This package aims to produce high-quality genome browser tracks that are highly customizable. Currently, it is possible to plot: bigwig, bed (many options), bedgraph, links (represented as arcs), and Hi-C matrices. pyGenomeTracks can make plots with or without Hi-C data.

F-Seq is a software package that generates a continuous tag sequence density estimation allowing identification of biologically meaningful sites such as transcription factor binding sites (ChIP-seq) or regions of open chromatin (DNase-seq). Output can be displayed directly in the UCSC Genome Browser.

This package is used for demultiplexing single-cell sequencing experiments of pooled cells. These cells are labeled with barcode oligonucleotides. The package implements methods to fit regression mixture models for a probabilistic classification of cells, including multiplet detection. Demultiplexing error rates can be estimated, and methods for quality control are provided.

CheckM provides a set of tools for assessing the quality of genomes recovered from isolates, single cells, or metagenomes. It provides robust estimates of genome completeness and contamination by using collocated sets of genes that are ubiquitous and single-copy within a phylogenetic lineage. Assessment of genome quality can also be examined using plots depicting key genomic characteristics (e.g., GC, coding density) which highlight sequences outside the expected distributions of a typical genome. CheckM also provides tools for identifying genome bins that are likely candidates for merging based on marker set compatibility, similarity in genomic characteristics, and proximity within a reference genome.

Prodigal runs smoothly on finished genomes, draft genomes, and metagenomes, providing gene predictions in GFF3, Genbank, or Sequin table format. It runs quickly, in an unsupervised fashion, handles gaps, handles partial genes, and identifies translation initiation sites.

dnaio is a Python library for fast parsing of FASTQ and also FASTA files. The code was previously part of the cutadapt tool.

This package provides a collection of methods to extract gene programs from single-cell gene expression data using non-negative matrix factorization (NMF). GeneNMF contains functions to directly interact with the Seurat toolkit and derive interpretable gene program signatures.

This is a collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using R.

WhatsHap is software for phasing genomic variants using DNA sequencing reads, also called read-based phasing or haplotype assembly. It is especially suitable for long reads, but works also well with short reads.

This package contains the Battenberg R package for subclonal copy number estimation, as described by Nik-Zainal et al.

StringTie is a fast and efficient assembler of RNA-Seq sequence alignments into potential transcripts. It uses a novel network flow algorithm as well as an optional de novo assembly step to assemble and quantitate full-length transcripts representing multiple splice variants for each gene locus. Its input can include not only the alignments of raw reads used by other transcript assemblers, but also alignments of longer sequences that have been assembled from those reads. To identify differentially expressed genes between experiments, StringTie's output can be processed either by the Cuffdiff or Ballgown programs.

This package contains a multicore Barnes-Hut implementation of the t-SNE algorithm. The implementation is described here: http://lvdmaaten.github.io/publications/papers/JMLR_2014.pdf.

This is an R package to build generic .loom files aligning with the default naming convention of the .loom format and to integrate other data types e.g.: regulons (SCENIC), clusters from Seurat, trajectory information... The package can also be used to extract data from .loom files.

This package provides an accurate VCF/GFF3/GTF LiftOver tool for new genome assemblies.

Pegasusio is a Python package for reading or writing single-cell genomics data.

Arriba is a command-line tool for the detection of gene fusions from RNA-Seq data. It was developed for the use in a clinical research setting. Therefore, short runtimes and high sensitivity were important design criteria. It is based on the fast STAR aligner and the post-alignment runtime is typically just around two minutes. In contrast to many other fusion detection tools which build on STAR, Arriba does not require to reduce the alignIntronMax parameter of STAR to detect small deletions.

Salad is a schema language for describing JSON or YAML structured linked data documents. Salad schema describes rules for preprocessing, structural validation, and hyperlink checking for documents described by a Salad schema. Salad supports rich data modeling with inheritance, template specialization, object identifiers, object references, documentation generation, code generation, and transformation to RDF. Salad provides a bridge between document and record oriented data modeling and the Semantic Web.

MethylDackel will process a coordinate-sorted and indexed BAM or CRAM file containing some form of BS-seq alignments and extract per-base methylation metrics from them. MethylDackel requires an indexed fasta file containing the reference genome as well.

Change-O is a collection of tools for processing the output of V(D)J alignment tools, assigning clonal clusters to immunoglobulin (Ig) sequences, and reconstructing germline sequences.

This is a package for the discovery of communities in Pore-C concatemers.

This package provides a computational toolkit in R for the integration, exploration, and analysis of high-dimensional single-cell cytometry and imaging data.

A tiny C library for managing SOM (Self-Organizing Maps) neural networks.

python-scanrbp is a Python package that provides the scanRBP tool that loads RNA-protein binding motif PWM and computes the log-odds scores for all the loaded RBPs across a given genomic sequence and draws a heatmap of the scores.

CoolBox is a toolkit for visual analysis of genomics data. It aims to be highly compatible with the Python ecosystem, easy to use and highly customizable with a well-designed user interface. It can be used in various visualization situations, for example, to produce high-quality genome track plots or fetch common used genomic data files with a Python script or command line, interactively explore genomic data within Jupyter environment or web browser.