raw files using a controlled vocabulary. The package is especially addressed towards users that acquire mass spectrometry data on a large scale (e.g. data sets from clinical settings consisting of several thousands of samples). The MsQuality package allows to calculate low-level quality metrics that require minimum information on mass spectrometry data: retention time, m/z values, and associated intensities. MsQuality relies on the Spectra package, or alternatively the MsExperiment package, and its infrastructure to store spectral data. Additionally, MsQuality supports Chromatograms objects from the Chromatograms package for chromatographic quality metrics.

A differential abundance analysis for the comparison of two or more conditions. Useful for analyzing data from standard RNA-seq or meta-RNA-seq assays as well as selected and unselected values from in-vitro sequence selections. Uses a Dirichlet-multinomial model to infer abundance from counts, optimized for three or more experimental replicates. The method infers biological and sampling variation to calculate the expected false discovery rate, given the variation, based on a Wilcoxon Rank Sum test and Welch's t-test (via aldex.ttest), a Kruskal-Wallis test (via aldex.kw), a generalized linear model (via aldex.glm), or a correlation test (via aldex.corr). All tests report predicted p-values and posterior Benjamini-Hochberg corrected p-values. ALDEx2 also calculates expected standardized effect sizes for paired or unpaired study designs. ALDEx2 can now be used to estimate the effect of scale on the results and report on the scale-dependent robustness of results.

The package implements a method for normalising microarray intensities from single- and multiple-color arrays. It can also be used for data from other technologies, as long as they have similar format. The method uses a robust variant of the maximum-likelihood estimator for an additive-multiplicative error model and affine calibration. The model incorporates data calibration step (a.k.a. normalization), a model for the dependence of the variance on the mean intensity and a variance stabilizing data transformation. Differences between transformed intensities are analogous to "normalized log-ratios". However, in contrast to the latter, their variance is independent of the mean, and they are usually more sensitive and specific in detecting differential transcription.

Provides R bindings for HiSpa, a hierarchical Bayesian model for inferring three-dimensional chromatin structures from Hi-C contact matrices using Markov Chain Monte Carlo (MCMC) sampling. The package implements a cluster-based hierarchical approach that efficiently handles large-scale Hi-C datasets. It uses Rcpp and RcppArmadillo for efficient C++ integration with the original HiSpa C++ implementation, enabling fast computation of chromatin structure inference through parallel MCMC sampling.

Provides an interface to build a unified database of genomic annotations and their coordinates (gene, transcript and exon levels). It is aimed to be used when simple tab-delimited annotations (or simple GRanges objects) are required instead of the more complex annotation Bioconductor packages. Also useful when combinatorial annotation elements are reuired, such as RefSeq coordinates with Ensembl biotypes. Finally, it can download, construct and handle annotations with versioned genes and transcripts (where available, e.g. RefSeq and latest Ensembl). This is particularly useful in precision medicine applications where the latter must be reported.

This package provides functions used in Seqtometry (Kousnetsov et al. 2024), a method for analyzing single cell (scRNA-seq or scATAC-seq) data via signature (gene set) enrichment scores. The Seqtometry scores may be useful for annotating or characterizing cells, either in a flow cytometry like workflow (where scores are standalone features used for progressive partitoning as described in the Seqtometry publication) or in a cluster-based workflow (as features of clusters). The exported impute function (a port of Python's MAGIC-impute, van Dijk et al. 2018), may also be useful for single cell analysis on its own.

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.

CPSM provides a comprehensive computational pipeline for predicting survival probability and risk groups in cancer patients. The package includes steps for data preprocessing, training/test split, and normalization. It enables feature selection using univariate survival analysis and computes a LASSO-based prognostic index (PI) score. CPSM supports the development of predictive models using various feature sets and offers a suite of visualization tools, including survival curves based on predicted probabilities, barplots for predicted mean and median survival times, KM plots overlaid with individual survival predictions, and nomograms for estimating 1-, 3-, 5-, and 10-year survival probabilities. This makes CPSM a versatile tool for survival analysis in cancer research.

Quantification and differential analysis of mass-spectrometry proteomics data, with probabilistic recovery of information from missing values. Avoids the need for imputation. Estimates the detection probability curve (DPC), which relates the probability of successful detection to the underlying log-intensity of each precursor ion, and uses it to incorporate missing values into protein quantification and into subsequent differential expression analyses. The package produces objects suitable for downstream analysis in limma. The package accepts precursor (or peptide) intensities including missing values and produces complete protein quantifications without the need for imputation. The uncertainty of the protein quantifications is propagated through to the limma analyses using variance modeling and precision weights, ensuring accurate error rate control. The analysis pipeline can alternatively work with PTM or protein level data. The package name "limpa" is an acronym for "Linear Models for Proteomics Data".

Provides functions for counting reads from high-throughput sequencing screen data (e.g., CRISPR, shRNA) to quantify barcode abundance. Currently supports single barcodes in single- or paired-end data, and combinatorial barcodes in paired-end data.

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.

This package implements algorithms and data structures for performing gene expression signature (GES) searches, and subsequently interpreting the results functionally with specialized enrichment methods.

omicsViewer visualizes ExpressionSet (or SummarizedExperiment) in an interactive way. The omicsViewer has a separate back- and front-end. In the back-end, users need to prepare an ExpressionSet that contains all the necessary information for the downstream data interpretation. Some extra requirements on the headers of phenotype data or feature data are imposed so that the provided information can be clearly recognized by the front-end, at the same time, keep a minimum modification on the existing ExpressionSet object. The pure dependency on R/Bioconductor guarantees maximum flexibility in the statistical analysis in the back-end. Once the ExpressionSet is prepared, it can be visualized using the front-end, implemented by shiny and plotly. Both features and samples could be selected from (data) tables or graphs (scatter plot/heatmap). Different types of analyses, such as enrichment analysis (using Bioconductor package fgsea or fisher's exact test) and STRING network analysis, will be performed on the fly and the results are visualized simultaneously. When a subset of samples and a phenotype variable is selected, a significance test on means (t-test or ranked based test; when phenotype variable is quantitative) or test of independence (chi-square or fisher’s exact test; when phenotype data is categorical) will be performed to test the association between the phenotype of interest with the selected samples. Additionally, other analyses can be easily added as extra shiny modules. Therefore, omicsViewer will greatly facilitate data exploration, many different hypotheses can be explored in a short time without the need for knowledge of R. In addition, the resulting data could be easily shared using a shiny server. Otherwise, a standalone version of omicsViewer together with designated omics data could be easily created by integrating it with portable R, which can be shared with collaborators or submitted as supplementary data together with a manuscript.

Used to determine which cell types are enriched within gene lists. The package provides tools for testing enrichments within simple gene lists (such as human disease associated genes) and those resulting from differential expression studies. The package does not depend upon any particular Single Cell Transcriptome dataset and user defined datasets can be loaded in and used in the analyses.

glmSparseNet is an R-package that generalizes sparse regression models when the features (e.g. genes) have a graph structure (e.g. protein-protein interactions), by including network-based regularizers. glmSparseNet uses the glmnet R-package, by including centrality measures of the network as penalty weights in the regularization. The current version implements regularization based on node degree, i.e. the strength and/or number of its associated edges, either by promoting hubs in the solution or orphan genes in the solution. All the glmnet distribution families are supported, namely "gaussian", "poisson", "binomial", "multinomial", "cox", and "mgaussian".

Create, handle, validate, visualize and convert networks in the Cytoscape exchange (CX) format to standard data types and objects. The package also provides conversion to and from objects of iGraph and graphNEL. The CX format is also used by the NDEx platform, a online commons for biological networks, and the network visualization software Cytocape.

CAGE is a widely used high throughput assay for measuring transcription start site (TSS) activity. CAGEfightR is an R/Bioconductor package for performing a wide range of common data analysis tasks for CAGE and 5'-end data in general. Core functionality includes: import of CAGE TSSs (CTSSs), tag (or unidirectional) clustering for TSS identification, bidirectional clustering for enhancer identification, annotation with transcript and gene models, correlation of TSS and enhancer expression, calculation of TSS shapes, quantification of CAGE expression as expression matrices and genome brower visualization.

R-based computational framework for a comprehensive in silico analysis of circRNAs. This computational framework allows to combine and analyze circRNAs previously detected by multiple publicly available annotation-based circRNA detection tools. It covers different aspects of circRNAs analysis from differential expression analysis, evolutionary conservation, biogenesis to functional analysis.

High level functions to assist in annotation of (metabolomics) data sets. These include functions to perform simple tentative annotations based on mass matching but also functions to consider m/z and retention times for annotation of LC-MS features given that respective reference values are available. In addition, the function provides high-level functions to simplify matching of LC-MS/MS spectra against spectral libraries and objects and functionality to represent and manage such matched data.

CompoundDb provides functionality to create and use (chemical) compound annotation databases from a variety of different sources such as LipidMaps, HMDB, ChEBI or MassBank. The database format allows to store in addition MS/MS spectra along with compound information. The package provides also a backend for Bioconductor's Spectra package and allows thus to match experimetal MS/MS spectra against MS/MS spectra in the database. Databases can be stored in SQLite format and are thus portable.

A comprehensive package for visualizing multi-set intersections and extracting detailed subset information. VennDetail generates high-resolution visualizations including traditional Venn diagrams, Venn-pie plots, and UpSet-style plots. It provides functions to extract and combine subset details with user datasets in various formats. The package is particularly useful for bioinformatics applications but can be used for any multi-set analysis.

Mass spectrometry (MS) data backend supporting import and export of MS/MS library spectra from MassBank record files. Different backends are available that allow handling of data in plain MassBank text file format or allow also to interact directly with MassBank SQL databases. Objects from this package are supposed to be used with the Spectra Bioconductor package. This package thus adds MassBank support to the Spectra package.

The kallisto | bustools pipeline is a fast and modular set of tools to convert single cell RNA-seq reads in fastq files into gene count or transcript compatibility counts (TCC) matrices for downstream analysis. Central to this pipeline is the barcode, UMI, and set (BUS) file format. This package serves the following purposes: First, this package allows users to manipulate BUS format files as data frames in R and then convert them into gene count or TCC matrices. Furthermore, since R and Rcpp code is easier to handle than pure C++ code, users are encouraged to tweak the source code of this package to experiment with new uses of BUS format and different ways to convert the BUS file into gene count matrix. Second, this package can conveniently generate files required to generate gene count matrices for spliced and unspliced transcripts for RNA velocity. Here biotypes can be filtered and scaffolds and haplotypes can be removed, and the filtered transcriptome can be extracted and written to disk. Third, this package implements utility functions to get transcripts and associated genes required to convert BUS files to gene count matrices, to write the transcript to gene information in the format required by bustools, and to read output of bustools into R as sparses matrices.

Provides a complete workflow for the identification, analysis, and functional annotation of long non-coding RNAs (lncRNAs) from RNA-Seq data. The package includes functions for filtering transcripts from GTF files, evaluating the performance of multiple coding potential prediction tools (e.g., CPC2, PLEK, CPAT), and summarizing their agreement. It enables systematic performance analysis of individual tools, "at least N" tool consensus, and all possible tool combinations. Functional analysis is supported through the identification of potential cis- and trans-acting interactions with protein-coding genes, followed by enrichment analysis. Results can be visualized using a variety of plots, including radar plots, clock plots, and interactive Sankey diagrams.

An elaborate molecular evolutionary framework that facilitates straightforward simulation of codon genetic sequences subjected to different degrees and/or patterns of Darwinian selection. The model is built upon the fitness landscape paradigm of Sewall Wright, as popularised by the mutation-selection model of Halpern and Bruno. This enables realistic evolutionary process of living organisms to be reproducible seamlessly. For example, an Ornstein-Uhlenbeck fitness update algorithm is incorporated herein. Consequently, otherwise complex biological processes, such as the effect of the interplay between genetic drift and fitness landscape fluctuations on the inference of diversifying selection, may now be investigated with minimal effort. Frequency-dependent and stochastic fitness landscape update techniques are available.

The tidySummarizedExperiment package provides a set of tools for creating and manipulating tidy data representations of SummarizedExperiment objects. SummarizedExperiment is a widely used data structure in bioinformatics for storing high-throughput genomic data, such as gene expression or DNA sequencing data. The tidySummarizedExperiment package introduces a tidy framework for working with SummarizedExperiment objects. It allows users to convert their data into a tidy format, where each observation is a row and each variable is a column. This tidy representation simplifies data manipulation, integration with other tidyverse packages, and enables seamless integration with the broader ecosystem of tidy tools for data analysis.

CCPlotR is an R package for visualising results from tools that predict cell-cell interactions from single-cell RNA-seq data. These plots are generic and can be used to visualise results from multiple tools such as Liana, CellPhoneDB, NATMI etc.

Provides customized print methods for 'SummarizedExperiment' objects to enhance readability and usability within a tidy workflow. It offers consistent, tidyverse-aligned console displays, including alternative tibble abstractions for large genomic data to improve discoverability and interpretation. The package also includes unified, contextual messaging utilities intended for the 'tidyomics' ecosystem.

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.

ChemmineOB provides an R interface to a subset of cheminformatics functionalities implemented by the OpelBabel C++ project. OpenBabel is an open source cheminformatics toolbox that includes utilities for structure format interconversions, descriptor calculations, compound similarity searching and more. ChemineOB aims to make a subset of these utilities available from within R. For non-developers, ChemineOB is primarily intended to be used from ChemmineR as an add-on package rather than used directly.

Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution.

ChemmineR is a cheminformatics package for analyzing drug-like small molecule data in R. Its latest version contains functions for efficient processing of large numbers of molecules, physicochemical/structural property predictions, structural similarity searching, classification and clustering of compound libraries with a wide spectrum of algorithms. In addition, it offers visualization functions for compound clustering results and chemical structures.

EpiDISH is a R package to infer the proportions of a priori known cell-types present in a sample representing a mixture of such cell-types. Right now, the package can be used on DNAm data of blood-tissue of any age, from birth to old-age, generic epithelial tissue and breast tissue. Besides, the package provides a function that allows the identification of differentially methylated cell-types and their directionality of change in Epigenome-Wide Association Studies.

msPurity R package was developed to: 1) Assess the spectral quality of fragmentation spectra by evaluating the "precursor ion purity". 2) Process fragmentation spectra. 3) Perform spectral matching. What is precursor ion purity? -What we call "Precursor ion purity" is a measure of the contribution of a selected precursor peak in an isolation window used for fragmentation. The simple calculation involves dividing the intensity of the selected precursor peak by the total intensity of the isolation window. When assessing MS/MS spectra this calculation is done before and after the MS/MS scan of interest and the purity is interpolated at the recorded time of the MS/MS acquisition. Additionally, isotopic peaks can be removed, low abundance peaks are removed that are thought to have limited contribution to the resulting MS/MS spectra and the isolation efficiency of the mass spectrometer can be used to normalise the intensities used for the calculation.

High-throughput omics data are often affected by systematic biases introduced throughout all the steps of a clinical study, from sample collection to quantification. Normalization methods aim to adjust for these biases to make the actual biological signal more prominent. However, selecting an appropriate normalization method is challenging due to the wide range of available approaches. Therefore, a comparative evaluation of unnormalized and normalized data is essential in identifying an appropriate normalization strategy for a specific data set. This R package provides different functions for preprocessing, normalizing, and evaluating different normalization approaches. Furthermore, normalization methods can be evaluated on downstream steps, such as differential expression analysis and statistical enrichment analysis. Spike-in data sets with known ground truth and real-world data sets of biological experiments acquired by either tandem mass tag (TMT) or label-free quantification (LFQ) can be analyzed.

ExpoRiskR provides tools for exposure-aware multi-omics risk modeling in translational and environmental health studies. The package aligns sample identifiers across exposure and multi-omics blocks, performs lightweight preprocessing, and fits exposure-adjusted association models to build interpretable microbe–metabolite networks. It also computes simple exposure perturbation summaries and generates publication-ready visualizations. Workflows support both matrix-based inputs and SummarizedExperiment objects.

Provide utilities for ROC, with microarray focus.

Provides tools for simulating copy-number alteration (CNA) profiles, applying a non-decimated Haar wavelet transform to genomic signals, and extracting wavelet-derived features for use in supervised learning. Multiple machine learning methods including lasso and elastic-net regularisation, random forest, partial least squares, neural networks and k-nearest neighbours are implemented to train predictive models from genomic feature vectors. The workflow enables end-to-end analysis from CNA simulation to feature extraction and classification.

fourSynergy is an ensemble algorithm leveraging synergies among the existing 4C-seq algorithms r3C-seq, peakC, r.4cker and fourSig. It uses a weighted voting approach to perform improved interaction calling. fourSynergy supports also differential interaction calling.

Pirat enables the imputation of missing values (either MNARs or MCARs) in bottom-up LC-MS/MS proteomics data using a penalized maximum likelihood strategy. It does not require any parameter tuning, it models the instrument censorship from the data available. It accounts for sibling peptides correlations and it can leverage complementary transcriptomics measurements.

Visualization of next generation sequencing (NGS) data is essential for interpreting high-throughput genomics experiment results. 'GenomicPlot' facilitates plotting of NGS data in various formats (bam, bed, wig and bigwig); both coverage and enrichment over input can be computed and displayed with respect to genomic features (such as UTR, CDS, enhancer), and user defined genomic loci or regions. Statistical tests on signal intensity within user defined regions of interest can be performed and represented as boxplots or bar graphs. Parallel processing is used to speed up computation on multicore platforms. In addition to genomic plots which is suitable for displaying of coverage of genomic DNA (such as ChIPseq data), metagenomic (without introns) plots can also be made for RNAseq or CLIPseq data as well.

This package addresses the mean-variance relationship in spatially resolved transcriptomics data. Precision weights are generated for individual observations using Empirical Bayes techniques. These weights are used to rescale the data and covariates, which are then used as input in spatially variable gene detection tools.

systemPipeR is a workflow management environment for reproducible data analysis that integrates R with command-line software. It enables researchers to design, execute, and report complex workflows on local machines and HPC systems. The framework combines R-based analysis with external tools through a Common Workflow Language (CWL) interface, manages workflow dependencies and restart capabilities, and automatically generates reproducible scientific analysis reports. The companion package systemPipeRdata provides ready-to-use workflow templates that simplify workflow setup and customization. Alternatively, workflow templates can be loaded from dedicated GitHub repositories.

cogeqc aims to facilitate systematic quality checks on standard comparative genomics analyses to help researchers detect issues and select the most suitable parameters for each data set. cogeqc can be used to asses: i. genome assembly and annotation quality with BUSCOs and comparisons of statistics with publicly available genomes on the NCBI; ii. orthogroup inference using a protein domain-based approach and; iii. synteny detection using synteny network properties. There are also data visualization functions to explore QC summary statistics.

The GENESIS package provides methodology for estimating, inferring, and accounting for population and pedigree structure in genetic analyses. The current implementation provides functions to perform PC-AiR (Conomos et al., 2015, Gen Epi) and PC-Relate (Conomos et al., 2016, AJHG). PC-AiR performs a Principal Components Analysis on genome-wide SNP data for the detection of population structure in a sample that may contain known or cryptic relatedness. Unlike standard PCA, PC-AiR accounts for relatedness in the sample to provide accurate ancestry inference that is not confounded by family structure. PC-Relate uses ancestry representative principal components to adjust for population structure/ancestry and accurately estimate measures of recent genetic relatedness such as kinship coefficients, IBD sharing probabilities, and inbreeding coefficients. Additionally, functions are provided to perform efficient variance component estimation and mixed model association testing for both quantitative and binary phenotypes.

Our scLANE model uses truncated power basis spline models to build flexible, interpretable models of single cell gene expression over pseudotime or latent time. The modeling architectures currently supported are Negative-binomial GLMs, GEEs, & GLMMs. Downstream analysis functionalities include model comparison, dynamic gene clustering, smoothed counts generation, gene set enrichment testing, & visualization.

Mass spectrometry (MS) data backend supporting import and export of MS/MS spectra data from Mascot Generic Format (mgf) files. Objects defined in this package are supposed to be used with the Spectra Bioconductor package. This package thus adds mgf file support to the Spectra package.