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.

PHYLIP (the PHYLogeny Inference Package) is a package of programs for inferring phylogenies (evolutionary trees).

SAIGE is a package for efficiently controlling for case-control imbalance and sample relatedness in single-variant assoc tests (SAIGE) and controlling for sample relatedness in region-based assoc tests in large cohorts and biobanks (SAIGE-GENE+).

Bioinformaticians often have to convert sequence files between formats and do little manipulations on them, and it's not worth writing scripts for that. Seqmagick is a utility to expose the file format conversion in BioPython in a convenient way. Instead of having a big mess of scripts, there is one that takes arguments.

This package provides basic routines for estimation of gene-specific transcriptional derivatives and visualization of the resulting velocity patterns.

bustools is a program for manipulating BUS files for single cell RNA-Seq datasets. It can be used to error correct barcodes, collapse UMIs, produce gene count or transcript compatibility count matrices, and is useful for many other tasks.

MafFilter is a program dedicated to the analysis of genome alignments. It parses and manipulates MAF files as well as more simple fasta files. This package can be used to design a pipeline as a series of consecutive filters, each performing a dedicated analysis. Many of the filters are available, from alignment cleaning to phylogeny reconstruction and population genetics analysis. Despite various filtering options and format conversion tools, MafFilter can compute a wide range of statistics (phylogenetic trees, nucleotide diversity, inference of selection, etc.).

Genrich is a peak-caller for genomic enrichment assays (e.g. ChIP-seq, ATAC-seq). It analyzes alignment files generated following the assay and produces a file detailing peaks of significant enrichment.

PhenoGraph is a clustering method designed for high-dimensional single-cell data. It works by creating a graph representing phenotypic similarities between cells and then identifying communities in this graph.

Centrifuge is a microbial classification engine that enables rapid, accurate and sensitive labeling of reads and quantification of species on desktop computers. The system uses an indexing scheme based on the Burrows-Wheeler transform (BWT) and the Ferragina-Manzini (FM) index, optimized specifically for the metagenomic classification problem. Centrifuge requires a relatively small index (4.7 GB for all complete bacterial and viral genomes plus the human genome) and classifies sequences at very high speed, allowing it to process the millions of reads from a typical high-throughput DNA sequencing run within a few minutes.

MUSCLE aims to be a fast and accurate multiple sequence alignment program for nucleotide and protein sequences.

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

The gdc-client provides several convenience functions over the GDC API which provides general download/upload via HTTPS.

Splicekit is a modular platform for splicing analysis from short-read RNA-seq datasets. The platform also integrates pybio for genomic operations and scanRBP for RNA-protein binding studies. The whole analysis is self-contained (one single directory) and the platform is written in Python, in a modular way.

This package provides bioinformatic tools to align, deduplicate, reformat, filter and normalize DNA and RNA-seq data. It includes the following tools: BBMap, a short read aligner for DNA and RNA-seq data; BBNorm, a kmer-based error-correction and normalization tool; Dedupe, a tool to simplify assemblies by removing duplicate or contained subsequences that share a target percent identity; Reformat, to convert reads between fasta/fastq/scarf/fasta+qual/sam, interleaved/paired, and ASCII-33/64, at over 500 MB/s; and BBDuk, a tool to filter, trim, or mask reads with kmer matches to an artifact/contaminant file.

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.

MAFFT offers a range of multiple alignment methods for nucleotide and protein sequences. For instance, it offers L-INS-i (accurate; for alignment of <~200 sequences) and FFT-NS-2 (fast; for alignment of <~30,000 sequences).

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

This package implements methods to project single-cell RNA-seq data onto a reference atlas, enabling interpretation of unknown cell transcriptomic states in the the context of known, reference states.

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.

MoFax is a Python package for transcription factor motif analysis. It provides convenience functions to load and visualize factor models trained with MOFA+ in Python.

This package provides the kentUtils, a selection of bioinformatics utilities used in combination with the UCSC genome browser.

This package offers a flexible statistical simulator for scRNA-seq data. It can generate data that captures gene correlation. Additionally, it allows for varying the number of cells and sequencing depth.

HOMER (Hypergeometric Optimization of Motif EnRichment) is a suite of tools for Motif Discovery and next-gen sequencing analysis. It is a collection of command line programs written in Perl and C++. HOMER was primarily written as a de novo motif discovery algorithm and is well suited for finding 8-20 bp motifs in large scale genomics data. HOMER contains many useful tools for analyzing ChIP-Seq, GRO-Seq, RNA-Seq, DNase-Seq, Hi-C and numerous other types of functional genomics sequencing data sets.