This R package enables the user to read pfam predictions into R. Most human protein domains exist as multiple distinct variants termed domain isotypes. This R package enables the identification and classification of such domain isotypes from pfam data.

AgiMicroRna provides useful functionality for the processing, quality assessment and differential expression analysis of Agilent microRNA array data. The package uses a limma-like structure to generate the processed data in order to make statistical inferences about differential expression using the linear model features implemented in limma. Standard Bioconductor objects are used so that other packages could be used as well.

This package provides tools for analyzing R expressions or blocks of code and determining the dependencies between them. It focuses on R scripts, but can be used on the bodies of functions. There are many facilities including the ability to summarize or get a high-level view of code, determining dependencies between variables, code improvement suggestions.

This package provides a set of tools for making TxDb objects from genomic annotations from various sources (e.g. UCSC, Ensembl, and GFF files). These tools allow the user to download the genomic locations of transcripts, exons, and CDS, for a given assembly, and to import them in a TxDb object. TxDb objects are implemented in the GenomicFeatures package, together with flexible methods for extracting the desired features in convenient formats.

This package provides tools for clustering and enhancing the resolution of spatial gene expression experiments. BayesSpace clusters a low-dimensional representation of the gene expression matrix, incorporating a spatial prior to encourage neighboring spots to cluster together. The method can enhance the resolution of the low-dimensional representation into "sub-spots", for which features such as gene expression or cell type composition can be imputed.

This package corrects GC and mappability biases for readcounts (i.e. coverage) in non-overlapping windows of fixed length for single whole genome samples, yielding a rough estimate of copy number for further analysis. It was designed for rapid correction of high coverage whole genome tumor and normal samples.

This package provides functions to plot data associated with arbitrary genomic intervals along chromosomal ideogram.

This package implements the circular binary segmentation (CBS) algorithm to segment DNA copy number data and identify genomic regions with abnormal copy number.

This is a package for the automated analysis of Affymetrix arrays. It is used for preprocessing the arrays.

This package supports data management of large-scale whole-genome sequencing variant calls with thousands of individuals: genotypic data (e.g., SNVs, indels and structural variation calls) and annotations in SeqArray GDS files are stored in an array-oriented and compressed manner, with efficient data access using the R programming language.

ATAC-seq, an assay for Transposase-Accessible Chromatin using sequencing, is a rapid and sensitive method for chromatin accessibility analysis. It was developed as an alternative method to MNase-seq, FAIRE-seq and DNAse-seq. The ATACseqQC package was developed to help users to quickly assess whether their ATAC-seq experiment is successful. It includes diagnostic plots of fragment size distribution, proportion of mitochondria reads, nucleosome positioning pattern, and CTCF or other Transcript Factor footprints.

This package provides tools for alignment, quantification and analysis of second and third generation sequencing data. It includes functionality for read mapping, read counting, SNP calling, structural variant detection and gene fusion discovery. It can be applied to all major sequencing techologies and to both short and long sequence reads.

This package discovers differential features in hetero- and homogeneous omic data by a two-step method including subsampling LIMMA and NSCA. DECO reveals feature associations to hidden subclasses not exclusively related to higher deregulation levels.

This package supports the application of diverse quality metrics to AffyBatch instances, summarizing these metrics via PCA, and then performing parametric outlier detection on the PCs to identify aberrant arrays with a fixed Type I error rate.

This package provides functions for visualizing hypergraphs.

This package provides full genome sequences for Danio rerio (Zebrafish) as provided by UCSC (danRer11, May 2017) and stored in Biostrings objects.

This package contains gene-level counts for a collection of public scRNA-seq datasets, provided as SingleCellExperiment objects with cell- and gene-level metadata.

The AffiXcan package imputes a genetically regulated expression (GReX) for a set of genes in a sample of individuals, using a method based on the total binding affinity (TBA). Statistical models to impute GReX can be trained with a training dataset where the real total expression values are known.

This package provides functions to detect and correct for batch effects in DNA methylation data. The core function is based on latent factor models and can also be used to predict missing values in any other matrix containing real numbers.

This package provides functions to compare two or more survival curves with:

• The Fleming-Harrington test for right-censored data based on permutations and on counting processes.

• An extension of the Fleming-Harrington test for interval-censored data based on a permutation distribution and on a score vector distribution.

This is a comprehensive package to automatically train and validate a multi-class SVM classifier based on gene expression data. It provides transparent selection of gene markers, their coexpression networks, and an interface to query the classifier.

This package is designed for visualization of RNA-related genomic features with respect to the landmarks of RNA transcripts, i.e., transcription starting site, start codon, stop codon and transcription ending site.

This package provides flexible, quantitative, and integrative genomic visualizations for publication-quality multi-panel figures.

This package implements a method that aims to identify enhancers on large scale. The STARR-seq data consists of two sequencing datasets of the same targets in a specific genome. The input sequences show which regions where tested for enhancers. Significant enriched peaks i.e. a lot more sequences in one region than in the input where enhancers in the genomic DNA are, can be identified. So the approach pursued is to call peak every region in which there is a lot more (significant in a binomial model) STARR-seq signal than input signal and propose an enhancer at that very same position. Enhancers then are called weak or strong dependent of there degree of enrichment in comparison to input.