CS466: Introduction to Bioinformatics Location: 0216 Siebel Center Time: 08:30AM - 9:50AM, M/W Instructor: Jian Peng Assistant Professor of Computer Science Email: jianpeng@illinois.edu Office: 2118 Siebel Center Office Hour: M/W 10 - 11am Teaching Assistant: Nate Russell PhD Student of Informatics Email: ntrusse2@illinois.edu Office: 1218 Siebel Center Office Hour: Wed 2-3pm Teaching Assistant: Casey Hanson PhD Student of Computer Science Email: crhanso2@illinois.edu Office: 1218 Siebel Center Office Hour: Mon 2-3pm > Forum: piazza Video Lectures: Echo360 Video Lectures Student Guide: EWS Student Guide

Course Objectives

Introduction to bioinformatics:

• Basic problems in computational biology
• Statistics and machine learning for data analysis
• Algorithms for data processing

Learning to do research:

• Course project experience
• Hands-on practice with real datasets
• Propose and perform independent research

Prerequisites

• Programming skills (equivalent to CS 225) for doing the mini-project.
• Knowledge of basic probability and statistics for understanding several lectures.
• No biology background is necessary.

Introductory materials

Molecular Biology for Computer Scientists by Larry Hunter.

An Introduction to Bioinformatics Algorithms by Neil C. Jones and Pavel A. Pevzner.

Tentative Grading Scheme

3-credit students:

• Five problem sets (30%)
• Midterm (25%)
• Final (25%)
• Team-based mini-project and report (20%)

4-credit students:

• Five problem sets (20%)
• Midterm (25%)
• Final (25%)
• Mini-project + individual report (30%)

Assignments

Problem Set 1

• Release Date: 2/04/2019
• Due Data: 2/11/2019 11:59 PM CST
• File: pdf

Problem Set 2

• Release Date: 2/25/2019
• Due Data: 3/6/2019 11:59PM CST
• File: pdf 1
• File: pdf 2

Problem Set 3

• Release Date: 4/05/2019
• Due Data: 4/12/2019 11:59PM CST
• File: See Compass2G

Exams

Midterm

• Date: March 13th
• Time: In Class
• Location: 0216 SC

Final

• Date: TBD
• Time: TBD
• Location: 0216 SC

Course Mini-Project

Topics

Computational techniques:

• Comparing algorithms
• Efficient implementation of algorithms that scale on large datasets
• New probabilistic models for biological data

Biological problems:

• Comparative analysis
• Interesting data analysis
• New computational biological problems

Team size

• One or two (4-credit students)
• Up to four (3-credit students)

* Make clear your contribution in the project report

Implementation

• Put your code/data on Github
• Get your hands dirty and work on real-world datasets

Assignments Policy

• Please see the University Policy on Academic Integrity, especially the section on plagiarism.
• Late submission within 3 days (72 hours) is worth 80% credit.
• A student may request an extension of 3 days at most once in the semester.

Schedule

Date	Presenter	Slides
01/14/2019	Jian Peng	Introduction [Slides]
01/16/2019	Jian Peng	Molecular biology [Slides]
01/23/2019	Jian Peng	Probability and Statistics [Slides]
01/28/2019	Jian Peng	Probability and Statistics with Sequences[Slides]
02/04/2019	Jian Peng	Sequences I [Slides]
02/06/2019	Jian Peng	Sequences II [Slides]
02/11/2019	Jian Peng	Alignment I [Slides]
02/13/2019	Jian Peng	Alignment II [Slides]
02/18/2019	Jian Peng	Alignment III [Slides]
02/20/2019	Jian Peng	Pattern I [Slides]
02/25/2019	Jian Peng	Pattern II [Slides]
02/27/2019	Jian Peng	Pattern III [Slides]
03/04/2019	Jian Peng	Review [Slides]
03/6/2019	Jian Peng	Review [Slides]
03/11/2019	Jian Peng	BLAST I [Slides]
03/25/2019	Jian Peng	BLAST I [Slides]
04/01/2019	Jian Peng	Seq Assembly [Slides]
04/03/2019	Jian Peng	Seq Assembly + HMM [Slides]
04/08/2019	Jian Peng	HMM [Slides]
04/15/2019	Jian Peng	HMM Gene finding [Slides]
04/22/2019	Jian Peng	Classification & Regression [Slides]
04/24/2019	Jian Peng	Clustering [Slides]