tn93 1.0.7

A python implementation of the Tamura-Nei pairwise distance calculation

Tamura-Nei Distance Calculation with Python

Overview

This repository contains a Python implementation of the Tamura-Nei (TN93) distance calculation. When provided with two sequences to compare, this software returns a distance value between 0 and 1 using the Tamura-Nei nucleotide substitution model. See Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees and NOTES for more information on the algorithm. This software has been developed by the Molecular Epidemiology and Bioinformatics Team in the Division of HIV/AIDS Prevention, NCHHSTP.

Usage

This tool is primarily meant as a library to be imported and used in custom analysis code, but can also be used to directly calculate the pairwise distances for a set of sequences in a FASTA file.

First, install using pip

pip install tn93

or clone this respository and copy src/tn93/tn93.py to your working directory. To calculate the distance between a pair of sequences,

from Bio import SeqIO
import tn93
# Read in a FASTA file to get sequences
seqs = [ x for x in SeqIo.parse("your_sequences.fasta", format="fasta") ]
tn93 = tn93.TN93()
distance = tn93.tn93_distance(seqs[0], seqs[1], "RESOLVE")

Alternatively, the module can be run from the command line and provided with a sequence file and match mode to produce a JSON file with the pairwise distances.

python tn93.py --input_file example_seqs.fasta --match_mode RESOLVE --output example_seqs_resolve_distance.json

There are four distinct match modes:

• SKIP, which ignores ambiguous positions
• GAPMM, which treats gaps appearing in only one sequence as mismatches
• AVERAGE, which takes the average of the possible resolution values
• RESOLVE, which tries to resolve the ambiguity to a single nucleotide, averages if that fails

Related documents

• Open Practices
• Rules of Behavior
• Thanks and Acknowledgements
• Disclaimer
• Contribution Notice
• Code of Conduct

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Additional Standard Notices

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