# 🐍✂️ PytrimAl [](https://github.com/althonos/pytrimal/stargazers)
*[Cython](https://cython.org/) bindings and Python interface to [trimAl](http://trimal.cgenomics.org/), a tool for automated alignment trimming. **Now with SIMD!***
[](https://github.com/althonos/pytrimal/actions)
[](https://codecov.io/gh/althonos/pytrimal/)
[](https://choosealicense.com/licenses/gpl-3.0/)
[](https://pypi.org/project/pytrimal)
[](https://anaconda.org/bioconda/pytrimal)
[](https://aur.archlinux.org/packages/python-pytrimal)
[](https://pypi.org/project/pytrimal/#files)
[](https://pypi.org/project/pytrimal/#files)
[](https://pypi.org/project/pytrimal/#files)
[](https://github.com/althonos/pytrimal/)
[](https://git.embl.de/larralde/pytrimal/)
[](https://github.com/althonos/pytrimal/issues)
[](https://pytrimal.readthedocs.io)
[](https://github.com/althonos/pytrimal/blob/main/CHANGELOG.md)
[](https://pepy.tech/project/pytrimal)
***⚠️ This package is based on the release candidate of trimAl 2.0, and results
may not be consistent across versions or with the trimAl 1.4 results.***
## 🗺️ Overview
PytrimAl is a Python module that provides bindings to [trimAl](http://trimal.cgenomics.org/)
using [Cython](https://cython.org/). It implements a user-friendly, Pythonic
interface to use one of the different trimming methods from trimAl and
access results directly. It interacts with the trimAl internals, which has
the following advantages:
- **single dependency**: PytrimAl is distributed as a Python package, so you
can add it as a dependency to your project, and stop worrying about the
trimAl 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 trimAl CLI using a
sub-process and temporary files.
[`Alignment`](https://pytrimal.readthedocs.io/en/latest/api/alignment.html#pytrimal.Alignment)
objects can be created directly from Python code.
- **friendly interface**: The different trimming methods are implement as
Python classes that can be configured independently.
- **error management**: Errors occuring in trimAl are converted
transparently into Python exceptions, including an informative
error message.
- **better performance**: PytrimAl uses *SIMD* instructions to compute
statistics like pairwise sequence similarity. This makes the whole
trimming process much faster for alignment with a large number of
sequences, at the expense of slightly higher memory consumption.
## 📋 Roadmap
The following features are available or considered for implementation:
- [x] **automatic trimming**: Support for trimming alignments using one of the
automatic heuristics implemented in trimAl.
- [x] **manual trimming**: Support for trimming alignments using manually
defined conservation and gap thresholds for each residue position.
- [x] **overlap trimming**: Trimming sequences using residue and sequence
overlaps to exclude regions with minimal conservation.
- [x] **representative trimming**: Select only representative sequences
from the alignment, either using a fixed number, or a maximum identity
threshold.
- [x] **alignment loading from disk**: Load an alignment from disk given
a filename.
- [x] **alignment loading from a file-like object**: Load an alignment from
a Python [file object](https://docs.python.org/3/glossary.html#term-file-object)
instead of a file on the local filesystem.
- [x] **aligment creation from Python**: Create an alignment from a collection
of sequences stored in Python strings.
- [x] **alignment formatting to disk**: Write an alignment to a file given
a filename in one of the supported file formats.
- [x] **alignment formatting to a file-like object**: Write an alignment to
a file-like object in one of the supported file formats.
- [ ] **reverse-translation**: Back-translate a protein alignment to align
the sequences in genomic space.
- [x] **alternative similarity matrix**: Specify an alternative similarity
matrix for the alignment (instead of BLOSUM62).
- [x] **similarity matrix creation**: Create a similarity matrix from scratch
from Python code.
- [x] **windows for manual methods**: Use a sliding window for computing
statistics in manual methods.
## 🔧 Installing
PytrimAl is available for all modern versions (3.6+), with no external dependencies.
It can be installed directly from [PyPI](https://pypi.org/project/pytrimal/),
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 pytrimal
```
Otherwise, pytrimal is also available as a [Bioconda](https://bioconda.github.io/)
package:
```console
$ conda install -c bioconda pytrimal
```
## 💡 Example
Let's load an `Alignment` from a file on the disk, and use the *strictplus*
method to trim it, before printing the `TrimmedAlignment` as a Clustal block:
```python
from pytrimal import Alignment, AutomaticTrimmer
ali = Alignment.load("pytrimal/tests/data/example.001.AA.clw")
trimmer = AutomaticTrimmer(method="strictplus")
trimmed = trimmer.trim(ali)
for name, seq in zip(trimmed.names, trimmed.sequences):
print(name.decode().rjust(6), seq)
```
This should output the following:
```
Sp8 GIVLVWLFPWNGLQIHMMGII
Sp10 VIMLEWFFAWLGLEINMMVII
Sp26 GLFLAAANAWLGLEINMMAQI
Sp6 GIYLSWYLAWLGLEINMMAII
Sp17 GFLLTWFQLWQGLDLNKMPVF
Sp33 GLHMAWFQAWGGLEINKQAIL
```
You can then use the
[`dump`](https://pytrimal.readthedocs.io/en/latest/api/alignment.html#pytrimal.Alignment.dump)
method to write the trimmed alignment to a file or file-like
object. For instance, save the results in
[PIR format](https://www.bioinformatics.nl/tools/crab_pir.html)
to a file named `example.trimmed.pir`:
```python
trimmed.dump("example.trimmed.pir", format="pir")
```
## 🧶 Thread-safety
Trimmer objects are thread-safe, and the `trim` method is re-entrant.
This means you can batch-process alignments in parallel using a
[`ThreadPool`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.ThreadPool)
with a single trimmer object:
```python
import glob
import multiprocessing.pool
from pytrimal import Alignment, AutomaticTrimmer
trimmer = AutomaticTrimmer()
alignments = map(Alignment.load, glob.iglob("pytrimal/tests/data/*.fasta"))
with multiprocessing.pool.ThreadPool() as pool:
trimmed_alignments = pool.map(trimmer.trim, alignments)
```
## ⏱️ Benchmarks
Benchmarks were run on a [i7-10710U CPU](https://ark.intel.com/content/www/us/en/ark/products/196448/intel-core-i710710u-processor-12m-cache-up-to-4-70-ghz.html)
@ 1.10GHz, using a single core to time the computation of several statistics,
on a variable number of sequences from
[`example.014.AA.EggNOG.COG0591.fasta`](https://github.com/inab/trimal/blob/trimAl/dataset/example.014.AA.EggNOG.COG0591.fasta),
an alignment of 3583 sequences and 7287 columns.
Each graph measures the computation time of a single trimAl statistic
(see the [Statistics page](https://pytrimal.readthedocs.io/en/stable/statistics.html)
of the [online documentation](https://pytrimal.readthedocs.io/) for more
information.)
The `None` curve shows the time using the internal trimAl 2.0 code,
the `Generic` curve shows a generic C implementation with some more
optimizations, and the `SSE` curve shows the time spent using a dedicated
class with [SIMD](https://en.wikipedia.org/wiki/Single_instruction,_multiple_data)
implementations of the statistic computation.
## 💭 Feedback
### ⚠️ Issue Tracker
Found a bug ? Have an enhancement request ? Head over to the [GitHub issue tracker](https://github.com/althonos/pytrimal/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/pytrimal/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/pytrimal/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/).
trimAl is developed by the [trimAl team](http://trimal.cgenomics.org/trimal_team) and is distributed under the
terms of the GPLv3 as well. See `vendor/trimal/LICENSE` for more information.
*This project is in no way not affiliated, sponsored, or otherwise endorsed
by the [trimAl authors](http://trimal.cgenomics.org/trimal_team). 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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"description": "# \ud83d\udc0d\u2702\ufe0f PytrimAl [](https://github.com/althonos/pytrimal/stargazers)\n\n*[Cython](https://cython.org/) bindings and Python interface to [trimAl](http://trimal.cgenomics.org/), a tool for automated alignment trimming. **Now with SIMD!***\n\n[](https://github.com/althonos/pytrimal/actions)\n[](https://codecov.io/gh/althonos/pytrimal/)\n[](https://choosealicense.com/licenses/gpl-3.0/)\n[](https://pypi.org/project/pytrimal)\n[](https://anaconda.org/bioconda/pytrimal)\n[](https://aur.archlinux.org/packages/python-pytrimal)\n[](https://pypi.org/project/pytrimal/#files)\n[](https://pypi.org/project/pytrimal/#files)\n[](https://pypi.org/project/pytrimal/#files)\n[](https://github.com/althonos/pytrimal/)\n[](https://git.embl.de/larralde/pytrimal/)\n[](https://github.com/althonos/pytrimal/issues)\n[](https://pytrimal.readthedocs.io)\n[](https://github.com/althonos/pytrimal/blob/main/CHANGELOG.md)\n[](https://pepy.tech/project/pytrimal)\n\n***\u26a0\ufe0f This package is based on the release candidate of trimAl 2.0, and results\nmay not be consistent across versions or with the trimAl 1.4 results.***\n\n## \ud83d\uddfa\ufe0f Overview\n\nPytrimAl is a Python module that provides bindings to [trimAl](http://trimal.cgenomics.org/)\nusing [Cython](https://cython.org/). It implements a user-friendly, Pythonic\ninterface to use one of the different trimming methods from trimAl and\naccess results directly. It interacts with the trimAl internals, which has\nthe following advantages:\n\n- **single dependency**: PytrimAl is distributed as a Python package, so you\n can add it as a dependency to your project, and stop worrying about the\n trimAl 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 trimAl CLI using a\n sub-process and temporary files.\n [`Alignment`](https://pytrimal.readthedocs.io/en/latest/api/alignment.html#pytrimal.Alignment)\n objects can be created directly from Python code.\n- **friendly interface**: The different trimming methods are implement as\n Python classes that can be configured independently.\n- **error management**: Errors occuring in trimAl are converted\n transparently into Python exceptions, including an informative\n error message.\n- **better performance**: PytrimAl uses *SIMD* instructions to compute\n statistics like pairwise sequence similarity. This makes the whole\n trimming process much faster for alignment with a large number of\n sequences, at the expense of slightly higher memory consumption.\n\n## \ud83d\udccb Roadmap\n\nThe following features are available or considered for implementation:\n\n- [x] **automatic trimming**: Support for trimming alignments using one of the\n automatic heuristics implemented in trimAl.\n- [x] **manual trimming**: Support for trimming alignments using manually\n defined conservation and gap thresholds for each residue position.\n- [x] **overlap trimming**: Trimming sequences using residue and sequence\n overlaps to exclude regions with minimal conservation.\n- [x] **representative trimming**: Select only representative sequences\n from the alignment, either using a fixed number, or a maximum identity\n threshold.\n- [x] **alignment loading from disk**: Load an alignment from disk given\n a filename.\n- [x] **alignment loading from a file-like object**: Load an alignment from\n a Python [file object](https://docs.python.org/3/glossary.html#term-file-object)\n instead of a file on the local filesystem.\n- [x] **aligment creation from Python**: Create an alignment from a collection\n of sequences stored in Python strings.\n- [x] **alignment formatting to disk**: Write an alignment to a file given\n a filename in one of the supported file formats.\n- [x] **alignment formatting to a file-like object**: Write an alignment to\n a file-like object in one of the supported file formats.\n- [ ] **reverse-translation**: Back-translate a protein alignment to align\n the sequences in genomic space.\n- [x] **alternative similarity matrix**: Specify an alternative similarity\n matrix for the alignment (instead of BLOSUM62).\n- [x] **similarity matrix creation**: Create a similarity matrix from scratch\n from Python code.\n- [x] **windows for manual methods**: Use a sliding window for computing\n statistics in manual methods.\n\n## \ud83d\udd27 Installing\n\nPytrimAl is available for all modern versions (3.6+), with no external dependencies.\n\nIt can be installed directly from [PyPI](https://pypi.org/project/pytrimal/),\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 compile\nfrom source with Cython:\n```console\n$ pip install pytrimal\n```\n\nOtherwise, pytrimal is also available as a [Bioconda](https://bioconda.github.io/)\npackage:\n```console\n$ conda install -c bioconda pytrimal\n```\n\n## \ud83d\udca1 Example\n\nLet's load an `Alignment` from a file on the disk, and use the *strictplus*\nmethod to trim it, before printing the `TrimmedAlignment` as a Clustal block:\n```python\nfrom pytrimal import Alignment, AutomaticTrimmer\n\nali = Alignment.load(\"pytrimal/tests/data/example.001.AA.clw\")\ntrimmer = AutomaticTrimmer(method=\"strictplus\")\n\ntrimmed = trimmer.trim(ali)\nfor name, seq in zip(trimmed.names, trimmed.sequences):\n print(name.decode().rjust(6), seq)\n```\n\nThis should output the following:\n```\nSp8 GIVLVWLFPWNGLQIHMMGII\nSp10 VIMLEWFFAWLGLEINMMVII\nSp26 GLFLAAANAWLGLEINMMAQI\nSp6 GIYLSWYLAWLGLEINMMAII\nSp17 GFLLTWFQLWQGLDLNKMPVF\nSp33 GLHMAWFQAWGGLEINKQAIL\n```\n\nYou can then use the\n[`dump`](https://pytrimal.readthedocs.io/en/latest/api/alignment.html#pytrimal.Alignment.dump)\nmethod to write the trimmed alignment to a file or file-like\nobject. For instance, save the results in\n[PIR format](https://www.bioinformatics.nl/tools/crab_pir.html)\nto a file named `example.trimmed.pir`:\n```python\ntrimmed.dump(\"example.trimmed.pir\", format=\"pir\")\n```\n\n## \ud83e\uddf6 Thread-safety\n\nTrimmer objects are thread-safe, and the `trim` method is re-entrant.\nThis means you can batch-process alignments in parallel using a\n[`ThreadPool`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.ThreadPool)\nwith a single trimmer object:\n```python\nimport glob\nimport multiprocessing.pool\nfrom pytrimal import Alignment, AutomaticTrimmer\n\ntrimmer = AutomaticTrimmer()\nalignments = map(Alignment.load, glob.iglob(\"pytrimal/tests/data/*.fasta\"))\n\nwith multiprocessing.pool.ThreadPool() as pool:\n trimmed_alignments = pool.map(trimmer.trim, alignments)\n```\n\n## \u23f1\ufe0f Benchmarks\n\nBenchmarks were run on a [i7-10710U CPU](https://ark.intel.com/content/www/us/en/ark/products/196448/intel-core-i710710u-processor-12m-cache-up-to-4-70-ghz.html)\n@ 1.10GHz, using a single core to time the computation of several statistics,\non a variable number of sequences from\n[`example.014.AA.EggNOG.COG0591.fasta`](https://github.com/inab/trimal/blob/trimAl/dataset/example.014.AA.EggNOG.COG0591.fasta),\nan alignment of 3583 sequences and 7287 columns.\n\n\n\nEach graph measures the computation time of a single trimAl statistic\n(see the [Statistics page](https://pytrimal.readthedocs.io/en/stable/statistics.html)\nof the [online documentation](https://pytrimal.readthedocs.io/) for more\ninformation.)\n\nThe `None` curve shows the time using the internal trimAl 2.0 code,\nthe `Generic` curve shows a generic C implementation with some more\noptimizations, and the `SSE` curve shows the time spent using a dedicated\nclass with [SIMD](https://en.wikipedia.org/wiki/Single_instruction,_multiple_data)\nimplementations of the statistic computation.\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/pytrimal/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/pytrimal/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/pytrimal/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/).\ntrimAl is developed by the [trimAl team](http://trimal.cgenomics.org/trimal_team) and is distributed under the\nterms of the GPLv3 as well. See `vendor/trimal/LICENSE` for more information.\n\n*This project is in no way not affiliated, sponsored, or otherwise endorsed\nby the [trimAl authors](http://trimal.cgenomics.org/trimal_team). 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",
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