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The BLOcks SUbstitution Matrices (BLOSUM) are used to score alignments between protein sequences and are therefore mainly used in bioinformatics.

Reading such matrices is not particularly difficult, yet most off the shelf packages are overloaded with strange dependencies. And why do we need to implement the same reader again if there is a simple module for that.

blosum offers a robust and easy-to-expand implementation without relying on third-party libraries.

Installation

Using pip / pip3:

pip install blosum

Or by source:

git clone git@github.com:not-a-feature/blosum.git
cd blosum
pip install .

Or by conda:

conda install blosum

How to use

Default BLOSUM

This package provides the most commonly used BLOSUM matrices. You can choose from BLOSUM 45, 50, 62, 80 and 90.

To load a matrix:

import blosum as bl
matrix = bl.BLOSUM(62)
val = matrix["A"]["Y"]

Custom matrix

In addition, own matrices can be loaded. For this, the path is given as an argument.

import blosum as bl
matrix = bl.BLOSUM("path/to/blosum.file")
val = matrix["A"]["Y"]

The matrices are required to have following format:

# Comments should start with #
# Each value should be seperated by one or many whitespace
   A  R  N  D
A  5 -2 -1 -2
R -2  7  0 -1
N -1  0  6  2
D -2 -1  2  7

Getting values:

Once loaded the matrix behaves like a defaultdict. To get a value use:

val = matrix["A"]["Y"]

To get a defaultdict of the row with a given key use:

val_dict = matrix["A"]

If the key cannot be found, the default value float("-inf") is returned. It is possible to set a custom default score:

matrix = bl.BLOSUM(62, default=0)

License

Copyright (C) 2025 by Jules Kreuer - @not_a_feature

This piece of software is published unter the GNU General Public License v3.0 TLDR:

Permissions Conditions Limitations
✓ Commercial use Disclose source ✕ Liability
✓ Distribution License and copyright notice ✕ Warranty
✓ Modification Same license
✓ Patent use State changes
✓ Private use

Go to LICENSE.md to see the full version.

关于

提供 BLOSUM 氨基酸替换矩阵,用于蛋白质序列比对打分和相似性评估。

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