We’re using programs like BLAST and HMMER now, and these are just the beginning of an entire world of bioinformatic algorithms. We unfortunately don’t have a lot of time to over these during class, but I compiled some resources here if you’d like to explore them later.
Nature Biotech primer – good place to start -> https://www.nature.com/articles/nbt1004-1315
Markov Chains – play around on this so you can get a feel for the underlying dynamics of a Hidden Markov Model: http://setosa.io/blog/2014/07/26/markov-chains/
Hidden Markov Models -> http://www.cs.cmu.edu/~./awm/tutorials/hmm.html
More Hidden Markov Models & Bayesian info -> https://github.com/laryamamoto/BayesianCourseNotes/blob/master/tex/bayesian.pdf
Many different algorithms (with some awesome example code in the form of Jupyter notebooks) http://www.langmead-lab.org/teaching-materials/
More examples (look at dishonest casino under the Viterbi section) -> http://comprna.upf.edu/courses/Master_AGB/
Whether or not you are comfortable with R at this point, there is a wealth of information to be found in the package documentation. Try searching the repository in google like this:
hidden markov model site:https://cran.r-project.org
You’ll find a lot of pdf links, generally these are documents written to accompany packages (aka vignettes) and will tell you more than you ever wanted to know about the algorithms we’re getting into these days (which is the natural progression of the introduction to biocomputing).
YouTube playlist of lectures explaining these concepts https://www.youtube.com/playlist?list=PL2mpR0RYFQsBiCWVJSvVAO3OJ2t7DzoHA