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Genomic analysis can be utilised to identify differences between RNA populations in two conditions, both in production and abundance. This includes the identification of RNAs produced by multiple genomes within a biological system. For example, RNA produced by pathogens within a host or mobile RNAs in plant graft systems. The mobileRNA package provides methods to pre-process, analyse and visualise the sRNA and mRNA populations based on the premise of mapping reads to all genotypes at the same time.

Mutational processes leave characteristic footprints in genomic DNA. This package provides a comprehensive set of flexible functions that allows researchers to easily evaluate and visualize a multitude of mutational patterns in base substitution catalogues of e.g. healthy samples, tumour samples, or DNA-repair deficient cells. The package covers a wide range of patterns including: mutational signatures, transcriptional and replicative strand bias, lesion segregation, genomic distribution and association with genomic features, which are collectively meaningful for studying the activity of mutational processes. The package works with single nucleotide variants (SNVs), insertions and deletions (Indels), double base substitutions (DBSs) and larger multi base substitutions (MBSs). The package provides functionalities for both extracting mutational signatures de novo and determining the contribution of previously identified mutational signatures on a single sample level. MutationalPatterns integrates with common R genomic analysis workflows and allows easy association with (publicly available) annotation data.

Standard methods for analysis of mutation data following error- corrected sequencing (ECS) for the purpose of mutagencity assessment. Functions include importing the mutation lists provided by a variant caller, and a set of analytical tools for statistical testing and visualization of mutation data; comparison to COSMIC and/or germline signatures; etc.

Tools for NanoString Technologies nCounter Technology. Provides support for reading RCC files into an ExpressionSet derived object. Also includes methods for QC and normalizaztion of NanoString data.

#' NetActivity enables to compute gene set scores from previously trained sparsely-connected autoencoders. The package contains a function to prepare the data (`prepareSummarizedExperiment`) and a function to compute the gene set scores (`computeGeneSetScores`). The package `NetActivityData` contains different pre-trained models to be directly applied to the data. Alternatively, the users might use the package to compute gene set scores using custom models.

While some non-coding RNAs (ncRNAs) are assigned critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together. This genomic proximity on the sequence can hint to a functional association. We present a tool, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out using the functional annotations of the coding genes located proximal to the input ncRNAs. Other biologically relevant information such as topologically associating domain (TAD) boundaries, co-expression patterns, and miRNA target prediction information can be incorporated to conduct a richer enrichment analysis. To this end, NoRCE includes several relevant datasets as part of its data repository, including cell-line specific TAD boundaries, functional gene sets, and expression data for coding & ncRNAs specific to cancer. Additionally, the users can utilize custom data files in their investigation. Enrichment results can be retrieved in a tabular format or visualized in several different ways. NoRCE is currently available for the following species: human, mouse, rat, zebrafish, fruit fly, worm, and yeast.

Performs outlier detection of sequences in a multiple sequence alignment using bootstrap of predefined distance metrics. Outlier sequences can make downstream analyses unreliable or make the alignments less accurate while they are being constructed. This package implements the OD-seq algorithm proposed by Jehl et al (doi 10.1186/s12859-015-0702-1) for aligned sequences and a variant using string kernels for unaligned sequences.

A Shiny app for visual exploration of omic datasets as compositions, and differential abundance analysis using ALDEx2. Useful for exploring RNA-seq, meta-RNA-seq, 16s rRNA gene sequencing with visualizations such as principal component analysis biplots (coloured using metadata for visualizing each variable), dendrograms and stacked bar plots, and effect plots (ALDEx2). Input is a table of counts and metadata file (if metadata exists), with options to filter data by count or by metadata to remove low counts, or to visualize select samples according to selected metadata.

omicRexposome systematizes the association evaluation between exposures and omic data, taking advantage of MultiDataSet for coordinated data management, rexposome for exposome data definition and limma for association testing. Also to perform data integration mixing exposome and omic data using multi co-inherent analysis (omicade4) and multi-canonical correlation analysis (PMA).

Omixer - an Bioconductor package for multivariate and reproducible sample randomization, which ensures optimal sample distribution across batches with well-documented methods. It outputs lab-friendly sample layouts, reducing the risk of sample mixups when manually pipetting randomized samples.

A client for the OmniPath web service (https://www.omnipathdb.org) and many other resources. It also includes functions to transform and pretty print some of the downloaded data, functions to access a number of other resources such as BioPlex, ConsensusPathDB, EVEX, Gene Ontology, Guide to Pharmacology (IUPHAR/BPS), Harmonizome, HTRIdb, Human Phenotype Ontology, InWeb InBioMap, KEGG Pathway, Pathway Commons, Ramilowski et al. 2015, RegNetwork, ReMap, TF census, TRRUST and Vinayagam et al. 2011. Furthermore, OmnipathR features a close integration with the NicheNet method for ligand activity prediction from transcriptomics data, and its R implementation `nichenetr` (available only on github).

The ORFhunteR package is a R and C++ library for an automatic determination and annotation of open reading frames (ORF) in a large set of RNA molecules. It efficiently implements the machine learning model based on vectorization of nucleotide sequences and the random forest classification algorithm. The ORFhunteR package consists of a set of functions written in the R language in conjunction with C++. The efficiency of the package was confirmed by the examples of the analysis of RNA molecules from the NCBI RefSeq and Ensembl databases. The package can be used in basic and applied biomedical research related to the study of the transcriptome of normal as well as altered (for example, cancer) human cells.

`orthos` decomposes RNA-seq contrasts, for example obtained from a gene knock-out or compound treatment experiment, into unspecific and experiment-specific components. Original and decomposed contrasts can be efficiently queried against a large database of contrasts (derived from ARCHS4, https://maayanlab.cloud/archs4/) to identify similar experiments. `orthos` furthermore provides plotting functions to visualize the results of such a search for similar contrasts.

This package implements the PAIRADISE procedure for detecting differential isoform expression between matched replicates in paired RNA-Seq data.

Pancreatic ductal adenocarcinoma (PDA) has a relatively poor prognosis and is one of the most lethal cancers. Molecular classification of gene expression profiles holds the potential to identify meaningful subtypes which can inform therapeutic strategy in the clinical setting. The Pancreatic Cancer Adenocarcinoma Tool-Kit (PDATK) provides an S4 class-based interface for performing unsupervised subtype discovery, cross-cohort meta-clustering, gene-expression-based classification, and subsequent survival analysis to identify prognostically useful subtypes in pancreatic cancer and beyond. Two novel methods, Consensus Subtypes in Pancreatic Cancer (CSPC) and Pancreatic Cancer Overall Survival Predictor (PCOSP) are included for consensus-based meta-clustering and overall-survival prediction, respectively. Additionally, four published subtype classifiers and three published prognostic gene signatures are included to allow users to easily recreate published results, apply existing classifiers to new data, and benchmark the relative performance of new methods. The use of existing Bioconductor classes as input to all PDATK classes and methods enables integration with existing Bioconductor datasets, including the 21 pancreatic cancer patient cohorts available in the MetaGxPancreas data package. PDATK has been used to replicate results from Sandhu et al (2019) [https://doi.org/10.1200/cci.18.00102] and an additional paper is in the works using CSPC to validate subtypes from the included published classifiers, both of which use the data available in MetaGxPancreas. The inclusion of subtype centroids and prognostic gene signatures from these and other publications will enable researchers and clinicians to classify novel patient gene expression data, allowing the direct clinical application of the classifiers included in PDATK. Overall, PDATK provides a rich set of tools to identify and validate useful prognostic and molecular subtypes based on gene-expression data, benchmark new classifiers against existing ones, and apply discovered classifiers on novel patient data to inform clinical decision making.

Protein domains is one of the most import annoation of proteins we have with the Pfam database/tool being (by far) the most used tool. This R package enables the user to read the pfam prediction from both webserver and stand-alone runs into R. We have recently shown most human protein domains exist as multiple distinct variants termed domain isotypes. Different domain isotypes are used in a cell, tissue, and disease-specific manner. Accordingly, we find that domain isotypes, compared to each other, modulate, or abolish the functionality of a protein domain. This R package enables the identification and classification of such domain isotypes from Pfam data.

PhIPData defines an S4 class for phage-immunoprecipitation sequencing (PhIP-seq) experiments. Buliding upon the RangedSummarizedExperiment class, PhIPData enables users to coordinate metadata with experimental data in analyses. Additionally, PhIPData provides specialized methods to subset and identify beads-only samples, subset objects using virus aliases, and use existing peptide libraries to populate object parameters.

Regularization and score distributions for position count matrices.

ProteoMM is a statistical method to perform model-based peptide-level differential expression analysis of single or multiple datasets. For multiple datasets ProteoMM produces a single fold change and p-value for each protein across multiple datasets. ProteoMM provides functionality for normalization, missing value imputation and differential expression. Model-based peptide-level imputation and differential expression analysis component of package follows the analysis described in “A statistical framework for protein quantitation in bottom-up MS based proteomics" (Karpievitch et al. Bioinformatics 2009). EigenMS normalisation is implemented as described in "Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition." (Karpievitch et al. Bioinformatics 2009).

Provides an R wrapper for BWA alignment algorithms. Both BWA-backtrack and BWA-MEM are available. Convenience function to build a BWA index from a reference genome is also provided. Currently not supported for Windows machines.

Interactive viewing and exploration of graphs, connecting R to Cytoscape.js, using websockets.

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

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.

SCAN is a microarray normalization method to facilitate personalized-medicine workflows. Rather than processing microarray samples as groups, which can introduce biases and present logistical challenges, SCAN normalizes each sample individually by modeling and removing probe- and array-specific background noise using only data from within each array. SCAN can be applied to one-channel (e.g., Affymetrix) or two-channel (e.g., Agilent) microarrays. The Universal exPression Codes (UPC) method is an extension of SCAN that estimates whether a given gene/transcript is active above background levels in a given sample. The UPC method can be applied to one-channel or two-channel microarrays as well as to RNA-Seq read counts. Because UPC values are represented on the same scale and have an identical interpretation for each platform, they can be used for cross-platform data integration.

In single cell RNA sequencing (scRNA-seq) data combinations of cells are sometimes considered a single cell (doublets). The scds package provides methods to annotate doublets in scRNA-seq data computationally.

An R implementation of the correlation-based method developed in the Joshi laboratory to analyse and filter processed single-cell RNAseq data. It returns a filtered version of the data containing only genes expression values unaffected by systematic noise.

ScreenR is a package suitable to perform hit identification in loss of function High Throughput Biological Screenings performed using barcoded shRNA-based libraries. ScreenR combines the computing power of software such as edgeR with the simplicity of use of the Tidyverse metapackage. ScreenR executes a pipeline able to find candidate hits from barcode counts, and integrates a wide range of visualization modes for each step of the analysis.

scRepertoire is a toolkit for processing and analyzing single-cell T-cell receptor (TCR) and immunoglobulin (Ig). The scRepertoire framework supports use of 10x, AIRR, BD, MiXCR, TRUST4, and WAT3R single-cell formats. The functionality includes basic clonal analyses, repertoire summaries, distance-based clustering and interaction with the popular Seurat and SingleCellExperiment/Bioconductor R single-cell workflows.

A pipeline which processes single cell RNA-seq (scRNA-seq) reads from CEL-seq and CEL-seq2 protocols. Demultiplex scRNA-seq FASTQ files, align reads to reference genome using Rsubread, and generate UMI filtered count matrix. Also provide visualizations of read alignments and pre- and post-alignment QC metrics.

The seqCAT package uses variant calling data (in the form of VCF files) from high throughput sequencing technologies to authenticate and validate the source, function and characteristics of biological samples used in scientific endeavours.

seqsetvis enables the visualization and analysis of sets of genomic sites in next gen sequencing data. Although seqsetvis was designed for the comparison of mulitple ChIP-seq samples, this package is domain-agnostic and allows the processing of multiple genomic coordinate files (bed-like files) and signal files (bigwig files pileups from bam file). seqsetvis has multiple functions for fetching data from regions into a tidy format for analysis in data.table or tidyverse and visualization via ggplot2.

Tools for compositional and other sample-level ecological analyses and visualizations tailored for single-cell RNA-seq data. SETA includes functions for taxonomizing celltypes, normalizing data, performing statistical tests, and visualizing results. Several tutorials are included to guide users and introduce them to key concepts. SETA is meant to teach users about statistical concepts underlying ecological analysis methods so they can apply them to their own single-cell data.

The Single Cell Toolkit (SCTK) in the singleCellTK package provides an interface to popular tools for importing, quality control, analysis, and visualization of single cell RNA-seq data. SCTK allows users to seamlessly integrate tools from various packages at different stages of the analysis workflow. A general "a la carte" workflow gives users the ability access to multiple methods for data importing, calculation of general QC metrics, doublet detection, ambient RNA estimation and removal, filtering, normalization, batch correction or integration, dimensionality reduction, 2-D embedding, clustering, marker detection, differential expression, cell type labeling, pathway analysis, and data exporting. Curated workflows can be used to run Seurat and Celda. Streamlined quality control can be performed on the command line using the SCTK-QC pipeline. Users can analyze their data using commands in the R console or by using an interactive Shiny Graphical User Interface (GUI). Specific analyses or entire workflows can be summarized and shared with comprehensive HTML reports generated by Rmarkdown. Additional documentation and vignettes can be found at camplab.net/sctk.

Using site polymorphism is one of the ways to cluster DNA/protein sequences but it is possible for the sequences with the same polymorphism on a single site to be genetically distant. This package is aimed at clustering sequences using site polymorphism and their corresponding phylogenetic trees. By considering their location on the tree, only the structurally adjacent sequences will be clustered. However, the adjacent sequences may not necessarily have the same polymorphism. So a branch-and-bound like algorithm is used to minimize the entropy representing the purity of site polymorphism of each cluster.

This package enables automated selection of group specific signature, especially for rare population. The package is developed for generating specifc lists of signature genes based on Term Frequency-Inverse Document Frequency (TF-IDF) modified methods. It can also be used as a new gene-set scoring method or data transformation method. Multiple visualization functions are implemented in this package.

The package uses exogenous enzyme imprinted information to map protein-DNA binding on individual sequenced DNA molecules. For example, GpC methyltransferase, CpG methyltransferase, and Adenine methyltransferases. Public datasets from such assays are compiled into tracks, and hosted at public servers like Galaxy for their seamless access by this package.

Spaniel includes a series of tools to aid the quality control and analysis of Spatial Transcriptomics data. Spaniel can import data from either the original Spatial Transcriptomics system or 10X Visium technology. The package contains functions to create a SingleCellExperiment Seurat object and provides a method of loading a histologial image into R. The spanielPlot function allows visualisation of metrics contained within the S4 object overlaid onto the image of the tissue.

SpatialCPie is an R package designed to facilitate cluster evaluation for spatial transcriptomics data by providing intuitive visualizations that display the relationships between clusters in order to guide the user during cluster identification and other downstream applications. The package is built around a shiny "gadget" to allow the exploration of the data with multiple plots in parallel and an interactive UI. The user can easily toggle between different cluster resolutions in order to choose the most appropriate visual cues.

Using spatial or bulk gene expression data, estimates abundance of mixed cell types within each observation. Based on "Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data", Danaher (2022). Designed for use with the NanoString GeoMx platform, but applicable to any gene expression data.

Tools for NanoString Technologies GeoMx Technology. Package to easily graph on top of an OME-TIFF image. Plotting annotations can range from tissue segment to gene expression.

This package generates pathway scores from expression data for single samples after training on a reference cohort. The score is generated by taking the expression of a gene set (pathway) from a reference cohort and performing linear discriminant analysis to distinguish samples in the cohort that have the pathway augmented and not. The separating hyperplane is then used to score new samples.

An R-based automated gating pipeline for flow cytometry data designed to mimic the manual gating strategy of defining flow biomarker positive populations relative to a unimodal background population to include cells with varying intensities of marker expression. The pipeline’s main feature is a flexible density-based gating strategy capable of capturing varying scenarios based on marker expression patterns to analyze a 29-marker flow panel that characterizes T-cell lineage, differentiation, and functional states.

This package contains the Summix2 method for estimating and adjusting for substructure in genetic summary allele frequency data. The function summix() estimates reference group proportions using a mixture model. The adjAF() function produces adjusted allele frequencies for an observed group with reference group proportions matching a target individual or sample. The summix_local() function estimates local ancestry mixture proportions and performs selection scans in genetic summary data.

Cell surface proteins form a major fraction of the druggable proteome and can be used for tissue-specific delivery of oligonucleotide/cell-based therapeutics. Alternatively spliced surface protein isoforms have been shown to differ in their subcellular localization and/or their transmembrane (TM) topology. Surface proteins are hydrophobic and remain difficult to study thereby necessitating the use of TM topology prediction methods such as TMHMM and Phobius. However, there exists a need for bioinformatic approaches to streamline batch processing of isoforms for comparing and visualizing topologies. To address this gap, we have developed an R package, surfaltr. It pairs inputted isoforms, either known alternatively spliced or novel, with their APPRIS annotated principal counterparts, predicts their TM topologies using TMHMM or Phobius, and generates a customizable graphical output. Further, surfaltr facilitates the prioritization of biologically diverse isoform pairs through the incorporation of three different ranking metrics and through protein alignment functions. Citations for programs mentioned here can be found in the vignette.

Synapsis is a Bioconductor software package for automated (unbiased and reproducible) analysis of meiotic immunofluorescence datasets. The primary functions of the software can i) identify cells in meiotic prophase that are labelled by a synaptonemal complex axis or central element protein, ii) isolate individual synaptonemal complexes and measure their physical length, iii) quantify foci and co-localise them with synaptonemal complexes, iv) measure interference between synaptonemal complex-associated foci. The software has applications that extend to multiple species and to the analysis of other proteins that label meiotic prophase chromosomes. The software converts meiotic immunofluorescence images into R data frames that are compatible with machine learning methods. Given a set of microscopy images of meiotic spread slides, synapsis crops images around individual single cells, counts colocalising foci on strands on a per cell basis, and measures the distance between foci on any given strand.

The tidyexposomics package is designed to facilitate the integration of exposure and omics data to identify exposure-omics associations. We structure our commands to fit into the tidyverse framework, where commands are designed to be simplified and intuitive. Here we provide functionality to perform quality control, sample and exposure association analysis, differential abundance analysis, multi-omics integration, and functional enrichment analysis.

The TissueEnrich package is used to calculate enrichment of tissue-specific genes in a set of input genes. For example, the user can input the most highly expressed genes from RNA-Seq data, or gene co-expression modules to determine which tissue-specific genes are enriched in those datasets. Tissue-specific genes were defined by processing RNA-Seq data from the Human Protein Atlas (HPA) (Uhlén et al. 2015), GTEx (Ardlie et al. 2015), and mouse ENCODE (Shen et al. 2012) using the algorithm from the HPA (Uhlén et al. 2015).The hypergeometric test is being used to determine if the tissue-specific genes are enriched among the input genes. Along with tissue-specific gene enrichment, the TissueEnrich package can also be used to define tissue-specific genes from expression datasets provided by the user, which can then be used to calculate tissue-specific gene enrichments.

`tomoseqr` is an R package for analyzing Tomo-seq data. Tomo-seq is a genome-wide RNA tomography method that combines combining high-throughput RNA sequencing with cryosectioning for spatially resolved transcriptomics. `tomoseqr` reconstructs 3D expression patterns from tomo-seq data and visualizes the reconstructed 3D expression patterns.

Contains a set of functions to perform large-scale analysis of toxicogenomic data, providing a standardized data structure to hold information relevant to annotation, visualization and statistical analysis of toxicogenomic data.

tradeSeq provides a flexible method for fitting regression models that can be used to find genes that are differentially expressed along one or multiple lineages in a trajectory. Based on the fitted models, it uses a variety of tests suited to answer different questions of interest, e.g. the discovery of genes for which expression is associated with pseudotime, or which are differentially expressed (in a specific region) along the trajectory. It fits a negative binomial generalized additive model (GAM) for each gene, and performs inference on the parameters of the GAM.