https://github.com/annapoorna-a-k/motif-discovery_gibbs-sampler-using-python

Motif Finding using Gibbs Sampler
https://github.com/annapoorna-a-k/motif-discovery_gibbs-sampler-using-python

gibbs-sampler gibbs-sampling gibbs-sampling-algorithm motif-discovery motif-finding python

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Motif Finding using Gibbs Sampler

• Host: GitHub
• URL: https://github.com/annapoorna-a-k/motif-discovery_gibbs-sampler-using-python
• Owner: annapoorna-a-k
• Created: 2022-08-01T17:08:05.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2022-08-01T17:22:22.000Z (almost 3 years ago)
• Last Synced: 2025-01-03T04:28:19.618Z (5 months ago)
• Topics: gibbs-sampler, gibbs-sampling, gibbs-sampling-algorithm, motif-discovery, motif-finding, python
• Language: Python
• Homepage:
• Size: 6.84 KB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# MOTIF-DISCOVERY_GIBBS-SAMPLER-using-python

Gibbs sampling (also called alternating conditional sampling) is a Markov Chain Monte Carlo algorithm for high-dimensional data.

GibbsSampler is a motif finding algorithm that finds one common motif and returns a list of bestMotifs containing the closest motif match from each string in dna.

# Problem Statement

Given: Integers k, t, and N, followed by a collection of strings Dna.


Return: The strings BestMotifs resulting from running GibbsSampler(Dna, k, t, N) with 20 random starts.



We have to code Gibbs Sampler for the purpose of motif discovery. Here,


           Dna -- A collection of DNA strings that are of the same length.


           "t" -- Is an integer indicating how many times to read the genetic algorithm.


           "k" -- An integer indicating the motif length being searched for.


           "N" -- The number of iterations before returning the best motif.


    

# Algorithm

GibbsSampler (Dna, k , t , N)


       randomly select k−Mers Motifs = ( Motif 1 , . . . , Motift ) in each string from Dna


       BestMotifs ←Motifs


       for j ←1 to N


              i ← Random ( t )


              Profile ←profile matrix constructed from all strings in Motifs except for Motifi


              Motifi ←Profile − randomly generated k-Mer in the i-th sequence


              if Score ( Motifs ) < Score ( BestMotifs )


                     BestMotifs ←Motifs


       return BestMotifs