This package provides mappings from Entrez gene identifiers to various annotations for the genome of the model mouse Mus musculus.

This package provides per-exon and per-gene read counts computed for selected genes from RNA-seq data that were presented in the article 'Conservation of an RNA regulatory map between Drosophila and mammals' by Brooks et al., Genome Research 2011.

This package identifies mutational signatures of single nucleotide variants (SNVs). It provides a infrastructure related to the methodology described in Nik-Zainal (2012, Cell), with flexibility in the matrix decomposition algorithms.

This package provides S4 generic functions needed by Bioconductor proteomics packages.

The aim of XINA is to determine which proteins exhibit similar patterns within and across experimental conditions, since proteins with co-abundance patterns may have common molecular functions. XINA imports multiple datasets, tags dataset in silico, and combines the data for subsequent subgrouping into multiple clusters. The result is a single output depicting the variation across all conditions. XINA not only extracts coabundance profiles within and across experiments, but also incorporates protein-protein interaction databases and integrative resources such as Kyoto encyclopedia of genes and genomes (KEGG) to infer interactors and molecular functions, respectively, and produces intuitive graphical outputs.

BioQC performs quality control of high-throughput expression data based on tissue gene signatures. It can detect tissue heterogeneity in gene expression data. The core algorithm is a Wilcoxon-Mann-Whitney test that is optimised for high performance.

Wrapping an array-like object (typically an on-disk object) in a DelayedArray object allows one to perform common array operations on it without loading the object in memory. In order to reduce memory usage and optimize performance, operations on the object are either delayed or executed using a block processing mechanism. Note that this also works on in-memory array-like objects like DataFrame objects (typically with Rle columns), Matrix objects, and ordinary arrays and data frames.

Filter genetic variants using different criteria such as inheritance model, amino acid change consequence, minor allele frequencies across human populations, splice site strength, conservation, etc.

LEA is an R package dedicated to population genomics, landscape genomics and genotype-environment association tests. LEA can run analyses of population structure and genome-wide tests for local adaptation, and also performs imputation of missing genotypes. The package includes statistical methods for estimating ancestry coefficients from large genotypic matrices and for evaluating the number of ancestral populations (snmf). It performs statistical tests using latent factor mixed models for identifying genetic polymorphisms that exhibit association with environmental gradients or phenotypic traits (lfmm2). In addition, LEA computes values of genetic offset statistics based on new or predicted environments (genetic.gap, genetic.offset). LEA is mainly based on optimized programs that can scale with the dimensions of large data sets.

In this package, a Hidden Semi Markov Model (HSMM) and one homogeneous segmentation model are designed and implemented for segmentation genomic data, with the aim of assisting in transcripts detection using high throughput technology like RNA-seq or tiling array, and copy number analysis using aCGH or sequencing.

BANDITS is a Bayesian hierarchical model for detecting differential splicing of genes and transcripts, via DTU (differential transcript usage), between two or more conditions. The method uses a Bayesian hierarchical framework, which allows for sample specific proportions in a Dirichlet-Multinomial model, and samples the allocation of fragments to the transcripts. Parameters are inferred via MCMC (Markov chain Monte Carlo) techniques and a DTU test is performed via a multivariate Wald test on the posterior densities for the average relative abundance of transcripts.

The package contains functions that can be used to compare expression measures for Affymetrix Oligonucleotide Arrays.

This package provides memory efficient string containers, string matching algorithms, and other utilities, for fast manipulation of large biological sequences or sets of sequences.

This package provides a set of tools for interacting with GO and microarray data. A variety of basic manipulation tools for graphs, hypothesis testing and other simple calculations.

IONiseR provides tools for the quality assessment of Oxford Nanopore MinION data. It extracts summary statistics from a set of fast5 files and can be used either before or after base calling. In addition to standard summaries of the read-types produced, it provides a number of plots for visualising metrics relative to experiment run time or spatially over the surface of a flowcell.

The anota2seq package provides analysis of translational efficiency and differential expression analysis for polysome-profiling and ribosome-profiling studies (two or more sample classes) quantified by RNA sequencing or DNA-microarray. Polysome-profiling and ribosome-profiling typically generate data for two RNA sources, translated mRNA and total mRNA. Analysis of differential expression is used to estimate changes within each RNA source. Analysis of translational efficiency aims to identify changes in translation efficiency leading to altered protein levels that are independent of total mRNA levels or buffering, a mechanism regulating translational efficiency so that protein levels remain constant despite fluctuating total mRNA levels.

This package implements an expiration system for access to versioned directories. Directories that have not been accessed by a registered function within a certain time frame are deleted. This aims to reduce disk usage by eliminating obsolete caches generated by old versions of packages.

This package provides functions for pathway analysis based on the REACTOME pathway database. It implements enrichment analysis, gene set enrichment analysis and several functions for visualization.

mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data.

This package provides the data that were used in the http://quinlanlab.org/tutorials/bedtools/bedtools.html. It includes a subset of the DnaseI hypersensitivity data from "Maurano et al. Systematic Localization of Common Disease-Associated Variation in Regulatory DNA. Science. 2012. Vol. 337 no. 6099 pp. 1190-1195." The rest of the tracks were originally downloaded from the UCSC table browser. See the HelloRanges vignette for a port of the bedtools tutorial to R.

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.

TreeSummarizedExperiment extends SingleCellExperiment to include hierarchical information on the rows or columns of the rectangular data.

This package contains microarray gene expression data on 57 bladder samples from 5 batches. The data are used as an illustrative example for the sva package.

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.