Produce highly customizable publication quality graphics for genomic data primarily at the cohort level.

Genome-wide association studies (GWAS) is a widely used tool for identification of genetic variants associated with phenotypes and diseases, though complex diseases featuring many genetic variants with small effects present difficulties for traditional these studies. By leveraging pleiotropy, the statistical power of a single GWAS can be increased. This package provides functions for fitting graph-GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy. GGPA package provides user-friendly interface to fit graph-GPA models, implement association mapping, and generate a phenotype graph.

G-quadruplexes (G4s) are unique nucleic acid secondary structures predominantly found in guanine-rich regions and have been shown to be involved in various biological regulatory processes. G4SNVHunter is an R package designed to rapidly identify genomic sequences with G4-forming propensity and to accurately screen user-provided single nucleotide variants—as well as other small-scale variants such as indels and MNVs—for their potential to destabilize these structures. This allows researchers to then screen these critical variants for deeper study, digging into how they might influence biological functions—think gene regulation, for instance—by impairing G4 formation propensity.

Classification using generalized partial least squares for two-group and multi-group (more than 2 group) classification.

This package provides tools for analyzing EWAS, methQTL and GxE genome widely.

Models and methods for fitting linear models to gene expression data, together with tools for computing and using various regression diagnostics.

GSABenchmark is a package designed for benchmarking scRNA-seq gene set analysis (scGSA) methods. It provides both traditional and novel benchmark metrics, as well as visualization tools. Currently, GSABenchmark supports 17 scGSA methods.

Simulated expression data for five large Gene Regulatory Networks from different simulators.

This package provides a series of statistical models using count generating distributions for background modelling, feature and sample QC, normalization and differential expression analysis on GeoMx RNA data. The application of these methods are demonstrated by example data analysis vignette.

GO-a-GO annotates Gene Ontology terms that are enriched in a given set of gene pairs. The enrichment is calculated from a permutation test for overrepresentation of gene pairs that are associated with a shared term. Such gene pairs are counted for the original set of gene pairs and compared against randomized sets in which the structure of the pairs is preserved, but the gene identities (including the associated terms) are permuted.

The method may be conceptualised as a test of overall significance in regression analysis, where the response variable is overdispersed and the number of explanatory variables exceeds the sample size. Useful for testing for association between RNA-Seq and high-dimensional data.

This package provides a package with focus on analysis of discrete regions of the genome. This package is useful for investigation of one or a few genes using Affymetrix data, since it will extract probe level data using the Affymetrix Power Tools application and wrap these data into a ProbeLevelSet. A ProbeLevelSet directly extends the expressionSet, but includes additional information about the sequence of each probe and the probe set it is derived from. The package includes a number of functions used for plotting these probe level data as a function of location along sequences of mRNA-strands. This can be used for analysis of variable splicing, and is especially well suited for use with exon-array data.

To classify Helicobacter pylori genomes according to genetic distance from nine reference populations. The nine reference populations are hpgpAfrica, hpgpAfrica-distant, hpgpAfroamerica, hpgpEuroamerica, hpgpMediterranea, hpgpEurope, hpgpEurasia, hpgpAsia, and hpgpAklavik86-like. The vertex populations are Africa, Europe and Asia.

This is a Hi-C analysis package using a cumulative binomial test to detect interactions between distal genomic loci that have significantly more reads than expected by chance in Hi-C experiments. It takes mapped paired NGS reads as input and gives back the list of significant interactions for a given bin size in the genome.

This package implements a metabolic network analysis pipeline to identify an active metabolic module based on high throughput data. The pipeline takes as input transcriptional and/or metabolic data and finds a metabolic subnetwork (module) most regulated between the two conditions of interest. The package further provides functions for module post-processing, annotation and visualization.

Given a vector of cluster memberships for a cell population, identifies a sequence of gates (polygon filters on 2D scatter plots) for isolation of that cell type.

This package is a comprehensive visualization tool specifically designed for exploring phylomorphospace. It not only simplifies the process of generating phylomorphospace, but also enhances it with the capability to add graphic layers to the plot with grammar of graphics to create fully annotated phylomorphospaces. It also provide some utilities to help interpret evolutionary patterns.

Simple visualizations of alignments of DNA or AA sequences as well as arbitrary strings. Compatible with Biostrings and ggplot2. The plots are fully customizable using ggplot2 modifiers such as theme().

This package enables regression and classification on high-dimensional data with different relative strengths of penalization for different feature groups, such as different assays or omic types. The optimal relative strengths are chosen adaptively. Optimisation is performed using a variational Bayes approach.

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.

This package provides a multi-objective optimization algorithm for disease sub-type discovery based on a non-dominated sorting genetic algorithm. The Galgo framework combines the advantages of clustering algorithms for grouping heterogeneous omics data and the searching properties of genetic algorithms for feature selection. The algorithm search for the optimal number of clusters determination considering the features that maximize the survival difference between sub-types while keeping cluster consistency high.

Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science. Machine learning has enabled us to generate useful protein sequences on a variety of scales. Generative models are machine learning methods which seek to model the distribution underlying the data, allowing for the generation of novel samples with similar properties to those on which the model was trained. Generative models of proteins can learn biologically meaningful representations helpful for a variety of downstream tasks. Furthermore, they can learn to generate protein sequences that have not been observed before and to assign higher probability to protein sequences that satisfy desired criteria. In this package, common deep generative models for protein sequences, such as variational autoencoder (VAE), generative adversarial networks (GAN), and autoregressive models are available. In the VAE and GAN, the Word2vec is used for embedding. The transformer encoder is applied to protein sequences for the autoregressive model.

This package provides long description of genes collected from the RefSeq database. The text in "COMMENT" section started with "Summary" is extracted as the description of the gene. The long text descriptions can be used for analysis such as text mining.

This package aims to import, parse, and analyze KEGG data such as KEGG PATHWAY and KEGG MODULE. The package supports visualizing KEGG information using ggplot2 and ggraph through using the grammar of graphics. The package enables the direct visualization of the results from various omics analysis packages.