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TENET identifies key transcription factors (TFs) and regulatory elements (REs) linked to a specific cell type by finding significantly correlated differences in gene expression and RE DNA methylation between case and control input datasets, and identifying the top genes by number of significant RE DNA methylation site links. It also includes many tools for visualization and analysis of the results, including plots displaying and comparing methylation and expression data and methylation site link counts, survival analysis, TF motif searching in the vicinity of linked RE DNA methylation sites, custom TAD and peak overlap analysis, and UCSC Genome Browser track file generation. A utility function is also provided to download methylation, expression, and patient survival data from The Cancer Genome Atlas (TCGA) for use in TENET or other analyses.

TEKRABber is made to provide a user-friendly pipeline for comparing orthologs and transposable elements (TEs) between two species. It considers the orthology confidence between two species from BioMart to normalize expression counts and detect differentially expressed orthologs/TEs. Then it provides one to one correlation analysis for desired orthologs and TEs. There is also an app function to have a first insight on the result. Users can prepare orthologs/TEs RNA-seq expression data by their own preference to run TEKRABber following the data structure mentioned in the vignettes.

This is an advanced version of TDbasedUFE, which is a comprehensive package to perform Tensor decomposition based unsupervised feature extraction. In contrast to TDbasedUFE which can perform simple the feature selection and the multiomics analyses, this package can perform more complicated and advanced features, but they are not so popularly required. Only users who require more specific features can make use of its functionality.

Gene signatures of TB progression, TB disease, and other TB disease states have been validated and published previously. This package aggregates known signatures and provides computational tools to enlist their usage on other datasets. The TBSignatureProfiler makes it easy to profile RNA-Seq data using these signatures and includes common signature profiling tools including ASSIGN, GSVA, and ssGSEA. Original models for some gene signatures are also available. A shiny app provides some functionality alongside for detailed command line accessibility.

TaxSEA is an R package for Taxon Set Enrichment Analysis, which utilises a Kolmogorov-Smirnov test analyses to investigate differential abundance analysis output for whether there are alternations in a-priori defined sets of taxa from public databases (BugSigDB, MiMeDB, GutMGene, mBodyMap, BacDive and GMRepoV2) and collated from the literature. TaxSEA takes as input a list of taxonomic identifiers (e.g. species names, NCBI IDs etc.) and a rank (E.g. fold change, correlation coefficient). TaxSEA be applied to any microbiota taxonomic profiling technology (array-based, 16S rRNA gene sequencing, shotgun metagenomics & metatranscriptomics etc.) and enables researchers to rapidly contextualize their findings within the broader literature to accelerate interpretation of results.

Implement the BETA algorithm for infering direct target genes from DNA-binding and perturbation expression data Wang et al. (2013) <doi: 10.1038/nprot.2013.150>. Extend the algorithm to predict the combined function of two DNA-binding elements from comprable binding and expression data.

Offers functions for plotting split (or implicit) networks (unrooted, undirected) and explicit networks (rooted, directed) with reticulations extending. 'ggtree' and using functions from 'ape' and 'phangorn'. It extends the 'ggtree' package [@Yu2017] to allow the visualization of phylogenetic networks using the 'ggplot2' syntax. It offers an alternative to the plot functions already available in 'ape' Paradis and Schliep (2019) <doi:10.1093/bioinformatics/bty633> and 'phangorn' Schliep (2011) <doi:10.1093/bioinformatics/btq706>.

This package provides functions to standardise the analysis of Differential Allelic Representation (DAR). DAR compromises the integrity of Differential Expression analysis results as it can bias expression, influencing the classification of genes (or transcripts) as being differentially expressed. DAR analysis results in an easy-to-interpret value between 0 and 1 for each genetic feature of interest, where 0 represents identical allelic representation and 1 represents complete diversity. This metric can be used to identify features prone to false-positive calls in Differential Expression analysis, and can be leveraged with statistical methods to alleviate the impact of such artefacts on RNA-seq data.

Differential expression analysis of RNA-seq using the Poisson-Tweedie (PT) family of distributions. PT distributions are described by a mean, a dispersion and a shape parameter and include Poisson and NB distributions, among others, as particular cases. An important feature of this family is that, while the Negative Binomial (NB) distribution only allows a quadratic mean-variance relationship, the PT distributions generalizes this relationship to any orde.

TRIP is a software framework that provides analytics services on antigen receptor (B cell receptor immunoglobulin, BcR IG | T cell receptor, TR) gene sequence data. It is a web application written in R Shiny. It takes as input the output files of the IMGT/HighV-Quest tool. Users can select to analyze the data from each of the input samples separately, or the combined data files from all samples and visualize the results accordingly.

This is a collection of utility functions that allow to perform exploration of and calculations to RNA sequencing data, in a modular, pipe-friendly and tidy fashion.

Provides functionality for untargeted LC-MS metabolomics research as specified in the associated protocol article in the 'Metabolomics Data Processing and Data Analysis—Current Best Practices' special issue of the Metabolites journal (2020). This includes tabular data preprocessing and quality control, uni- and multivariate analysis as well as quality control visualizations, feature-wise visualizations and results visualizations. Raw data preprocessing and functionality related to biological context, such as pathway analysis, is not included.