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    BIOL Z620 Spring 2013

    Selected Topics in Zoology (Section 28329):
    Introduction to Bioinformatics and Genomics

    BIOL Z620 Spring 2013

    Time & Location:    Mon, Wed 12:45p - 2:15p (Credits: 3.0); Lindley Hall (LH) 030
    Instructors:    Irene Newton (Jordan Hall 221C; Tel.: 855-3883);    Volker Brendel (205C Simon Hall; Tel.: 855-7074)
    Email:     IN, irnewton@indiana.edu;     VB, vbrendel@indiana.edu
    WWW:     Irene Newton     https://brendelgroup.org/
    Office Hours:     Mon, Wed after class and by appointment.
    Grades:    will be determined as described below.
    Schedule:     https://brendelgroup.org/teaching/2013/ItBG13Sschedule.php
    Computing Resources:     You will have access to networked computer terminals in class and will need such basic access outside of the classroom for assignments.

    btn_printerFriendly.gif version of this syllabus

    Synopsis

    Extensive use of computing resources pervades all of modern biological and medical research. Bioinformatics is just one label for large-scale efforts to store and analyze data sets accumulating from both community and individual laboratory projects. This course provides a comprehensive hands-on (keyboards and optical mouse) introduction to computational skills and approaches for analyzing a wide range of genomic data. Students will learn basic scripting language techniques and apply their knowledge to simulated and real data in pursuit of relevant scientific questions. An explicit goal of the class is to for students to evaluate, design, and execute common bioinformatics and computational biology approaches to widely occurring data analysis problems in genomics.

    Prerequisites

    This class is directed primarily at first- and second-year graduate students in the Biology Ph.D. program. Participants are expected to have passed the introductory http://brendelgroup.org/teaching/BiolZ620FA12.php|BIOL Z620 Bioinformatics2Go course; similar background experience can serve as a substitute with permission of the instructors. Classes will be taught in a computer lab. Students are required to be familiar with basic computer operational skills, although no programming language knowledge will be assumed. Class messages and materials, including assignments, will be shared through our Oncourse site in addition to these web pages, and students are required to regularly check these relevant communication channels. IU is committed to Creating a Positive Environment for teaching and learning. If you have any concerns or suggestions, please let the instructor know.

    Assignments

    Most class periods will begin with a brief introduction to a particular topic, followed by group and individual work on associated in-class assignment. In addition to the in-class work, there will be three types of homework assignments: (1) 5 scripting assignments (in the first few weeks of the course); (2) 9 reading assignments, and (3) 6 data analysis assignments (after the first few weeks of the course). Students are expected to prepare for class by completing posted assignments in timely fashion. It is anticipated that completion of the assignments will require about 3 hours of work outside the classroom per week.

    Grading

    Grades will be based on completion of the weekly assignments (68%) and an individual final project (32%). The assignment grade will be calculated as follows: 4 scripting assignments, each counting for 3% of the grade for a subtotal of 12% (if students hand in all 5 scripting assignments, only the best 4 will be scored); 7 reading assignments, each counting for 3% of the grade for a subtotal of 21% (if students hand in all 9 reading assignments, only the best 7 will be scored); 5 data analysis assignments, each counting for 7% of the grade for a subtotal of 35% (if students hand in all 6 reading assignments, only the best 5 will be scored). Final projects will consist of short, manuscript-style reports on individual data analysis projects covering multiple themes of the class. The main teaching goal with the final projects is for students to learn best practices for design and documentation of typical computational approaches in genome science.

    Text books

    The course does not rely on a textbook. However, we suggest the following background material for those students wishing to consult additional sources: Learning Perl by Schwartz, Phoenix & Foy Understanding Bioinformatics by Zvelebil & Baum. Web-accessible resources will be posted on the appropriate schedule pages.

    Selected journals

    Students are encouraged to review current research literature that provides many examples of application of the class topics. The following list provides a selection of relevant journals that are electronically accessible.