High-throughput experimental data are accumulating exponentially in public databases. However, mining valid scientific discoveries from these abundant resources is hampered by technical artifacts and inherent biological heterogeneity. The former are usually termed "batch effects," and the latter is often modelled by "subtypes." The R package BUScorrect fits a Bayesian hierarchical model, the Batch-effects-correction-with-Unknown-Subtypes model (BUS), to correct batch effects in the presence of unknown subtypes. BUS is capable of (a) correcting batch effects explicitly, (b) grouping samples that share similar characteristics into subtypes, (c) identifying features that distinguish subtypes, and (d) enjoying a linear-order computation complexity.

Full genome sequences for Danio rerio (Zebrafish) as provided by UCSC (danRer5, Jul. 2007) and stored in Biostrings objects.

Full genome sequences for Pan paniscus (Bonobo) as provided by UCSC (panPan1, May 2012) and stored in Biostrings objects.

Genexpression data from a breast cancer study published by van't Veer et al. in 2002 and van de Vijver et al. in 2002, provided as an eSet.

Precise knowledge on the binding sites of an RNA-binding protein (RBP) is key to understand (post-) transcriptional regulatory processes. Here we present a workflow that describes how exact binding sites can be defined from iCLIP data. The package provides functions for binding site definition and result visualization. For details please see the vignette.

Full genome sequences for Macaca mulatta (Rhesus) as provided by UCSC (rheMac3, Oct. 2010) and stored in Biostrings objects.

Full reference nuclear genome sequences for Vitis vinifera subsp. vinifera PN40024 (derived from Pinot Noir and close to homozygosity after 6-9 rounds of selfing) as assembled by the IGGP (version 12Xv0) and available at the URGI (INRA).

This package provides an interface to infer the parameters of BASiCS using the variational inference (ADVI), Markov chain Monte Carlo (NUTS), and maximum a posteriori (BFGS) inference engines in the Stan programming language. BASiCS is a Bayesian hierarchical model that uses an adaptive Metropolis within Gibbs sampling scheme. Alternative inference methods provided by Stan may be preferable in some situations, for example for particularly large data or posterior distributions with difficult geometries.

Represents the OpenAPI v2 Azul API as an R object for performing requests. The infrastructure uses the AnVIL and rapiclient packages. Users can connect to either the AnVIL or Human Cell Atlas Data Explorers.

R interface for importing and analyzing enzyme information from the BRENDA database.

This is a probabilistic modelling pipeline for computing per- nucleotide posterior probabilities of modification from the data collected in structure probing experiments. The model supports multiple experimental replicates and empirically corrects coverage- and sequence-dependent biases. The model utilises the measure of a "drop-off rate" for each nucleotide, which is compared between replicates through a log-ratio (LDR). The LDRs between control replicates define a null distribution of variability in drop-off rate observed by chance and LDRs between treatment and control replicates gets compared to this distribution. Resulting empirical p-values (probability of being "drawn" from the null distribution) are used as observations in a Hidden Markov Model with a Beta-Uniform Mixture model used as an emission model. The resulting posterior probabilities indicate the probability of a nucleotide of having being modified in a structure probing experiment.

Use BridgeDb functions and load identifier mapping databases in R. It uses GitHub, Zenodo, and Figshare if you use this package to download identifier mappings files.

Saccharomyces cerevisiae (Yeast) full genome as provided by UCSC (sacCer3, April 2011) and stored in Biostrings objects.

Full genome sequences for Mus musculus (Mouse) as provided by UCSC (genome mm39, based on assembly GRCm39) and stored in Biostrings objects.

Blacksheep is a tool designed for outlier analysis in the context of pairwise comparisons in an effort to find distinguishing characteristics from two groups. This tool was designed to be applied for biological applications such as phosphoproteomics or transcriptomics, but it can be used for any data that can be represented by a 2D table, and has two sub populations within the table to compare.

Full genome sequences for Mustela putorius furo (Ferret) as provided by UCSC (musFur1, Apr. 2011) and stored in Biostrings objects.

Full genome sequences for Pan troglodytes (Chimp) as provided by UCSC (panTro2, Mar. 2006) and stored in Biostrings objects. The sequences are the same as in BSgenome.Ptroglodytes.UCSC.panTro2, 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.

Full genome sequences for Chlamydomonas reinhardtii (v5.6) as provided by JGI and stored in Biostrings objects. The data in this package is public. See citation("BSgenome.Creinhardtii.JGI.v5.6") for how to cite in publications.

Full reference nuclear genome sequences for Vitis vinifera subsp. vinifera PN40024 (derived from Pinot Noir and close to homozygosity after 6-9 rounds of selfing) as assembled by the IGGP (version 12Xv2) and available at the URGI (INRA).

Model-based analysis of single-cell methylation data.

Full genome sequences for Arabidopsis thaliana as provided by TAIR (snapshot from April 23, 2008) and stored in Biostrings objects.

Full genome sequences for Bos taurus (Cow) as provided by UCSC (bosTau3, Aug. 2006) and stored in Biostrings objects.

bnem combines the use of indirect measurements of Nested Effects Models (package mnem) with the Boolean networks of CellNOptR. Perturbation experiments of signalling nodes in cells are analysed for their effect on the global gene expression profile. Those profiles give evidence for the Boolean regulation of down-stream nodes in the network, e.g., whether two parents activate their child independently (OR-gate) or jointly (AND-gate).

The BloodGen3Module package provides functions for R user performing module repertoire analyses and generating fingerprint representations. Functions can perform group comparison or individual sample analysis and visualization by fingerprint grid plot or fingerprint heatmap. Module repertoire analyses typically involve determining the percentage of the constitutive genes for each module that are significantly increased or decreased. As we describe in details;https://www.biorxiv.org/content/10.1101/525709v2 and https://pubmed.ncbi.nlm.nih.gov/33624743/, the results of module repertoire analyses can be represented in a fingerprint format, where red and blue spots indicate increases or decreases in module activity. These spots are subsequently represented either on a grid, with each position being assigned to a given module, or in a heatmap where the samples are arranged in columns and the modules in rows.