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 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

bash 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,

```python from Bio import SeqIO import tn93

Read in a FASTA file to get sequences

seqs = [ x for x in SeqIo.parse("yoursequences.fasta", format="fasta") ] tn93 = tn93.TN93() distance = tn93.tn93distance(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.

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

By default, the software produces distances in the form ID1,ID2,Distance

Selecting JSON output produces distances in the form {"ID1": ID1, "ID2": ID2, "Distance": Distance}

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

``` usage: tn93.py [-h] -i INPUTFILE -m MATCHMODE -o OUTPUT [-g MAXAMBIGFRACTION] [-v] [-n] [-j]

optional arguments: -h, --help show this help message and exit -i INPUTFILE, --inputfile INPUTFILE Path to the input fasta file -m MATCHMODE, --matchmode MATCHMODE How to handle ambiguities. This can be one of four options: average - Averages the possible nucleotide values for each ambiguity in a sequence; resolve - Tries to resolve ambiguities; skip - Ignores gaps and ambiguities; gapmm - Treats gaps in only one sequence as 'N's; -o OUTPUT, --output OUTPUT The name of the output file to create -g MAXAMBIGFRACTION, --maxambigfraction MAXAMBIGFRACTION Sequences that have proportions of ambiguities lower than this value will be resolved, otherwise they will be averaged (RESOLVE only) (Default: 1.0) -v, --verbose Verbosity, One copy prints intermediate values and final counts, two copies produces a CSV file with pairwise counts for each non-gap nucleotide -n, --ignoreterminalgaps Should gaps at the beginning and end of a sequence be ignored (GAPMM only)? (Default: False) -j, --json_output Should the output be in JSON format? (Default: False) ```

Related documents

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

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