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Gibbs Sampler for motif discovery

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This program runs the Gibbs Sampler algorithm for de novo motif discovery. Given a set of sequences, the program will calculate the most likely motif instance as well as the position weight matrix and position specific scoring matrix (the log2 normalized frequency scores).

The input sequence file should be provided in fasta format. Output is written to the console. A eps file containing the weblogo of the motif is also created. #Getting Started ##Prerequisites Python 3.8 or later is required. ##Installation The program can be installed with pip by running the following command:

$ pip install gibbs_sampler


To run, simply call gibbs_sampler from the command line along with the path to the sequence file and expected width of the motif.

$ gibbs_sampler <primary sequence file> <width>

Optional arguments for the title of the weblogo file and number of iterations of the sampler can be specified using the -t and -n flags respectively.


gibbs_sampler was created by Monty Python. It is licensed under the terms of the CC0 v1.0 Universal license.


Thank you to Professor Hendrix for teaching me how to use python to investigate biologically relevant questions.

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