A fast tool to calculate Hamming distances

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Maintainers

dkempf iulusoy lkeegan

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Project description

A small and fast 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])

Output formats

The constructed distances matrix can then be written to disk in several different formats:

# 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")

# Or in sparse format (`sparse` Ripser format: space-delimited triplet of `i j d(i,j)`
# with one line for each distance entry i > j which is not above threshold):
data.dump_sparse("sparse.txt", threshold=3)

# 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")

# The lower-triangular distance elements can also be directly accessed as a 1-d numpy array:
lt_array = data.lt_array
# The elements in this array correspond to the 2-d indices (row=1,col=0), (row=2,col=0), (row=2,col=1), ...
# These indices can be generated using the numpy tril_indices function, e.g. to construct the lower-triangular matrix:
lt_matrix = np.zeros((n_seq, n_seq))
lt_matrix[np.tril_indices(n_seq, -1)] = lt_array

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)

Maximum distance values

By default, the elements in the distances matrix returned by hammingdist.from_fasta have a maximum value of 255. You can also set a smaller maximum value using the max_distance argument. 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

On linux hammingdist is built with OpenMP (multithreading) support, and will automatically make use of all available CPU threads.

CUDA on linux

On linux hammingdist is also built with CUDA (Nvidia GPU) support. To use the GPU instead of the CPU, set use_gpu=True when calling from_fasta. Here we also set the maximum distance to 2:

import hammingdist

data = hammingdist.from_fasta("example.fasta", use_gpu=True, max_distance=2)

Additionally, the lower triangular matrix file can now be directly constructed from the fasta file using the GPU with the from_fasta_to_lower_triangular function. This avoids storing the entire distances matrix in memory and interleaves computation on the GPU with disk I/O on the CPU, which means it requires less RAM and runs faster.

import hammingdist

hammingdist.from_fasta_to_lower_triangular('input_fasta.txt', 'output_lower_triangular.txt', use_gpu=True, max_distance=2)

overview

Performance history

A rough measure of the impact of the different performance improvements in hammingdist:

overview

Project details

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Maintainers

dkempf iulusoy lkeegan

These details have not been verified by PyPI

Release history Release notifications | RSS feed

This version

1.3.0

Aug 20, 2024

1.2.0

Mar 4, 2024

1.1.0

Feb 19, 2024

1.0.0

Jan 10, 2024

0.21.0

Nov 13, 2023

0.20.0

Aug 10, 2023

0.19.0

Jan 30, 2023

0.18.0

Sep 5, 2022

0.17.0

Sep 5, 2022

0.16.0

Apr 13, 2022

0.15.0

Apr 11, 2022

0.13.0

Dec 3, 2021

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Jul 19, 2021

0.11.0

Jul 19, 2021

0.10.0

Jun 23, 2021

0.8

Nov 25, 2020

0.7

Nov 24, 2020

0.6

Nov 16, 2020

0.5

Nov 16, 2020

0.4

Nov 13, 2020

0.3

Oct 30, 2020

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hammingdist 1.3.0

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Algorithm	Hash digest
SHA256	`ea14011b6f0c280ad6764ec3f931877b46cf3c96003394c86ddd7bd45b177a5e`
MD5	`97666a18ccf17f289a218db7fb933106`
BLAKE2b-256	`aa7d9a797842e60eaa5f30332f6dcf0eea02dc83c21a993b0bba8271fac8435b`

Algorithm	Hash digest
SHA256	`6ee7dde95c52a27ab380582eeeb2d1162015c2c31de6a4748ba2386893af541c`
MD5	`f91c98b4e65fb81c90cb0ef661cde8d6`
BLAKE2b-256	`7bb1f60574c31cd6a7c96f402cedbef9c27ec0cd8bcc59cf815df7b3c559ba40`

Algorithm	Hash digest
SHA256	`52363bb8d075a071981abfc22c5c56e6a0bdc3b98158093e711bc63ada2a368e`
MD5	`3e59fc6a571aae2bea85b53d7dd138e5`
BLAKE2b-256	`42cbbb55738f1341fbc685c34e2bf0630b648fb41b795ca9c22857aaa3722f8f`

Algorithm	Hash digest
SHA256	`181a0f045d9ece79c1cd0c0561a44cde55a2e77f3d391d57c09c35f609b42908`
MD5	`f05ea00507bfc9edf5adc02f1d071976`
BLAKE2b-256	`3cdb2b43042888fc0b89eb25e5cf39a86f50fbf4ab258df5f0590c30a6259851`

Algorithm	Hash digest
SHA256	`265de82489f99df6e12bc5a45e90868086d625ffec9bb7bc20ba4f3a91ec3033`
MD5	`1c259ca3255961ec0ed19e778e70297b`
BLAKE2b-256	`d63bb10f677b68bc6ccc1ca0ffb39955544a4dab9f0c8befdcf0e2272006fcf2`

Algorithm	Hash digest
SHA256	`22ccd1e857ed9590639cd589ab27f883d8abfe6f5874b6303762c565a1065e17`
MD5	`f1ddf1c6d56a6fd2143db35fce85fd71`
BLAKE2b-256	`e738a75cc2e45015e85213225d27181ed6a39f447f7f981f5bc776d5ef60dd9c`

Algorithm	Hash digest
SHA256	`2cebae7771535f4f81b35019cedab850c6806a5f52f4e6a222b2d57b1c43fbf2`
MD5	`ff6f8fb53c75225d831c4fbf9a51ad52`
BLAKE2b-256	`b152742aba72008845e4ce25738b987e94ed44e30ec8c3ed784d63a42797bbcd`

Algorithm	Hash digest
SHA256	`03c8cee8cdb413e5ab9630867458bfc822e464ff02e953b0fb0b24ddb7239e06`
MD5	`48aac60253d43b5631f5ccb8926568fb`
BLAKE2b-256	`a292e2839dc06559d77d20a69085eca92779ab65a8a5fff9b579ce95ac1fa74f`

Algorithm	Hash digest
SHA256	`57658c26a67e87548d0cb9e709508a3fb3a45d73512ff5d6708328e1f101e394`
MD5	`3f2813cbd6913bec1414cd92b4bd09af`
BLAKE2b-256	`2ba43145f5fb971c1e4bea8a9a146ffbd83232097ea4b7f935b592dff1869e02`

Algorithm	Hash digest
SHA256	`faf68b742a76e0e7d087bc7d62c706a94324101e8ba0a8ef0beed1167ffd787b`
MD5	`c2a1a87bc9308d9343c6806df111d92d`
BLAKE2b-256	`51dab6f14a90c831028e593c41960c7d1877dae8e4bbe7601db828e9ee30b5c1`