This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was Vitis\_Vinifera\_probe\_tab.

The main objective of ViSEAGO package is to carry out a data mining of biological functions and establish links between genes involved in the study. We developed ViSEAGO in R to facilitate functional Gene Ontology (GO) analysis of complex experimental design with multiple comparisons of interest. It allows to study large-scale datasets together and visualize GO profiles to capture biological knowledge. The acronym stands for three major concepts of the analysis: Visualization, Semantic similarity and Enrichment Analysis of Gene Ontology. It provides access to the last current GO annotations, which are retrieved from one of NCBI EntrezGene, Ensembl or Uniprot databases for several species. Using available R packages and novel developments, ViSEAGO extends classical functional GO analysis to focus on functional coherence by aggregating closely related biological themes while studying multiple datasets at once. It provides both a synthetic and detailed view using interactive functionalities respecting the GO graph structure and ensuring functional coherence supplied by semantic similarity. ViSEAGO has been successfully applied on several datasets from different species with a variety of biological questions. Results can be easily shared between bioinformaticians and biologists, enhancing reporting capabilities while maintaining reproducibility.

Vulcan (VirtUaL ChIP-Seq Analysis through Networks) is a package that interrogates gene regulatory networks to infer cofactors significantly enriched in a differential binding signature coming from ChIP-Seq data. In order to do so, our package combines strategies from different BioConductor packages: DESeq for data normalization, ChIPpeakAnno and DiffBind for annotation and definition of ChIP-Seq genomic peaks, csaw to define optimal peak width and viper for applying a regulatory network over a differential binding signature.

This package provides a dummy regulatory network and ChIP-Seq dataset for running examples in the vulcan package.

High-throughput single-cell measurements of DNA methylation allows studying inter-cellular epigenetic heterogeneity, but this task faces the challenges of sparsity and noise. We present vmrseq, a statistical method that overcomes these challenges and identifies variably methylated regions accurately and robustly.

SpatialFeatureExperiment (SFE) is a new S4 class for working with spatial single-cell genomics data. The voyager package implements basic exploratory spatial data analysis (ESDA) methods for SFE. Univariate methods include univariate global spatial ESDA methods such as Moran's I, permutation testing for Moran's I, and correlograms. Bivariate methods include Lee's L and cross variogram. Multivariate methods include MULTISPATI PCA and multivariate local Geary's C recently developed by Anselin. The Voyager package also implements plotting functions to plot SFE data and ESDA results.

Data from the sequencing of a 50/50 mixture of HapMap trio samples NA12878 (CEU) and NA19240 (YRI), subset to the TP53 region.

Mutations that rapidly accumulate in viral genomes during a pandemic can be used to track the evolution of the virus and, accordingly, unravel the viral infection network. To this extent, sequencing samples of the virus can be employed to estimate models from genomic epidemiology and may serve, for instance, to estimate the proportion of undetected infected people by uncovering cryptic transmissions, as well as to predict likely trends in the number of infected, hospitalized, dead and recovered people. VERSO is an algorithmic framework that processes variants profiles from viral samples to produce phylogenetic models of viral evolution. The approach solves a Boolean Matrix Factorization problem with phylogenetic constraints, by maximizing a log-likelihood function. VERSO includes two separate and subsequent steps; in this package we provide an R implementation of VERSO STEP 1.

This package provides two multiplex imaging datasets collected on Vectra instruments at the University of Colorado Anschutz Medical Campus. Data are provided as a Spatial Experiment objects. Data is provided in tabular form and has been segmented and phenotyped using Inform software. Raw .tiff files are not included.

This package enables the interpretation and analysis of results from a gene set enrichment analysis using network-based and text-mining approaches. Most enrichment analyses result in large lists of significant gene sets that are difficult to interpret. Tools in this package help build a similarity-based network of significant gene sets from a gene set enrichment analysis that can then be investigated for their biological function using text-mining approaches.

The VISTA (Visualization and Integrated System for Transcriptomic Analysis) platform streamlines differential expression workflows by wrapping DESeq2 and edgeR into a SummarizedExperiment-based container with consistent metadata. The package includes visualization utilities, MSigDB enrichment helpers, and optional deconvolution support to simplify interactive exploration of RNA-seq experiments.

This package provides helper functions for working with multiple Visium capture areas that overlap each other. This package was developed along with the companion example use case data available from https://github.com/LieberInstitute/visiumStitched_brain. visiumStitched prepares SpaceRanger (10x Genomics) output files so you can stitch the images from groups of capture areas together with Fiji. Then visiumStitched builds a SpatialExperiment object with the stitched data and makes an artificial hexagonal grid enabling the seamless use of spatial clustering methods that rely on such grid to identify neighboring spots, such as PRECAST and BayesSpace. The SpatialExperiment objects created by visiumStitched are compatible with spatialLIBD, which can be used to build interactive websites for stitched SpatialExperiment objects. visiumStitched also enables casting SpatialExperiment objects as Seurat objects.

The package allows users to readily import spatial data obtained from either the 10X website or from the Space Ranger pipeline. Supported formats include tar.gz, h5, and mtx files. Multiple files can be imported at once with *List type of functions. The package represents data mainly as SpatialExperiment objects.

This package provides a comprehensive package for visualizing multi-set intersections and extracting detailed subset information. VennDetail generates high-resolution visualizations including traditional Venn diagrams, Venn-pie plots, and UpSet-style plots. It provides functions to extract and combine subset details with user datasets in various formats. The package is particularly useful for bioinformatics applications but can be used for any multi-set analysis.

variant-transcription factor-phenotype networks, inspired by Maurano et al., Science (2012), PMID 22955828.

This package provides Bioconductor-friendly wrappers for RNA velocity calculations in single-cell RNA-seq data. We use the basilisk package to manage Conda environments, and the zellkonverter package to convert data structures between SingleCellExperiment (R) and AnnData (Python). The information produced by the velocity methods is stored in the various components of the SingleCellExperiment class.

Hidden Markov Models for characterizing chromosomal alteration in high throughput SNP arrays.

VeloViz uses each cell’s current observed and predicted future transcriptional states inferred from RNA velocity analysis to build a nearest neighbor graph between cells in the population. Edges are then pruned based on a cosine correlation threshold and/or a distance threshold and the resulting graph is visualized using a force-directed graph layout algorithm. VeloViz can help ensure that relationships between cell states are reflected in the 2D embedding, allowing for more reliable representation of underlying cellular trajectories.

This package provides a package containing an environment representing the Vitis_Vinifera.cdf file.

Feature-based variance-sensitive clustering of omics data. Optimizes cluster assignment by taking into account individual feature variance. Includes several modules for statistical testing, clustering and enrichment analysis.

VCFArray extends the DelayedArray to represent VCF data entries as array-like objects with on-disk / remote VCF file as backend. Data entries from VCF files, including info fields, FORMAT fields, and the fixed columns (REF, ALT, QUAL, FILTER) could be converted into VCFArray instances with different dimensions.

Discovery of genome-wide variable alternative splicing events from short-read RNA-seq data and visualizations of gene splicing information for publication-quality multi-panel figures in a population. (Warning: The visualizing function is removed due to the dependent package Sushi deprecated. If you want to use it, please change back to an older version.).

The aim of vidger is to rapidly generate information-rich visualizations for the interpretation of differential gene expression results from three widely-used tools: Cuffdiff, DESeq2, and edgeR.

VarCon is an R package which converts the positional information from the annotation of an single nucleotide variation (SNV) (either referring to the coding sequence or the reference genomic sequence). It retrieves the genomic reference sequence around the position of the single nucleotide variation. To asses, whether the SNV could potentially influence binding of splicing regulatory proteins VarCon calcualtes the HEXplorer score as an estimation. Besides, VarCon additionally reports splice site strengths of splice sites within the retrieved genomic sequence and any changes due to the SNV.