setriq 1.2.1

Python package written in C++ for pairwise distance computation for sequences.

setriq: pairwise sequence distances

A Python package written in C++ for computing pairwise distances between (immunoglobulin) sequences.

Install

This package is available on PyPI

pip install setriq

Quickstart

setriq inherits from the torch philosophy of callable objects. Each Metric subclass is a callable upon initialisation, taking a list of objects (usually str) and returning a list of float values.

import setriq
metric = setriq.CdrDist()

sequences = [
    'CASSLKPNTEAFF',
    'CASSAHIANYGYTF',
    'CASRGATETQYF'
]
distances = metric(sequences)

The returned list is flat and contains N * (N - 1) / 2 elements, i.e. the lower (or upper) triangle of the distance matrix. To get the square form of the matrix, use scipy.spatial.distance.squareform on the returned distances.

About

As the header suggests, setriq is a no-frills Python package for fast computation of pairwise sequence distances, with a focus on immunoglobulins. It is a declarative framework and borrows many concepts from the popular torch library. It has been optimized for parallel compute on CPU architectures.

It can only perform pairwise, all-v-all distance computations. This decision was made to maximize consistency and cohesion.

Requirements

A Python version of 3.7 or above is required, as well as a C++ compiler equipped with OpenMP. The package has been tested on Linux and macOS. To get the required OpenMP resources, run:

On Linux:

sudo apt install libomp-dev && sudo apt show libomp-dev

On macOS:

brew install libomp llvm

References

1. Dash, P., Fiore-Gartland, A.J., Hertz, T., Wang, G.C., Sharma, S., Souquette, A., Crawford, J.C., Clemens, E.B., Nguyen, T.H., Kedzierska, K. and La Gruta, N.L., 2017. Quantifiable predictive features define epitope-specific T cell receptor repertoires. Nature, 547(7661), pp.89-93. (https://doi.org/10.1038/nature22383)
2. Levenshtein, V.I., 1966, February. Binary codes capable of correcting deletions, insertions, and reversals. In Soviet physics doklady (Vol. 10, No. 8, pp. 707-710).
3. python-Levenshtein (https://github.com/ztane/python-Levenshtein)
4. Thakkar, N. and Bailey-Kellogg, C., 2019. Balancing sensitivity and specificity in distinguishing TCR groups by CDR sequence similarity. BMC bioinformatics, 20(1), pp.1-14. (https://doi.org/10.1186/s12859-019-2864-8)