Bioparser is a C++ header only parsing library for several bioinformatics formats (FASTA/Q, MHAP/PAF/SAM), with support for zlib compressed files.

iVar is a computational package that contains functions broadly useful for viral amplicon-based sequencing.

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.

Minimap2 is a versatile sequence alignment program that aligns DNA or mRNA sequences against a large reference database. Typical use cases include:

1. mapping PacBio or Oxford Nanopore genomic reads to the human genome;

2. finding overlaps between long reads with error rate up to ~15%;

3. splice-aware alignment of PacBio Iso-Seq or Nanopore cDNA or Direct RNA reads against a reference genome;

4. aligning Illumina single- or paired-end reads;

5. assembly-to-assembly alignment;

6. full-genome alignment between two closely related species with divergence below ~15%.

Bamnostic is a pure Python Binary Alignment Map (BAM) file parser and random access tool.

Scanorama enables batch-correction and integration of heterogeneous scRNA-seq datasets, which is described in the paper "Efficient integration of heterogeneous single-cell transcriptomes using Scanorama" by Brian Hie, Bryan Bryson, and Bonnie Berger.

Bloom-filter-based error correction solution for high-throughput sequencing reads (BLESS) uses a single minimum-sized bloom filter is a correction tool for genomic reads produced by Next-generation sequencing (NGS). BLESS produces accurate correction results with much less memory compared with previous solutions and is also able to tolerate a higher false-positive rate. BLESS can extend reads like DNA assemblers to correct errors at the end of reads.

METAL is a tool for meta-analysis genomewide association scans. METAL can combine either test statistics and standard errors or p-values across studies (taking sample size and direction of effect into account). METAL analysis is a convenient alternative to a direct analysis of merged data from multiple studies. It is especially appropriate when data from the individual studies cannot be analyzed together because of differences in ethnicity, phenotype distribution, gender or constraints in sharing of individual level data imposed. Meta-analysis results in little or no loss of efficiency compared to analysis of a combined dataset including data from all individual studies.

The WiggleTools package allows genomewide data files to be manipulated as numerical functions, equipped with all the standard functional analysis operators (sum, product, product by a scalar, comparators), and derived statistics (mean, median, variance, stddev, t-test, Wilcoxon's rank sum test, etc).

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.

Sambamba is a high performance modern robust and fast tool (and library), written in the D programming language, for working with SAM and BAM files. Current parallelised functionality is an important subset of samtools functionality, including view, index, sort, markdup, and depth.

SNAP is a fast and accurate aligner for short DNA reads. It is optimized for modern read lengths of 100 bases or higher, and takes advantage of these reads to align data quickly through a hash-based indexing scheme.

This package is a set of R functions for generating precise figures. This tool helps you to create clean markdown reports about what you just discovered with your analysis script.

libmaus2 is a collection of data structures and algorithms. It contains:

• I/O classes (single byte and UTF-8);

• bitio classes (input, output and various forms of bit level manipulation);

• text indexing classes (suffix and LCP array, fulltext and minute (FM), etc.);

• BAM sequence alignment files input/output (simple and collating); and many lower level support classes.

This is a package for normalizing Hi-C contact counts efficiently.

The goal of anpan is to consolidate statistical methods for strain analysis. This includes automated filtering of metagenomic functional profiles, testing genetic elements for association with outcomes, phylogenetic association testing, and pathway-level random effects models.

This package provides a library and collection of scripts to work with Illumina paired-end data (for CASAVA 1.8+).

PDBFixer is designed to rectify issues in Protein Data Bank files. Its intuitive interface simplifies the process of resolving problems encountered in PDB files prior to simulation tasks.

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

BEDOPS is a suite of tools to address common questions raised in genomic studies---mostly with regard to overlap and proximity relationships between data sets. It aims to be scalable and flexible, facilitating the efficient and accurate analysis and management of large-scale genomic data.

BEDOPS provides tools that perform highly efficient and scalable Boolean and other set operations, statistical calculations, archiving, conversion and other management of genomic data of arbitrary scale. Tasks can be easily split by chromosome for distributing whole-genome analyses across a computational cluster.

Morpheus is a modeling and simulation environment for the study of multi-scale and multicellular systems.

pySCENIC is a Python implementation of the SCENIC pipeline (Single-Cell rEgulatory Network Inference and Clustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from single-cell RNA-seq data.

Smithlab CPP is a C++ library that includes functions used in many of the Smith lab bioinformatics projects, such as a wrapper around Samtools data structures, classes for genomic regions, mapped sequencing reads, etc.

IMP's broad goal is to contribute to a comprehensive structural characterization of biomolecules ranging in size and complexity from small peptides to large macromolecular assemblies, by integrating data from diverse biochemical and biophysical experiments. IMP provides a C++ and Python toolbox for solving complex modeling problems, and a number of applications for tackling some common problems in a user-friendly way.