This package implements a variety of methods for combining p-values in differential analyses of genome-scale datasets. Functions can combine p-values across different tests in the same analysis (e.g., genomic windows in ChIP-seq, exons in RNA-seq) or for corresponding tests across separate analyses (e.g., replicated comparisons, effect of different treatment conditions). Support is provided for handling log-transformed input p-values, missing values and weighting where appropriate.

This package provides full masked genome sequences for Drosophila melanogaster (Fly) as provided by UCSC (dm3, April 2006) and stored in Biostrings objects. The sequences are the same as in BSgenome.Dmelanogaster.UCSC.dm3, except that each of them has the 4 following masks on top: (1) the mask of assembly gaps (AGAPS mask), (2) the mask of intra-contig ambiguities (AMB mask), (3) the mask of repeats from RepeatMasker (RM mask), and (4) the mask of repeats from Tandem Repeats Finder (TRF mask). Only the AGAPS and AMB masks are "active" by default.

This package provides memory efficient S4 classes for storing sequences "externally" (behind an R external pointer, or on disk).

This package makes GREAT (Genomic Regions Enrichment of Annotations Tool) analysis automatic by constructing a HTTP POST request according to user's input and automatically retrieving results from GREAT web server.

This package offers functionality for taking methtuple or Bismark outputs to calculate ASM scores and compute DAMEs regions. It also offers nice visualization of methyl-circle plots.

This package provides RcisTarget databases: Gene-based motif rankings and annotation to transcription factors. This package contains a subset of 4.6k motifs (cisbp motifs), scored only within 500bp upstream and the TSS. See RcisTarget tutorial to download the full databases, containing 20k motifs and search space up to 10kbp around the TSS.

Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data from a gene by sample matrix to a gene-set by sample matrix, thereby allowing the evaluation of pathway enrichment for each sample. This new matrix of GSVA enrichment scores facilitates applying standard analytical methods like functional enrichment, survival analysis, clustering, CNV-pathway analysis or cross-tissue pathway analysis, in a pathway-centric manner.

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

This package provides tools to import transcript-level abundance, estimated counts and transcript lengths, and to summarize them into matrices for use with downstream gene-level analysis packages. Average transcript length, weighted by sample-specific transcript abundance estimates, is provided as a matrix which can be used as an offset for different expression of gene-level counts.

This package provides tools to analyze and visualize Illumina Infinium methylation arrays.

This package provides functions to annotate microarrays, find orthologs, and integrate heterogeneous gene expression profiles using annotation and other molecular biology information available as flat file database (plain text files).

This package performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently.

SCONE is an R package for comparing and ranking the performance of different normalization schemes for single-cell RNA-seq and other high-throughput analyses.

This package provides an R wrapper around the popular bowtie short read aligner and around SpliceMap, a de novo splice junction discovery and alignment tool.

This package provides tools to efficiently represent and manipulate genomic annotations and alignments is playing a central role when it comes to analyzing high-throughput sequencing data (a.k.a. NGS data). The GenomicRanges package defines general purpose containers for storing and manipulating genomic intervals and variables defined along a genome.

This package contains the helper files that are required to run the Bioconductor package CopywriteR. It contains pre-assembled 1kb bin GC-content and mappability files for the reference genomes hg18, hg19, hg38, mm9 and mm10. In addition, it contains a blacklist filter to remove regions that display copy number variation. Files are stored as GRanges objects from the GenomicRanges Bioconductor package.

This package defines classes for "class discovery" in the OOMPA project. Class discovery primarily consists of unsupervised clustering methods with attempts to assess their statistical significance.

This package is developed for the analysis and visualization of clonal tracking data. The required data is formed by samples and tag abundances in matrix form, usually from cellular barcoding experiments, integration site retrieval analyses, or similar technologies.

This package provides an implementation of the BRGE's (Bioinformatic Research Group in Epidemiology from Center for Research in Environmental Epidemiology) MultiDataSet and ResultSet. MultiDataSet is designed for integrating multi omics data sets and ResultSet is a container for omics results. This package contains base classes for MEAL and rexposome packages.

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

This package corrects GC and mappability biases for readcounts (i.e. coverage) in non-overlapping windows of fixed length for single whole genome samples, yielding a rough estimate of copy number for further analysis. It was designed for rapid correction of high coverage whole genome tumor and normal samples.

This package provides a set of protein ID mappings for PFAM, assembled using data from public repositories.

This package provides basic functions for filtering genes from high-throughput sequencing experiments.

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.