This package implements algorithms for calculating microarray enrichment (ACME), and it is a set of tools for analysing tiling array of combined chromatin immunoprecipitation with DNA microarray (ChIP/chip), DNAse hypersensitivity, or other experiments that result in regions of the genome showing enrichment. It does not rely on a specific array technology (although the array should be a tiling array), is very general (can be applied in experiments resulting in regions of enrichment), and is very insensitive to array noise or normalization methods. It is also very fast and can be applied on whole-genome tiling array experiments quite easily with enough memory.

This package provides a toolset for deciphering and managing biological sequences.

This package provides functionality for the compression and decompression of raw bead-level data from the Illumina BeadArray platform.

This package takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values and local false discovery rate (FDR) values. The q-value of a test measures the proportion of false positives incurred when that particular test is called significant. The local FDR measures the posterior probability the null hypothesis is true given the test's p-value. Various plots are automatically generated, allowing one to make sensible significance cut-offs. The software can be applied to problems in genomics, brain imaging, astrophysics, and data mining.

This package is designed to facilitate the automated gating methods in a sequential way to mimic the manual gating strategy.

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 a set of functions to create and interact with dynamic documents and vignettes.

This package contains useful helper functions for dealing with structural variants in VCF format. The packages contains functions for parsing VCFs from a number of popular callers as well as functions for dealing with breakpoints involving two separate genomic loci encoded as GRanges objects.

This package provides an interface to simulate metabolic reconstruction from the BiGG database and other metabolic reconstruction databases. The package facilitates flux balance analysis (FBA) and the sampling of feasible flux distributions. Metabolic networks and estimated fluxes can be visualized with hypergraphs.

This package implements a method to rapidly assess cell type identity using both functional and random gene sets and it allows users to quantify cell type replicability across datasets using neighbor voting. MetaNeighbor works on the basis that cells of the same type should have more similar gene expression profiles than cells of different types.

This package lets you programmatically access the NIH/NCI Genomic Data Commons RESTful service.

ASICS quantifies concentration of metabolites in a complex spectrum. The identification of metabolites is performed by fitting a mixture model to the spectra of the library with a sparse penalty.

This package performs hybrid multiple testing that incorporates method selection and assumption evaluations into the analysis using EBP estimates obtained by Grenander density estimation. For instance, for 3-group comparison analysis, Hybrid Multiple testing considers EBPs as weighted EBPs between F-test and H-test with EBPs from Shapiro Wilk test of normality as weight. Instead of just using EBPs from F-test only or using H-test only, this methodology combines both types of EBPs through EBPs from Shapiro Wilk test of normality. This methodology uses then the law of total EBPs.

This package provides tools for alignment, quantification and analysis of second and third generation sequencing data. It includes functionality for read mapping, read counting, SNP calling, structural variant detection and gene fusion discovery. It can be applied to all major sequencing techologies and to both short and long sequence reads.

This package offers functions to process multiple ChIP-seq BAM files and detect allele-specific events. It computes allele counts at individual variants (SNPs/SNVs), implements extensive QC (quality control) steps to remove problematic variants, and utilizes a Bayesian framework to identify statistically significant allele-specific events. BaalChIP is able to account for copy number differences between the two alleles, a known phenotypical feature of cancer samples.

Independent hypothesis weighting (IHW) is a multiple testing procedure that increases power compared to the method of Benjamini and Hochberg by assigning data-driven weights to each hypothesis. The input to IHW is a two-column table of p-values and covariates. The covariate can be any continuous-valued or categorical variable that is thought to be informative on the statistical properties of each hypothesis test, while it is independent of the p-value under the null hypothesis.

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 tools for representing and modeling data in the EMBL-EBI GWAS catalog.

ASEB is an R package to predict lysine sites that can be acetylated by a specific KAT (K-acetyl-transferases) family. Lysine acetylation is a well-studied posttranslational modification on kinds of proteins. About four thousand lysine acetylation sites and over 20 lysine KATs have been identified. However, which KAT is responsible for a given protein or lysine site acetylation is mostly unknown. In this package, we use a GSEA-like (Gene Set Enrichment Analysis) method to make predictions. GSEA method was developed and successfully used to detect coordinated expression changes and find the putative functions of the long non-coding RNAs.

This package provides a flexible method for fitting regression models that can be used to find genes that are differentially expressed along one or multiple lineages in a trajectory. Based on the fitted models, it uses a variety of tests suited to answer different questions of interest, e.g. the discovery of genes for which expression is associated with pseudotime, or which are differentially expressed (in a specific region) along the trajectory. It fits a negative binomial generalized additive model (GAM) for each gene, and performs inference on the parameters of the GAM.

This package is used for the analysis of long-range chromatin interactions from 3C-seq assay.

The method implemented in this package performs bottom-up hierarchical clustering, using a Dirichlet Process (infinite mixture) to model uncertainty in the data and Bayesian model selection to decide at each step which clusters to merge. This avoids several limitations of traditional methods, for example how many clusters there should be and how to choose a principled distance metric. This implementation accepts multinomial (i.e. discrete, with 2+ categories) or time-series data. This version also includes a randomised algorithm which is more efficient for larger data sets.

This package is focused on finding differential exon usage using RNA-seq exon counts between samples with different experimental designs. It provides functions that allows the user to make the necessary statistical tests based on a model that uses the negative binomial distribution to estimate the variance between biological replicates and generalized linear models for testing. The package also provides functions for the visualization and exploration of the results.

This package provides quantitative variant callers for detecting subclonal mutations in ultra-deep (>=100x coverage) sequencing experiments. The deepSNV algorithm is used for a comparative setup with a control experiment of the same loci and uses a beta-binomial model and a likelihood ratio test to discriminate sequencing errors and subclonal SNVs. The shearwater algorithm computes a Bayes classifier based on a beta-binomial model for variant calling with multiple samples for precisely estimating model parameters - such as local error rates and dispersion - and prior knowledge, e.g. from variation data bases such as COSMIC.