The package ASGSCA (Association Study using Generalized Structured Component Analysis) provides tools to model and test the association between multiple genotypes and multiple traits, taking into account the prior biological knowledge. Genes, and clinical pathways are incorporated in the model as latent variables.

This package provides a set of low-level utilities to retrieve data from the UCSC Genome Browser. Most functions in the package access the data via the UCSC REST API but some of them query the UCSC MySQL server directly. Note that the primary purpose of the package is to support higher-level functionalities implemented in downstream packages like GenomeInfoDb or txdbmaker.

GOfuncR performs a gene ontology enrichment analysis based on the ontology enrichment software FUNC. GO-annotations are obtained from OrganismDb or OrgDb packages (Homo.sapiens by default); the GO-graph is included in the package and updated regularly. GOfuncR provides the standard candidate vs background enrichment analysis using the hypergeometric test, as well as three additional tests:

1. the Wilcoxon rank-sum test that is used when genes are ranked,

2. a binomial test that is used when genes are associated with two counts, and

3. a Chi-square or Fisher's exact test that is used in cases when genes are associated with four counts.

To correct for multiple testing and interdependency of the tests, family-wise error rates are computed based on random permutations of the gene-associated variables. GOfuncR also provides tools for exploring the ontology graph and the annotations, and options to take gene-length or spatial clustering of genes into account. It is also possible to provide custom gene coordinates, annotations and ontologies.

This package implements `import()` and `export()` standard generics for importing and exporting biological data formats. `import()` supports whole-file as well as chunk-wise iterative import. The `import()` interface optionally provides a standard mechanism for 'lazy' access via `filter()` (on row or element-like components of the file resource), `select()` (on column-like components of the file resource) and `collect()`. The `import()` interface optionally provides transparent access to remote (e.g. via https) as well as local access. Developers can register a file extension, e.g., `.loom` for dispatch from character-based URIs to specific `import()` / `export()` methods based on classes representing file types, e.g., `LoomFile()`.

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 can be used to test two sets of gene lists and visualize the results.

This package provides a database of PolyPhen predictions for Homo sapiens dbSNP build 131.

Feature selection is critical in omics data analysis to extract restricted and meaningful molecular signatures from complex and high-dimension data, and to build robust classifiers. This package implements a method to assess the relevance of the variables for the prediction performances of the classifier. The approach can be run in parallel with the PLS-DA, Random Forest, and SVM binary classifiers. The signatures and the corresponding 'restricted' models are returned, enabling future predictions on new datasets.

The package includes functions to retrieve the sequences around the peak, obtain enriched Gene Ontology (GO) terms, find the nearest gene, exon, miRNA or custom features such as most conserved elements and other transcription factor binding sites supplied by users. Starting 2.0.5, new functions have been added for finding the peaks with bi-directional promoters with summary statistics (peaksNearBDP), for summarizing the occurrence of motifs in peaks (summarizePatternInPeaks) and for adding other IDs to annotated peaks or enrichedGO (addGeneIDs).

This package provides full genome sequences for Drosophila melanogaster (Fly) as provided by UCSC (dm6) and stored in Biostrings objects.

ChIPComp implements a statistical method for quantitative comparison of multiple ChIP-seq datasets. It detects differentially bound sharp binding sites across multiple conditions considering matching control in ChIP-seq datasets.

The enrichplot package implements several visualization methods for interpreting functional enrichment results obtained from ORA or GSEA analyses. All the visualization methods are developed based on ggplot2 graphics.

BBCAnalyzer is a package for visualizing the relative or absolute number of bases, deletions and insertions at defined positions in sequence alignment data available as bam files in comparison to the reference bases. Markers for the relative base frequencies, the mean quality of the detected bases, known mutations or polymorphisms and variants called in the data may additionally be included in the plots.

This package supports the application of diverse quality metrics to AffyBatch instances, summarizing these metrics via PCA, and then performing parametric outlier detection on the PCs to identify aberrant arrays with a fixed Type I error rate.

This package provides a dplyr-like interface for interacting with the common Bioconductor classes Ranges and GenomicRanges. By providing a grammatical and consistent way of manipulating these classes their accessibility for new Bioconductor users is hopefully increased.

This package provides tools for the computationally efficient analysis of quantitative trait loci (QTL) data, including eQTL, mQTL, dsQTL, etc. The software in this package aims to support refinements and functional interpretation of members of a collection of association statistics on a family of feature/genome hypotheses.

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.

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

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.

This package provides summarized MinION sequencing data for Salmonella Typhi published by Ashton et al. in 2015. Three replicate runs are each provided as Fast5Summary objects.

This package provides tools to produce a graphical display, as a heat map, of measures of pairwise linkage disequilibria between SNPs. Users may optionally include the physical locations or genetic map distances of each SNP on the plot.

Microarray quality assessment is a major concern of microarray analysts. This package provides some simple approaches to in silico creation of quality problems in CEL-level data to help evaluate performance of quality metrics.

This package generates microarray quality metrics reports for data in Bioconductor microarray data containers (ExpressionSet, NChannelSet, AffyBatch). One and two color array platforms are supported.

The scRepertoire package was built to process data derived from the 10x Genomics Chromium Immune Profiling for both TCR and Ig enrichment workflows and subsequently interacts with the popular Seurat and SingleCellExperiment R packages. It also allows for general analysis of single-cell clonotype information without the use of expression information. The package functions as a wrapper for Startrac and powerTCR R packages.