A fast tool to calculate Hamming distances

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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

Project details

These details have not been verified by PyPI

Project links

Release history Release notifications | RSS feed

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

This version

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

0.12.0

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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