mitology allows to study the mitochondrial activity throught high-throughput RNA-seq data. It is based on a collection of genes whose proteins localize in to the mitochondria. From these, mitology provides a reorganization of the pathways related to mitochondria activity from Reactome and Gene Ontology. Further a ready-to-use implementation of MitoCarta3.0 pathways is included.

MiDAS is a R package for immunogenetics data transformation and statistical analysis. MiDAS accepts input data in the form of HLA alleles and KIR types, and can transform it into biologically meaningful variables, enabling HLA amino acid fine mapping, analyses of HLA evolutionary divergence, KIR gene presence, as well as validated HLA-KIR interactions. Further, it allows comprehensive statistical association analysis workflows with phenotypes of diverse measurement scales. MiDAS closes a gap between the inference of immunogenetic variation and its efficient utilization to make relevant discoveries related to T cell, Natural Killer cell, and disease biology.

Affymetrix mogene11 annotation data (chip mogene11sttranscriptcluster) assembled using data from public repositories.

MetaDICT is a method for the integration of microbiome data. This method is designed to remove batch effects and preserve biological variation while integrating heterogeneous datasets. MetaDICT can better avoid overcorrection when unobserved confounding variables are present.

This package provides functions for interfacing with the Metabolomics Workbench RESTful API. Study, compound, protein and gene information can be searched for using the API. Methods to obtain study data in common Bioconductor formats such as SummarizedExperiment and MultiAssayExperiment are also included.

MWASTools provides a complete pipeline to perform metabolome-wide association studies. Key functionalities of the package include: quality control analysis of metabonomic data; MWAS using different association models (partial correlations; generalized linear models); model validation using non-parametric bootstrapping; visualization of MWAS results; NMR metabolite identification using STOCSY; and biological interpretation of MWAS results.

FHCRC Genomics Shared Resource Mu15v1 Annotation Data (Mu15v1) assembled using data from public repositories.

Package to integrate methylation and expression data. It can also perform methylation or expression analysis alone. Several plotting functionalities are included as well as a new region analysis based on redundancy analysis. Effect of SNPs on a region can also be estimated.

Mass spectrometry (MS) data backend supporting import and handling of MS/MS spectra from NIST MSP Format (msp) files. Import of data from files with different MSP *flavours* is supported. Objects from this package add support for MSP files to Bioconductor's Spectra package. This package is thus not supposed to be used without the Spectra package that provides a complete infrastructure for MS data handling.

Agilent "Mouse Genome, Whole" annotation data (chip mgug4122a) assembled using data from public repositories.

This package provides a package containing an environment representing the MG_U74C.cdf file.

Simple and efficient workflow for time-course gene expression data, built on publictly available open-source projects hosted on CRAN and bioconductor. moanin provides helper functions for all the steps required for analysing time-course data using functional data analysis: (1) functional modeling of the timecourse data; (2) differential expression analysis; (3) clustering; (4) downstream analysis.

This package fits a model to the pattern of dropouts in single-cell RNASeq data. This model is used as a null to identify significantly variable (i.e. differentially expressed) genes for use in downstream analysis, such as clustering cells. Also includes an method for calculating exact Pearson residuals in UMI-tagged data using a library-size aware negative binomial model.

The package is designed to detect marker genes from Microarray gene expression data sets.

This package provides methods for genetic finemapping in inbred mice by taking advantage of their very high homozygosity rate (>95%).

This package provides a collection of tools for doing various analyses of multi-state QTL data, with a focus on visualization and interpretation. The package multistateQTL contains functions which can remove or impute missing data, identify significant associations, as well as categorise features into global, multi-state or unique. The analysis results are stored in a QTLExperiment object, which is based on the SummarisedExperiment framework.

mitch is an R package for multi-contrast enrichment analysis. At it’s heart, it uses a rank-MANOVA based statistical approach to detect sets of genes that exhibit enrichment in the multidimensional space as compared to the background. The rank-MANOVA concept dates to work by Cox and Mann (https://doi.org/10.1186/1471-2105-13-S16-S12). mitch is useful for pathway analysis of profiling studies with one, two or more contrasts, or in studies with multiple omics profiling, for example proteomic, transcriptomic, epigenomic analysis of the same samples. mitch is perfectly suited for pathway level differential analysis of scRNA-seq data. We have an established routine for pathway enrichment of Infinium Methylation Array data (see vignette). The main strengths of mitch are that it can import datasets easily from many upstream tools and has advanced plotting features to visualise these enrichments.

MUSCLE performs multiple sequence alignments of nucleotide or amino acid sequences.

Easily visualize and inspect microarrays for spatial artifacts.

Data used by the barcode package for microarrays of type mouse4302.

miaDash provides a Graphical User Interface for the exploration of microbiome data. This way, no knowledge of programming is required to perform analyses. Datasets can be imported, manipulated, analysed and visualised with a user-friendly interface.

The matchBox package enables comparing ranked vectors of features, merging multiple datasets, removing redundant features, using CAT-plots and Venn diagrams, and computing statistical significance.

This package provides a package containing an environment representing the MG_U74B.cdf file.

Assorted utilities for multi-modal analyses of single-cell datasets. Includes functions to combine multiple modalities for downstream analysis, perform MNN-based batch correction across multiple modalities, and to compute correlations between assay values for different modalities.