MAGIC is an interactive tool to impute missing values in single-cell sequencing data and to restore the structure of the data. It also provides data pre-processing functionality such as dimensionality reduction and gene expression visualization.

Maxent is a stand-alone Java application for modelling species geographic distributions.

This package provides an assortment of R functions that is suitable for all types of microbial diversity analyses.

PiGX ChIPseq is an analysis pipeline for preprocessing, peak calling and reporting for ChIP sequencing experiments. It is easy to use and produces high quality reports. The inputs are reads files from the sequencing experiment, and a configuration file which describes the experiment. In addition to quality control of the experiment, the pipeline enables to set up multiple peak calling analysis and allows the generation of a UCSC track hub in an easily configurable manner.

Mash is a fast sequence distance estimator that uses the MinHash algorithm and is designed to work with genomes and metagenomes in the form of assemblies or reads.

gkm-SVM, a sequence-based method for predicting regulatory DNA elements, is a useful tool for studying gene regulatory mechanisms. LS-GKM is an effort to improve the method. It offers much better scalability and provides further advanced gapped k-mer based kernel functions. As a result, LS-GKM achieves considerably higher accuracy than the original gkm-SVM.

Flexbar preprocesses high-throughput nucleotide sequencing data efficiently. It demultiplexes barcoded runs and removes adapter sequences. Moreover, trimming and filtering features are provided. Flexbar increases read mapping rates and improves genome and transcriptome assemblies. It supports next-generation sequencing data in fasta/q and csfasta/q format from Illumina, Roche 454, and the SOLiD platform.

This package is designed to streamline scATAC analyses in R.

Cyvcf2 is a Cython wrapper around htslib built for fast parsing of Variant Call Format (VCF) files.

Delly is an integrated structural variant prediction method that can discover and genotype deletions, tandem duplications, inversions and translocations at single-nucleotide resolution in short-read massively parallel sequencing data. It uses paired-ends and split-reads to sensitively and accurately delineate genomic rearrangements throughout the genome.

PySnpTools is a library for reading and manipulating genetic data. It can, for example, efficiently read whole PLINK *.bed/bim/fam files or parts of those files. It can also efficiently manipulate ranges of integers using set operators such as union, intersection, and difference.

CodeAndRoll2 is a set of more than 130 productivity functions. These functions are used by MarkdownReports, ggExpress, and SeuratUtils.

CGAT-core is a set of libraries and helper functions used to enable researchers to design and build computational workflows for the analysis of large-scale data-analysis.

Logomaker is a Python package for generating publication-quality sequence logos. Logomaker can generate both standard and highly customized logos illustrating the properties of DNA, RNA, or protein sequences. Logos are rendered as vector graphics embedded within native matplotlib Axes objects, making them easy to style and incorporate into multi-panel figures.

This package contains gatingTemplates, example fcs files and compensation controls for use in CytoExploreR.

An interval map structure that is optimized for low memory (each interval is represented by about 3 words + whatever the cargo is) and has semantics that are appropriate for genomic intervals (namely, intervals can overlap and queries will return all matches together). It also designed to be used in two phases: a construction phase + query phase).

This package provides tools for handling BAM, SAM, Tabix, bgzf, CRAM, CSIv1, CSIv2 and FAI files.

This package provides a C library for parsing local and remote BigWig files.

The package graph implements graph manipulation functions.

Psupertime is supervised pseudotime for single cell RNAseq data. It uses single cell RNAseq data, where the cells have a known ordering. This ordering helps to identify a small number of genes which place cells in that known order. It can be used for discovery of relevant genes, for identification of subpopulations, and characterization of further unknown or differently labelled data.

CD-HIT is a program for clustering and comparing protein or nucleotide sequences. CD-HIT is designed to be fast and handle extremely large databases.

RSeQC provides a number of modules that can comprehensively evaluate high throughput sequence data, especially RNA-seq data. Some basic modules inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while RNA-seq specific modules evaluate sequencing saturation, mapped reads distribution, coverage uniformity, strand specificity, etc.

FAN-C provides a pipeline for analysing Hi-C data starting at mapped paired-end sequencing reads.

BayesPrism includes deconvolution and embedding learning modules. The deconvolution module models a prior from cell type-specific expression profiles from scRNA-seq to jointly estimate the posterior distribution of cell type composition and cell type-specific gene expression from bulk RNA-seq expression of tumor samples. The embedding learning module uses Expectation-maximization (EM) to approximate the tumor expression using a linear combination of malignant gene programs while conditional on the inferred expression and fraction of non-malignant cells estimated by the deconvolution module.