Save MultiAssayExperiments to h5mu files supported by muon and mudata. Muon is a Python framework for multimodal omics data analysis. It uses an HDF5-based format for data storage.

Predicts the sex of samples in gene expression microarray datasets.

Microbiome time series simulation with generalized Lotka-Volterra model, Self-Organized Instability (SOI), and other models. Hubbell's Neutral model is used to determine the abundance matrix. The resulting abundance matrix is applied to (Tree)SummarizedExperiment objects.

Useful functions to work with sequence motifs in the analysis of genomics data. These include methods to annotate genomic regions or sequences with predicted motif hits and to identify motifs that drive observed changes in accessibility or expression. Functions to produce informative visualizations of the obtained results are also provided.

Experimental data for use with the MutSeqR vignette and examples. This dataset is taken from LeBlanc et al., 2022. 24 MutaMouse animals were exposed to one of three doses of benzo[a]pyrene or a vehicle control for 28 days by oral gavage. 28 days after the end of the exposure, bone marrow of the femurs was harvested from euthanized animals. DNA extraction was conducted via DNeasy Blood and Tissue kit. DNA samples were sequenced using TwinStrand's Duplex Sequencing on the Mouse Mutagenesis Panel at > 10,000 depth. The Mouse Mutagenesis Panel comprises 20 2.4kb genomic targets with one located on each mouse autosome (two on chromosome 1). Pre-processing of sequence reads was redone since publication using an updated version of TwinStrand's Mutagenesis App (v. 3.20.1) which produced tabular mutation data files for each sample. Data contained herein are only those required for running MutSeqR examples and vignette.

Annotation data file for mouseCHRLOC assembled using data from public data repositories.

Affymetrix mta10 annotation data (chip mta10transcriptcluster) assembled using data from public repositories.

This package provides a collection of datasets to accompany the R package MOFA and illustrate running and analysing MOFA models.

MOGAMUN is a multi-objective genetic algorithm that identifies active modules in a multiplex biological network. This allows analyzing different biological networks at the same time. MOGAMUN is based on NSGA-II (Non-Dominated Sorting Genetic Algorithm, version II), which we adapted to work on networks.

The package implements MBASED algorithm for detecting allele-specific gene expression from RNA count data, where allele counts at individual loci (SNVs) are integrated into a gene-specific measure of ASE, and utilizes simulations to appropriately assess the statistical significance of observed ASE.

The missRows package implements the MI-MFA method to deal with missing individuals ('biological units') in multi-omics data integration. The MI-MFA method generates multiple imputed datasets from a Multiple Factor Analysis model, then the yield results are combined in a single consensus solution. The package provides functions for estimating coordinates of individuals and variables, imputing missing individuals, and various diagnostic plots to inspect the pattern of missingness and visualize the uncertainty due to missing values.

MSstatsQCgui is a Shiny app which provides longitudinal system suitability monitoring and quality control tools for proteomic experiments.

The Molecular Degree of Perturbation webtool quantifies the heterogeneity of samples. It takes a data.frame of omic data that contains at least two classes (control and test) and assigns a score to all samples based on how perturbed they are compared to the controls. It is based on the Molecular Distance to Health (Pankla et al. 2009), and expands on this algorithm by adding the options to calculate the z-score using the modified z-score (using median absolute deviation), change the z-score zeroing threshold, and look at genes that are most perturbed in the test versus control classes.

The package provides a comprehensive mapping table of nine different Metabolite ID formats and their common name. The data has been collected and merged from four publicly available source, including HMDB, Comptox Dashboard, ChEBI, and the graphite Bioconductor R package.

Motivation: The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). Results: We present an R/bioconductor package called MoonlightR which returns a list of candidate driver genes for specific cancer types on the basis of TCGA expression data. The method first infers gene regulatory networks and then carries out a functional enrichment analysis (FEA) (implementing an upstream regulator analysis, URA) to score the importance of well-known biological processes with respect to the studied cancer type. Eventually, by means of random forests, MoonlightR predicts two specific roles for the candidate driver genes: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, MoonlightR can be used to discover OCGs and TSGs in the same cancer type. This may help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV) in breast cancer. In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments.

msPurity R package was developed to: 1) Assess the spectral quality of fragmentation spectra by evaluating the "precursor ion purity". 2) Process fragmentation spectra. 3) Perform spectral matching. What is precursor ion purity? -What we call "Precursor ion purity" is a measure of the contribution of a selected precursor peak in an isolation window used for fragmentation. The simple calculation involves dividing the intensity of the selected precursor peak by the total intensity of the isolation window. When assessing MS/MS spectra this calculation is done before and after the MS/MS scan of interest and the purity is interpolated at the recorded time of the MS/MS acquisition. Additionally, isotopic peaks can be removed, low abundance peaks are removed that are thought to have limited contribution to the resulting MS/MS spectra and the isolation efficiency of the mass spectrometer can be used to normalise the intensities used for the calculation.

The package contains functions for inferece of target gene regulation by miRNA, based on only target gene expression profile.

Memory efficient analysis of base resolution DNA methylation data in both the CpG and non-CpG sequence context. Integration of DNA methylation data derived from any methodology providing base- or low-resolution data.

It contains functions for estimating the DNA copy number profile using mBPCR with the aim of detecting regions with copy number changes.

MOSim package simulates multi-omic experiments that mimic regulatory mechanisms within the cell, allowing flexible experimental design including time course and multiple groups.

Affymetrix Affymetrix MG_U74C Array annotation data (chip mgu74c) assembled using data from public repositories.

Affymetrix mogene20 annotation data (chip mogene20stprobeset) assembled using data from public repositories.

Mass spectrometry (MS) data backend supporting import and export of MS/MS library spectra from MassBank record files. Different backends are available that allow handling of data in plain MassBank text file format or allow also to interact directly with MassBank SQL databases. Objects from this package are supposed to be used with the Spectra Bioconductor package. This package thus adds MassBank support to the Spectra package.

Analysis of de novo copy number variants in trios from high-dimensional genotyping platforms.