Full genome sequences for Bos taurus (Cow) as provided by UCSC (bosTau6, Nov. 2009) and stored in Biostrings objects.

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

Saccharomyces cerevisiae (Yeast) full genome as provided by UCSC (sacCer2, June 2008) and stored in Biostrings objects.

This package reads remote parquet files that have processed Bioconductor build report logs. Users may query the tables directly for specific information or use pre-defined helper functions for common queries. The logs processed are from https://bioconductor.org/checkResults/. In the future we will extend this package out to include processing of r-universe logs.

Base annotation databases for bovine, intended ONLY to be used by AnnotationDbi to produce regular annotation packages.

Full genome sequences for Taeniopygia guttata (Zebra finch) as provided by UCSC (taeGut2, Feb. 2013) 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).

Full genome sequences for Mus musculus (Mouse) as provided by UCSC (genome mm10, based on GRCm38.p6) and stored in Biostrings objects. The sequences are the same as in BSgenome.Mmusculus.UCSC.mm10, except that each of them has the 2 following masks on top: (1) the mask of assembly gaps (AGAPS mask), and (2) the mask of intra-contig ambiguities (AMB mask).

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

This package implements a variety of methods for batch correction in single-cell RNA sequencing (scRNA-seq) data. It incorporates quantitative metrics (e.g. Wasserstein distance, Adjusted Rand Index) to evaluate their performance. Furthermore, the package assists users in identifying and applying the optimal method for specific datasets.

COSMIC: Catalogue Of Somatic Mutations In Cancer, version 67 (2013-10-24).

This package provides a curated dataset of publicly available ChIP-sequencing of transcription factors, chromatin remodelers and histone modifications in the 3T3-L1 pre-adipocyte cell line. The package document the data collection, pre-processing and processing of the data. In addition to the documentation, the package contains the scripts that was used to generated the data.

COMPASS is a statistical framework that enables unbiased analysis of antigen-specific T-cell subsets. COMPASS uses a Bayesian hierarchical framework to model all observed cell-subsets and select the most likely to be antigen-specific while regularizing the small cell counts that often arise in multi-parameter space. The model provides a posterior probability of specificity for each cell subset and each sample, which can be used to profile a subject's immune response to external stimuli such as infection or vaccination.

Quality metrics for ChIPseq data.

cosmiq is a tool for the preprocessing of liquid- or gas - chromatography mass spectrometry (LCMS/GCMS) data with a focus on metabolomics or lipidomics applications. To improve the detection of low abundant signals, cosmiq generates master maps of the mZ/RT space from all acquired runs before a peak detection algorithm is applied. The result is a more robust identification and quantification of low-intensity MS signals compared to conventional approaches where peak picking is performed in each LCMS/GCMS file separately. The cosmiq package builds on the xcmsSet object structure and can be therefore integrated well with the package xcms as an alternative preprocessing step.

Package designed to visualize genomic data along the chromosomes, where the vertical chromosomes are sorted by number, with sex chromosomes at the end.

The cfTools R package provides methods for cell-free DNA (cfDNA) methylation data analysis to facilitate cfDNA-based studies. Given the methylation sequencing data of a cfDNA sample, for each cancer marker or tissue marker, we deconvolve the tumor-derived or tissue-specific reads from all reads falling in the marker region. Our read-based deconvolution algorithm exploits the pervasiveness of DNA methylation for signal enhancement, therefore can sensitively identify a trace amount of tumor-specific or tissue-specific cfDNA in plasma. cfTools provides functions for (1) cancer detection: sensitively detect tumor-derived cfDNA and estimate the tumor-derived cfDNA fraction (tumor burden); (2) tissue deconvolution: infer the tissue type composition and the cfDNA fraction of multiple tissue types for a plasma cfDNA sample. These functions can serve as foundations for more advanced cfDNA-based studies, including cancer diagnosis and disease monitoring.

The Connectivity Map (CMap) is a massive resource of perturbational gene expression profiles built by researchers at the Broad Institute and funded by the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. Please visit https://clue.io for more information. The cmapR package implements methods to parse, manipulate, and write common CMap data objects, such as annotated matrices and collections of gene sets.

The package includes DNA methylation data for the primary Chronic Lymphocytic Leukemia samples included in the Primary Blood Cancer Encyclopedia (PACE) project. Raw data from the 450k DNA methylation arrays is stored in the European Genome-Phenome Archive (EGA) under accession number EGAS0000100174. For more information concerning the project please refer to the paper "Drug-perturbation-based stratification of blood cancer" by Dietrich S, Oles M, Lu J et al., J. Clin. Invest. (2018) and R/Bioconductor package BloodCancerMultiOmics2017.

NChannelSet for rat hepatocytes treated with Carbon Tetrachloride (CCl4) data from LGC company.

Base annotation databases for canine, intended ONLY to be used by AnnotationDbi to produce regular annotation packages.

This package provides tools for computational epigenomics developed for the analysis, integration and simultaneous visualization of various (epi)genomics data types across multiple genomic regions in multiple samples.

This R package provides an R Shiny application that enables the user to generate, manage, and edit data and metadata files suitable for the import in cBioPortal for Cancer Genomics. Create cancer studies and edit its metadata. Upload mutation data of a patient that will be concatenated to the data_mutation_extended.txt file of the study. Create and edit clinical patient data, sample data, and timeline data. Create custom timeline tracks for patients.

Datasets and workflows for Cardinal: DESI and MALDI examples including pig fetus, cardinal painting, and human RCC.