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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MD5 | be6e42e1806e731dc602343b72b5916f |
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BLAKE2b-256 | 7b72bce875283243f45001dcbf5c99b280da5a7ba5ddfe924b37b62ffc218c75 |
Hashes for hammingdist-0.19.0-cp36-cp36m-musllinux_1_1_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | 84e0c6a91207e126017f5cd3bd2b012fbb46ae8ed81873ee38b20d631680dd11 |
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MD5 | 96cbeb6e67fda89bb7c72fe897d23927 |
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BLAKE2b-256 | 9b603db1e560488ef527c472de0c862045dfb0a01349aa95c125f673d685e135 |
Hashes for hammingdist-0.19.0-cp36-cp36m-musllinux_1_1_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 7d9f3be8512f17271b7f7425a4ff7c581bf0261e3ab58b05aebb258a02717695 |
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MD5 | e638b1d018f84d6ad20f457507a7bd89 |
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BLAKE2b-256 | 0ebd965a58fca267be91575581beae2e3c9c75294f349a2899d6caf5c5551af9 |
Hashes for hammingdist-0.19.0-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 838e99778867059867a73523c9a4040604c636c71477cd32188f3cab8b3a2a7d |
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MD5 | 470a364d3e749a8101bef0e5511d9169 |
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BLAKE2b-256 | 6052ff61cccaa472f1049572b0e0227a04136cb05b2bf295bbc781aef37ec6f6 |
Hashes for hammingdist-0.19.0-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 420849f2ed58bb8d463084469ade4804ccc50e7419e005d1b56a257b6addb3d3 |
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MD5 | 11cf70eb3097be13beb7dd28a3314e98 |
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BLAKE2b-256 | 2e54372339fffba839672339d4c9b522c1adc63d37f16fa7d1c60fadf7f3c194 |