This package provides microarray data (from the Illumina Ref-8 BeadChips platform) and phenotype-level data from an epidemiological investigation of benzene exposure, packaged using SummarizedExperiemnt, for use as an example with the biotmle R package.

This package provides SNP locations and alleles for Homo sapiens extracted from NCBI dbSNP Build 144. The source data files used for this package were created by NCBI on May 29-30, 2015, and contain SNPs mapped to reference genome GRCh37.p13. Note that the GRCh37.p13 genome is a patched version of GRCh37. However the patch doesn't alter chromosomes 1-22, X, Y, MT. GRCh37 itself is the same as the hg19 genome from UCSC *except* for the mitochondrion chromosome. Therefore, the SNPs in this package can be injected in BSgenome.Hsapiens.UCSC.hg19 and they will land at the correct position but this injection will exclude chrM (i.e. nothing will be injected in that sequence).

This package contains genome-wide annotations for Human, primarily based on mapping using Entrez Gene identifiers.

This package implements the density-preserving modification to t-SNE and UMAP described by Narayan et al. (2020) <doi:10.1101/2020.05.12.077776>. den-SNE and densMAP aim to enable more accurate visual interpretation of high-dimensional datasets by producing lower-dimensional embeddings that accurately represent the heterogeneity of the original high-dimensional space, enabling the identification of homogeneous and heterogeneous cell states. This accuracy is accomplished by including in the optimisation process a term which considers the local density of points in the original high-dimensional space. This can help to create visualisations that are more representative of heterogeneity in the original high-dimensional space.

This package provides an annotation database of Homo sapiens genome data. It is derived from the UCSC hg38 genome and based on the "knownGene" track. The database is exposed as a TxDb object.

The package includes functions to retrieve the sequences around the peak, obtain enriched Gene Ontology (GO) terms, find the nearest gene, exon, miRNA or custom features such as most conserved elements and other transcription factor binding sites supplied by users. Starting 2.0.5, new functions have been added for finding the peaks with bi-directional promoters with summary statistics (peaksNearBDP), for summarizing the occurrence of motifs in peaks (summarizePatternInPeaks) and for adding other IDs to annotated peaks or enrichedGO (addGeneIDs).

This package provides genome wide annotations for Zebrafish, primarily based on mapping using Entrez Gene identifiers.

This package provides a client for the gypsum REST API (https://gypsum.artifactdb.com), a cloud-based file store in the ArtifactDB ecosystem. This package provides functions for uploads, downloads, and various administrative and management tasks. Check out the documentation at https://github.com/ArtifactDB/gypsum-worker for more details.

This package works analogous to BiocManager but for Docker images. Use the BiocDockerManager package to install and manage Docker images provided by the Bioconductor project.

The purpose of this GO.db annotation package is to provide detailed information about the latest version of the Gene Ontologies.

This is a package for the assessment and comparison of the performance of risk prediction (survival) models.

This package provides a manifest package for Illumina's EPIC v2.0 methylation arrays. The version 2 covers more than 935K CpG sites in the human genome hg38. It is an update of the original EPIC v1.0 array (i.e., the 850K methylation array).

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

This is a package for the automated analysis of Affymetrix arrays. It provides reporting features.

This package provides repository information for the appropriate version of Bioconductor.

The data consist of microarrays from 128 different individuals with acute lymphoblastic leukemia (ALL). A number of additional covariates are available. The data have been normalized (using rma) and it is the jointly normalized data that are available here. The data are presented in the form of an exprSet object.

This package provides a package that provides a client interface to the Kyoto Encyclopedia of Genes and Genomes (KEGG) REST server.

This package provides a set of tools and methods for making and manipulating transcript centric annotations. With these tools the user can easily download the genomic locations of the transcripts, exons and cds of a given organism, from either the UCSC Genome Browser or a BioMart database (more sources will be supported in the future). This information is then stored in a local database that keeps track of the relationship between transcripts, exons, cds and genes. Flexible methods are provided for extracting the desired features in a convenient format.

The msa package provides a unified R/Bioconductor interface to the multiple sequence alignment algorithms ClustalW, ClustalOmega, and Muscle. All three algorithms are integrated in the package, therefore, they do not depend on any external software tools and are available for all major platforms. The multiple sequence alignment algorithms are complemented by a function for pretty-printing multiple sequence alignments using the LaTeX package TeXshade.

This package contains example data for Illumina microarray output files, for testing purposes.

This package provides mappings from Entrez gene identifiers to various annotations for the genome of the model mouse Mus musculus.

This package provides a new clustering algorithm, binary cut, for clustering similarity matrices of functional terms is implemented in this package. It also provides functionalities for visualizing, summarizing and comparing the clusterings.

This package implements clustering of microarray gene expression profiles according to functional annotations. For each term genes are annotated to, splits into two subclasses are computed and a significance of the supporting gene set is determined.

The objective of AGDEX is to evaluate whether the results of a pair of two-group differential expression analysis comparisons show a level of agreement that is greater than expected if the group labels for each two-group comparison are randomly assigned. The agreement is evaluated for the entire transcriptome and (optionally) for a collection of pre-defined gene-sets. Additionally, the procedure performs permutation-based differential expression and meta analysis at both gene and gene-set levels of the data from each experiment.