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Do you have any suggestions on a suitable introductory text on bioinformatics for a computer scientist? Any recommendations with pros and cons of different books would be appreciated. I'm mainly looking for a brief introduction since I'm a novice to this field.

I found some books like

  1. Systems Biology by Edda Klipp,
  2. Introduction to Bioinformatics by Arthur Lesk
  3. Bioinformatics For Dummies
  4. Bioinformatics and Functional Genomics by Jonathan Pevsner
  5. An Introduction to Bioinformatics Algorithms by Neil C. Jones and Pavel A. Pevzner

I would like to develop software related to sequencing applications, so any suggestions should ideally aid in this goal.

kmm
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twid
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  • Can you narrow down your requirements any, as I think this is too broad to get a reasonable answer. – jonsca Jul 04 '13 at 13:02
  • I was looking for something related to sequencing, Since looking for developing software for it – twid Jul 04 '13 at 13:03
  • I would put as much information as possible into the question itself. Asking for the "best" book is going to get a host of opinion-based answers. – jonsca Jul 04 '13 at 13:05
  • With a slightly reformulated Q I do not see a big problem - BioSE has many open questions about book suggestions. Adding specifics about future software development and therefore need for technical aspects of sequencing would probably be useful though. – fileunderwater Jul 04 '13 at 13:25
  • @fileunderwater why did you remove one of the books from the OP's list? – terdon Jul 04 '13 at 13:25
  • @terdon Mistake on my part or the Q was updated while I edited. Maybe a mod can put it back in. – fileunderwater Jul 04 '13 at 13:27
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    @fileunderwater no worries, I put it back. I was just asking in case there was a valid reason. – terdon Jul 04 '13 at 13:28
  • I added the bit from your comment - I think it's an important point that actually narrows the books under consideration. Please edit it as you would like, but I'd encourage you to put specific information into the question directly as @jonsca suggested, since few people are going to read this long comments section. – blep Jul 04 '13 at 19:50
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    "Biological Sequence Analysis" by Durbin et al. is the classic bioinformatics text for people with a CS background. If you are interested in a more quantitative approach to biology without necessarily focusing on algorithms, you will also probably find "An Introduction to Systems Biology" by Uri Alon and "Physical Biology of the Cell" by Phillips et al. interesting (depending on your area of interest). Additionally, any Machine Learning text will be relevant and some often have examples from biology/bioinformatics. – Bitwise Jul 05 '13 at 01:00
  • @Bitwise I've reopened the question now, could you please create an answer from your comment. – Mad Scientist Jul 05 '13 at 07:55

5 Answers5

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Systems biology and bioinformatics are quite diverse subjects, but here are some options:

  • Biological Sequence Analysis by Durbin et al.: the classic bioinformatics text for people with a CS background. Does not deal with networks.
  • An Introduction to Systems Biology by Uri Alon: A physicist's view of systems biology, focusing on design principles and networks. Interesting concepts and some math (no algorithms). Doesn't really deal with methodology.
  • Physical Biology of the Cell by Phillips et al.: Nice introduction to biophysics and quantitative biology (no algorithms). Many parts are relevant for systems biology.
  • Any diverse Machine Learning text will be relevant and many have examples from biology/bioinformatics.
  • Probabilistic Graphical Models are also quite popular in bioinformatics (e.g. Hidden Markov Models, Expectation-Maximization, Bayesian Networks). Probabilistic Graphical Models by Koller et al. is a very comprehensive CS book on the subject.

Another suggestion is to look for primers/reviews in relevant journals. This is important because the field is new, so once a new technology is developed, it takes time until the appropriate computational methodology is developed enough to make it into a text book. Also, they can point you to relevant research papers. For example, PLoS Computational Biology has an Education Collection which features a large variety of such reviews.

Bitwise
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I second these two I used both and liked them overall:

  • Bioinformatics and Functional Genomics by Jonathan Pevsner
  • An Introduction to Bioinformatics Algorithms by Neil C. Jones and Pavel A. Pevzner
kmm
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trigun99
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For my bioinformatics class a few years ago, we used these:

Was excellent as an intro text. Explained a lot of the biological concepts while it was teaching you the methods. Useful either for biologists or computer scientists bridging into bioinformatics.

The go to book for more comprehensive coverage of algorithms and applications. Did a good job covering the details of a method, like dynamic programming, from start to finish. Much more computer science oriented.

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For a really brief introduction, I recommend the 37 page paper

Bioinformatics - An Introduction for Computer Scientists by Jacques Cohen.

It is a well written text, assuming some knowledge about algorithms, computing and maths, but not about biology. It helped me a lot getting comfortable with the field.

Afterwards, I'd stick to Durbin's "Biological Sequnce Analysis" (as already mentioned by Bitwise) as a book that provides you with the theoretical foundations of the models used in bioinformatics. It focuses on Sequence processing, which is what you want as far as I understood your question. It is also quite accessible from a computer scientist's or mathematician's point of view.

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There several popular books for bioinformatic

  1. A First Course In Computers 2003 Edition (With Cd)
  2. Foundations of Computing (English, Paperback, Sinhs Pradeep K.)
  3. Introduction to Bioinformatics
  4. Bioinformatics: Methods and Applications: Genomics, Proteomics and Drug Discovery by S. C. Rastogi
  5. Bioinformatics: Concepts, Skills & Applications
  6. Principles of Genome Analysis and Genomics

You can find the list of bioinformatic books here 6 Best Books for Bioinformatics

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