MEIGOR provides a comprehensive environment for performing global optimization tasks in bioinformatics and systems biology. It leverages advanced metaheuristic algorithms to efficiently search the solution space and is specifically tailored to handle the complexity and high-dimensionality of biological datasets. This package supports various optimization routines and is integrated with Bioconductor's infrastructure for a seamless analysis workflow.

Affymetrix Affymetrix MG_U74Bv2 Array annotation data (chip mgu74bv2) assembled using data from public repositories.

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.

Raw amplification data from a large microRNA mixture / dilution study. These data are used by the miRcomp package to assess the performance of methods that estimate expression from the amplification curves.

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.

Standard methods for analysis of mutation data following error- corrected sequencing (ECS) for the purpose of mutagencity assessment. Functions include importing the mutation lists provided by a variant caller, and a set of analytical tools for statistical testing and visualization of mutation data; comparison to COSMIC and/or germline signatures; etc.

This package stores two merged expressionSet objects that contain the gene expression profile and clinical information of -a- six breast cancer cohorts and -b- four colorectal cancer cohorts. Breast cancer data are employed in the vignette of the hrunbiased package for survival analysis of gene signatures.

The package is designed to detect marker genes from RNA-seq data.

MEDIPS was developed for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq). However, MEDIPS provides functionalities for the analysis of any kind of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential coverage between groups of samples and saturation and correlation analysis.

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

This package provides a package containing an environment representing the MOE430B.CDF file.

Single-cell RNA-sequencing (scRNA-seq) has made it possible to profile gene expression in tissues at high resolution. An important preprocessing step prior to performing downstream analyses is to identify and remove cells with poor or degraded sample quality using quality control (QC) metrics. Two widely used QC metrics to identify a ‘low-quality’ cell are (i) if the cell includes a high proportion of reads that map to mitochondrial DNA encoded genes (mtDNA) and (ii) if a small number of genes are detected. miQC is data-driven QC metric that jointly models both the proportion of reads mapping to mtDNA and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset.

Provide functions for performing abundance and compositional based binning on metagenomic samples, directly from FASTA or FASTQ files. Functions are implemented in Java and called via rJava. Parallel implementation that operates directly on input FASTA/FASTQ files for fast execution. Inputs may be file paths or Biostrings/ShortRead sequence objects; results are returned as a MetabinResult S4 object wrapping cluster assignments, algorithm parameters, and input metadata.

The MAIT package contains functions to perform end-to-end statistical analysis of LC/MS Metabolomic Data. Special emphasis is put on peak annotation and in modular function design of the functions.

Bedgraph files generated by Bisulfite pipelines often come in various flavors. Critical downstream step requires summarization of these files into methylation/coverage matrices. This step of data aggregation is done by Methrix, including many other useful downstream functions.

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.

Data to test the msPurity package.

Multi-omic Pathway Analysis of Cells (MPAC), integrates multi-omic data for understanding cellular mechanisms. It predicts novel patient groups with distinct pathway profiles as well as identifying key pathway proteins with potential clinical associations. From CNA and RNA-seq data, it determines genes’ DNA and RNA states (i.e., repressed, normal, or activated), which serve as the input for PARADIGM to calculate Inferred Pathway Levels (IPLs). It also permutes DNA and RNA states to create a background distribution to filter IPLs as a way to remove events observed by chance. It provides multiple methods for downstream analysis and visualization.

Codelink UniSet Mouse I Bioarray (~10 000 mouse gene targets) annotation data (chip m10kcod) assembled using data from public repositories.

This package provides a toolkit for simulating differential microbiome data designed for longitudinal analyses. Several functional forms may be specified for the mean trend. Observations are drawn from a multivariate normal model. The objective of this package is to be able to simulate data in order to accurately compare different longitudinal methods for differential abundance.

An R package for deeping mining gene co-expression networks in multi-trait expression data. Provides functions for analyzing, comparing, and visualizing WGCNA networks across conditions. multiWGCNA was designed to handle the common case where there are multiple biologically meaningful sample traits, such as disease vs wildtype across development or anatomical region.

This package provides a package to merge, filter sort, organise and otherwise mash together metabolite annotation tables. Metabolite annotations can be imported from multiple sources (software) and combined using workflow steps based on S4 class templates derived from the `struct` package. Other modular workflow steps such as filtering, merging, splitting, normalisation and rest-api queries are included.

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

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.