Programs like BLAST and HMMER are key tools in bioinformatics, and the algorithms that they use are helpful to understand when you incorporate them into your workflows. These are just the tip of the iceburg, but they’re a really good place to start. I compiled some resources that I’ve found helpful in learning them.
Image: Atomic Force Microscopy of circuit on DNA nanostructure. More information
- 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).
- For the folks who’d rather watch someone explain it, here’s a YouTube playlist of lectures explaining these concepts https://www.youtube.com/playlist?list=PL2mpR0RYFQsBiCWVJSvVAO3OJ2t7DzoHA
- The site Rosalind has a series of problems you can test out for those who have interest in learning how to code the algorithms themselves. It’s a great resource!
- Ready for more?