A fast tool to calculate Hamming distances
Project description
A small C++ tool to calculate pairwise distances between gene sequences given in fasta format.
Python interface
To use the Python interface, you should install it from PyPI:
python -m pip install hammingdist
Distances matrix
Then, you can e.g. use it in the following way from Python:
import hammingdist
# To see the different optional arguments available:
help(hammingdist.from_fasta)
# To import all sequences from a fasta file
data = hammingdist.from_fasta("example.fasta")
# To import only the first 100 sequences from a fasta file
data = hammingdist.from_fasta("example.fasta", n=100)
# To import all sequences and remove any duplicates
data = hammingdist.from_fasta("example.fasta", remove_duplicates=True)
# To import all sequences from a fasta file, also treating 'X' as a valid character
data = hammingdist.from_fasta("example.fasta", include_x=True)
# The distance data can be accessed point-wise, though looping over all distances might be quite inefficient
print(data[14,42])
# The data can be written to disk in csv format (default `distance` Ripser format) and retrieved:
data.dump("backup.csv")
retrieval = hammingdist.from_csv("backup.csv")
# It can also be written in lower triangular format (comma-delimited row-major, `lower-distance` Ripser format):
data.dump_lower_triangular("lt.txt")
retrieval = hammingdist.from_lower_triangular("lt.txt")
# If the `remove_duplicates` option was used, the sequence indices can also be written.
# For each input sequence, this prints the corresponding index in the output:
data.dump_sequence_indices("indices.txt")
# Finally, we can pass the data as a list of strings in Python:
data = hammingdist.from_stringlist(["ACGTACGT", "ACGTAGGT", "ATTTACGT"])
Duplicates
When from_fasta
is called with the option remove_duplicates=True
, duplicate sequences are removed before constructing the differences matrix.
For example given this set of three input sequences:
Index | Sequence |
---|---|
0 | ACG |
1 | ACG |
2 | TAG |
The distances matrix would be a 2x2 matrix of distances between ACG
and TAG
:
ACG | TAG | |
---|---|---|
ACG | 0 | 2 |
TAG | 2 | 0 |
The row of the distances matrix corresponding to each index in the original sequence would be:
Index | Sequence | Row in distances matrix |
---|---|---|
0 | ACG | 0 |
1 | ACG | 0 |
2 | TAT | 1 |
This last column is what is written to disk by DataSet.dump_sequence_indices
.
It can also be constructed (as a numpy array) without calculating the distances matrix by using hammingdist.fasta_sequence_indices
import hammingdist
sequence_indices = hammingdist.fasta_sequence_indices(fasta_file)
Large distance values
By default, the elements in the distances matrix returned by hammingdist.from_fasta
have a maximum value of 255.
For distances larger than this hammingdist.from_fasta_large
supports distances up to 65535 (but uses twice as much RAM)
Distances from reference sequence
The distance of each sequence in a fasta file from a given reference sequence can be calculated using:
import hammingdist
distances = hammingdist.fasta_reference_distances(sequence, fasta_file, include_x=True)
This function returns a numpy array that contains the distance of each sequence from the reference sequence.
You can also calculate the distance between two individual sequences:
import hammingdist
distance = hammingdist.distance("ACGTX", "AAGTX", include_x=True)
OpenMP on linux
The latest versions of hammingdist on linux are now built with OpenMP (multithreading) support. If this causes any issues, you can install a previous version of hammingdist without OpenMP support:
pip install hammingdist==0.11.0
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SHA256 | 7766814e84c2e883b8c9a5743d93628af345a24fb7b4413dc1587743133e206c |
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MD5 | 525ffad2389d1012ef1f439185334a86 |
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BLAKE2b-256 | 155e0c1b7d6f4befbecb777d0ca3785d2820c05929bd2be9955dadac608deec1 |
Hashes for hammingdist-0.21.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a581bd76d41cee05a66149b756b77ad253bf2f49d2afd841bd4654d018b16037 |
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MD5 | ff8774ec446e10ca93f464078791f6de |
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BLAKE2b-256 | 75881c97f695c75829ff774687dcdb339ac959b30b9a7a4cbe54bec59df46db7 |
Hashes for hammingdist-0.21.0-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2d93f35202c8e2dc2b0b3f0315312bde4ca929f8f629a7ae508ab5ffd22ce10f |
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MD5 | 2a753251e536195c386199e7d6727b3f |
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BLAKE2b-256 | 537a868b397da1998dfff2387f2cad1b61f7e94287e2bdd49e3ec508d82c286c |
Hashes for hammingdist-0.21.0-cp38-cp38-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 454ac065e6585715520a9f336e3a36c2ef9eb37d61468760b0cf89f9f1e65dd2 |
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MD5 | f2be45fbcf72a9688448d36e59750fab |
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BLAKE2b-256 | b7ac2bb000e13230937b2bbadc3e5187ffd7fbbab3373f7b1d66d7adcf666e86 |
Hashes for hammingdist-0.21.0-cp38-cp38-win32.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 30629d88bb7a0b3e5f9e29a33a54115c5178734c79fa555ba4226c8ace11247a |
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MD5 | f59b8e1dd338698c3da5ab6d5c347b93 |
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BLAKE2b-256 | a7021b84e194e84878a39aae7344f531b5a9c244c9bcfaf2fcabfeb7dc572d16 |
Hashes for hammingdist-0.21.0-cp38-cp38-musllinux_1_1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | fcd80f695aa237f6d16b5ee6bbee05802f6f8552c717d1ab1ec82b7eb0b21eb4 |
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MD5 | 467d7f6c874f14ca5bd8698c6d106293 |
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BLAKE2b-256 | 234f789622668368b3c7ff4e0d30a642184ab46398aa062505aed02956e2ff4a |
Hashes for hammingdist-0.21.0-cp38-cp38-musllinux_1_1_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 6c27aa71330b19b8df42eaa032eb59b56f015bfdb1b5bf0b7ac7f79a63ba5d89 |
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MD5 | fbd964ff21b9a5ef062f46c345c60230 |
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BLAKE2b-256 | dd38d91a3b9bff7166cb42a49ae3aea2ef6ee8610b75e494595375fbd5b3b790 |
Hashes for hammingdist-0.21.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 055b7d917d6e8460d8627aab266f1771b6a2685174d91b0939c88d611244361b |
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MD5 | 753e3e6e71a2d41ab9e25eecb9c6ab57 |
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BLAKE2b-256 | fb545e61cb2afb1e9dbed7a6fb980d67ec9d33461322cde4c8bab15bd22f1481 |
Hashes for hammingdist-0.21.0-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2d97269580887e7be03059ca30285f11513eedac0358ff5f5f07d1eb6d9f5fe3 |
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MD5 | 401bb1d4dc2d2639833e2af14823aaac |
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BLAKE2b-256 | f4c17a7ff2623289926d8c6a8e9a496f84a76054a295ed6ebc1e7a9307a5a016 |
Hashes for hammingdist-0.21.0-cp37-cp37m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2e77328fcc722b7e9607635178afb6d5fdb60138a79a8468b91af54f002351b6 |
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MD5 | 1914093697ff0db9c9f196a2cb5f2aa9 |
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BLAKE2b-256 | 1ca9f99c566a9666173ccba18f2fda3013b64c6addd69d9a0c2c00016b17ec6f |
Hashes for hammingdist-0.21.0-cp37-cp37m-win32.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e5ad497b9551fef61c70d0ed9664b4ff288c250c78a6b3aa10d152329ae1d01b |
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MD5 | dc7fd8c9861ca74ae91a963356e6e774 |
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BLAKE2b-256 | ce612c4b1bf759656fe81807c603ffd00a7e7f920c53f436c36840c10c7f3d52 |
Hashes for hammingdist-0.21.0-cp37-cp37m-musllinux_1_1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 7e15f9bbd45f9c9048ea61fe955ddb094f21d6eb402740201bacf36f6d15fe97 |
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MD5 | 2049f3f7657c9b39f98c4fbb3947d051 |
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BLAKE2b-256 | 1a7f7b4d4b7a18d1e53e789dc6868386673e673787e0889996a5b92d0c9b9e6c |
Hashes for hammingdist-0.21.0-cp37-cp37m-musllinux_1_1_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 744b6c7356e598502f0d6446a43209f683aee26008491ef0167080bd28350539 |
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MD5 | f5f9efffa5ff54d3b0ed83ed086b0573 |
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BLAKE2b-256 | 5628700c85f8e8af3cafa68fc8f4eb5eb2d949751680889cec148158494086e8 |
Hashes for hammingdist-0.21.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a76311efdb58a41dd0b932402870b7d881b7546cf9329860f44140bb32bc6699 |
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MD5 | 9d73f01979255c8fd97e6296e5e485a2 |
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BLAKE2b-256 | bfe6160e2faf709865a3a38ee6302c246750bab2d4c07a78c9cd08166dd26072 |
Hashes for hammingdist-0.21.0-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1c2cf49aa23df5b09a5f7f6355f12823c617b1c9af6d39d1ebcb34b8664ee915 |
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MD5 | f9836651af23658f1254b8b0cda3ec2c |
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BLAKE2b-256 | 53fcc589612d15e2b4d5705bdc20179574dddbc463156d19b78f99e0db4f8cd7 |