Hi all! I’ve recently used Github to build a website that I’ve copied all the content here to, and will be updating that site with new content in the future. I’d be stoked if you checked it out!

crivaldi.github.io

I don’t have plans to shut this site down, but I have no idea how long it will be maintained by the university in the future.

P.S. – I build my site by forking the Beautiful Jekyll

respository. It was a lot easier than I thought it would be! There’s a great tutorial by the author, but if you’re curious about any of the details, feel free to ask me about it!

*Note to all users: As of 07/14/2019, all posts have been moved to (and are being updated at crivaldi.github.io . I don’t have plans to shut this site down, but I have no idea how long it will be maintained by the university in the future.*

 

I taught at this workshop and made this lesson with the brillian Shannon Joslin (@IntrprtngGnmcs). If you have questions about getting started cluster computing, this may answer some of them! Feel free to contact me via email or twitter for more questions or suggestions for the lesson.

 

Link to lesson!

*Note to all users: As of 07/14/2019, all posts have been moved to (and are being updated at crivaldi.github.io . I don’t have plans to shut this site down, but I have no idea how long it will be maintained by the university in the future.*

Bioinformatics

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.

Algorithms

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

Ready for more?