# 🐍⛓️🧬 Pyskani [](https://github.com/althonos/pyskani/stargazers)
*[PyO3](https://pyo3.rs/) bindings and Python interface to [skani](https://github.com/bluenote-1577/skani), a method for fast fast genomic identity calculation using sparse chaining.*
[](https://github.com/althonos/pyskani/actions)
[](https://codecov.io/gh/althonos/pyskani/)
[](https://choosealicense.com/licenses/mit/)
[](https://pypi.org/project/pyskani)
[](https://anaconda.org/bioconda/pyskani)
[](https://aur.archlinux.org/packages/python-pyskani)
[](https://pypi.org/project/pyskani/#files)
[](https://pypi.org/project/pyskani/#files)
[](https://pypi.org/project/pyskani/#files)
[](https://github.com/althonos/pyskani/)
[](https://git.embl.de/larralde/pyskani/)
[](https://github.com/althonos/pyskani/issues)
[](https://pyskani.readthedocs.io)
[](https://github.com/althonos/pyskani/blob/master/CHANGELOG.md)
[](https://pepy.tech/project/pyskani)
## 🗺️ Overview
`skani`[\[1\]](#ref1) is a method developed by [Jim Shaw](https://jim-shaw-bluenote.github.io/)
and [Yun William Yu](https://github.com/yunwilliamyu) for fast and robust
metagenomic sequence comparison through sparse chaining. It improves on
FastANI by being more accurate and much faster, while requiring less memory.
`pyskani` is a Python module, implemented using the [PyO3](https://pyo3.rs/)
framework, that provides bindings to `skani`. It directly links to the
`skani` code, which has the following advantages over CLI wrappers:
- **pre-built wheels**: `pyskani` is distributed on PyPI and features
pre-built wheels for common platforms, including x86-64 and Arm64 UNIX.
- **single dependency**: If your software or your analysis pipeline is
distributed as a Python package, you can add `pyskani` as a dependency to
your project, and stop worrying about the `skani` binary being present on
the end-user machine.
- **sans I/O**: Everything happens in memory, in Python objects you control,
making it easier to pass your sequences to `skani` without having to write
them to a temporary file.
*This library is still a work-in-progress, and in an experimental stage,
but it should already pack enough features to be used in a standard pipeline.*
## 🔧 Installing
Pyskani can be installed directly from [PyPI](https://pypi.org/project/pyskani/),
which hosts some pre-built CPython wheels for x86-64 Unix platforms, as well
as the code required to compile from source with Rust:
```console
$ pip install pyskani
```
<!-- Otherwise, pyskani is also available as a [Bioconda](https://anaconda.org/bioconda/pyskani)
package:
```console
$ conda install -c bioconda pyskani
``` -->
In the event you have to compile the package from source, all the required
Rust libraries are vendored in the source distribution, and a Rust compiler
will be setup automatically if there is none on the host machine.
## 🔖 Citation
Pyskani is scientific software, and builds on top of `skani`. Please cite [`skani`](https://github.com/bluenote-1577/skani) if you are using it in
an academic work, for instance as:
> `pyskani`, a Python library binding to `skani` (Shaw & Yu, 2023).
## 💡 Examples
### 📝 Creating a database
A database can be created either in memory or using a folder on the machine
filesystem to store the sketches. Independently of the storage, a database
can be used immediately for querying, or saved to a different location.
Here is how to create a database into memory,
using [Biopython](https://github.com/biopython/biopython)
to load the record:
```python
database = pyskani.Database()
record = Bio.SeqIO.read("vendor/skani/test_files/e.coli-EC590.fasta", "fasta")
database.sketch("E. coli EC590", bytes(record.seq))
```
For draft genomes, simply pass more arguments to the `sketch` method, for
which you can use the splat operator:
```python
database = pyskani.Database()
records = Bio.SeqIO.parse("vendor/skani/test_files/e.coli-o157.fasta", "fasta")
sequences = (bytes(record.seq) for record in records)
database.sketch("E. coli O157", *sequences)
```
### 🗒️ Loading a database
To load a database, either created from `skani` or `pyskani`, you can either
load all sketches into memory, for fast querying:
```python
database = pyskani.Database.load("path/to/sketches")
```
Or load the files lazily to save memory, at the cost of slower querying:
```python
database = pyskani.Database.open("path/to/sketches")
```
### 🔎 Querying a database
Once a database has been created or loaded, use the `Database.query` method
to compute ANI for some query genomes:
```python
record = Bio.SeqIO.read("vendor/skani/test_files/e.coli-K12.fasta", "fasta")
hits = database.query("E. coli K12", bytes(record.seq))
```
## 🔎 See Also
Computing ANI for closed genomes? You may also be interested in
[`pyfastani`, a Python package for computing ANI](https://github.com/althonos/pyfastani)
using the [FastANI method](https://www.nature.com/articles/s41467-018-07641-9)
developed by [Chirag Jain](https://github.com/cjain7) *et al.*
## 💭 Feedback
### ⚠️ Issue Tracker
Found a bug ? Have an enhancement request ? Head over to the
[GitHub issue tracker](https://github.com/althonos/pyskani/issues) if you need
to report or ask something. If you are filing in on a bug, please include as
much information as you can about the issue, and try to recreate the same bug
in a simple, easily reproducible situation.
### 🏗️ Contributing
Contributions are more than welcome! See
[`CONTRIBUTING.md`](https://github.com/althonos/pyskani/blob/master/CONTRIBUTING.md)
for more details.
## ⚖️ License
This library is provided under the [MIT License](https://choosealicense.com/licenses/mit/).
The `skani` code was written by [Jim Shaw](https://jim-shaw-bluenote.github.io/)
and is distributed under the terms of the [MIT License](https://choosealicense.com/licenses/mit/)
as well. See `vendor/skani/LICENSE` for more information. Source distributions
of `pyskani` vendors additional sources under their own terms using
the [`cargo vendor`](https://doc.rust-lang.org/cargo/commands/cargo-vendor.html)
command.
*This project is in no way not affiliated, sponsored, or otherwise endorsed
by the [original `skani` authors](https://jim-shaw-bluenote.github.io/).
It was developed by [Martin Larralde](https://github.com/althonos/) during his
PhD project at the [European Molecular Biology Laboratory](https://www.embl.de/)
in the [Zeller team](https://github.com/zellerlab).*
## 📚 References
- <a id="ref1">\[1\]</a> Jim Shaw and Yun William Yu. ast and robust metagenomic sequence comparison through sparse chaining with skani (2023). Nature Methods. [doi:10.1038/s41592-023-02018-3](https://doi.org/10.1038/s41592-023-02018-3). [PMID:37735570](https://pubmed.ncbi.nlm.nih.gov/37735570/).
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"description": "# \ud83d\udc0d\u26d3\ufe0f\ud83e\uddec Pyskani [](https://github.com/althonos/pyskani/stargazers)\n\n*[PyO3](https://pyo3.rs/) bindings and Python interface to [skani](https://github.com/bluenote-1577/skani), a method for fast fast genomic identity calculation using sparse chaining.*\n\n[](https://github.com/althonos/pyskani/actions)\n[](https://codecov.io/gh/althonos/pyskani/)\n[](https://choosealicense.com/licenses/mit/)\n[](https://pypi.org/project/pyskani)\n[](https://anaconda.org/bioconda/pyskani)\n[](https://aur.archlinux.org/packages/python-pyskani)\n[](https://pypi.org/project/pyskani/#files)\n[](https://pypi.org/project/pyskani/#files)\n[](https://pypi.org/project/pyskani/#files)\n[](https://github.com/althonos/pyskani/)\n[](https://git.embl.de/larralde/pyskani/)\n[](https://github.com/althonos/pyskani/issues)\n[](https://pyskani.readthedocs.io)\n[](https://github.com/althonos/pyskani/blob/master/CHANGELOG.md)\n[](https://pepy.tech/project/pyskani)\n\n\n## \ud83d\uddfa\ufe0f Overview\n\n`skani`[\\[1\\]](#ref1) is a method developed by [Jim Shaw](https://jim-shaw-bluenote.github.io/)\nand [Yun William Yu](https://github.com/yunwilliamyu) for fast and robust\nmetagenomic sequence comparison through sparse chaining. It improves on\nFastANI by being more accurate and much faster, while requiring less memory.\n\n`pyskani` is a Python module, implemented using the [PyO3](https://pyo3.rs/)\nframework, that provides bindings to `skani`. It directly links to the\n`skani` code, which has the following advantages over CLI wrappers:\n\n- **pre-built wheels**: `pyskani` is distributed on PyPI and features\n pre-built wheels for common platforms, including x86-64 and Arm64 UNIX.\n- **single dependency**: If your software or your analysis pipeline is\n distributed as a Python package, you can add `pyskani` as a dependency to\n your project, and stop worrying about the `skani` binary being present on\n the end-user machine.\n- **sans I/O**: Everything happens in memory, in Python objects you control,\n making it easier to pass your sequences to `skani` without having to write\n them to a temporary file.\n\n*This library is still a work-in-progress, and in an experimental stage,\nbut it should already pack enough features to be used in a standard pipeline.*\n\n\n## \ud83d\udd27 Installing\n\nPyskani can be installed directly from [PyPI](https://pypi.org/project/pyskani/),\nwhich hosts some pre-built CPython wheels for x86-64 Unix platforms, as well\nas the code required to compile from source with Rust:\n```console\n$ pip install pyskani\n```\n<!-- Otherwise, pyskani is also available as a [Bioconda](https://anaconda.org/bioconda/pyskani)\npackage:\n```console\n$ conda install -c bioconda pyskani\n``` -->\n\nIn the event you have to compile the package from source, all the required\nRust libraries are vendored in the source distribution, and a Rust compiler\nwill be setup automatically if there is none on the host machine.\n\n## \ud83d\udd16 Citation\n\nPyskani is scientific software, and builds on top of `skani`. Please cite [`skani`](https://github.com/bluenote-1577/skani) if you are using it in\nan academic work, for instance as:\n\n> `pyskani`, a Python library binding to `skani` (Shaw & Yu, 2023).\n\n## \ud83d\udca1 Examples\n\n### \ud83d\udcdd Creating a database\n\nA database can be created either in memory or using a folder on the machine\nfilesystem to store the sketches. Independently of the storage, a database\ncan be used immediately for querying, or saved to a different location.\n\nHere is how to create a database into memory,\nusing [Biopython](https://github.com/biopython/biopython)\nto load the record:\n```python\ndatabase = pyskani.Database()\nrecord = Bio.SeqIO.read(\"vendor/skani/test_files/e.coli-EC590.fasta\", \"fasta\")\ndatabase.sketch(\"E. coli EC590\", bytes(record.seq))\n```\n\nFor draft genomes, simply pass more arguments to the `sketch` method, for\nwhich you can use the splat operator:\n```python\ndatabase = pyskani.Database()\nrecords = Bio.SeqIO.parse(\"vendor/skani/test_files/e.coli-o157.fasta\", \"fasta\")\nsequences = (bytes(record.seq) for record in records)\ndatabase.sketch(\"E. coli O157\", *sequences)\n```\n\n### \ud83d\uddd2\ufe0f Loading a database\n\nTo load a database, either created from `skani` or `pyskani`, you can either\nload all sketches into memory, for fast querying:\n```python\ndatabase = pyskani.Database.load(\"path/to/sketches\")\n```\n\nOr load the files lazily to save memory, at the cost of slower querying:\n```python\ndatabase = pyskani.Database.open(\"path/to/sketches\")\n```\n\n### \ud83d\udd0e Querying a database\n\nOnce a database has been created or loaded, use the `Database.query` method\nto compute ANI for some query genomes:\n```python\nrecord = Bio.SeqIO.read(\"vendor/skani/test_files/e.coli-K12.fasta\", \"fasta\")\nhits = database.query(\"E. coli K12\", bytes(record.seq))\n```\n\n## \ud83d\udd0e See Also\n\nComputing ANI for closed genomes? You may also be interested in\n[`pyfastani`, a Python package for computing ANI](https://github.com/althonos/pyfastani)\nusing the [FastANI method](https://www.nature.com/articles/s41467-018-07641-9)\ndeveloped by [Chirag Jain](https://github.com/cjain7) *et al.*\n\n## \ud83d\udcad Feedback\n\n### \u26a0\ufe0f Issue Tracker\n\nFound a bug ? Have an enhancement request ? Head over to the\n[GitHub issue tracker](https://github.com/althonos/pyskani/issues) if you need\nto report or ask something. If you are filing in on a bug, please include as\nmuch information as you can about the issue, and try to recreate the same bug\nin a simple, easily reproducible situation.\n\n### \ud83c\udfd7\ufe0f Contributing\n\nContributions are more than welcome! See\n[`CONTRIBUTING.md`](https://github.com/althonos/pyskani/blob/master/CONTRIBUTING.md)\nfor more details.\n\n\n## \u2696\ufe0f License\n\nThis library is provided under the [MIT License](https://choosealicense.com/licenses/mit/).\n\nThe `skani` code was written by [Jim Shaw](https://jim-shaw-bluenote.github.io/)\nand is distributed under the terms of the [MIT License](https://choosealicense.com/licenses/mit/)\nas well. See `vendor/skani/LICENSE` for more information. Source distributions\nof `pyskani` vendors additional sources under their own terms using\nthe [`cargo vendor`](https://doc.rust-lang.org/cargo/commands/cargo-vendor.html)\ncommand.\n\n*This project is in no way not affiliated, sponsored, or otherwise endorsed\nby the [original `skani` authors](https://jim-shaw-bluenote.github.io/).\nIt was developed by [Martin Larralde](https://github.com/althonos/) during his\nPhD project at the [European Molecular Biology Laboratory](https://www.embl.de/)\nin the [Zeller team](https://github.com/zellerlab).*\n\n## \ud83d\udcda References\n\n- <a id=\"ref1\">\\[1\\]</a> Jim Shaw and Yun William Yu. ast and robust metagenomic sequence comparison through sparse chaining with skani (2023). Nature Methods. [doi:10.1038/s41592-023-02018-3](https://doi.org/10.1038/s41592-023-02018-3). [PMID:37735570](https://pubmed.ncbi.nlm.nih.gov/37735570/).\n\n",
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