motifFinder-Bioinformatics

This project involves developing a "motif finding" program and testing the program on a set of synthetic datasets.

The first step is to build a benchmark of the collection of synthetic datasets, and each dataset contains a set of DNA sequences, into which a "motif" has been "planted".

The second step is to write a program to read the dataset generated in the first step and find the "motif" planted in the first step. We employed the Gibbs sampling algorithm for implementing the motif finder.

Lastly, we evaluated the performance of the motif finder using three metrics:
(1) Relative Entropy: difference between the motif our algorithm found and the planted motif.
(2) Overlapping Sites: the correct sites our algorithm found.
(3) Running time: how long our algorithm takes to find the motif.