A.2 Computer Science

Even though you're programming for biological applications, you'll often find yourself venturing into the realm of traditional computer science. Here are some published resources to help you find your way.

A.2.1 Algorithms

Mastering Algorithms with Perl; by Jon Orwant, Jarkho Hietaniemi, and John Macdonald; O'Reilly & Associates. The best book for noncomputer scientists who program in Perl.

Introduction to Algorithms; by Thomas H. Cormen, Charles E. Leiserson, and Ronald L. Rivest; MIT Press and McGraw-Hill. This is a really good book on algorithms—in many ways, the best. It's one of the standard university texts (arguably the standard text) at both the graduate and undergraduate levels. It works well as a textbook and as a reference. Its target audience is computer-science students, so there is a fair amount of math included, but even nonmathematical programmers will find this book very helpful.

Fundamentals of Algorithmics, by Gilles Brassard and Paul Bratley, Prentice Hall. An easy overview of algorithmic techniques.

Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology; by Dan Gusfield; Cambridge University Press. This book specializes in algorithms for strings, including such topics as sequence alignment. It's very detailed, but even so, not complete: this is a big field! The best single source on string algorithms, with lots of information about biological sequence similarity.

The following books are for advanced study.

The Design and Analysis of Computer Algorithms; by Alfred V. Aho, John E. Hopcroft, and Jeffrey D. Ullman; Addison-Wesley. This is the classic book on the science of algorithms.

Introduction to Parallel Algorithms and Architectures: Arrays, Trees, Hypercubes; by Frank Thomson Leighton; Morgan Kaufmann. A comprehensive and rigorous text and reference.

Randomized Algorithms, by Rajeev Motwani and Prabhakar Raghavan, Cambridge University Press. A clear, rigorous book.

A.2.2 Software Engineering

Software Engineering, Second Edition; by Ian Sommerville; Addison-Wesley. A good, general book that covers lots of important topics and generally avoids taking sides for or against competing styles.

A.2.3 Theory of Computer Science

Introduction to Automata Theory, Languages, and Computation, Second Edition; by John E. Hopcroft, Rajeev Motwani, and Jeffrey D. Ullman; Addison-Wesley. The classic text on computer-science theory.

Computers and Intractability: A Guide to the Theory of Np-Completeness, by Michael R. Garey and David S. Johnson, W.H. Freeman & Co. The classic, and superb, book on the topic.

A.2.4 General Programming

The Unix Programmers Manual, Steven V. Earhart, ed., Harcourt, Brace and Jovanovich School. This manual for Unix (whatever version of Unix) is a crash course in computer science with an emphasis on programming. The design of the interacting programs, and the concepts of pipes, redirection, processes, and so on, has been one of the great success stories of programming. This manual summarizes the system: Part I documents user programs; Parts II and III document the programming interface. The programmable shell, and the programs grep, awk, and sed were some of the primary inspirations for Perl.

The C Programming Language, by Brian W. Kernighan and Dennis M. Ritchie, Prentice Hall PTR. C and C++ are important languages in bioinformatics, and this classic book teaches C. If you work through the book, attempting all the programming exercises, you'll have some excellent programming training.

Structure and Interpretation of Computer Programs; by Harold Abelson, Gerald Jay Sussman, and Juke Sussman; MIT Press. A really interesting book that looks deeply at programming in the context of learning a dialect of Lisp.

The Unix Programming Environment, by Brian W. Kernighan and Robert Pike, Prentice Hall. This book is fun, and it talks about good software design.