The funOmics package ggregates or summarizes omics data into higher level functional representations such as GO terms gene sets or KEGG metabolic pathways. The aggregated data matrix represents functional activity scores that facilitate the analysis of functional molecular sets while allowing to reduce dimensionality and provide easier and faster biological interpretations. Coordinated functional activity scores can be as informative as single molecules!

Computational evaluation of variability across DNA or RNA sequencing datasets is a crucial step in genomics, as it allows both to evaluate reproducibility of replicates, and to compare different datasets to identify potential correlations. fCCAC applies functional Canonical Correlation Analysis to allow the assessment of: (i) reproducibility of biological or technical replicates, analyzing their shared covariance in higher order components; and (ii) the associations between different datasets. fCCAC represents a more sophisticated approach that complements Pearson correlation of genomic coverage.

This package provides a package to analyze flow cytometric data using gate information to follow population/community dynamics.

Processed RNA-seq data for 1,139 human primary colorectal tissue samples across three phenotypes, including tumor, normal adjacent-to-tumor, and healthy, available as Synapse ID syn22237139 on synapse.org. Data have been parsed into SummarizedExperiment objects available via ExperimentHub to facilitate reproducibility and extension of results from Dampier et al. (PMCID: PMC7386360, PMID: 32764205).

This package extends the function of the LiquidAssociation package for genome-wide application. It integrates a screening method into the LA analysis to reduce the number of triplets to be examined for a high LA value and provides code for use in subsequent significance analyses.

This package provides a visual exploration tool for multiple sequence alignment and associated data. Supports MSA of DNA, RNA, and protein sequences using ggplot2'. Multiple sequence alignment can easily be combined with other ggplot2 plots, such as phylogenetic tree Visualized by ggtree', boxplot, genome map and so on. More features: visualization of sequence logos, sequence bundles, RNA secondary structures and detection of sequence recombinations.

GA4GHclient provides an easy way to access public data servers through Global Alliance for Genomics and Health (GA4GH) genomics API. It provides low-level access to GA4GH API and translates response data into Bioconductor-based class objects.

GSNAP and GMAP are a pair of tools to align short-read data written by Tom Wu. This package provides convenience methods to work with GMAP and GSNAP from within R. In addition, it provides methods to tally alignment results on a per-nucleotide basis using the bam_tally tool.

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".

The geomeTriD (Three-Dimensional Geometry) Package provides interactive 3D visualization of chromatin structures using the WebGL-based three.js (https://threejs.org/) or the rgl rendering library. It is designed to identify and explore spatial chromatin patterns within genomic regions. The package generates dynamic 3D plots and HTML widgets that integrate seamlessly with Shiny applications, enabling researchers to visualize chromatin organization, detect spatial features, and compare structural dynamics across different conditions and data types.

GDS files are widely used to represent genotyping or sequence data. The GDSArray package implements the `GDSArray` class to represent nodes in GDS files in a matrix-like representation that allows easy manipulation (e.g., subsetting, mathematical transformation) in _R_. The data remains on disk until needed, so that very large files can be processed.

The package clusters gene activity along chromosome into zones, detects differential zones as outstanding, and visualizes maps of outstanding zones across the genome. It enables characterization of effects on multiple genes within adaptive genomic neighborhoods, which could arise from genome reorganization, structural variation, or epigenome alteration. It guarantees cluster optimality, linear runtime to sample size, and reproducibility. One can apply it on genome-wide activity measurements such as copy number, transcriptomic, proteomic, and methylation data.

Offers a set of autoplot methods to visualize tree-like structures (e.g., hierarchical clustering and classification/regression trees) using ggtree'. You can adjust graphical parameters using grammar of graphic syntax and integrate external data to the tree.

This package provides a collection of meta-analysis tools for analysing high throughput experimental data.

This package provides classes and methods for handling pedigree data. It also includes functions to calculate genetic relationship measures as relationship and inbreeding coefficients and other utilities. Note that package is not yet stable. Use it with care!

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.

This package contains utility functions used throughout the gDR platform to fit data, manipulate data, and convert and validate data structures. This package also has the necessary default constants for gDR platform. Many of the functions are utilized by the gDRcore package.

Recurrent breakpoint gene detection on copy number aberration profiles.

Gene lists derived from the results of genomic analyses are rich in biological information. For instance, differentially expressed genes (DEGs) from a microarray or RNA-Seq analysis are related functionally in terms of their response to a treatment or condition. Gene lists can vary in size, up to several thousand genes, depending on the robustness of the perturbations or how widely different the conditions are biologically. Having a way to associate biological relatedness between hundreds and thousands of genes systematically is impractical by manually curating the annotation and function of each gene. Over-representation analysis (ORA) of genes was developed to identify biological themes. Given a Gene Ontology (GO) and an annotation of genes that indicate the categories each one fits into, significance of the over-representation of the genes within the ontological categories is determined by a Fisher's exact test or modeling according to a hypergeometric distribution. Comparing a small number of enriched biological categories for a few samples is manageable using Venn diagrams or other means for assessing overlaps. However, with hundreds of enriched categories and many samples, the comparisons are laborious. Furthermore, if there are enriched categories that are shared between samples, trying to represent a common theme across them is highly subjective. goSTAG uses GO subtrees to tag and annotate genes within a set. goSTAG visualizes the similarities between the over-representation of DEGs by clustering the p-values from the enrichment statistical tests and labels clusters with the GO term that has the most paths to the root within the subtree generated from all the GO terms in the cluster.

Statistic methods to evaluate variations of differential expression (DE) between multiple biological conditions. It takes into account the fold-changes and p-values from previous differential expression (DE) results that use large-scale data (*e.g.*, microarray and RNA-seq) and evaluates which genes would react in response to the distinct experiments. This evaluation involves an unique pipeline of statistical methods, including weighted summarization, quantile detection, cluster analysis, and ANOVA tests, in order to classify a subset of relevant genes whose DE is similar or dependent to certain biological factors.

This package provides tools for NanoString Technologies GeoMx Technology. Package provides functions for reading in DCC and PKC files based on an ExpressionSet derived object. Normalization and QC functions are also included.

Illumina Golden Gate Human Methylation Cancer Panel Version 1 annotation data (chip GGHumanMethCancerPanelv1) assembled using data from public repositories.

This package contains a targeted clustering algorithm for the analysis of microarray data. The algorithm can aid in the discovery of new genes with similar functions to a given list of genes already known to have closely related functions.

Genetically modified organisms (GMOs) and cell lines are widely used models in all kinds of biological research. As part of characterising these models, DNA sequencing technology and bioinformatics analyses are used systematically to study their genomes. Therefore, large volumes of data are generated and various algorithms are applied to analyse this data, which introduces a challenge on representing all findings in an informative and concise manner. `gmoviz` provides users with an easy way to visualise and facilitate the explanation of complex genomic editing events on a larger, biologically-relevant scale.