# 🎼🧬 `lightmotif` [](https://github.com/althonos/lightmotif/stargazers)
*A lightweight [platform-accelerated](https://en.wikipedia.org/wiki/Single_instruction,_multiple_data) library for [biological motif](https://en.wikipedia.org/wiki/Sequence_motif) scanning using [position weight matrices](https://en.wikipedia.org/wiki/Position_weight_matrix)*.
[](https://github.com/althonos/lightmotif/actions)
[](https://codecov.io/gh/althonos/lightmotif/)
[](https://choosealicense.com/licenses/mit/)
[](https://lightmotif.readthedocs.io/en/stable/)
[](https://crates.io/crates/lightmotif-py)
[](https://pypi.org/project/lightmotif)
[](https://pypi.org/project/lightmotif/#files)
[](https://anaconda.org/bioconda/lightmotif)
[](https://pypi.org/project/lightmotif/#files)
[](https://pypi.org/project/lightmotif/#files)
[](https://github.com/althonos/lightmotif/tree/main/lightmotif-py)
[](https://git.embl.de/larralde/lightmotif/)
[](https://github.com/althonos/lightmotif/issues)
[](https://github.com/althonos/lightmotif/blob/master/CHANGELOG.md)
[](https://pepy.tech/project/lightmotif)
## 🗺️ Overview
[Motif](https://en.wikipedia.org/wiki/Sequence_motif) scanning with
[position weight matrices](https://en.wikipedia.org/wiki/Position_weight_matrix)
(also known as position-specific scoring matrices) is a robust method for
identifying motifs of fixed length inside a
[biological sequence](https://en.wikipedia.org/wiki/Sequence_(biology)). They can be
used to identify [transcription factor](https://en.wikipedia.org/wiki/Transcription_factor)
[binding sites in DNA](https://en.wikipedia.org/wiki/DNA_binding_site),
or [protease](https://en.wikipedia.org/wiki/Protease) [cleavage](https://en.wikipedia.org/wiki/Proteolysis) site in [polypeptides](https://en.wikipedia.org/wiki/Proteolysis).
Position weight matrices are often viewed as [sequence logos](https://en.wikipedia.org/wiki/Sequence_logo):
[](https://www.prodoric.de/matrix/MX000274.html)
The `lightmotif` library provides a Python module to run very efficient
searches for a motif encoded in a position weight matrix. The position
scanning combines several techniques to allow high-throughput processing
of sequences:
- Compile-time definition of alphabets and matrix dimensions.
- Sequence symbol encoding for fast table look-ups, as implemented in
HMMER[\[1\]](#ref1) or MEME[\[2\]](#ref2)
- Striped sequence matrices to process several positions in parallel,
inspired by Michael Farrar[\[3\]](#ref3).
- Vectorized matrix row look-up using `permute` instructions of [AVX2](https://fr.wikipedia.org/wiki/Advanced_Vector_Extensions).
*This is the Python version, there is a [Rust crate](https://crates.io/crates/lightmotif) available as well.*
## 🔧 Installing
`lightmotif` can be installed directly from [PyPI](https://pypi.org/project/lightmotif/),
which hosts some pre-built wheels for most mainstream platforms, as well as the
code required to compile from source with Rust:
```console
$ pip install lightmotif
```
<!-- Otherwise, lightmotif is also available as a [Bioconda](https://anaconda.org/bioconda/lightmotif)
package:
```console
$ conda install -c bioconda lightmotif
``` -->
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.
## 💡 Example
The motif interface should be mostly compatible with the
[`Bio.motifs`](https://biopython-tutorial.readthedocs.io/en/latest/notebooks/14%20-%20Sequence%20motif%20analysis%20using%20Bio.motifs.html#)
module from [Biopython](https://biopython.org/). The notable difference is that
the `calculate` method of PSSM objects expects a *striped* sequence instead.
```python
import lightmotif
# Create a count matrix from an iterable of sequences
motif = lightmotif.create(["GTTGACCTTATCAAC", "GTTGATCCAGTCAAC"])
# Create a PSSM with 0.1 pseudocounts and uniform background frequencies
pwm = motif.counts.normalize(0.1)
pssm = pwm.log_odds()
# Encode the target sequence into a striped matrix
seq = "ATGTCCCAACAACGATACCCCGAGCCCATCGCCGTCATCGGCTCGGCATGCAGATTCCCAGGCG"
striped = lightmotif.stripe(seq)
# Compute scores using the fastest backend implementation for the host machine
scores = pssm.calculate(sseq)
```
## ⏱️ Benchmarks
Benchmarks use the [MX000001](https://www.prodoric.de/matrix/MX000001.html)
motif from [PRODORIC](https://www.prodoric.de/)[\[4\]](#ref4), and the
[complete genome](https://www.ncbi.nlm.nih.gov/nuccore/U00096) of an
*Escherichia coli K12* strain.
*Benchmarks were run on a [i7-10710U CPU](https://ark.intel.com/content/www/us/en/ark/products/196448/intel-core-i7-10710u-processor-12m-cache-up-to-4-70-ghz.html) running @1.10GHz, compiled with `--target-cpu=native`*.
```console
lightmotif (avx2): 5,479,884 ns/iter (+/- 3,370,523) = 807.8 MiB/s
Bio.motifs: 334,359,765 ns/iter (+/- 11,045,456) = 13.2 MiB/s
MOODS.scan: 182,710,624 ns/iter (+/- 9,459,257) = 24.2 MiB/s
pymemesuite.fimo: 239,694,118 ns/iter (+/- 7,444,620) = 18.5 MiB/s
```
## 💭 Feedback
### ⚠️ Issue Tracker
Found a bug ? Have an enhancement request ? Head over to the [GitHub issue
tracker](https://github.com/althonos/lightmotif/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/lightmotif/blob/master/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/lightmotif/blob/master/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 3.0 or later](https://choosealicense.com/licenses/gpl-3.0/),
as it contains the GPL-licensed code of the TFM-PVALUE algorithm. The TFM-PVALUE dependency can be disabled by disabling
the `pvalue` crate feature, in which case the code can be used and redistributed under the terms
of the [MIT license](https://choosealicense.com/licenses/mit/).
*This project 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> Eddy, Sean R. ‘Accelerated Profile HMM Searches’. PLOS Computational Biology 7, no. 10 (20 October 2011): e1002195. [doi:10.1371/journal.pcbi.1002195](https://doi.org/10.1371/journal.pcbi.1002195).
- <a id="ref2">\[2\]</a> Grant, Charles E., Timothy L. Bailey, and William Stafford Noble. ‘FIMO: Scanning for Occurrences of a given Motif’. Bioinformatics 27, no. 7 (1 April 2011): 1017–18. [doi:10.1093/bioinformatics/btr064](https://doi.org/10.1093/bioinformatics/btr064).
- <a id="ref3">\[3\]</a> Farrar, Michael. ‘Striped Smith–Waterman Speeds Database Searches Six Times over Other SIMD Implementations’. Bioinformatics 23, no. 2 (15 January 2007): 156–61. [doi:10.1093/bioinformatics/btl582](https://doi.org/10.1093/bioinformatics/btl582).
- <a id="ref4">\[4\]</a> Dudek, Christian-Alexander, and Dieter Jahn. ‘PRODORIC: State-of-the-Art Database of Prokaryotic Gene Regulation’. Nucleic Acids Research 50, no. D1 (7 January 2022): D295–302. [doi:10.1093/nar/gkab1110](https://doi.org/10.1093/nar/gkab1110).
Raw data
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"description": "# \ud83c\udfbc\ud83e\uddec `lightmotif` [](https://github.com/althonos/lightmotif/stargazers)\n\n*A lightweight [platform-accelerated](https://en.wikipedia.org/wiki/Single_instruction,_multiple_data) library for [biological motif](https://en.wikipedia.org/wiki/Sequence_motif) scanning using [position weight matrices](https://en.wikipedia.org/wiki/Position_weight_matrix)*.\n\n[](https://github.com/althonos/lightmotif/actions)\n[](https://codecov.io/gh/althonos/lightmotif/)\n[](https://choosealicense.com/licenses/mit/)\n[](https://lightmotif.readthedocs.io/en/stable/)\n[](https://crates.io/crates/lightmotif-py)\n[](https://pypi.org/project/lightmotif)\n[](https://pypi.org/project/lightmotif/#files)\n[](https://anaconda.org/bioconda/lightmotif)\n[](https://pypi.org/project/lightmotif/#files)\n[](https://pypi.org/project/lightmotif/#files)\n[](https://github.com/althonos/lightmotif/tree/main/lightmotif-py)\n[](https://git.embl.de/larralde/lightmotif/)\n[](https://github.com/althonos/lightmotif/issues)\n[](https://github.com/althonos/lightmotif/blob/master/CHANGELOG.md)\n[](https://pepy.tech/project/lightmotif)\n\n## \ud83d\uddfa\ufe0f Overview\n\n[Motif](https://en.wikipedia.org/wiki/Sequence_motif) scanning with \n[position weight matrices](https://en.wikipedia.org/wiki/Position_weight_matrix)\n(also known as position-specific scoring matrices) is a robust method for \nidentifying motifs of fixed length inside a \n[biological sequence](https://en.wikipedia.org/wiki/Sequence_(biology)). They can be \nused to identify [transcription factor](https://en.wikipedia.org/wiki/Transcription_factor) \n[binding sites in DNA](https://en.wikipedia.org/wiki/DNA_binding_site), \nor [protease](https://en.wikipedia.org/wiki/Protease) [cleavage](https://en.wikipedia.org/wiki/Proteolysis) site in [polypeptides](https://en.wikipedia.org/wiki/Proteolysis). \nPosition weight matrices are often viewed as [sequence logos](https://en.wikipedia.org/wiki/Sequence_logo):\n\n[](https://www.prodoric.de/matrix/MX000274.html)\n\nThe `lightmotif` library provides a Python module to run very efficient\nsearches for a motif encoded in a position weight matrix. The position\nscanning combines several techniques to allow high-throughput processing\nof sequences:\n\n- Compile-time definition of alphabets and matrix dimensions.\n- Sequence symbol encoding for fast table look-ups, as implemented in\n HMMER[\\[1\\]](#ref1) or MEME[\\[2\\]](#ref2)\n- Striped sequence matrices to process several positions in parallel,\n inspired by Michael Farrar[\\[3\\]](#ref3).\n- Vectorized matrix row look-up using `permute` instructions of [AVX2](https://fr.wikipedia.org/wiki/Advanced_Vector_Extensions).\n\n*This is the Python version, there is a [Rust crate](https://crates.io/crates/lightmotif) available as well.*\n\n## \ud83d\udd27 Installing\n\n`lightmotif` can be installed directly from [PyPI](https://pypi.org/project/lightmotif/),\nwhich hosts some pre-built wheels for most mainstream platforms, as well as the \ncode required to compile from source with Rust:\n```console\n$ pip install lightmotif\n```\n<!-- Otherwise, lightmotif is also available as a [Bioconda](https://anaconda.org/bioconda/lightmotif)\npackage:\n```console\n$ conda install -c bioconda lightmotif\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 Example\n\nThe motif interface should be mostly compatible with the \n[`Bio.motifs`](https://biopython-tutorial.readthedocs.io/en/latest/notebooks/14%20-%20Sequence%20motif%20analysis%20using%20Bio.motifs.html#)\nmodule from [Biopython](https://biopython.org/). The notable difference is that \nthe `calculate` method of PSSM objects expects a *striped* sequence instead.\n\n```python\nimport lightmotif\n\n# Create a count matrix from an iterable of sequences\nmotif = lightmotif.create([\"GTTGACCTTATCAAC\", \"GTTGATCCAGTCAAC\"])\n\n# Create a PSSM with 0.1 pseudocounts and uniform background frequencies\npwm = motif.counts.normalize(0.1)\npssm = pwm.log_odds()\n\n# Encode the target sequence into a striped matrix\nseq = \"ATGTCCCAACAACGATACCCCGAGCCCATCGCCGTCATCGGCTCGGCATGCAGATTCCCAGGCG\"\nstriped = lightmotif.stripe(seq)\n\n# Compute scores using the fastest backend implementation for the host machine\nscores = pssm.calculate(sseq)\n```\n\n## \u23f1\ufe0f Benchmarks\n\nBenchmarks use the [MX000001](https://www.prodoric.de/matrix/MX000001.html)\nmotif from [PRODORIC](https://www.prodoric.de/)[\\[4\\]](#ref4), and the\n[complete genome](https://www.ncbi.nlm.nih.gov/nuccore/U00096) of an\n*Escherichia coli K12* strain. \n*Benchmarks were run on a [i7-10710U CPU](https://ark.intel.com/content/www/us/en/ark/products/196448/intel-core-i7-10710u-processor-12m-cache-up-to-4-70-ghz.html) running @1.10GHz, compiled with `--target-cpu=native`*.\n\n```console\nlightmotif (avx2): 5,479,884 ns/iter (+/- 3,370,523) = 807.8 MiB/s\nBio.motifs: 334,359,765 ns/iter (+/- 11,045,456) = 13.2 MiB/s\nMOODS.scan: 182,710,624 ns/iter (+/- 9,459,257) = 24.2 MiB/s\npymemesuite.fimo: 239,694,118 ns/iter (+/- 7,444,620) = 18.5 MiB/s\n```\n\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\ntracker](https://github.com/althonos/lightmotif/issues) if you need to report\nor ask something. If you are filing in on a bug, please include as much\ninformation 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 [`CONTRIBUTING.md`](https://github.com/althonos/lightmotif/blob/master/CONTRIBUTING.md) for more details. -->\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/lightmotif/blob/master/CHANGELOG.md)\nin the [Keep a Changelog](http://keepachangelog.com/en/1.0.0/) format.\n\n## \u2696\ufe0f License\n\nThis library is provided under the [GNU General Public License 3.0 or later](https://choosealicense.com/licenses/gpl-3.0/),\nas it contains the GPL-licensed code of the TFM-PVALUE algorithm. The TFM-PVALUE dependency can be disabled by disabling\nthe `pvalue` crate feature, in which case the code can be used and redistributed under the terms \nof the [MIT license](https://choosealicense.com/licenses/mit/).\n\n*This project was developed by [Martin Larralde](https://github.com/althonos/)\nduring his PhD project at the [European Molecular Biology Laboratory](https://www.embl.de/)\nin the [Zeller team](https://github.com/zellerlab).*\n\n\n## \ud83d\udcda References\n\n- <a id=\"ref1\">\\[1\\]</a> Eddy, Sean R. \u2018Accelerated Profile HMM Searches\u2019. PLOS Computational Biology 7, no. 10 (20 October 2011): e1002195. [doi:10.1371/journal.pcbi.1002195](https://doi.org/10.1371/journal.pcbi.1002195).\n- <a id=\"ref2\">\\[2\\]</a> Grant, Charles E., Timothy L. Bailey, and William Stafford Noble. \u2018FIMO: Scanning for Occurrences of a given Motif\u2019. Bioinformatics 27, no. 7 (1 April 2011): 1017\u201318. [doi:10.1093/bioinformatics/btr064](https://doi.org/10.1093/bioinformatics/btr064).\n- <a id=\"ref3\">\\[3\\]</a> Farrar, Michael. \u2018Striped Smith\u2013Waterman Speeds Database Searches Six Times over Other SIMD Implementations\u2019. Bioinformatics 23, no. 2 (15 January 2007): 156\u201361. [doi:10.1093/bioinformatics/btl582](https://doi.org/10.1093/bioinformatics/btl582).\n- <a id=\"ref4\">\\[4\\]</a> Dudek, Christian-Alexander, and Dieter Jahn. \u2018PRODORIC: State-of-the-Art Database of Prokaryotic Gene Regulation\u2019. Nucleic Acids Research 50, no. D1 (7 January 2022): D295\u2013302. [doi:10.1093/nar/gkab1110](https://doi.org/10.1093/nar/gkab1110).\n",
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