BI524
Computational Foundations of Bioinformatics
Tu Th 1:30--3:00 in Higgins 425.

Course description

Biology is increasingly a field dominated by high-throughput methods, yielding large data sets which require data analysis using both public domain/commercial software as well as new algorithms to be implemented in a programming language. Bioinformatics is an interdisciplinary area concerned with the application of mathematics, statistics and programming to solve mainstream problems in biology, problems such as the following.

• According to the ENCODE Consortium article in Nature (June 14, 2007), the human genome is "pervasively transcribed", much of it in RNA transcripts of unknown function (i.e. not tRNA, mRNA, rRNA, miRNA, etc.). What does all this biological "dark matter" really do?
• According to an article of Aldman and Terzic in the Journal of the American Medical Association (November 2005), "the field of clinical oncology is poised for unprecedented innovation, reflecting the confluence of breakthroughs in decoding disease pathobiology in the context of high-throuhput enabling technologies". A key issue in molecular pathology is the development of statistical models and computer programs to determine biologically significant gene expression patterns for certain kinds of disease.

In this course, you will learn how to navigate through public databases containing protein conformations, nucleotide and amino acid sequences, etc. and how to use bioinformatics software (BLAST, ClustalW, Vienna RNA Package, etc.). This is fun and easy, but not the main focus of the course -- a more in depth treatment of such issues is given in the course BI420 Introduction to Bioinformatics.

The goal of the course, which assumes no prior experience in computer programming, is to learn to use the UNIX operating system and how to write interpreted programs called scripts, in order to parse biological files (PDB, GenBank, etc.), to implement some bioinformatics algorithms, to invoke executable code from within a program, etc. In the course, we will focus principally on the language Python, a simple, elegant scripting language, and towards the end of the course will additionally cover aspects of Perl.

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Texts

1. "Developing Bioinformatics Computer Skills", by Cynthia Gibas and Per Jambeck, O'Reilly & Associates, Inc. (2001), ISBN 1-56592-664-1. Perl run-time environment, documentation and tutorial: http://www.perl.org/.
   This text is a well-written introduction to biological databases, tools and public domain software, UNIX, and Perl.
2. Python, by Chris Fehily, Peachpit Press, Visual Quickstart Guide, 0-201-74884-3 (2002). Python run-time environment, documentation and tutorial: http://www.python.org/.
   This text presents the basics of Python programming language.

1. Beginning Perl for Bioinformatics: An introduction to Perl for Biologists, by J. Tisdall, O'Reilly (2001).
2. Python course in Bioinformatics, by Katja Schuerer and Catherine Letondal (Institut Pasteur). You can download and print off the pdf file which is available as well.
3. "Python Essential Reference", Second Edition, David M. Beazley, New Riders Publishing (a Prentice-Hall company), ISBN 0-7357-1091-0
   Excellent reference work with good glossary for finding Python syntax. See http://islab.cs.uchicago.edu/python/.
   If you'd really like program efficiently in Python, then I've found this book to be indispensible (i.e. strongly recommended).
4. "Bioinformatics: A practical guide to the analysis of genes and proteins", edited by A.D. Baxevanis and B.F.F. Ouellette, second edition, Wiley & Sons, Inc. (2001).
5. "Learning Python", by Mark Lutz and David Ascher, O'Reilly Publishing Co., (1999), ISBN 1-56592-464-9.
   Introductory text to Python programming with some example programs and good overview of basic syntax and applications of the language. I've found that the glossary is of limited use, since many important terms are not listed there.
6. "Learning the UNIX Operating System", Fourth Edition, by Jerry Peek, Grace Todino & John Strang, O'Reilly & Associates, Inc., ISBN: 1-56592-390-1
   Unix is the best platform for efficient work in bioinformatics, so this tutorial will help you to learn Unix. Though not required in this introductory course, since I work on Unix, all class examples, etc. will be demonstrated from a Linux platform, rather than Macintosh or Windows. We will not spend class time covering Unix; however, if you plan to do research in computational biology, you'll need to learn Unix on your own.
7. "A Primer of Genome Science", by G. Gibson and S.V. Muse, Sinauer Associates, Inc. (2002).

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Grading Policy

Homework, class participation	30%
Midterm	30%
Final Exam	40%

The grading policy is subject to change. If so, then this will be clearly announced with ample time.

Boston College Academic Integrity Policy

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