Full genome sequences for Sus scrofa (Pig) as provided by UCSC (susScr3, Aug. 2011) and stored in Biostrings objects.

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

This package provides a novel approach utilizing a homogeneous hidden Markov model. And effectively model untransformed beta values. To identify DMCs while considering the spatial. Correlation of the adjacent CpG sites.

An small dataset that can be used to run examples from the beadarray vignette and examples.

Full genome sequences for Gallus gallus (Chicken) as provided by UCSC (galGal6, Mar. 2018) and stored in Biostrings objects.

Full genome sequences for Bos taurus (Cow) as provided by UCSC (bosTau9, Apr. 2018) and stored in Biostrings objects.

Full genome sequences for Asparagus officinalis (Garden asparagus) as provided by NCBI (Aspof.V1, Feb. 2017) and stored in Biostrings objects.

Gene expression data from a breast cancer study published by Sotiriou et al. in 2007, provided as an eSet.

This package is built to perform GWAS analysis for non-Gaussian data using BG2. The BG2 method uses penalized quasi-likelihood along with nonlocal priors in a two step manner to identify SNPs in GWAS analysis. The research related to this package was supported in part by National Science Foundation awards DMS 1853549 and DMS 2054173.

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

Microarray analysis methods that use BufferedMatrix objects.

Full genome sequences for Callithrix jacchus (Marmoset) as provided by UCSC (calJac3, Mar. 2009) and stored in Biostrings objects.

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

This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was Bovine\_probe\_tab.

Oryza sativa full genome as provided by MSU (MSU7 Genome Release) and stored in Biostrings objects.

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.

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 Rattus norvegicus (Rat) as provided by UCSC (rn5, Mar. 2012) and stored in Biostrings objects. The sequences are the same as in BSgenome.Rnorvegicus.UCSC.rn5, 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 Gasterosteus aculeatus (Stickleback) as provided by UCSC (gasAcu1, Feb. 2006) and stored in Biostrings objects.

This package contains several sets of omics data including Gene Expression (ExpressionSet), Methylation (GenomicRatioSet), Proteome and Exposome (ExposomeSet). This data is used in vignettes and exaples at MEAL, MultiDataSet and omicRexposome.

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.

`BatchSVG` is a feature-based Quality Control (QC) to identify SVGs on spatial transcriptomics data with specific types of batch effect. Regarding to the spatial transcriptomics data experiments, the batch can be defined as "sample", "sex", and etc.The `BatchSVG` method is based on binomial deviance model (Townes et al, 2019) and applies cutoffs based on the number of standard deviation (nSD) of relative change in deviance and rank difference as the data-driven thresholding approach to detect the batch-biased outliers.

The package provides a bioimage dataset for the image analysis using machine learning and deep learning. The dataset includes microscopy imaging data with supervised labels. The data is provided as R list data that can be loaded to Keras/tensorflow in R.

Full genome sequences for Danio rerio (Zebrafish) as provided by UCSC (danRer5, Jul. 2007) and stored in Biostrings objects. The sequences are the same as in BSgenome.Drerio.UCSC.danRer5, 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.