Welcome to sourmash!

sourmash is a command-line tool and Python library for computing hash sketches from DNA sequences, comparing them to each other, and plotting the results. This allows you to estimate sequence similarity between even very large data sets quickly and accurately.

sourmash can be used to quickly search large databases of genomes for matches to query genomes and metagenomes; see our list of available databases.

sourmash also includes k-mer based taxonomic exploration and classification routines for genome and metagenome analysis. These routines can use the NCBI taxonomy but do not depend on it in any way.

We have several tutorials available! Start with Making signatures, comparing, and searching.

The paper Large-scale sequence comparisons with sourmash (Pierce et al., 2019) gives an overview of how sourmash works and what its major use cases are. Please also see the mash software and the mash paper (Ondov et al., 2016) for background information on how and why MinHash works.

Questions? Thoughts? Ask us on the sourmash issue tracker!

To use sourmash, you must be comfortable with the UNIX command line; programmers may find the Python library and API useful as well.

If you use sourmash, please cite us!

Brown and Irber (2016), sourmash: a library for MinHash sketching of DNA Journal of Open Source Software, 1(5), 27, doi:10.21105/joss.00027

sourmash in brief

sourmash uses MinHash-style sketching to create “signatures”, compressed representations of DNA/RNA sequence. These signatures can then be stored, searched, explored, and taxonomically annotated.

  • sourmash provides command line utilities for creating, comparing, and searching signatures, as well as plotting and clustering signatures by similarity (see the command-line docs).

  • sourmash can search very large collections of signatures to find matches to a query.

  • sourmash can also identify parts of metagenomes that match known genomes, and can taxonomically classify genomes and metagenomes against databases of known species.

  • sourmash can be used to search databases of public sequences (e.g. all of GenBank) and can also be used to create and search databases of private sequencing data.

  • sourmash supports saving, loading, and communication of signatures via JSON, a ~human-readable & editable format.

  • sourmash also has a simple Python API for interacting with signatures, including support for online updating and querying of signatures (see the API docs).

  • sourmash isn’t terribly slow, and relies on an underlying Cython module.

  • sourmash is developed on GitHub and is freely and openly available under the BSD 3-clause license. Please see the README for more information on development, support, and contributing.

You can take a look at sourmash analyses on real data in a saved Jupyter notebook, and experiment with it yourself interactively in a Jupyter Notebook at mybinder.org.

Installing sourmash

We currently suggest installing the latest pre-release in the sourmash 2.0 series; please see the README file in github.com/dib-lab/sourmash for information. You can use pip or conda equally well.

Memory and speed

sourmash has relatively small disk and memory requirements compared to many other software programs used for genome search and taxonomic classification.

First, mash beats sourmash in speed and memory, so if you can use mash, more power to you :)

sourmash search and sourmash gather can be used to search all genbank microbial genomes (using our prepared databases) with about 20 GB of disk and in under 1 GB of RAM. Typically a search for a single genome takes about 30 seconds on a laptop.

sourmash lca can be used to search/classify against all genbank microbial genomes with about 200 MB of disk space and about 10 GB of RAM. Typically a metagenome classification takes about 1 minute on a laptop.


sourmash cannot find matches across large evolutionary distances.

sourmash seems to work well to search and compare data sets for matches at the species and genus level, but does not have much sensitivity beyond that. (It seems to be particularly good at strain-level analysis.) You should use protein-based analyses to do searches across larger evolutionary distances.

sourmash signatures can be very large.

We use a modification of the MinHash sketch approach that allows us to search the contents of metagenomes and large genomes with no loss of sensitivity, but there is a tradeoff: there is no guaranteed limit to signature size when using ‘scaled’ signatures.

Indices and tables