This package provides manifests and annotation for Illumina's 450k array data.

This package provides full genome sequences for Homo sapiens from 1000genomes phase2 reference genome sequence (hs37d5), based on NCBI GRCh37.

DSS is an R library performing differential analysis for count-based sequencing data. It detects differentially expressed genes (DEGs) from RNA-seq, and differentially methylated loci or regions (DML/DMRs) from bisulfite sequencing (BS-seq). The core of DSS is a dispersion shrinkage method for estimating the dispersion parameter from Gamma-Poisson or Beta-Binomial distributions.

This package provides a collection of compression filters for use with HDF5 datasets.

This package computes differentially bound sites from multiple ChIP-seq experiments using affinity (quantitative) data. Also enables occupancy (overlap) analysis and plotting functions.

This package implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. It provides a robust toolbox supporting over 70 methods for semantic similarity analysis.

This package contains functions for the efficient design of factorial two-colour microarray experiments and for the statistical analysis of factorial microarray data.

This package provides examples and code that make use of the different graph related packages produced by Bioconductor.

rtracklayer is an extensible framework for interacting with multiple genome browsers (currently UCSC built-in) and manipulating annotation tracks in various formats (currently GFF, BED, bedGraph, BED15, WIG, BigWig and 2bit built-in). The user may export/import tracks to/from the supported browsers, as well as query and modify the browser state, such as the current viewport.

This is an annotation package for Illumina Infinium DNA methylation probes. It contains the compiled HumanMethylation27 and HumanMethylation450 annotations.

This package contains default datasets used by the Bioconductor package biscuiteer.

This package provides tools for identifying preferential usage of APA sites, comparing two biological conditions, starting from known alternative sites and alignments obtained from standard RNA-seq experiments.

This package provides methods and functionality to analyze flow data that is beyond the basic infrastructure provided by the flowCore package.

Single-cell RNA-seq (scRNA-seq) is widely used to investigate the composition of complex tissues since the technology allows researchers to define cell-types using unsupervised clustering of the transcriptome. However, due to differences in experimental methods and computational analyses, it is often challenging to directly compare the cells identified in two different experiments. scmap is a method for projecting cells from a scRNA-seq experiment onto the cell-types or individual cells identified in a different experiment.

This R package provides tools for handling genomic interaction data, such as ChIA-PET/Hi-C, annotating genomic features with interaction information and producing various plots and statistics.

This package provides a subset of BAM files untreated1.bam (single-end reads) and untreated3.bam (paired-end reads) from "Pasilla" experiment (Pasilla knock-down by Brooks et al., Genome Research 2011). See the vignette in the pasilla data package for how BAM files untreated1.bam and untreated3.bam were obtained from the RNA-Seq read sequence data that is provided by NCBI Gene Expression Omnibus under accession numbers GSM461176 to GSM461181. It also contains the DNA sequence for fly chromosome 4 to which the reads can be mapped.

This package provides full genome sequences for Homo sapiens as provided by UCSC (hg19, February 2009) and stored in Biostrings objects.

This package implements different performance measures for classification and ranking tasks. Area under curve (AUC), precision at a given recall, F-score for single and multiple classes are available.

The necessary external data to run the flowWorkspace and openCyto vignette is found in this package. This data package contains two flowJo, one diva xml workspace and the associated fcs files as well as three GatingSets for testing the flowWorkspace, openCyto and CytoML packages.

Explore and download data from the recount project available at https://jhubiostatistics.shinyapps.io/recount/. Using the recount package you can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level, the raw counts, the phenotype metadata used, the urls to the sample coverage bigWig files or the mean coverage bigWig file for a particular study. The RangedSummarizedExperiment objects can be used by different packages for performing differential expression analysis. Using http://bioconductor.org/packages/derfinder you can perform annotation-agnostic differential expression analyses with the data from the recount project as described at https://www.nature.com/nbt/journal/v35/n4/full/nbt.3838.html.

This package provides functions to estimate variance-mean dependence in count data from high-throughput nucleotide sequencing assays and test for differential expression based on a model using the negative binomial distribution.

EBImage provides general purpose functionality for image processing and analysis. In the context of (high-throughput) microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. This allows the automation of such tasks using the R programming language and facilitates the use of other tools in the R environment for signal processing, statistical modeling, machine learning and visualization with image data.

This package provides dataset samples (Affymetrix: Expression, Gene, Exon, SNP; NimbleGen: Expression, Tiling) to be used with the oligo package.

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.