Fastseg implements a very fast and efficient segmentation algorithm. It can segment data from DNA microarrays and data from next generation sequencing for example to detect copy number segments. Further it can segment data from RNA microarrays like tiling arrays to identify transcripts. Most generally, it can segment data given as a matrix or as a vector. Various data formats can be used as input to fastseg like expression set objects for microarrays or GRanges for sequencing data.

This package provides mappings from Entrez gene identifiers to various annotations for the genome of the model fruit fly Drosophila melanogaster.

This package contains data for mapping between NCBI taxonomy ID and species. It is used by functions in the GenomeInfoDb package.

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 contains whole-genome single cell sequencing data for demonstration purposes in the AneuFinder package.

This package contains functions for building GenomicState objects from different annotation sources such as Gencode. It also provides access to these files at JHPCE.

This package exposes an annotation databases generated from UCSC by exposing these as TxDb objects.

This package provides functions and datasets for maximum likelihood fitting of some classes of graphical Markov models.

RnBeads facilitates comprehensive analysis of various types of DNA methylation data at the genome scale.

Monocle 3 performs clustering, differential expression and trajectory analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle 3 also performs differential expression analysis, clustering, visualization, and other useful tasks on single-cell expression data. It is designed to work with RNA-Seq data, but could be used with other types as well.

This package provides an expressionSet containing gene expression data from 60 bone marrow samples of patients with one of the four main types of leukemia (ALL, AML, CLL, CML) or non-leukemia.

This package efficiently obtains count vectors from indexed bam files. It counts the number of nucleotide sequence reads in given genomic ranges and it computes reads profiles and coverage profiles. It also handles paired-end data.

satuRn provides a framework for performing differential transcript usage analyses. The package consists of three main functions. The first function, fitDTU, fits quasi-binomial generalized linear models that model transcript usage in different groups of interest. The second function, testDTU, tests for differential usage of transcripts between groups of interest. Finally, plotDTU visualizes the usage profiles of transcripts in groups of interest.

This package contains methods for converting standard objects constructed by bioinformatics packages, especially those in Bioconductor, and converting them to tidy data. It thus serves as a complement to the broom package, and follows the same tidy, augment, glance division of tidying methods. Tidying data makes it easy to recombine, reshape and visualize bioinformatics analyses.

This package provides functions for plotting genomic data.

This package can do differential expression analysis of RNA-seq expression profiles with biological replication. It implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests. It be applied to differential signal analysis of other types of genomic data that produce counts, including ChIP-seq, SAGE and CAGE.

This package provides functions for annotation-agnostic differential expression analysis of RNA-seq data. Two implementations of the DER Finder approach are included in this package:

1. single base-level F-statistics and

2. DER identification at the expressed regions-level.

The DER Finder approach can also be used to identify differentially bounded ChIP-seq peaks.

This package provides an alternative interface to Bioconductor annotation resources, in particular the gene identifier mapping functionality of the org packages (e.g., org.Hs.eg.db) and the genome coordinate functionality of the TxDb packages (e.g., TxDb.Hsapiens.UCSC.hg38.knownGene).

This package vendors an assortment of useful header-only C++ libraries. Bioconductor packages can use these libraries in their own C++ code by LinkingTo this package without introducing any additional dependencies. The use of a central repository avoids duplicate vendoring of libraries across multiple R packages, and enables better coordination of version updates across cohorts of interdependent C++ libraries.

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 an annotation database of Homo sapiens genome data. It is derived from the UCSC hg19 genome and based on the "knownGene" track. The database is exposed as a TxDb object.

This package aims to make NMR spectroscopy data analysis as easy as possible. It only requires a small set of functions to perform an entire analysis. Speaq offers the possibility of raw spectra alignment and quantitation but also an analysis based on features whereby the spectra are converted to peaks which are then grouped and turned into features. These features can be processed with any number of statistical tools either included in speaq or available elsewhere on CRAN.

This package contains the basic methods needed to generate interactive Shiny-based display methods for Bioconductor objects.

rGADEM is an efficient de novo motif discovery tool for large-scale genomic sequence data.