pyskani


Namepyskani JSON
Version 0.1.2 PyPI version JSON
download
home_pagehttps://github.com/althonos/pyskani
SummaryPyO3 bindings and Python interface to skani, a method for fast fast genomic identity calculation using sparse chaining.
upload_time2024-06-13 21:49:42
maintainerNone
docs_urlNone
authorMartin Larralde
requires_python>=3.7
licenseMIT
keywords bioinformatics genomics average nucleotide identity
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            # πŸβ›“οΈπŸ§¬ Pyskani [![Stars](https://img.shields.io/github/stars/althonos/pyskani.svg?style=social&maxAge=3600&label=Star)](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.*

[![Actions](https://img.shields.io/github/actions/workflow/status/althonos/pyskani/test.yml?branch=main&logo=github&style=flat-square&maxAge=300)](https://github.com/althonos/pyskani/actions)
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[![PyPI](https://img.shields.io/pypi/v/pyskani.svg?style=flat-square&maxAge=3600)](https://pypi.org/project/pyskani)
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## πŸ—ΊοΈ Overview

`skani` 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.*


## πŸ”§ 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.


## πŸ’‘ Examples

### πŸ“ 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)
```

### πŸ—’οΈ 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")
```

### πŸ”Ž 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))
```

## πŸ”Ž 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.*

## πŸ’­ 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.

### πŸ—οΈ Contributing

Contributions are more than welcome! See
[`CONTRIBUTING.md`](https://github.com/althonos/pyskani/blob/master/CONTRIBUTING.md)
for more details.


## βš–οΈ 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).*

            

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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\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? 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