# 🐍🧮 PyFAMSA [](https://github.com/althonos/pyfamsa/stargazers)
*[Cython](https://cython.org/) bindings and Python interface to [FAMSA](https://github.com/refresh-bio/FAMSA), an algorithm for ultra-scale multiple sequence alignments.*
[](https://github.com/althonos/pyfamsa/actions)
[](https://codecov.io/gh/althonos/pyfamsa/)
[](https://choosealicense.com/licenses/gpl-3.0/)
[](https://pypi.org/project/pyfamsa)
[](https://anaconda.org/bioconda/pyfamsa)
[](https://aur.archlinux.org/packages/python-pyfamsa)
[](https://pypi.org/project/pyfamsa/#files)
[](https://pypi.org/project/pyfamsa/#files)
[](https://pypi.org/project/pyfamsa/#files)
[](https://github.com/althonos/pyfamsa/)
[](https://git.embl.de/larralde/pyfamsa/)
[](https://github.com/althonos/pyfamsa/issues)
[](https://pyfamsa.readthedocs.io)
[](https://github.com/althonos/pyfamsa/blob/main/CHANGELOG.md)
[](https://pepy.tech/project/pyfamsa)
***⚠️ This package is based on FAMSA 2.***
## 🗺️ Overview
[FAMSA](https://github.com/refresh-bio/FAMSA) is a method published in
2016 by Deorowicz *et al.*[\[1\]](#ref1) for large-scale multiple sequence alignments.
It uses state-of-the-art time and memory optimizations as well as a fast
guide tree heuristic to reach very high performance and accuracy.
PyFAMSA is a Python module that provides bindings to [FAMSA](https://github.com/refresh-bio/FAMSA)
using [Cython](https://cython.org/). It implements a user-friendly, Pythonic
interface to align protein sequences using different parameters and access
results directly. It interacts with the FAMSA library interface, which has
the following advantages:
- **single dependency**: pyfamsa is distributed as a Python package, so you
can add it as a dependency to your project, and stop worrying about the
FAMSA binary being present on the end-user machine.
- **no intermediate files**: Everything happens in memory, in a Python object
you control, so you don't have to invoke the FAMSA CLI using a
sub-process and temporary files.
- **friendly interface**: The different guide tree build methods and
heuristics can be selected from the Python code with a simple keyword
argument when configuring a new [`Aligner`](https://pyfamsa.readthedocs.io/en/stable/api/aligner.html#pyfamsa.Aligner).
## 🔧 Installing
PyFAMSA can be installed directly from [PyPI](https://pypi.org/project/pyfamsa/),
which hosts some pre-built wheels for the x86-64 architecture (Linux/OSX)
and the Aarch64 architecture (Linux only), as well as the code required to
compile from source with Cython:
```console
$ pip install pyfamsa
```
<!-- Otherwise, pyfamsa is also available as a [Bioconda](https://bioconda.github.io/)
package:
```console
$ conda install -c bioconda pyfamsa
``` -->
Otherwise, have a look at the [Installation page](https://pyfamsa.readthedocs.io/en/stable/install.html) of the [online documentation](https://pyfamsa.readthedocs.io/)
## 💡 Example
Let's create some sequences in memory, align them using the UPGMA method,
(without any heuristic), and simply print the alignment on screen:
```python
from pyfamsa import Aligner, Sequence
sequences = [
Sequence(b"Sp8", b"GLGKVIVYGIVLGTKSDQFSNWVVWLFPWNGLQIHMMGII"),
Sequence(b"Sp10", b"DPAVLFVIMLGTITKFSSEWFFAWLGLEINMMVII"),
Sequence(b"Sp26", b"AAAAAAAAALLTYLGLFLGTDYENFAAAAANAWLGLEINMMAQI"),
Sequence(b"Sp6", b"ASGAILTLGIYLFTLCAVISVSWYLAWLGLEINMMAII"),
Sequence(b"Sp17", b"FAYTAPDLLLIGFLLKTVATFGDTWFQLWQGLDLNKMPVF"),
Sequence(b"Sp33", b"PTILNIAGLHMETDINFSLAWFQAWGGLEINKQAIL"),
]
aligner = Aligner(guide_tree="upgma")
msa = aligner.align(sequences)
for sequence in msa:
print(sequence.id.decode().ljust(10), sequence.sequence.decode())
```
This should output the following:
```
Sp10 --------DPAVLFVIMLGTIT-KFS--SEWFFAWLGLEINMMVII
Sp17 ---FAYTAPDLLLIGFLLKTVA-TFG--DTWFQLWQGLDLNKMPVF
Sp26 AAAAAAAAALLTYLGLFLGTDYENFA--AAAANAWLGLEINMMAQI
Sp33 -------PTILNIAGLHMETDI-NFS--LAWFQAWGGLEINKQAIL
Sp6 ------ASGAILTLGIYLFTLCAVIS--VSWYLAWLGLEINMMAII
Sp8 ------GLGKVIVYGIVLGTKSDQFSNWVVWLFPWNGLQIHMMGII
```
## 🧶 Thread-safety
`Aligner` objects are thread-safe, and the `align` method is re-entrant. You
could batch process several alignments in parallel using a
[`ThreadPool`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.ThreadPool) with a single
aligner object:
```python
import glob
import multiprocessing.pool
import Bio.SeqIO
from pyfamsa import Aligner, Sequence
families = [
[ Sequence(r.id.encode(), r.seq.encode()) for r in Bio.SeqIO.parse(file, "fasta") ]
for file in glob.glob("pyfamsa/tests/data/*.faa")
]
aligner = Aligner()
with multiprocessing.pool.ThreadPool() as pool:
alignments = pool.map(aligner.align, families)
```
<!-- ## ⏱️ Benchmarks -->
## 🔎 See Also
Done with your protein alignment? You may be interested in trimming it: in that
case, you could use the [`pytrimal`](https://github.com/althonos/pytrimal) Python
package, which wraps [trimAl](http://trimal.cgenomics.org/) 2.0. Or perhaps
you want to build a HMM from the alignment? Then maybe have a look at
[`pyhmmer`](https://github.com/althonos/pyhmmer), a Python package which
wraps [HMMER](http://hmmer.org/).
## 💭 Feedback
### ⚠️ Issue Tracker
Found a bug ? Have an enhancement request ? Head over to the [GitHub issue tracker](https://github.com/althonos/pyfamsa/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/pyfamsa/blob/main/CONTRIBUTING.md)
for more details.
## 📋 Changelog
This project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.html)
and provides a [changelog](https://github.com/althonos/pyfamsa/blob/main/CHANGELOG.md)
in the [Keep a Changelog](http://keepachangelog.com/en/1.0.0/) format.
## ⚖️ License
This library is provided under the [GNU General Public License v3.0](https://choosealicense.com/licenses/gpl-3.0/). FAMSA is developed by the
[REFRESH Bioinformatics Group](https://refresh-bio.github.io/) and is
distributed under the terms of the GPLv3 as well. See `vendor/FAMSA/LICENSE`
for more information. In addition, FAMSA vendors several libraries for
compatibility, all of which are redistributed with PyFAMSA under their own
terms: `atomic_wait` (MIT License), `mimalloc` (MIT License), `libdeflate`
(MIT License), Boost (Boost Software License).
*This project is in no way not affiliated, sponsored, or otherwise endorsed
by the [FAMSA authors](https://github.com/refresh-bio). 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> Deorowicz, Sebastian, Debudaj-Grabysz, Agnieszka & Gudyś, Adam. ‘FAMSA: Fast and accurate multiple sequence alignment of huge protein families’. Sci Rep 6, 33964 (2016). [doi:10.1038/srep33964](https://doi.org/10.1038/srep33964)
Raw data
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"description": "# \ud83d\udc0d\ud83e\uddee PyFAMSA [](https://github.com/althonos/pyfamsa/stargazers)\n\n*[Cython](https://cython.org/) bindings and Python interface to [FAMSA](https://github.com/refresh-bio/FAMSA), an algorithm for ultra-scale multiple sequence alignments.*\n\n[](https://github.com/althonos/pyfamsa/actions)\n[](https://codecov.io/gh/althonos/pyfamsa/)\n[](https://choosealicense.com/licenses/gpl-3.0/)\n[](https://pypi.org/project/pyfamsa)\n[](https://anaconda.org/bioconda/pyfamsa)\n[](https://aur.archlinux.org/packages/python-pyfamsa)\n[](https://pypi.org/project/pyfamsa/#files)\n[](https://pypi.org/project/pyfamsa/#files)\n[](https://pypi.org/project/pyfamsa/#files)\n[](https://github.com/althonos/pyfamsa/)\n[](https://git.embl.de/larralde/pyfamsa/)\n[](https://github.com/althonos/pyfamsa/issues)\n[](https://pyfamsa.readthedocs.io)\n[](https://github.com/althonos/pyfamsa/blob/main/CHANGELOG.md)\n[](https://pepy.tech/project/pyfamsa)\n\n\n***\u26a0\ufe0f This package is based on FAMSA 2.***\n\n## \ud83d\uddfa\ufe0f Overview\n\n[FAMSA](https://github.com/refresh-bio/FAMSA) is a method published in\n2016 by Deorowicz *et al.*[\\[1\\]](#ref1) for large-scale multiple sequence alignments.\nIt uses state-of-the-art time and memory optimizations as well as a fast\nguide tree heuristic to reach very high performance and accuracy.\n\nPyFAMSA is a Python module that provides bindings to [FAMSA](https://github.com/refresh-bio/FAMSA)\nusing [Cython](https://cython.org/). It implements a user-friendly, Pythonic\ninterface to align protein sequences using different parameters and access\nresults directly. It interacts with the FAMSA library interface, which has\nthe following advantages:\n\n- **single dependency**: pyfamsa is distributed as a Python package, so you\n can add it as a dependency to your project, and stop worrying about the\n FAMSA binary being present on the end-user machine.\n- **no intermediate files**: Everything happens in memory, in a Python object\n you control, so you don't have to invoke the FAMSA CLI using a\n sub-process and temporary files.\n- **friendly interface**: The different guide tree build methods and\n heuristics can be selected from the Python code with a simple keyword\n argument when configuring a new [`Aligner`](https://pyfamsa.readthedocs.io/en/stable/api/aligner.html#pyfamsa.Aligner).\n\n## \ud83d\udd27 Installing\n\nPyFAMSA can be installed directly from [PyPI](https://pypi.org/project/pyfamsa/),\nwhich hosts some pre-built wheels for the x86-64 architecture (Linux/OSX)\nand the Aarch64 architecture (Linux only), as well as the code required to\ncompile from source with Cython:\n```console\n$ pip install pyfamsa\n```\n\n<!-- Otherwise, pyfamsa is also available as a [Bioconda](https://bioconda.github.io/)\npackage:\n```console\n$ conda install -c bioconda pyfamsa\n``` -->\n\nOtherwise, have a look at the [Installation page](https://pyfamsa.readthedocs.io/en/stable/install.html) of the [online documentation](https://pyfamsa.readthedocs.io/)\n\n## \ud83d\udca1 Example\n\nLet's create some sequences in memory, align them using the UPGMA method,\n(without any heuristic), and simply print the alignment on screen:\n\n```python\nfrom pyfamsa import Aligner, Sequence\n\nsequences = [\n Sequence(b\"Sp8\", b\"GLGKVIVYGIVLGTKSDQFSNWVVWLFPWNGLQIHMMGII\"),\n Sequence(b\"Sp10\", b\"DPAVLFVIMLGTITKFSSEWFFAWLGLEINMMVII\"),\n Sequence(b\"Sp26\", b\"AAAAAAAAALLTYLGLFLGTDYENFAAAAANAWLGLEINMMAQI\"),\n Sequence(b\"Sp6\", b\"ASGAILTLGIYLFTLCAVISVSWYLAWLGLEINMMAII\"),\n Sequence(b\"Sp17\", b\"FAYTAPDLLLIGFLLKTVATFGDTWFQLWQGLDLNKMPVF\"),\n Sequence(b\"Sp33\", b\"PTILNIAGLHMETDINFSLAWFQAWGGLEINKQAIL\"),\n]\n\naligner = Aligner(guide_tree=\"upgma\")\nmsa = aligner.align(sequences)\n\nfor sequence in msa:\n print(sequence.id.decode().ljust(10), sequence.sequence.decode())\n```\n\nThis should output the following:\n```\nSp10 --------DPAVLFVIMLGTIT-KFS--SEWFFAWLGLEINMMVII\nSp17 ---FAYTAPDLLLIGFLLKTVA-TFG--DTWFQLWQGLDLNKMPVF\nSp26 AAAAAAAAALLTYLGLFLGTDYENFA--AAAANAWLGLEINMMAQI\nSp33 -------PTILNIAGLHMETDI-NFS--LAWFQAWGGLEINKQAIL\nSp6 ------ASGAILTLGIYLFTLCAVIS--VSWYLAWLGLEINMMAII\nSp8 ------GLGKVIVYGIVLGTKSDQFSNWVVWLFPWNGLQIHMMGII\n```\n\n## \ud83e\uddf6 Thread-safety\n\n`Aligner` objects are thread-safe, and the `align` method is re-entrant. You\ncould batch process several alignments in parallel using a\n[`ThreadPool`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.ThreadPool) with a single\naligner object:\n```python\nimport glob\nimport multiprocessing.pool\nimport Bio.SeqIO\nfrom pyfamsa import Aligner, Sequence\n\nfamilies = [\n [ Sequence(r.id.encode(), r.seq.encode()) for r in Bio.SeqIO.parse(file, \"fasta\") ]\n for file in glob.glob(\"pyfamsa/tests/data/*.faa\")\n]\n\naligner = Aligner()\nwith multiprocessing.pool.ThreadPool() as pool:\n alignments = pool.map(aligner.align, families)\n```\n\n<!-- ## \u23f1\ufe0f Benchmarks -->\n\n## \ud83d\udd0e See Also\n\nDone with your protein alignment? You may be interested in trimming it: in that\ncase, you could use the [`pytrimal`](https://github.com/althonos/pytrimal) Python\npackage, which wraps [trimAl](http://trimal.cgenomics.org/) 2.0. Or perhaps\nyou want to build a HMM from the alignment? Then maybe have a look at\n[`pyhmmer`](https://github.com/althonos/pyhmmer), a Python package which\nwraps [HMMER](http://hmmer.org/).\n\n## \ud83d\udcad Feedback\n\n### \u26a0\ufe0f Issue Tracker\n\nFound a bug ? Have an enhancement request ? Head over to the [GitHub issue tracker](https://github.com/althonos/pyfamsa/issues)\nif you need to report or ask something. If you are filing in on a bug,\nplease include as much information as you can about the issue, and try to\nrecreate the same bug in a simple, easily reproducible situation.\n\n\n### \ud83c\udfd7\ufe0f Contributing\n\nContributions are more than welcome! See\n[`CONTRIBUTING.md`](https://github.com/althonos/pyfamsa/blob/main/CONTRIBUTING.md)\nfor more details.\n\n\n## \ud83d\udccb Changelog\n\nThis project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.html)\nand provides a [changelog](https://github.com/althonos/pyfamsa/blob/main/CHANGELOG.md)\nin the [Keep a Changelog](http://keepachangelog.com/en/1.0.0/) format.\n\n\n## \u2696\ufe0f License\n\nThis library is provided under the [GNU General Public License v3.0](https://choosealicense.com/licenses/gpl-3.0/). FAMSA is developed by the\n[REFRESH Bioinformatics Group](https://refresh-bio.github.io/) and is\ndistributed under the terms of the GPLv3 as well. See `vendor/FAMSA/LICENSE`\nfor more information. In addition, FAMSA vendors several libraries for\ncompatibility, all of which are redistributed with PyFAMSA under their own\nterms: `atomic_wait` (MIT License), `mimalloc` (MIT License), `libdeflate`\n(MIT License), Boost (Boost Software License).\n\n*This project is in no way not affiliated, sponsored, or otherwise endorsed\nby the [FAMSA authors](https://github.com/refresh-bio). It was developed\nby [Martin Larralde](https://github.com/althonos/) during his PhD project\nat the [European Molecular Biology Laboratory](https://www.embl.de/) in\nthe [Zeller team](https://github.com/zellerlab).*\n\n\n## \ud83d\udcda References\n\n- <a id=\"ref1\">\\[1\\]</a> Deorowicz, Sebastian, Debudaj-Grabysz, Agnieszka & Gudy\u015b, Adam. \u2018FAMSA: Fast and accurate multiple sequence alignment of huge protein families\u2019. Sci Rep 6, 33964 (2016). [doi:10.1038/srep33964](https://doi.org/10.1038/srep33964)\n",
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