AbSeq is a comprehensive bioinformatic pipeline for the analysis of sequencing datasets generated from antibody libraries and abseqR is one of its packages. AbseqR empowers the users of abseqPy with plotting and reporting capabilities and allows them to generate interactive HTML reports for the convenience of viewing and sharing with other researchers. Additionally, abseqR extends abseqPy to compare multiple repertoire analyses and perform further downstream analysis on its output.

This package offers tools to create DNA barcode sets capable of correcting insertion, deletion, and substitution errors. Existing barcodes can be analyzed regarding their minimal, maximal and average distances between barcodes. Finally, reads that start with a (possibly mutated) barcode can be demultiplexed, i.e. assigned to their original reference barcode.

CelliD is a clustering-free method for extracting per-cell gene signatures from scRNA-seq. CelliD allows unbiased cell identity recognition across different donors, tissues-of-origin, model organisms and single-cell omics protocols. The package can also be used to explore functional pathways enrichment in single cell data.

The package implements an algorithm for fast gene set enrichment analysis. Using the fast algorithm makes more permutations and gets more fine grained p-values, which allows using accurate standard approaches to multiple hypothesis correction.

This package contains classes used in model-view-controller (MVC) design.

This library contains functions that calculate various statistics of differential expression for microarray data, including t statistics, fold change, F statistics, SAM, moderated t and F statistics and B statistics. It also implements a new methodology called DEDS (Differential Expression via Distance Summary), which selects differentially expressed genes by integrating and summarizing a set of statistics using a weighted distance approach.

This package provides classes and statistical methods for large single-nucleotide polymorphism (SNP) association studies. This extends the earlier snpMatrix package, allowing for uncertainty in genotypes.

The package provides functions to create and use transcript-centric annotation databases/packages. The annotation for the databases are directly fetched from Ensembl using their Perl API. The functionality and data is similar to that of the TxDb packages from the GenomicFeatures package, but, in addition to retrieve all gene/transcript models and annotations from the database, the ensembldb package also provides a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes.

This package allows for persistent storage, access, exploration, and manipulation of Cufflinks high-throughput sequencing data. In addition, provides numerous plotting functions for commonly used visualizations.

The SummarizedExperiment container contains one or more assays, each represented by a matrix-like object of numeric or other mode. The rows typically represent genomic ranges of interest and the columns represent samples.

The sparse nature of single cell epigenomics data can be overruled using probabilistic modelling methods such as Latent Dirichlet Allocation (LDA). This package allows the probabilistic modelling of cis-regulatory topics (cisTopics) from single cell epigenomics data, and includes functionalities to identify cell states based on the contribution of cisTopics and explore the nature and regulatory proteins driving them.

Principal Component Analysis (PCA) extracts the fundamental structure of the data without the need to build any model to represent it. This "summary" of the data is arrived at through a process of reduction that can transform the large number of variables into a lesser number that are uncorrelated (i.e. the 'principal components'), while at the same time being capable of easy interpretation on the original data. PCAtools provides functions for data exploration via PCA, and allows the user to generate publication-ready figures. PCA is performed via BiocSingular; users can also identify an optimal number of principal components via different metrics, such as the elbow method and Horn's parallel analysis, which has relevance for data reduction in single-cell RNA-seq (scRNA-seq) and high dimensional mass cytometry data.

This is a tool for human B-cell context-specific transcriptional regulatory network. In addition, this package provides a human normal B-cells dataset for the examples in package viper.

The NCBI Gene Expression Omnibus (GEO) is a public repository of microarray data. Given the rich and varied nature of this resource, it is only natural to want to apply BioConductor tools to these data. GEOquery is the bridge between GEO and BioConductor.

Monocle 3 performs clustering, differential expression and trajectory analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle 3 also performs differential expression analysis, clustering, visualization, and other useful tasks on single-cell expression data. It is designed to work with RNA-Seq data, but could be used with other types as well.

IONiseR provides tools for the quality assessment of Oxford Nanopore MinION data. It extracts summary statistics from a set of fast5 files and can be used either before or after base calling. In addition to standard summaries of the read-types produced, it provides a number of plots for visualising metrics relative to experiment run time or spatially over the surface of a flowcell.

This package provides a package for the annotation and gene expression data download from Bgee database, and TopAnat analysis: GO-like enrichment of anatomical terms, mapped to genes by expression patterns.

This package provides the lengths of mRNA transcripts for a number of genomes and gene ID formats, largely based on the UCSC table browser.

This package translates bedtools command-line invocations to R code calling functions from the Bioconductor *Ranges infrastructure. This is intended to educate novice Bioconductor users and to compare the syntax and semantics of the two frameworks.

It has been shown that both DNA methylation and RNA transcription are linked to chronological age and age related diseases. Several estimators have been developed to predict human aging from DNA level and RNA level. Most of the human transcriptional age predictor are based on microarray data and limited to only a few tissues. To date, transcriptional studies on aging using RNASeq data from different human tissues is limited. The aim of this package is to provide a tool for across-tissue and tissue-specific transcriptional age calculation based on GTEx RNASeq data.

This package provides functions for pathway analysis based on the REACTOME pathway database. It implements enrichment analysis, gene set enrichment analysis and several functions for visualization.

This is the classification package for the automated analysis of Affymetrix arrays.

The package includes quality control metrics, a selection of normalization methods and novel methods to identify differentially methylated regions and to highlight copy number alterations.

This package ofers functions for importation, normalization, visualization, and quality control to correct identified sources of variability in array of CGH experiments.