MetCirc comprises a workflow to interactively explore high-resolution MS/MS metabolomics data. MetCirc uses the Spectra object infrastructure defined in the package Spectra that stores MS/MS spectra. MetCirc offers functionality to calculate similarity between precursors based on the normalised dot product, neutral losses or user-defined functions and visualise similarities in a circular layout. Within the interactive framework the user can annotate MS/MS features based on their similarity to (known) related MS/MS features.

Store minor allele frequency data from the Phase 1 of the 1000 Genomes Project for the human genome version GRCh38.

Agilent annotation data (chip mgug4120a) assembled using data from public repositories.

The package is unified implementation of MeSH.db, MeSH.AOR.db, and MeSH.PCR.db and also is interface to construct Gene-MeSH package (MeSH.XXX.eg.db). loadMeSHDbiPkg import sqlite file and generate MeSH.XXX.eg.db.

Clontech BD Atlas Long Oligos Mouse 5K annotation data (chip mguatlas5k) assembled using data from public repositories.

Base-level (i.e. cytosine-level) counts for a collection of public bisulfite-seq datasets (e.g., WGBS and RRBS), provided as SummarizedExperiment objects with sample- and base-level metadata.

Data package containing a multi-sample multi-group spatial dataset in SpatialExperiment Bioconductor object format.

mastR is an R package designed for automated screening of signatures of interest for specific research questions. The package is developed for generating refined lists of signature genes from multiple group comparisons based on the results from edgeR and limma differential expression (DE) analysis workflow. It also takes into account the background noise of tissue-specificity, which is often ignored by other marker generation tools. This package is particularly useful for the identification of group markers in various biological and medical applications, including cancer research and developmental biology.

RNG_MRC Mouse Pangenomic 24k Set annotation data (chip mm24kresogen) assembled using data from public repositories.

Affymetrix mogene10 annotation data (chip mogene10sttranscriptcluster) assembled using data from public repositories.

microbiomeDataSets is a collection of microbiome datasets loaded from Bioconductor'S ExperimentHub infrastructure. The datasets serve as reference for workflows and vignettes published adjacent to the microbiome analysis tools on Bioconductor. Additional datasets can be added overtime and additions from authors are welcome.

Classification of pediatric tumors into biologically defined subtypes is challenging and multifaceted approaches are needed. For this aim, we developed a diagnostic classifier based on DNA methylation profiles. We offer MethPed as an easy-to-use toolbox that allows researchers and clinical diagnosticians to test single samples as well as large cohorts for subclass prediction of pediatric brain tumors. The current version of MethPed can classify the following tumor diagnoses/subgroups: Diffuse Intrinsic Pontine Glioma (DIPG), Ependymoma, Embryonal tumors with multilayered rosettes (ETMR), Glioblastoma (GBM), Medulloblastoma (MB) - Group 3 (MB_Gr3), Group 4 (MB_Gr3), Group WNT (MB_WNT), Group SHH (MB_SHH) and Pilocytic Astrocytoma (PiloAstro).

This package implements functions for calling methylation for all cytosines in the genome.

Affymetrix mogene21 annotation data (chip mogene21sttranscriptcluster) assembled using data from public repositories.

MoleculeExperiment contains functions to create and work with objects from the new MoleculeExperiment class. We introduce this class for analysing molecule-based spatial transcriptomics data (e.g., Xenium by 10X, Cosmx SMI by Nanostring, and Merscope by Vizgen). This allows researchers to analyse spatial transcriptomics data at the molecule level, and to have standardised data formats accross vendors.

This package provides tools for manipulating paired ranges and working with Hi-C data in R. Functionality includes manipulating/merging paired regions, generating paired ranges, extracting/aggregating interactions from `.hic` files, and visualizing the results. Designed for compatibility with plotgardener for visualization.

Codelink UniSet Mouse 20k I Bioarray annotation data (chip m20kcod) assembled using data from public repositories.

Affymetrix moex10 annotation data (chip moex10sttranscriptcluster) assembled using data from public repositories.

FHCRC Genomics Shared Resource Mu22v3 Annotation Data (Mu22v3) assembled using data from public repositories.

msqrob2 provides a robust linear mixed model framework for assessing differential abundance in MS-based Quantitative proteomics experiments. Our workflows can start from raw peptide intensities or summarised protein expression values. The model parameter estimates can be stabilized by ridge regression, empirical Bayes variance estimation and robust M-estimation. msqrob2's hurde workflow can handle missing data without having to rely on hard-to-verify imputation assumptions, and, outcompetes state-of-the-art methods with and without imputation for both high and low missingness. It builds on QFeature infrastructure for quantitative mass spectrometry data to store the model results together with the raw data and preprocessed data.

Store minor allele frequency data from the Exome Aggregation Consortium (ExAC release 1.0 subset of nonTCGA exomes) for the human genome version GRCh38.

Agilent Mouse annotation data (chip mgug4121a) assembled using data from public repositories.

MODA can be used to estimate and construct condition-specific gene co-expression networks, and identify differentially expressed subnetworks as conserved or condition specific modules which are potentially associated with relevant biological processes.

An increasing number of microbiome datasets have been generated and analyzed with the help of rapidly developing sequencing technologies. At present, analysis of taxonomic profiling data is mainly conducted using composition-based methods, which ignores interactions between community members. Besides this, a lack of efficient ways to compare microbial interaction networks limited the study of community dynamics. To better understand how community diversity is affected by complex interactions between its members, we developed a framework (Microbial community dIversity and Network Analysis, mina), a comprehensive framework for microbial community diversity analysis and network comparison. By defining and integrating network-derived community features, we greatly reduce noise-to-signal ratio for diversity analyses. A bootstrap and permutation-based method was implemented to assess community network dissimilarities and extract discriminative features in a statistically principled way.