Biological sequence analysis
Probabilistic models of proteins and nucleic acids
by Richard M. Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison · first published 1998
90% recommend10% neutral
Why HN recommends it
Covers modeling DNA, RNA and proteins as probabilistic sequences, with clear treatment of dynamic programming and hidden Markov models; suited to readers with a math or statistics background entering bioinformatics.
Summarised by Claude from recommending comments
A reference of probabilistic methods of sequence analysis.
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…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/
on “BioBlender: Blender for Biologists” ↗…Sussman The Little Schemer, Friedman The C Programming Language (K&R), Kernighan & Ritchie The Analysis of Algorithms, Purdom & Brown The Elements of Statistical Learning, Hastie, Tibshirani, Friedman Biological Sequence Analysis, Durbin et al. Artificial Intelligence: A Modern Approach, Russell & Norvig Introduction to the Theory of Computation, Sipser Introduction to Linear Algebra, Strang GEB is my 1/2
on “The Top 9½ Books In a Hacker's Bookshelf” ↗> [...] where we mostly learned things from outdated books I can recommend 3 really good books, SICP-level good: 1.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…
on “I should have loved biology” ↗…HR modules, but good enough to make a decent living and buy a nice car etc. ps: if you really into math, statistics, and feel good about everyday job, the best book to read and get into the field is "Biological sequence analysis" by Durbin, Eddy, Krogh and Mitchison.
on “Are you going to change the world? (Really?)” ↗…Hermann Hesse - most Haruki Murakami, especially "The Wind-up Bird Chronicle" - Joan Didion, "Where I Was From" - Richard P. Gabriel, "Patterns of Software" - Durbin, Eddy, Krogh, and Mitchison, "Biological Sequence Analysis" - Italo Calvino, "if on a winter's night a traveller"
on “Ask YC: favorite books” ↗…like that you would really need domain experts in each field to look for the important books (even if they are in low quality). Here for example a quick glance at Bioinformatics tells me that Durbin's Biological sequence analysis and Xiang's Essential Bioinformatics is missing even though it can be found on torrents. They are relevant much more than the rest of the books
"Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids" by Durbin, Eddy, Krogh, and Mitchison is a classic, very readable intro to sequence analysis algorithms (dynamic programming, HMMs) that would be interesting…
on “200 TB of 1000 genomes data on Amazon S3 as a public dataset” ↗…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.
on “Ask HN: What are good resources for studying Dynamic Programming?” ↗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.
on “Ask HN: Best place to start learning about Markov Chains?” ↗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…
on “Improved protein structure prediction using potentials from deep learning” ↗Skim through Durbin et al. Biological Sequence Analysis.
on “Ask HN: How to Get Started in Genomics?” ↗…-------------------------- ##Ungot An Introduction to Bioinformatics Algorithms Bioinformatics : Machine learning approaches Computational Genome Analysis, Understanding Bioinformatics by Marketa Zvelebil Biological Sequence Analysis, Durbin et al Bioinformatics by David Mount : Statistical Methods in Bioinformatics Warren J. Ewens, Gregory R. Grant Bioinformatics Basics: Applications in Biological Science and Medicine Time Warps,…
on “Peter Thiel's Startup Class 16 Notes: Decoding Ourselves” ↗…https://www.jstor.org/stable/29774782?seq=1#page_scan_tab_co... it applies the chomsky hierarchy to sequence analysis, there is in fact a ton of interesting literature around graphical models like HMMs, see https://www.amazon.com/Biological-Sequence-Analysis-Probabil... for more details.
on “23andMe Pulls Off Massive Crowdsourced Depression Study” ↗