The `TrIdent` R package automates the analysis of transductomics data by detecting, classifying, and characterizing read coverage patterns associated with potential transduction events. Transductomics is a DNA sequencing-based method for the detection and characterization of transduction events in pure cultures and complex communities. Transductomics relies on mapping sequencing reads from a viral-like particle (VLP)-fraction of a sample to contigs assembled from the metagenome (whole-community) of the same sample. Reads from bacterial DNA carried by VLPs will map back to the bacterial contigs of origin creating read coverage patterns indicative of ongoing transduction.

Rank results by confident effect sizes, while maintaining False Discovery Rate and False Coverage-statement Rate control. Topconfects is an alternative presentation of TREAT results with improved usability, eliminating p-values and instead providing confidence bounds. The main application is differential gene expression analysis, providing genes ranked in order of confident log2 fold change, but it can be applied to any collection of effect sizes with associated standard errors.

This package provides a R interface to the TnT javascript library (https://github.com/ tntvis) to provide interactive and flexible visualization of track-based genomic data.

Download Barcode, UMI, and Set (BUS) format of 10x datasets from within R. This package accompanies the package BUSpaRse, which can load BUS format into R as a sparse matrix, and which has utility functions related to using the C++ command line package bustools.

Design primers for targeted single-cell RNA-seq used by TAP-seq. Create sequence templates for target gene panels and design gene-specific primers using Primer3. Potential off-targets can be estimated with BLAST. Requires working installations of Primer3 and BLASTn.

This package provides a first step in the data analysis of Mass Spectrometry (MS) based proteomics data is to identify peptides and proteins. With this respect the huge number of experimental mass spectra typically have to be assigned to theoretical peptides derived from a sequence database. Search engines are used for this purpose. These tools compare each of the observed spectra to all candidate theoretical spectra derived from the sequence data base and calculate a score for each comparison. The observed spectrum is then assigned to the theoretical peptide with the best score, which is also referred to as the peptide to spectrum match (PSM). It is of course crucial for the downstream analysis to evaluate the quality of these matches. Therefore False Discovery Rate (FDR) control is used to return a reliable list PSMs. The FDR, however, requires a good characterisation of the score distribution of PSMs that are matched to the wrong peptide (bad target hits). In proteomics, the target decoy approach (TDA) is typically used for this purpose. The TDA method matches the spectra to a database of real (targets) and nonsense peptides (decoys). A popular approach to generate these decoys is to reverse the target database. Hence, all the PSMs that match to a decoy are known to be bad hits and the distribution of their scores are used to estimate the distribution of the bad scoring target PSMs. A crucial assumption of the TDA is that the decoy PSM hits have similar properties as bad target hits so that the decoy PSM scores are a good simulation of the target PSM scores. Users, however, typically do not evaluate these assumptions. To this end we developed TargetDecoy to generate diagnostic plots to evaluate the quality of the target decoy method.

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.

Exposes an annotation databases generated from UCSC by exposing these as TxDb objects.

transmogR provides the tools needed to crate a new reference genome or reference transcriptome, using a set of variants. Variants can be any combination of SNPs, Insertions and Deletions. The intended use-case is to enable creation of variant-modified reference transcriptomes for incorporation into transcriptomic pseudo-alignment workflows, such as salmon.

Exposes an annotation databases generated from BioMart by exposing these as TxDb objects.

This R package provides tools for the statistical analysis of integrative genomic data that involve some combination of: genotypes, high-dimensional intermediate traits (e.g., gene expression, protein abundance), and higher-order traits (phenotypes). The package includes functions to: (1) construct global linkage maps between genetic markers and gene expression; (2) analyze multiple-locus linkage (epistasis) for gene expression; (3) quantify the proportion of genome-wide variation explained by each locus and identify eQTL hotspots; (4) estimate pair-wise causal gene regulatory probabilities and construct gene regulatory networks; and (5) identify causal genes for a quantitative trait of interest.

This package provides many easy-to-use methods to analyze and visualize tomo-seq data. The tomo-seq technique is based on cryosectioning of tissue and performing RNA-seq on consecutive sections. (Reference: Kruse F, Junker JP, van Oudenaarden A, Bakkers J. Tomo-seq: A method to obtain genome-wide expression data with spatial resolution. Methods Cell Biol. 2016;135:299-307. doi:10.1016/bs.mcb.2016.01.006) The main purpose of the package is to find zones with similar transcriptional profiles and spatially expressed genes in a tomo-seq sample. Several visulization functions are available to create easy-to-modify plots.

This package provides a tool to search and download a collection of tumour microenvironment single-cell RNA sequencing datasets and their metadata. TMExplorer aims to act as a single point of entry for users looking to study the tumour microenvironment at the single cell level. Users can quickly search available datasets using the metadata table and then download the ones they are interested in for analysis.

tidySpatialExperiment provides a bridge between the SpatialExperiment package and the tidyverse ecosystem. It creates an invisible layer that allows you to interact with a SpatialExperiment object as if it were a tibble; enabling the use of functions from dplyr, tidyr, ggplot2 and plotly. But, underneath, your data remains a SpatialExperiment object.

Variance-stabilizing transformations help with the analysis of heteroskedastic data (i.e., data where the variance is not constant, like count data). This package provide two types of variance stabilizing transformations: (1) methods based on the delta method (e.g., acosh', log(x+1)'), (2) model residual based (Pearson and randomized quantile residuals).

transite is a computational method that allows comprehensive analysis of the regulatory role of RNA-binding proteins in various cellular processes by leveraging preexisting gene expression data and current knowledge of binding preferences of RNA-binding proteins.

This package contains a collection of trans-omics datasets generated using various sequencing technologies such as RNA-seq, Mass spectrometry and ChIP-seq. Modalities include the bulk profiling of the phosphoproteome, proteome, transcriptome and epigenome. Data reflects the timecourses of different developmental systems from the mouse or human.

The package provides S4 classes and methods to filter, summarise and visualise genetic variation data stored in VCF files. In particular, the package extends the FilterRules class (S4Vectors package) to define news classes of filter rules applicable to the various slots of VCF objects. Functionalities are integrated and demonstrated in a Shiny web-application, the Shiny Variant Explorer (tSVE).

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.

ExperimentHub package containing datasets for use in the TENET package's vignette and function examples. These include a variety of different objects to illustrate different datasets used in TENET functions. Where applicable, all datasets are aligned to the hg38 human genome.

Exposes an annotation databases generated from UCSC by exposing these as TxDb objects.

Collection of Xenium spatial transcriptomics datasets provided by 10x Genomics, formatted into the Bioconductor classes, the SpatialExperiment or SpatialFeatureExperiment (SFE), to facilitate seamless integration into various applications, including examples, demonstrations, and tutorials. The constructed data objects include gene expression profiles, per-transcript location data, centroid, segmentation boundaries (e.g., cell or nucleus boundaries), and image.

Leverage the existing open access TCGA data on Terra with well-established Bioconductor infrastructure. Make use of the Terra data model without learning its complexities. With a few functions, you can copy / download and generate a MultiAssayExperiment from the TCGA example workspaces provided by Terra.

Exposes an annotation databases generated from UCSC by exposing these as TxDb objects.