Full genome sequences for Rattus norvegicus (Rat) as provided by UCSC (genome rn7) and stored in Biostrings objects.

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

Full genome sequences for Homo sapiens (Human) as provided by UCSC (hg17, May 2004) and stored in Biostrings objects.

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.

This package provides a set of tools to forge BSgenome data packages. Supersedes the old seed-based tools from the BSgenome software package. This package allows the user to create a BSgenome data package in one function call, simplifying the old seed-based process.

Inference of ligand-receptor (LR) interactions from bulk expression (transcriptomics/proteomics) data, or spatial transcriptomics. BulkSignalR bases its inferences on the LRdb database included in our other package, SingleCellSignalR available from Bioconductor. It relies on a statistical model that is specific to bulk data sets. Different visualization and data summary functions are proposed to help navigating prediction results.

BEER implements a Bayesian model for analyzing phage-immunoprecipitation sequencing (PhIP-seq) data. Given a PhIPData object, BEER returns posterior probabilities of enriched antibody responses, point estimates for the relative fold-change in comparison to negative control samples, and more. Additionally, BEER provides a convenient implementation for using edgeR to identify enriched antibody responses.

Gene expression data from the breast cancer study published by Schmidt et al. in 2008, provided as an eSet.

Full genome sequences for Canis lupus familiaris (Dog) as provided by UCSC (canFam2, May 2005) and stored in Biostrings objects. The sequences are the same as in BSgenome.Cfamiliaris.UCSC.canFam2, 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.

Raw data objects used to estimate saliva cell proportion estimates in ewastools. The FlowSorted.Saliva.EPIC object is constructed from samples assayed by Lauren Middleton et. al. (2021).

Full genome sequences for Sus scrofa (Pig) as provided by UCSC (susScr11, Feb. 2017) and stored in Biostrings objects.

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

The package contains data of the Primary Blood Cancer Encyclopedia (PACE) project together with a complete executable transcript of the statistical analysis and reproduces figures presented in the paper "Drug-perturbation-based stratification of blood cancer" by Dietrich S, Oleś M, Lu J et al., J. Clin. Invest. (2018) 128(1):427-445. doi:10.1172/JCI93801.

Genetic algorithm are a class of optimization algorithms inspired by the process of natural selection and genetics. This package allows users to analyze and optimize high throughput genomic data using genetic algorithms. The functions provided are implemented in C++ for improved speed and efficiency, with an easy-to-use interface for use within R.

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

coMethDMR identifies genomic regions associated with continuous phenotypes by optimally leverages covariations among CpGs within predefined genomic regions. Instead of testing all CpGs within a genomic region, coMethDMR carries out an additional step that selects co-methylated sub-regions first without using any outcome information. Next, coMethDMR tests association between methylation within the sub-region and continuous phenotype using a random coefficient mixed effects model, which models both variations between CpG sites within the region and differential methylation simultaneously.

Normalization and centralization of array comparative genomic hybridization (aCGH) data. The algorithm uses an iterative procedure that effectively eliminates the influence of imbalanced copy numbers. This leads to a more reliable assessment of copy number alterations (CNAs).

Example text-converted C01 image files for use in the CONFESS Bioconductor package.

The crisprVerse is a modular ecosystem of R packages developed for the design and manipulation of CRISPR guide RNAs (gRNAs). All packages share a common language and design principles. This package is designed to make it easy to install and load the crisprVerse packages in a single step. To learn more about the crisprVerse, visit <https://www.github.com/crisprVerse>.

The curatedBladderData package provides relevant functions and data for gene expression analysis in patients with bladder cancer.

This package provides a comprehensive suite of functions to design and annotate CRISPR guide RNA (gRNAs) sequences. This includes on- and off-target search, on-target efficiency scoring, off-target scoring, full gene and TSS contextual annotations, and SNP annotation (human only). It currently support five types of CRISPR modalities (modes of perturbations): CRISPR knockout, CRISPR activation, CRISPR inhibition, CRISPR base editing, and CRISPR knockdown. All types of CRISPR nucleases are supported, including DNA- and RNA-target nucleases such as Cas9, Cas12a, and Cas13d. All types of base editors are also supported. gRNA design can be performed on reference genomes, transcriptomes, and custom DNA and RNA sequences. Both unpaired and paired gRNA designs are enabled.

The CytoGLMM R package implements two multiple regression strategies: A bootstrapped generalized linear model (GLM) and a generalized linear mixed model (GLMM). Most current data analysis tools compare expressions across many computationally discovered cell types. CytoGLMM focuses on just one cell type. Our narrower field of application allows us to define a more specific statistical model with easier to control statistical guarantees. As a result, CytoGLMM finds differential proteins in flow and mass cytometry data while reducing biases arising from marker correlations and safeguarding against false discoveries induced by patient heterogeneity.

Annotation files of the formatted genomic annotation for ChromHMM. Three types of text files are included the chromosome sizes, region coordinates and anchors specifying the transcription start and end sites. The package includes data for two versions of the genome of humans and mice.

Store University of Washington CADD v1.6 hg38 pathogenicity scores AnnotationHub Resource Metadata. Provide provenance and citation information for University of Washington CADD v1.6 hg38 pathogenicity score AnnotationHub resources. Illustrate in a vignette how to access those resources.