Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes preprocessing, visualization, clustering, pseudotime and trajectory inference and differential expression testing. The Python-based implementation efficiently deals with datasets of more than one million cells.

hdWGCNA is an R package for performing weighted gene co-expression network analysis in high dimensional -omics such as single-cell RNA-seq or spatial transcriptomics.

Skewer implements the bit-masked k-difference matching algorithm dedicated to the task of adapter trimming and it is specially designed for processing next-generation sequencing (NGS) paired-end sequences.

Very fast parallel big-data BLAST XML file parser which can be used as command line utility. Use blastxmlparser to: Parse BLAST XML; filter output; generate FASTA, JSON, YAML, RDF, JSON-LD, HTML, CSV, tabular output etc.

This package provides several programs that perform operations on SAM/BAM files. All of these programs are built into a single executable called bam.

Bio++ is a set of C++ libraries for Bioinformatics, including sequence analysis, phylogenetics, molecular evolution and population genetics. This library provides sequence-related modules.

Bandage is a program for visualising de novo assembly graphs. It allows users to interact with the assembly graphs made by de novo assemblers such as Velvet, SPAdes, MEGAHIT and others. De novo assembly graphs contain not only assembled contigs but also the connections between those contigs, which were previously not easily accessible. Bandage visualises assembly graphs, with connections, using graph layout algorithms. Nodes in the drawn graph, which represent contigs, can be automatically labelled with their ID, length or depth. Users can interact with the graph by moving, labelling and colouring nodes. Sequence information can also be extracted directly from the graph viewer. By displaying connections between contigs, Bandage opens up new possibilities for analysing and improving de novo assemblies that are not possible by looking at contigs alone.

The FASTX-Toolkit is a collection of command line tools for Short-Reads FASTA/FASTQ files preprocessing.

Next-Generation sequencing machines usually produce FASTA or FASTQ files, containing multiple short-reads sequences. The main processing of such FASTA/FASTQ files is mapping the sequences to reference genomes. However, it is sometimes more productive to preprocess the files before mapping the sequences to the genome---manipulating the sequences to produce better mapping results. The FASTX-Toolkit tools perform some of these preprocessing tasks.

python-cwlformat is a specification and a reference implementation for a very opinionated CWL code formatter. It outputs CWL in a standardized YAML format.

This is a package that lets you process UMI-4C data from scratch to produce nice plots.

The Filtlong package is a tool for filtering long reads by quality. It can take a set of long reads and produce a smaller, better subset. It uses both read length (longer is better) and read identity (higher is better) when choosing which reads pass the filter.

Implementation of the Smith-Waterman algorithm.

This package analyses the Oxford Nanopore sequencing data at signal-level. Nanopolish can calculate an improved consensus sequence for a draft genome assembly, detect base modifications, call SNPs (Single nucleotide polymorphisms) and indels with respect to a reference genome and more.

This package implements bindings for zarr store that are compatible with Bioconductor S4 data structures, namely the DataFrame and DelayedArray. This allows Zarr-backed data to be easily used as data frames with arbitrary sets of columns.

This package aims to bring the power and flexibility of AnnData to the R ecosystem, allowing you to effortlessly manipulate and analyze your single-cell data. This package lets you work with backed h5ad and zarr files, directly access various slots (e.g. X, obs, var), or convert the data into SingleCellExperiment and Seurat objects.

Anglemania extracts genes from multi-batch scRNA-seq experiments for downstream dataset integration. It improves conventional usage of highly-variable genes for integration tasks.

CNVkit is a Python library and command-line software toolkit to infer and visualize copy number from high-throughput DNA sequencing data. It is designed for use with hybrid capture, including both whole-exome and custom target panels, and short-read sequencing platforms such as Illumina and Ion Torrent.

PAML (for Phylogentic Analysis by Maximum Likelihood) contains a few programs for model fitting and phylogenetic tree reconstruction using nucleotide or amino-acid sequence data.

The BIOM file format is designed to be a general-use format for representing counts of observations e.g. operational taxonomic units, KEGG orthology groups or lipid types, in one or more biological samples e.g. microbiome samples, genomes, metagenomes.

This package infers, visualizes and analyzes the cell-cell communication networks from scRNA-seq data.

genomepy is designed to provide a simple and straightforward way to download and use genomic data. This includes

1. searching available data,

2. showing the available metadata,

3. automatically downloading, preprocessing and matching data, and

4. generating optional aligner indexes.

All with sensible, yet controllable defaults.

eXpress is a streaming tool for quantifying the abundances of a set of target sequences from sampled subsequences. Example applications include transcript-level RNA-Seq quantification, allele-specific/haplotype expression analysis (from RNA-Seq), transcription factor binding quantification in ChIP-Seq, and analysis of metagenomic data.

This package has been developed under ROpenSci gudelines to integrate conventional and cutting edge cytometry analysis tools under a unified framework. It aims to represent an intuitive and interactive approach to analysing cytometry data in R.

BSeq-sc is a bioinformatics analysis pipeline that leverages single-cell sequencing data to estimate cell type proportion and cell type-specific gene expression differences from RNA-seq data from bulk tissue samples. This is a companion package to the publication "A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure." Baron et al. Cell Systems (2016) https://www.ncbi.nlm.nih.gov/pubmed/27667365.