sysbench is a scriptable multi-threaded benchmark tool based on LuaJIT. It is most frequently used for database benchmarks, but can also be used to create arbitrarily complex workloads that do not involve a database server. sysbench comes with the following bundled benchmarks:

oltp_*.lua

A collection of OLTP-like database benchmarks.

fileio

A filesystem-level benchmark.

cpu

A simple CPU benchmark.

memory

A memory access benchmark.

threads

A thread-based scheduler benchmark.

mutex

A POSIX mutex benchmark.

It includes features such as:

• Extensive statistics about rate and latency is available, including latency percentiles and histograms.

• Low overhead even with thousands of concurrent threads. sysbench is capable of generating and tracking hundreds of millions of events per second.

• New benchmarks can be easily created by implementing pre-defined hooks in user-provided Lua scripts.

The package AlphaBeta is a computational method for estimating epimutation rates and spectra from high-throughput DNA methylation data in plants. The method has been specifically designed to:

• analyze germline epimutations in the context of multi-generational mutation accumulation lines;

• analyze somatic epimutations in the context of plant development and aging.

This package implements utilities for installation of the basilisk package, primarily for creation of the underlying Conda instance.

This is a package for saving GenomicRanges, IRanges and related data structures into file artifacts, and loading 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 contains gene-level counts for a collection of public scRNA-seq datasets, provided as SingleCellExperiment objects with cell- and gene-level metadata.

Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. decoupleR is a Bioconductor package containing different statistical methods to extract these signatures within a unified framework. decoupleR allows the user to flexibly test any method with any resource. It incorporates methods that take into account the sign and weight of network interactions. decoupleR can be used with any omic, as long as its features can be linked to a biological process based on prior knowledge. For example, in transcriptomics gene sets regulated by a transcription factor, or in phospho-proteomics phosphosites that are targeted by a kinase.

This package provides a function to infer pathway activity from gene expression. It contains the linear model inferred in the publication "Perturbation-response genes reveal signaling footprints in cancer gene expression".

This is a package with metadata for genotyping Illumina 370k arrays using the crlmm package.

This R package provides tools for building and running automated end-to-end analysis workflows for a wide range of next generation sequence (NGS) applications such as RNA-Seq, ChIP-Seq, VAR-Seq and Ribo-Seq. Important features include a uniform workflow interface across different NGS applications, automated report generation, and support for running both R and command-line software, such as NGS aligners or peak/variant callers, on local computers or compute clusters. Efficient handling of complex sample sets and experimental designs is facilitated by a consistently implemented sample annotation infrastructure.

R-escape streamlines gene set enrichment analysis for single-cell RNA sequencing. Using raw count information, Seurat objects, or SingleCellExperiment format, users can perform and visualize GSEA across individual cells.

This package is able to perform an automatic or interactive quality control on FCS data acquired using flow cytometry instruments. By evaluating three different properties:

1. flow rate

2. signal acquisition, and

3. dynamic range,

the quality control enables the detection and removal of anomalies.

The affyILM package is a preprocessing tool which estimates gene expression levels for Affymetrix Gene Chips. Input from physical chemistry is employed to first background subtract intensities before calculating concentrations on behal of the Langmuir model.

This package exposes a C elegans annotation database generated from UCSC by exposing these as TxDb objects.

ChemmineOB provides an R interface to a subset of cheminformatics functionalities implemented by the OpelBabel C++ project. OpenBabel is a free cheminformatics toolbox that includes utilities for structure format interconversions, descriptor calculations, compound similarity searching and more. ChemineOB aims to make a subset of these utilities available from within R. For non-developers, ChemineOB is primarily intended to be used from ChemmineR as an add-on package rather than used directly.

The PSMatch package helps proteomics practitioners to load, handle and manage peptide spectrum matches. It provides functions to model peptide-protein relations as adjacency matrices and connected components, visualise these as graphs and make informed decision about shared peptide filtering. The package also provides functions to calculate and visualise MS2 fragment ions.

This is a package for the assessment and comparison of the performance of risk prediction (survival) models.

The polyester package simulates RNA-seq reads from differential expression experiments with replicates. The reads can then be aligned and used to perform comparisons of methods for differential expression.

This package aggregateBioVar contains tools to summarize single cell gene expression profiles at the level of subject for single cell RNA-seq data collected from more than one subject (e.g. biological sample or technical replicates). A SingleCellExperiment object is taken as input and converted to a list of SummarizedExperiment objects, where each list element corresponds to an assigned cell type. The SummarizedExperiment objects contain aggregate gene-by-subject count matrices and inter-subject column metadata for individual subjects that can be processed using downstream bulk RNA-seq tools.

This package provides a differential abundance analysis for the comparison of two or more conditions. Useful for analyzing data from standard RNA-seq or meta-RNA-seq assays as well as selected and unselected values from in-vitro sequence selections. Uses a Dirichlet-multinomial model to infer abundance from counts, optimized for three or more experimental replicates. The method infers biological and sampling variation to calculate the expected false discovery rate, given the variation, based on a Wilcoxon Rank Sum test and Welch's t-test, a Kruskal-Wallis test, a generalized linear model, or a correlation test. All tests report p-values and Benjamini-Hochberg corrected p-values. ALDEx2 also calculates expected standardized effect sizes for paired or unpaired study designs.

This is a package for detection of differentially bound regions in ChIP-seq data with sliding windows, with methods for normalization and proper FDR control.

This package provides tools for differential expression biomarker discovery based on microarray and next-generation sequencing data that leverage efficient semiparametric estimators of the average treatment effect for variable importance analysis. Estimation and inference of the (marginal) average treatment effects of potential biomarkers are computed by targeted minimum loss-based estimation, with joint, stable inference constructed across all biomarkers using a generalization of moderated statistics for use with the estimated efficient influence function. The procedure accommodates the use of ensemble machine learning for the estimation of nuisance functions.

This package computes differentially bound sites from multiple ChIP-seq experiments using affinity (quantitative) data. Also enables occupancy (overlap) analysis and plotting functions.

This package implements R bindings to C++ code for analyzing single-cell (expression) data, mostly from various libscran libraries. Each function performs an individual step in the single-cell analysis workflow, ranging from quality control to clustering and marker detection. It is mostly intended for other Bioconductor package developers to build more user-friendly end-to-end workflows.

This package offers the possibility to access the ArrayExpress repository at EBI (European Bioinformatics Institute) and build Bioconductor data structures: ExpressionSet, AffyBatch, NChannelSet.