Introduction to Bioinformatics (I519/I617/Y790 Fall 2015; 3 CR)

Instructor: Yuzhen Ye (yye@indiana.edu)
Association instructor: Fatemeh Sharifi (fsharifi@umail.iu.edu)
Class meets: 9:30-10:45AM MW BH 148 (Ballantine Hall)
Lab meets: 10:45AM-12:00PM F I109 (Informatics West)
Office hours: 3-4PM Tuesday (Lindley Abyss, Fatemeh), 2-3PM Thursday (Lindley 301G, Yuzhen)

Syllabus

Description We aim to introduce the broad frontiers of bioinformatics topics from fundamental algorithms to practical tools. The first two weeks of this course will introduce necessary backgrounds in molecular biology and computer science (basic algorithms; programming with python) to understand the content of the entire course. The important themes that will be covered by this course include: DNA and protein sequence comparison; genome/metagenome sequencing; gene finding and genome annotation; discovery of DNA regulatory elements; non-coding RNA finding; phylogeny; protein structure comparison and prediction; high-throughput data (next-generation sequencing data--NGS; and a little on SMRT and Moleculo sequencing data) analyses and applications (RNA-seq, metagenomics and epigenomics); biological network/pathway reconstruction and modeling; and integrative genomics.
Learning outcome
- Understand fundamental concepts in bioinformatics and know the different subareas;
- Know and be able to use state-of-the-art bioinformatics tools and databases;
- Be able to read bioinformatics papers and use tools critically;
- Understand fundamental algorithms and data structures in bioinformatics;
- Understand the Big-O notation and time and space complexity of fundamental bioinformatics algorithms;
- Be able to implement simple algorithms in python/C;
- Understand common probability distributions used in bioinformatics;
- Be able to apply commonly used statistical tests, and prepare plots in R;
- Understand the basic computational and statistical procedure to analyze high-throughput sequencing data;
- Be able to implement simple pipelines using existing tools for customized analyses.
Important Dates:
- Midterm: normal class time on Oct 21st (Wed)
- Final exam: Dec 11th (in lab, Friday)
Programming language Python and C (or C++) are the languages we choose for this course (you are welcome to use either one or both). And if you are already good at other programming languages, like java or perl, and you do not want to learn yet another programming language, you need to get permission from the instructor in advance. You will also need to know/learn R, which is good for statistical computing, and plotting.
Textbook/References
- Optional textbooks:
  Understanding Bioinformatics by Marketa Zvelebil and Jeremy O. Baum (you may buy it from IU bookstore or online)
  Statistics : An Introduction Using R by Michael J Crawley
- Course webpage: http://mendel.informatics.indiana.edu/~yye/lab/teaching/fall2015-I519/ (link)
  (note: course webpage from last year is available here which you may find useful)
Online resources Bioinformatics Algorithms (slides & videos) | Bioalgorithms | Learn Python | 2014 NGS Field Guide | UCSC genome browser (tracks)

Grading: midterm exam (30%) + final exam (20%) + assignments (25%) + class project (25%)

Some rules:

No make-up exams;
We don't accept homeworks that are two days late; and we apply late penalties.