Package: tn93
Tamura-Nei Distance Calculation with PythonOverviewThis 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. SeeEstimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzeesfor 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.UsageThis 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 pippipinstalltn93or clone this respository and copysrc/tn93/tn93.pyto your working directory. To calculate the distance between a pair of sequences,fromBioimportSeqIOimporttn93# Read in a FASTA file to get sequencesseqs=[xforxinSeqIo.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.pythontn93.py--input_fileexample_seqs.fasta--match_modeRESOLVE--outputexample_seqs_resolve_distance.jsonBy default, the software produces distances in the formID1,ID2,DistanceSelecting JSON output produces distances in the form{"ID1": ID1, "ID2": ID2, "Distance": Distance}There are four distinct match modes:SKIP, which ignores ambiguous positionsGAPMM, which treats gaps appearing in only one sequence as mismatchesAVERAGE, which takes the average of the possible resolution valuesRESOLVE, which tries to resolve the ambiguity to a single nucleotide, averages if that failsusage: tn93.py [-h] -i INPUT_FILE -m MATCH_MODE -o OUTPUT
               [-g MAX_AMBIG_FRACTION] [-v] [-n] [-j]

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT_FILE, --input_file INPUT_FILE
                        Path to the input fasta file
  -m MATCH_MODE, --match_mode MATCH_MODE
                        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 MAX_AMBIG_FRACTION, --max_ambig_fraction MAX_AMBIG_FRACTION
                        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, --ignore_terminal_gaps
                        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 documentsOpen PracticesRules of BehaviorThanks and AcknowledgementsDisclaimerContribution NoticeCode of ConductPublic Domain Standard NoticeThis repository constitutes a work of the United States Government and is not
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