Core Course Preparation:

Bioinformatics and Computational Biology is, by nature, an interdisciplinary field, requiring expertise in statistics, computer science, and biology. Many students entering the BCB program have background in a subset of these disciplines, but rarely have training in the breadth of material covered by BCB. Our core courses are rigorous, and students typically need background/bridge training before they are prepared to begin the core. Please see the guidance we provide below and contact our core course instructors if you have questions about your readiness for their courses.

BCB5670, Bioinformatics Algorithms (Fall, annually), Instructors: Iddo Friedberg, idoerg@iastate.edu (odd years) and Xiaoqiu Huang, xqhuang@iastate.edu (even years)

Unless students have an extensive background in programming, they are encouraged to take COMS5010X prior to taking BCB5670. COMS5010X covers the following topics:

Design of data structures and algorithms with an object-oriented programming methodology. Abstract data type specification and correctness. Collections including lists, stacks, queues, trees and hash tables. Searching and Sorting. Discrete mathematics concepts as applied to computer science. Basics of logic, set theory, functions, relations, combinatorics. Proof techniques such as induction and recursion. Background in Math and Computer Science equivalent to MATH 1650 and COMS 2270 required.

BCB5680, Statistical Bioinformatics (Spring, annually), Instructor: Karin Dorman, kdorman@iastate.edu

Prior to taking BCB5680, all students must take STAT4830/5830, which covers the following topics:

Statistical methods for research involving computers; exploratory data analysis; selected topics from analysis of designed experiments - analysis of variance, hypothesis testing, interaction among variables; linear regression, logistic regression, Poisson regression; parameter estimation, prediction, confidence regions, dimension reduction techniques, model diagnostics and sensitivity analysis; Markov chains and processes; simulation techniques and bootstrap methods; applications to computer science, bioinformatics, computer engineering - programs, models and systems as objects of empirical study; communicating results of empirical studies. Statistical software: R. Knowledge of linear algebra recommended.

STAT4830/5830 is a fast-moving course, and, if these topics are new to you, it is recommended that you first take STAT3300, which covers many of these topics at a more basic level. For both STAT4830/5830 and BCB5680, background in differential calculus (covered in MATH1650 at ISU) and integral calculus (covered in MATH1660 at ISU) is necessary.

Statistical course offerings can vary considerably, and STAT 4830/5830 covers both statistical theory and methods, which are often taken in separate courses.  If you are unsure about your readiness for taking BCB5680 or need help comparing your statistical coursework to these requirements, please contact Dr. Dorman via email.