This is the core package for the automated analysis of Affymetrix arrays.

This package provides infrastructure for parallel computations distributed by file or by range. User defined mapper and reducer functions provide added flexibility for data combination and manipulation.

This package implements functions for finding breakpoints, plotting and export of Strand-seq data.

This package offers interactive Shiny displays for Bioconductor objects. In addition, this package empowers users to develop engaging visualizations and interfaces for working with Bioconductor data.

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

With the dedicated fortify method implemented for flowSet, ncdfFlowSet and GatingSet classes, both raw and gated flow cytometry data can be plotted directly with ggplot. The ggcyto wrapper and some custom layers also make it easy to add gates and population statistics to the plot.

This package provides functions for handling data from Bioconductor Affymetrix annotation data packages. It produces compact HTML and text reports including experimental data and URL links to many online databases. It allows searching of biological metadata using various criteria.

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 facilitates phyloseq exploration and analysis of taxonomic profiling data. This package provides tools for the manipulation, statistical analysis, and visualization of taxonomic profiling data. In addition to targeted case-control studies, microbiome facilitates scalable exploration of population cohorts. This package supports the independent phyloseq data format and expands the available toolkit in order to facilitate the standardization of the analyses and the development of best practices.

This package implements a general and flexible zero-inflated negative binomial model that can be used to provide a low-dimensional representations of single-cell RNA-seq data. The model accounts for zero inflation (dropouts), over-dispersion, and the count nature of the data. The model also accounts for the difference in library sizes and optionally for batch effects and/or other covariates, avoiding the need for pre-normalize the data.

This package provides a suite of helper functions for checking and manipulating TCGA data including data obtained from the curatedTCGAData experiment package. These functions aim to simplify and make working with TCGA data more manageable. Exported functions include those that import data from flat files into Bioconductor objects, convert row annotations, and identifier translation via the GDC API.

This is a manifest package for Illumina's EPIC methylation arrays.

This package provides example datasets that represent 'real world examples' of Affymetrix data, unlike the artificial examples included in the package affy.

This package provides supporting data for the TCGAbiolinksGUI package.

This is a package for creating na HTML report of differential expression analyses of count data. It integrates some of the code mentioned in DESeq2 and edgeR vignettes, and report a ranked list of genes according to the fold changes mean and variability for each selected gene.

Perform large scale genomic data retrieval and functional annotation retrieval. This package aims to provide users with a standardized way to automate genome, proteome, RNA, coding sequence (CDS), GFF, and metagenome retrieval from NCBI RefSeq, NCBI Genbank, ENSEMBL, and UniProt databases. Furthermore, an interface to the BioMart database allows users to retrieve functional annotation for genomic loci. In addition, users can download entire databases such as NCBI RefSeq, NCBI nr, NCBI nt, NCBI Genbank, etc with only one command.

This package provides supporting annotation and test data for SeSAMe package. This includes chip tango addresses, mapping information, performance annotation, and trained predictor for Infinium array data. This package provides user access to essential annotation data for working with many generations of the Infinium DNA methylation array. It currently supports human array (HM27, HM450, EPIC), mouse array (MM285) and the HorvathMethylChip40 (Mammal40) array.

RcisTarget identifies transcription factor binding motifs (TFBS) over-represented on a gene list. In a first step, RcisTarget selects DNA motifs that are significantly over-represented in the surroundings of the transcription start site (TSS) of the genes in the gene-set. This is achieved by using a database that contains genome-wide cross-species rankings for each motif. The motifs that are then annotated to TFs and those that have a high Normalized Enrichment Score (NES) are retained. Finally, for each motif and gene-set, RcisTarget predicts the candidate target genes (i.e. genes in the gene-set that are ranked above the leading edge).

This package provides tools for Bayesian integrated analysis of Affymetrix GeneChips.

This package implements the circular binary segmentation (CBS) algorithm to segment DNA copy number data and identify genomic regions with abnormal copy number.

This package provides genome wide annotations for Bovine, primarily based on mapping using Entrez Gene identifiers.

The HiTC package was developed to explore high-throughput "C" data such as 5C or Hi-C. Dedicated R classes as well as standard methods for quality controls, normalization, visualization, and further analysis are also provided.

Many modern biological datasets consist of small counts that are not well fit by standard linear-Gaussian methods such as principal component analysis. This package provides implementations of count-based feature selection and dimension reduction algorithms. These methods can be used to facilitate unsupervised analysis of any high-dimensional data such as single-cell RNA-seq.

This package implements some simple graph handling capabilities for R.