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 TAT
:
ACG | TAT | |
---|---|---|
ACG | 0 | 2 |
TAT | 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 | df5e25df5f88aedc5eddc6f112b3d15b96e937df0ee27e329f59337d7ef07040 |
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MD5 | 9a3a9c1c602162341798438eb98dce88 |
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BLAKE2b-256 | b3a1dcb552f04b2fcd6ef2bbb722e0de1963881e228bda8f239de383e10a226e |
Hashes for hammingdist-0.18.0-cp36-cp36m-musllinux_1_1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 22b662fa6e36992fa5edf16a8e28cbaf94ff1051aab4b256f1b87cc2131f611b |
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MD5 | 4ab0f98e2960515ef27edf1f536d2431 |
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BLAKE2b-256 | 9efae9bc9dabf6dbcc1d621dc0cb41c385312a7b486a6b6e39c283132685afb3 |
Hashes for hammingdist-0.18.0-cp36-cp36m-musllinux_1_1_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 28c7e1eb1a5f4dfab4d8db89cacfb3f88de8143be5d0153d2d2c818736b49aa5 |
|
MD5 | b334ada743a9283c045f617f9bc4741a |
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BLAKE2b-256 | 8ee99fd48d345b16393926fd637a3a1acacf612d31abfee4083874c9b98db53b |
Hashes for hammingdist-0.18.0-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0b6530ad3ef762c15dca55a2ecc4ffd85407c8401c15e28f2c611cf0013ce9fb |
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MD5 | 95c6ee18447a6bf5d5d15a1a2aead986 |
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BLAKE2b-256 | 65efba04df0373e5f1a50505326a1c455e8730360021c15c53139744fff81d31 |
Hashes for hammingdist-0.18.0-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 22226aae0511fa9172d72f5e4c9da820ad2464d3ae0d57380944423d597e7cfa |
|
MD5 | 688e7bde115d80678ac5afaa1613216c |
|
BLAKE2b-256 | 4f9b485edcf71716b34cdb9bcac3eb6695ca786e7258ab2ed571d15bf98d29d8 |