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 ->

Markov Chains – play around on this so you can get a feel for the underlying dynamics of a Hidden Markov Model:

Hidden Markov Models ->

More Hidden Markov Models & Bayesian info ->

Many different algorithms (with some awesome example code in the form of Jupyter notebooks)

More examples (look at dishonest casino under the Viterbi section) ->

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:

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

Ready for more?


Holiday Announcements

Thxgiving break announcements

Hi Biocomputing folx! For those of you who will be around during the Thanksgiving break, note that I will not be in my usual spot for office hours (Jordan Lobby, Thursday 5-6pm), but I will be around all week and am happy to meet – just bounce me an email.

For group projects, I am the contact person for the bioinformatics topic (This includes R folx – we aren’t doing a lot of R/python things since most of this work will be on the remote machines or at least L/unix). I’m happy to help with the other two, but your point person will be better prepared to answer questions about specific grading/requirements.