Codelink ADME Rat 16-Assay Bioarray annotation data (chip adme16cod) assembled using data from public repositories.

Supplies AnnotationHub with `MeSHDb` NIH MeSH annotation databases for many species. All the SQLite files and metadata.csv are generated by our Snakemake workflow [mesh-workflow](https://github.com/rikenbit/mesh-workflow).

The AWAggregatorData package contains the data associated with the AWAggregator R package. It includes two pre-trained random forest models, one incorporating the average coefficient of variation as a feature, and the other one not including it. It also contains the PSMs in Benchmark Set 1~3 derived from the psm.tsv output files generated by FragPipe, which are used to train the random forest models.

Colon normal tissue and cancer samples used in Corrada Bravo, et al. gene expression anti-profiles paper: BMC Bioinformatics 2012, 13:272 doi:10.1186/1471-2105-13-272. Measurements are z-scores obtained from the GeneExpression Barcode in the frma package.

ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).

The appreci8R is an R version of our appreci8-algorithm - A Pipeline for PREcise variant Calling Integrating 8 tools. Variant calling results of our standard appreci8-tools (GATK, Platypus, VarScan, FreeBayes, LoFreq, SNVer, samtools and VarDict), as well as up to 5 additional tools is combined, evaluated and filtered.

Data frame containing alternative splicing events. The splicing events were compiled from the annotation files used by the alternative splicing quantification tools MISO, VAST-TOOLS, SUPPA and rMATS.

Supplies AnnotationHub with some preprocessed sqlite, tibble, and data.table datasets of PubMed. All the datasets are generated by our Snakemake workflow [pubmed-workflow](https://github.com/rikenbit/pubmed-workflow). For the details, see the README.md of pubmed-workflow.

Data needed by the affycomp package.

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 ATH1-121501\_probe\_tab.

This package provides a package containing an environment representing the AG.CDF file.

Umbrella for the alabaster suite, providing a single-line import for all alabaster.* packages. Installing this package ensures that all known alabaster.* packages are also installed, avoiding problems with missing packages when a staging method or loading function is dynamically requested. Obviously, this comes at the cost of needing to install more packages, so advanced users and application developers may prefer to install the required alabaster.* packages individually.

Store Google DeepMind AlphaMissense v2023 hg38 pathogenicity scores AnnotationHub Resource Metadata. Provide provenance and citation information for Google DeepMind AlphaMissense v2023 hg38 pathogenicity score AnnotationHub resources. Illustrate in a vignette how to access those resources.

ADAPT carries out differential abundance analysis for microbiome metagenomics data in phyloseq format. It has two innovations. One is to treat zero counts as left censored and use Tobit models for log count ratios. The other is an innovative way to find non-differentially abundant taxa as reference, then use the reference taxa to find the differentially abundant ones.

Save common bioinformatics file formats within the alabaster framework. This includes BAM, BED, VCF, bigWig, bigBed, FASTQ, FASTA and so on. We save and load additional metadata for each file, and we support linkage between each file and its corresponding index.

This package unifies access to Statistal Modeling of Omics Data. Across linear modeling engines (lm, lme, lmer, limma, and wilcoxon). Across coding systems (treatment, difference, deviation, etc). Across model formulae (with/without intercept, random effect, interaction or nesting). Across omics platforms (microarray, rnaseq, msproteomics, affinity proteomics, metabolomics). Across projection methods (pca, pls, sma, lda, spls, opls). Across clustering methods (hclust, pam, cmeans). Across survival methods (coxph, survdiff, coin). It provides a fast enrichment analysis implementation.

Save SpatialExperiment objects and their images into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

This package provides access to mapping and results objects generated by the AssessORF package, as well as the genome sequences for the strains corresponding to those objects.

Save Biostrings objects to file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

Save variant calling SummarizedExperiment to file and load them back as VCF objects. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

The package provides a comprehensive mapping table of metabolites and proteins linked to PathBank pathways. The tables include HMDB, KEGG, ChEBI, CAS, Drugbank, Uniprot IDs. The tables are provided for each of the 10 species ("Homo sapiens", "Escherichia coli", "Mus musculus", "Arabidopsis thaliana", "Saccharomyces cerevisiae", "Bos taurus", "Caenorhabditis elegans", "Rattus norvegicus", "Drosophila melanogaster", and "Pseudomonas aeruginosa"). These table information can be used for Metabolite Set (and other) Enrichment Analysis.

Save BumpyMatrix objects into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

Store Google DeepMind AlphaMissense v2023 hg19 pathogenicity scores AnnotationHub Resource Metadata. Provide provenance and citation information for Google DeepMind AlphaMissense v2023 hg19 pathogenicity score AnnotationHub resources. Illustrate in a vignette how to access those resources.

This package contains pre-built mouse (GPL1261) database of gene expression profiles. The gene expression data was downloaded from NCBI GEO, preprocessed and normalized consistently. The biological context of each sample was recorded and manually verified based on the sample description in GEO.