This was a project for my bioinformatics class. We studied a couple of motif finding algorithms for finding a potential motif in n many randomly generated DNA sequences. This project
- randomly generates n many DNA sequences
- randomly generates a kmer
- edits up to d many positions in that kmer & inserts that kmer (now motif) into each sequence From here,
- blindly reads these sequences (with emplaced motifs) and
- brute force searches all n sequences to find n many motifs with the lowest hamming distance from a consensus string
- Utilizes Gibbs Sampling algorithm (a randomized algorithm) to find the lowest overall hamming distance from a motif profile Implemented in Swift.