Biological sequence analysis

What Hacker News says

1. nextosMar 2024
   Skim through Durbin et al. Biological Sequence Analysis.
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2. nextosJul 2022
   > [...] where we mostly learned things from outdated books

   I can recommend 3 really good books, SICP-level good:

   1. Biological Sequence Analysis by Durbin et al.

   How to model DNA, RNA and proteins as probabilistic languages / generative models. There is also a companion book with all exercises solved.

   2. Physical Biology of the Cell by Phillips et al.

   A massive amount of quantitative cell biology models that use simple undergrad physics.

   Originated from a Caltech course. After reading this book, all other intro to biology books look like a bag of tricks.

   3. An Introduction to Systems Biology: Design Principles of Biological Circuits by Alon.

   Bacterial circuits from the perspective of an electrical engineer.

   Sadly, NSF discontinued funding for a totally epic Cold Spring Harbor Summer school for postdocs that taught [2,3].

   A shame, we need more quantitative biology and less bags of tricks.

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3. dekhnFeb 2020
   Personally, I prefer the classic textbook approach, so I recommend Principles of Biochemistry (Lehninger), Biological Sequence Analysis (Durbin, Eddy, Krogh, Mitchison) which is sadly pretty dated now, a general Biology (Campbell), and finally if you really want to dive down the rabbit hole of a complex biological problem with huge health applications, Biology of Cancer (Weinberg).

   I've had this argument with folks before and some people seem fine learning in other ways, but I really prefer the textbook approach, especially textbooks which are basically just summaries of the current understanding of the field, with direct links to the detailed review articles.

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4. AlexCoventryApr 2019
   You could try Gelman et al.'s Bayesian Data Analysis. It has a good overview of MCMC.

   If you want an overview of Markov chains as statistical models in their own right, Durbin et al.'s Biological Sequence Analysis is a well-motivated overview.

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5. jdale27Nov 2016
   I second the recommendation of CLRS. Another good source of applied examples is the bioinformatics literature. It may be a little tough if you don't have much background in genetics, but a lot of the basic algorithms in computational biology are essentially dynamic programming algorithms that analyze strings over the alphabets of DNA or amino acids. One of my favorite books, which is a classic and (I think) is pretty approachable for the non-biologist, is Biological Sequence Analysis by Durbin, Eddy, Krogh, and Mitchison.
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6. clarleMar 2013
   Rosalind [1] (previously posted on HN) is pretty good for something like a Project Euler for bioinformatics. The problems are pretty similar to the introductory computational biology course at my school for upper-year undergrads and first-year grad students.

   It's a really good way to get a good overview of the field, and what some of the problems have been already solved in bioinformatics. It's definitely best to combine it with a more traditional textbook such as "Introduction to Computational Biology" (Haubold and Wiehe) or "Biological Sequence Analysis" (Mitchison, Krogh, Durbin, and Eddy).

   Hope that helps!

   [1]: http://rosalind.info/

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