TrackViewer offers multi-omics analysis with web based tracks and lollipops. Visualize mapped reads along with annotation as track layers for NGS datasets such as ChIP-seq, RNA-seq, miRNA-seq, DNA-seq, SNPs and methylation data.

The package includes quality control metrics, a selection of normalization methods and novel methods to identify differentially methylated regions and to highlight copy number alterations.

This package provides fast maximum-likelihood phylogeny inference from noisy single-cell data using the ScisTree algorithm proposed by doi.org/10.1093/bioinformatics/btz676, Yufeng Wu (2019). It makes the method applicable to massive single-cell datasets (>10,000 cells).

Analysis of Ct values from high throughput quantitative real-time PCR (qPCR) assays across multiple conditions or replicates. The input data can be from spatially-defined formats such ABI TaqMan Low Density Arrays or OpenArray; LightCycler from Roche Applied Science; the CFX plates from Bio-Rad Laboratories; conventional 96- or 384-well plates; or microfluidic devices such as the Dynamic Arrays from Fluidigm Corporation. HTqPCR handles data loading, quality assessment, normalization, visualization and parametric or non-parametric testing for statistical significance in Ct values between features (e.g. genes, microRNAs).

This package implements transcript quantification import from Salmon and alevin with automatic attachment of transcript ranges and release information, and other associated metadata. De novo transcriptomes can be linked to the appropriate sources with linkedTxomes and shared for computational reproducibility.

tRNAdbImport imports the entries of the tRNAdb and mtRNAdb as GRanges object.

This package provides processed 22 samples from BrainSpan keeping only the information for chromosome 21. Data is stored in the BigWig format and is used for examples in other packages.

This package provides an R interface to libsbml for SBML parsing, validating output, provides an S4 SBML DOM, converts SBML to R graph objects.

The ReportingTools package enables users to easily display reports of analysis results generated from sources such as microarray and sequencing data. The package allows users to create HTML pages that may be viewed on a web browser, or in other formats. Users can generate tables with sortable and filterable columns, make and display plots, and link table entries to other data sources such as NCBI or larger plots within the HTML page. Using the package, users can also produce a table of contents page to link various reports together for a particular project that can be viewed in a web browser.

The PSMatch package helps proteomics practitioners to load, handle and manage peptide spectrum matches. It provides functions to model peptide-protein relations as adjacency matrices and connected components, visualise these as graphs and make informed decision about shared peptide filtering. The package also provides functions to calculate and visualise MS2 fragment ions.

This package provides an interface to simulate metabolic reconstruction from the BiGG database and other metabolic reconstruction databases. The package facilitates flux balance analysis (FBA) and the sampling of feasible flux distributions. Metabolic networks and estimated fluxes can be visualized with hypergraphs.

MetagenomeSeq is designed to determine features (be it OTU, species, etc.) that are differentially abundant between two or more groups of multiple samples. This package is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations.

This package provides classes and statistical methods for large single-nucleotide polymorphism (SNP) association studies. This extends the earlier snpMatrix package, allowing for uncertainty in genotypes.

This package aggregateBioVar contains tools to summarize single cell gene expression profiles at the level of subject for single cell RNA-seq data collected from more than one subject (e.g. biological sample or technical replicates). A SingleCellExperiment object is taken as input and converted to a list of SummarizedExperiment objects, where each list element corresponds to an assigned cell type. The SummarizedExperiment objects contain aggregate gene-by-subject count matrices and inter-subject column metadata for individual subjects that can be processed using downstream bulk RNA-seq tools.

This package provides the full genome sequences for Bos taurus (UCSC version bosTau8).

This package provides genome wide annotation for E coli strain K12, primarily based on mapping using Entrez Gene identifiers. Entrez Gene is National Center for Biotechnology Information (NCBI)’s database for gene-specific information. Entrez Gene maintains records from genomes which have been completely sequenced, which have an active research community to submit gene-specific information, or which are scheduled for intense sequence analysis.

This package provides tools for analyzing R expressions or blocks of code and determining the dependencies between them. It focuses on R scripts, but can be used on the bodies of functions. There are many facilities including the ability to summarize or get a high-level view of code, determining dependencies between variables, code improvement suggestions.

This package provides the core data structure and API to represent and interact with gated cytometry data.

This package provides functionality for the compression and decompression of raw bead-level data from the Illumina BeadArray platform.

This package provides a simple single-sample gene signature scoring method that uses rank-based statistics to analyze the sample's gene expression profile. It scores the expression activities of gene sets at a single-sample level.

This package provides tools for processing short read data from ChIPseq experiments.

This package provides tools to analyze and visualize high-throughput metabolomics data acquired using chromatography-mass spectrometry. These tools preprocess data in a way that enables reliable and powerful differential analysis.

The package uses quadratic programming for signature refitting, i.e., to decompose the mutation catalog from an individual tumor sample into a set of given mutational signatures (either Alexandrov-model signatures or Shiraishi-model signatures), computing weights that reflect the contributions of the signatures to the mutation load of the tumor.

This package enables you to read and manipulate genome intervals and signals. It provides functionality similar to command-line tool suites within R, enabling interactive analysis and visualization of genome-scale data.