# Implement GibbsSampler solved by 425

July 29, 2015, 1:07 a.m. by Rosalind Team

We have previously defined the notion of a Profile-most probable k-mer in a string. We now define a Profile-randomly generated k-mer in a string Text. For each k-mer Pattern in Text, compute the probability Pr(Pattern | Profile), resulting in n = |Text| - k + 1 probabilities (p1, …, pn). These probabilities do not necessarily sum to 1, but we can still form the random number generator Random(p1, …, pn) based on them. GIBBSSAMPLER uses this random number generator to select a Profile-randomly generated k-mer at each step: if the die rolls the number i, then we define the Profile-randomly generated k-mer as the i-th k-mer in Text.

    GIBBSSAMPLER(Dna, k, t, N)        randomly select k-mers Motifs = (Motif1, …, 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

## Implement GibbsSampler

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. Remember to use pseudocounts!

## Sample Dataset

8 5 100
CGCCCCTCTCGGGGGTGTTCAGTAAACGGCCA
GGGCGAGGTATGTGTAAGTGCCAAGGTGCCAG
TAGTACCGAGACCGAAAGAAGTATACAGGCGT
TAGATCAAGTTTCAGGTGCACGTCGGTGAACC
AATCCACCAGCTCCACGTGCAATGTTGGCCTA


## Sample Output

TCTCGGGG
CCAAGGTG
TACAGGCG
TTCAGGTG
TCCACGTG