Bioinformatics

Course Objectives

• Understand the fundamentals of the field of bioinformatics
• Learn the main bioinformatics problems and algorithms proposed to solve them
• Explore available bioinformatics tools
• Read/present/review papers on state-of-the-art research in bioinformatics
• Prepare for original research in bioinformatics

Course Information

• Schedule: Mondays between 12:00-14:50 at BM 18
• Website: Course content will be available at Moodle. You are expected to check the course web site for announcements regularly.

Prerequisites

Medium level programming skills in the Python programming language, background on data structures and algorithms.

Textbook (Optional)

1. Bioinformatics Algorithms: An Active Learning Approach
   • Authors: P. Compeau, P. Pevzner
   • Edition: 3rd Edition (Volume 1 and 2 of the Second Edition is also fine)
   • Website: http://bioinformaticsalgorithms.com
   • Note: First five chapters of the book and complementary video lectures for all chapters from the textbook authors are available on the supplementary website, free of charge.

Tentative List of Topics

• Review of relevant background material from molecular biology
• Dynamic Programming and Sequence Alignment
• BLAST (Basic Local Similarity Search Algorithm) (FASTA and BLAST)
• Phylogenetic Trees
• Gene expression analysis, Clustering and Classification Algorithms
• Graph/path based algorithms
• Motif finding and Randomized Algorithms
• Expectation-Maximization Pattern Matching and Suffix Trees
• Text mining for biology

Course Requirements

Programming Assignments

There will be 2-3 programming assignments involving intermediate-level programming, where you will implement and test some of the algorithms that we cover in class. The assignments will be completed individually.

Tool/Resource Presentation

Presentation teams will consist of 1-2 people (you can form your own team, or ask us to assign you to a team). Each team will give a 20-min talk about a certain practical aspect of bioinformatics. For example, you can describe an available commonly used bioinformatics tool that implements a certain algorithm (e.g., pipeline for processing sequence data and identifying mutations), a commonly used library (e.g., BioConductor, BioPython), certain types of data or problem (e.g. databases for protein sequence and structure) and how to use these resources to obtain the data, file format of the data, etc.

Term Project

The project teams will consist of 2-3 people (there teams do not have to be the same as the tool/resource presentation teams). Each team will choose a project topic by involving choosing the definition of the existing work, and extending or improving it in some way. The teams will give short project progress and project final presentations in front of the class describing the methods used, the results obtained, as well as the limitations and future work.

Grading

• Assignments: 30%
• Tool/Resource presentation: 10%
• Term Project: 30%
• Exam: 30%