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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Algorithm | Hash digest | |
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SHA256 | 16eefb9e5b386c64adc2541290413eef24c65df22ea9da1389c82564f3b61cad |
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MD5 | c799106b9bf69708fb5d8ef308772759 |
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BLAKE2b-256 | 03660178a9ca4e77731408a4821b27bc51ce0355eeb5fd5ea007cc587e52a7d3 |
Hashes for hammingdist-0.20.0-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 16f37d72c6d3d8ac48305f886a1c8a7a13a75b81b4673fa7300ca46387c90d99 |
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MD5 | 906c4bbfbef2e8d826da253e33af356c |
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BLAKE2b-256 | 64d0d291bc248e6f18f3a0a837279c0c4e6ecb60c4f5755d8a57c9d2cc171770 |
Hashes for hammingdist-0.20.0-cp37-cp37m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e69b9575d8dea2fcebd949e97c6bcfc82650c74f9b14f21442be28eeed23f9e1 |
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MD5 | ebf954b9e5d92d7c4407a19944b49dae |
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BLAKE2b-256 | 4445a45e1d2ad7241dc32f3afed98d024b5a25875850421bded7b5586166e5cd |
Hashes for hammingdist-0.20.0-cp37-cp37m-win32.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 8fc31418b93bffc4d71969b479f35349de803d6e249fb64e1ec4af1bf4fddeec |
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MD5 | 597daade4ecce3966c657ed638d80911 |
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BLAKE2b-256 | a36fac842e4844a25bbe0cfa55bc4ddf7b5383021ad14c488eeaebcc6cce0665 |
Hashes for hammingdist-0.20.0-cp37-cp37m-musllinux_1_1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c2371dedfc6b6a4c9d433c44e058933179baf912130f6fad023a3ddbc131eeee |
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MD5 | 5dc272b49900fa3d4100c3b15fa4e945 |
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BLAKE2b-256 | a3e1da933179ac1692a2c3fa97a1dfe5476c6a9e33672d3378368ba0bb2bcc7d |
Hashes for hammingdist-0.20.0-cp37-cp37m-musllinux_1_1_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e6c955b7ae2824ac6fe54bf1b00ed72f363e68b1663f38659bdb24f423247ccd |
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MD5 | 24b32217a6df9d7b4ca2a18a7aa8f1a0 |
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BLAKE2b-256 | 1860dee48fbf81b8df5f0a1aeeba9c318b4619c8bee9f656977f1bcee3d4f410 |
Hashes for hammingdist-0.20.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 8be42d00b06fd363ca718b0adef5d0ff0f1637bb8f1ff7896e1d157ddeab1429 |
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MD5 | b2b6c27b2de685992d9d058365e1fc61 |
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BLAKE2b-256 | 70f3925ad1aa23e66db3fd07604b3372c3f0ca04689cbeb7493a746f98203d13 |
Hashes for hammingdist-0.20.0-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c6b05c0429ff2c0a52b81dad24c31d51b99a1361189155db4a4ebeae5c35ecff |
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MD5 | 492b5c2b030554a2e9638282a8cfd5c0 |
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BLAKE2b-256 | 8c36a440eb83e2f231396cc13f568e012b182163668f490f67369848c997c4ca |