Computational Genomics 2013 JHU Final Project Put main notes here: year month day 13.4.12 enhancer data: sequences around 800bp long kmer size to use: 20 number of enhancer sequences: 2500 number of negative (not enhancer) sequences: 4000 use java for bloomfilter -working on javabloomfilter - Kyle 13.4.12 -working on kmers (python) Neighborhoods (hamming distance) - Alessandro 13.4.12 -currently working on hmm tutorial in matlab (profile analysis) - Guannan 13.4.14 -first draft of kmer neighberhood feature extraction. (terrible performance) - Alessandro 13.4.14 -first draft of bloomfilter: Kyle 13.4.17 (runs fast but ~50% accuracy with defaults) kmer_size | %positive called correctly | % negative called correctly | average difference of +/- kmer calls per read 100 46.00% 52% 5.9293 50 49.75% 50.4% 7.1878 30 54.81% 54.5% 7.8611 20 50.89% 54.5% 8.5579 10 0.16% 99.9% 67.8815 5 0% 100% 0 I think as the kmer size gets small, the number of false positives from having a larger negative input file causes everything to be identified as a negative read (not classified as enhancer). Either way, the default hash functions end up with only about 50% correctness.