Computational Bioinformatics

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This site represents a class in a box that you are welcome to use to develop and /or apply to your own class of a similar theme.

Here, you will find a wealth of objectives, notes, assignments, ideas and systems of structure which may help to strengthen your approach to pedagogy, for seemingly any level of teaching.

These materials comprise a course that continues to be developed by Oliver Bonham-Carter, PhD. at Allegheny College in Meadville, Pennsylvania, USA.

If you find these materials helpful, I invite you to use them to the benefit of your students and to enrich your class!

I look forward to hearing from you if you would like to get in-touch.

  • Oliver Bonham-Carter
  • obonhamcarter (a) allegheny (dot) edu


An introduction to the development and application of methods, from the computational and information sciences, for the investigation of biological phenomena. In this interdisciplinary course, students integrate computational techniques with biological knowledge to develop and use analytical tools for extracting, organizing, and interpreting information from genetic sequence data. Often participating in team-based and hands-on activities, students implement and apply useful bioinformatics algorithms. During a weekly laboratory session students employ cutting-edge software tools and programming environments to complete projects, reporting on their results through both written documents and oral presentations. Students are invited to use their own departmentally approved laptop in this course; a limited number of laptops are available for use during class and lab sessions.

Course Objectives

Students successfully completing this class will have developed:

  • A “big-picture” view of bioinformatics.
  • An understanding of the objectives and limitations of bioinformatics.
  • An understanding of the biological foundations of bioinformatics (genes and genomes, gene expression, etc.).
  • An understanding of the computational foundations of bioinformatics (programming, databases, etc.).
  • An understanding of how genetic information is obtained and processed.
  • The ability to use basic bioinformatics software tools to study genetic information.

Throughout the semester students also will enhance their ability to write and present ideas about bioinformatics in a clear and compelling fashion. Students will gain practical experience in the design, implementation, and analysis of bioinformatics research during laboratory sessions and a final project. Finally, students will develop a richer understanding of the fascinating connections between biological systems, analysis and automation.

An Ethical Interest

Throughout the semester students will be exposed to famous dilemmas in technology which will arrive with discussions to encourage positive thinking in ethics. For example, the course will introduce students to ethically inclined concepts in the generation of technology. Such terms include liability, ethics, responsibility, privacy, information governance, data security and others.


  • Exploring Bioinformatics: A Project-based Approach by Caroline St. Clair and Jonathan E. Visick.
  • Think Python by Allen B. Downey.
  • Along with reading the required books, you will be asked to study many additional articles from a wide variety of conference proceedings, journals, and the popular press.

Other Useful Textbooks

  • Think Python by Allen B. Downey.

  • BUGS in Writing: A Guide to Debugging Your Prose by Lyn Dupr'e. Addison-Wesley Professional. ISBN-10: 020137921X and ISBN-13: 978-0201379211, 704 pages, 1998. References to the textbook are abbreviated as “BIW”.

  • Writing for Computer Science (Second Edition). Justin Zobel. Springer ISBN-10: 1852338024 and ISBN-13:978-1852338022, 270 pages, 2004. References to the textbook are abbreviated as “WFCS”.

  • On Being a Scientist: A Guide to Responsible Conduct in Research (Third Edition). Committee on Science, Engineering, and Public Policy, National Academy of Sciences, National Academy of Engineering, and Institute of Medicine. ISBN: 0309119715, 82 pages, 2009. References to the textbook are abbreviated as “OBAS”.

Welcome to a resources page for Bioinformatics research. Here you will find a list of links for data, tools, tutorials and related resources that may be very helpful to your work. Software Installations RStudio R Programming Language Python Programming Language Git for Mac Git for Windows Git for all platforms Python Programming Language Python for Biologists BioPython Python Programming Resources
How can I write better?! When completing any assignment in research or in your classes, the quality of your writing is very important. Below are some resources that may help in your writing. Online Resources Online guide to writing: Maytum Center for Student Success;Writing and Speaking Consultants at Allegheny College’s Writing Center Online guide to writing: Available from the University of Maryland