# 🧬🔠 `scoring-matrices` [](https://github.com/althonos/scoring-matrices/stargazers)
*Dependency free, [Cython](https://cython.org/)-compatible scoring matrices to use with biological sequences.*
[](https://github.com/althonos/scoring-matrices/actions)
[](https://codecov.io/gh/althonos/scoring-matrices/)
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[](https://pypi.org/project/scoring-matrices)
[](https://anaconda.org/bioconda/scoring-matrices)
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[](https://pypi.org/project/scoring-matrices/#files)
[](https://github.com/althonos/scoring-matrices/)
[](https://github.com/althonos/scoring-matrices/issues)
[](https://scoring-matrices.readthedocs.io)
[](https://github.com/althonos/scoring-matrices/blob/main/CHANGELOG.md)
[](https://pepy.tech/project/scoring-matrices)
## 🗺️ Overview
*Scoring Matrices* are matrices used to score the matches and mismatches between
two characters are the same position in a sequence alignment. Some of these
matrices are derived from *substitution matrices*, which uses evolutionary
modeling.
The `scoring-matrices` package is a dependency-free, batteries included library
to handle and distribute common substitution matrices:
- **no external dependencies**: The matrices are distributed as-is: you don't
need the whole [Biopython](https://biopython.org) ecosystem, or even
[NumPy](https://numpy.org/).
- **Cython compatibility**: The `ScoringMatrix` is a Cython class that can be
inherited, and the matrix data can be accessed as either a raw pointer, or
a [typed memoryview](https://cython.readthedocs.io/en/latest/src/userguide/memoryviews.html).
- **most common matrices**: The package distributes most common matrices, such as
those used by the NCBI BLAST+ suite, including:
- [*PAM*](https://en.wikipedia.org/wiki/Point_accepted_mutation#) matrices by Dayhoff *et al.* (1978).
- [*BLOSUM*](https://en.wikipedia.org/wiki/BLOSUM) matrices by Henikoff & Henikoff (1992).
- *VTML* matrices by Muller *et al.* (2002).
- *BENNER* matrices by Benner *et al.* (1994).
## 🔧 Installing
`scoring-matrices` can be installed directly from [PyPI](https://pypi.org/project/scoring-matrices/),
which hosts some pre-built wheels for the x86-64 architecture (Linux/OSX/Windows)
and the Aarch64 architecture (Linux/OSX), as well as the code required to
compile from source with Cython:
```console
$ pip install scoring-matrices
```
Otherwise, `scoring-matrices` is also available as a [Bioconda](https://bioconda.github.io/)
package:
```console
$ conda install bioconda::scoring-matrices
```
## 💡 Usage
### Python
- Import the `ScoringMatrix` class from the installed module:
```python
from scoring_matrices import ScoringMatrix
```
- Load one of the built-in matrices:
```python
blosum62 = ScoringMatrix.from_name("BLOSUM62")
```
- Get individual matrix weights either by index or by alphabet letter:
```python
x = blosum62[0, 0]
y = blosum62['A', 'A']
```
- Get a row of the matrix either by index or by alphabet letter:
```python
row_x = blosum62[0]
row_y = blosum62['A']
```
### Cython
- Access the matrix weights as raw pointers to constant data:
```cython
from scoring_matrices cimport ScoringMatrix
cdef ScoringMatrix blosum = ScoringMatrix.from_name("BLOSUM62")
cdef const float* data = blosum.data_ptr() # dense array
cdef const float** matrix = blosum.matrix_ptr() # array of pointers
```
- Access the `ScoringMatrix` weights as a [typed memoryview](https://cython.readthedocs.io/en/latest/src/userguide/memoryviews.html)
using the *buffer protocol* in more recents versions of Python:
```cython
from scoring_matrices cimport ScoringMatrix
cdef ScoringMatrix blosum = ScoringMatrix.from_name("BLOSUM62")
cdef const float[:, :] weights = blosum
```
## 💭 Feedback
### ⚠️ Issue Tracker
Found a bug ? Have an enhancement request ? Head over to the [GitHub issue tracker](https://github.com/althonos/scoring-matrices/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/scoring-matrices/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/scoring-matrices/blob/main/CHANGELOG.md)
in the [Keep a Changelog](http://keepachangelog.com/en/1.0.0/) format.
## ⚖️ License
This library is provided under the [MIT License](https://choosealicense.com/licenses/mit/).
Matrices were collected from the [MMseqs2](https://github.com/soedinglab/MMseqs2),
[Biopython](https://github.com/biopython/biopython/tree/master/Bio/Align/substitution_matrices/data)
and [NCBI BLAST+](https://ftp.ncbi.nih.gov/blast/matrices/) sources and are believed to
be in the public domain.
*This project was developed by [Martin Larralde](https://github.com/althonos/)
during his PhD project at the [Leiden University Medical Center](https://www.lumc.nl/en/) in the [Zeller team](https://github.com/zellerlab).*
Raw data
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"description": "# \ud83e\uddec\ud83d\udd20 `scoring-matrices` [](https://github.com/althonos/scoring-matrices/stargazers)\n\n*Dependency free, [Cython](https://cython.org/)-compatible scoring matrices to use with biological sequences.*\n\n[](https://github.com/althonos/scoring-matrices/actions)\n[](https://codecov.io/gh/althonos/scoring-matrices/)\n[](https://choosealicense.com/licenses/mit/)\n[](https://pypi.org/project/scoring-matrices)\n[](https://anaconda.org/bioconda/scoring-matrices)\n[](https://aur.archlinux.org/packages/python-scoring-matrices)\n[](https://pypi.org/project/scoring-matrices/#files)\n[](https://pypi.org/project/scoring-matrices/#files)\n[](https://pypi.org/project/scoring-matrices/#files)\n[](https://github.com/althonos/scoring-matrices/)\n[](https://github.com/althonos/scoring-matrices/issues)\n[](https://scoring-matrices.readthedocs.io)\n[](https://github.com/althonos/scoring-matrices/blob/main/CHANGELOG.md)\n[](https://pepy.tech/project/scoring-matrices)\n\n## \ud83d\uddfa\ufe0f Overview\n\n*Scoring Matrices* are matrices used to score the matches and mismatches between\ntwo characters are the same position in a sequence alignment. Some of these\nmatrices are derived from *substitution matrices*, which uses evolutionary \nmodeling.\n\nThe `scoring-matrices` package is a dependency-free, batteries included library\nto handle and distribute common substitution matrices:\n\n- **no external dependencies**: The matrices are distributed as-is: you don't \n need the whole [Biopython](https://biopython.org) ecosystem, or even \n [NumPy](https://numpy.org/).\n- **Cython compatibility**: The `ScoringMatrix` is a Cython class that can be\n inherited, and the matrix data can be accessed as either a raw pointer, or\n a [typed memoryview](https://cython.readthedocs.io/en/latest/src/userguide/memoryviews.html).\n- **most common matrices**: The package distributes most common matrices, such as \n those used by the NCBI BLAST+ suite, including:\n - [*PAM*](https://en.wikipedia.org/wiki/Point_accepted_mutation#) matrices by Dayhoff *et al.* (1978).\n - [*BLOSUM*](https://en.wikipedia.org/wiki/BLOSUM) matrices by Henikoff & Henikoff (1992).\n - *VTML* matrices by Muller *et al.* (2002).\n - *BENNER* matrices by Benner *et al.* (1994).\n\n## \ud83d\udd27 Installing\n\n`scoring-matrices` can be installed directly from [PyPI](https://pypi.org/project/scoring-matrices/),\nwhich hosts some pre-built wheels for the x86-64 architecture (Linux/OSX/Windows)\nand the Aarch64 architecture (Linux/OSX), as well as the code required to\ncompile from source with Cython:\n```console\n$ pip install scoring-matrices\n```\n\nOtherwise, `scoring-matrices` is also available as a [Bioconda](https://bioconda.github.io/)\npackage:\n```console\n$ conda install bioconda::scoring-matrices\n```\n\n## \ud83d\udca1 Usage\n\n### Python\n\n- Import the `ScoringMatrix` class from the installed module:\n ```python\n from scoring_matrices import ScoringMatrix\n ```\n- Load one of the built-in matrices:\n ```python\n blosum62 = ScoringMatrix.from_name(\"BLOSUM62\")\n ```\n- Get individual matrix weights either by index or by alphabet letter:\n ```python\n x = blosum62[0, 0]\n y = blosum62['A', 'A']\n ```\n- Get a row of the matrix either by index or by alphabet letter:\n ```python\n row_x = blosum62[0]\n row_y = blosum62['A']\n ```\n\n### Cython\n\n- Access the matrix weights as raw pointers to constant data:\n ```cython\n from scoring_matrices cimport ScoringMatrix\n\n cdef ScoringMatrix blosum = ScoringMatrix.from_name(\"BLOSUM62\")\n cdef const float* data = blosum.data_ptr() # dense array\n cdef const float** matrix = blosum.matrix_ptr() # array of pointers\n ```\n- Access the `ScoringMatrix` weights as a [typed memoryview](https://cython.readthedocs.io/en/latest/src/userguide/memoryviews.html) \n using the *buffer protocol* in more recents versions of Python:\n ```cython\n from scoring_matrices cimport ScoringMatrix\n\n cdef ScoringMatrix blosum = ScoringMatrix.from_name(\"BLOSUM62\")\n cdef const float[:, :] weights = blosum\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 tracker](https://github.com/althonos/scoring-matrices/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### \ud83c\udfd7\ufe0f Contributing\n\nContributions are more than welcome! 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