QuickMers

PyPI Version License

Overview

QuickMers is a high-performance library for computing distances between k-mers (short DNA sequences), implemented in C with Python bindings. It provides fast functions for:

  • Hamming distance – number of mismatches between equal-length sequences.
  • Levenshtein (edit) distance – minimum number of insertions, deletions, or substitutions needed to transform one sequence into another.

The library leverages bitwise operations for Hamming distance and the Myers bit-parallel algorithm for edit distance. On systems with AVX2 instructions, computations are further accelerated.

Installation

Planned: Quickmers will be available on PyPI in the future.
Install using pip:

python -m pip install quickmers

or manually:

python setup.py install

Usage

import numpy as np
from quickmers import (
    hamming_distance_array,
    hamming_distance,
    levenshtein_distance,
    levenshtein_distance_array,
    levenshtein_distance_array_with_min_dist
)

1. Hamming Distance

Signature:

hamming_distance(sequence1: str, sequence2: str) -> int

Compute the Hamming distance between two sequences of equal length.

d = hamming_distance("ACGT", "TCGA")
print(d)  # Output: 2

2. Hamming Distance (array)

Signature

hamming_distance_array(query: str, targets: Union[List[str], np.ndarray]) -> np.ndarray

Compute Hamming distances between one query sequence and a list of target sequences.

query = "ACGT"
targets_list = ["ACGT", "TCGA", "CGTA"]
targets_array = np.array(targets_list, dtype=object)

distances1 = hamming_distance_array(query, targets_list)
distances2 = hamming_distance_array(query, targets_array)

print(distances1) # Output: array([0, 2, 4])
print(distances2) # Output: array([0, 2, 4])

3. Levenshtein Distance

Signature

levenshtein_distance(sequence1: str, sequence2: str) -> int

Compute the edit distance between two sequences.

d = levenshtein_distance("ACGT", "CGTA")
print(d)  # Output: 2

4. Levenshtein Distance (array)

Signature

levenshtein_distance_array(query: str, targets: Union[List[str], np.ndarray]) -> np.ndarray

Compute edit distances between a query sequence and a list of sequences.

query = "ACGT"
targets = ["ACGT", "TCGA", "CGTA"]
distances = levenshtein_distance_array(query, targets)
print(distances)  # Output: array([0, 2, 2])

5. Levenshtein Distance with Minimum Distance

Signature

levenshtein_distance_array_with_min_dist(query: str, targets: Union[List[str], np.ndarray], min_distance: int) -> Tuple[bool, List[Optional[int]]]

Compute the Levenshtein (edit) distance between a query k-mer and a list of target k-mers, with early exit if a minimum distance is violated.

This function is useful when you want to skip unnecessary calculations once a target is "too close" to the query, which can save significant computation time for long lists.

query = "ACGT"
targets = ["AGGG", "TCGA", "CGTA"]
early_exit, distances = levenshtein_distance_array_with_min_dist(query, targets, 1)
print(early_exit) # Output: False
print(distances)  # Output: array([2, 2, 2])

Implementation Details

  • Hamming distance: computed using bitwise operations on 64-bit integers for speed. The sequence length has to be 32 or lower.
  • Levenshtein distance: implemented with Myers bit-parallel algorithm for rapid edit distance computation. The sequence length has to be 64 or lower.
  • Array functions: can take Python lists or NumPy object arrays as input.
  • AVX2 acceleration: if your CPU supports AVX2, internal loops are vectorized for faster computation.
  • Memory management: all functions use efficient pre-allocated arrays to minimize Python overhead.

Performance

Benchmarking was performed on randomly generated k-mers of varying lengths. Values represent the number of string pairs distances calculated per second.

Benchmark Results

Function k=5 k=10 k=15 k=20 k=25 k=30
hamming_distance 9,505,940 9,192,009 8,866,906 8,555,942 8,249,809 7,946,207
hamming_distance_array 49,038,568 47,807,278 44,236,469 38,861,603 35,788,375 33,238,156
levenshtein_distance 7,499,375 6,407,775 5,797,060 5,131,730 4,688,698 4,267,584
levenshtein_distance_array 26,354,634 20,227,311 16,396,735 13,399,246 11,772,501 10,360,021

Results may vary depending on CPU architecture, compiler optimizations, and whether AVX2 instructions are available.

References

[1] Gene Myers. 1999. A fast bit-vector algorithm for approximate string matching based on dynamic programming. J. ACM 46, 3 (May 1999), 395-415. https://doi.org/10.1145/316542.316550

License

This software is licensed under LGPL-3.0-or-later.

Copyright 2025 Alexander Schliep

Project links

Data verified by PyPI on Aug 11, 2026
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Owner

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Credits

Author: Kian Jalilian

Maintainer: Nathalie Gocht

GitHub Statistics

Data verified by PyPI on Aug 11, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

LGPL-3.0-or-later
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Requires

Python >=3.9

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