Bioinformatics Algorithms (Part 1)

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Course Date: 15 September 2014 to 24 November 2014 (10 weeks)

Price: free

Course Summary

This course is the first in a two-part series covering some of the algorithms underlying bioinformatics. It will cover some of the algorithms underlying the following fundamental topics in bioinformatics: assembling genomes, comparing DNA and protein sequences, finding regulatory motifs, analyzing genome rearrangements, identifying proteins, and many other topics.


Estimated Workload: 8-12 hours/week

Course Instructors

Pavel Pevzner

Pavel Pevzner (http://cseweb.ucsd.edu/~ppevzner/) is Professor of Computer Science and Engineering at University of California San Diego (UCSD), where he holds the Ronald R. Taylor Chair and has taught a Bioinformatics Algorithms course for the last 12 years.  In 2006, he was named a Howard Hughes Medical Institute Professor. In 2011, he founded the Algorithmic Biology Laboratory in St. Petersburg, Russia, which develops online bioinformatics platform Rosalind (http://rosalind.info). His research concerns the creation of bioinformatics algorithms for analyzing genome rearrangements, DNA sequencing, and computational proteomics. He authored Computational Molecular Biology (The MIT Press, 2000), co-authored (jointly with Neil Jones) An Introduction to Bioinformatics Algorithms (The MIT Press, 2004), and co-edited (with Ron Shamir) Bioinformatics for Biologists (Cambridge University Press, 2011). For his research, he has been named a Fellow of both the Association for Computing Machinery (ACM) and the International Society for Computational Biology (ISCB).



Phillip Compeau

Phillip Compeau is a Ph.D. candidate and instructor in the department of mathematics at UC San Diego. Together with Nikolay Vyahhi, he co-founded Rosalind, a free online resource for learning algorithmic biology , and he serves as the site's content editor.  He is interested in the combinatorics of genome rearrangements, bioinformatics pedagogy, and online education. In addition to his work with Rosalind, he contributed a chapter to Bioinformatics for Biologists (Cambridge University Press, 2011) and co-authored a primer on genome assembly in Nature Biotechnology (2011).  He is the recent co-author (with Pavel Pevzner) of Bioinformatics Algorithms: An Active Learning Approach.

Nikolay Vyahhi

Nikolay Vyahhi is a Visiting Scholar in the Department of Computer Science and Engineering at University of California San Diego (UCSD). Together with Phillip Compeau, he co-founded Rosalind, a free online resource for learning algorithmic biology. Nikolay directs the M.S. Program in Bioinformatics in the Academic University of St. Petersburg, Russian Academy of Sciences and recently founded the Bioinformatics Institute in St. Petersburg as well as Stepic, a project focusing on content delivery for online education.

Course Description

The sequencing of the human genome fueled a computational revolution in biology. As a result, modern biology produces as many new algorithms as any other fundamental realm of science.  Accordingly, the newly formed links between computer science and biology affect the way we teach applied algorithms to computer scientists.

Genome sequencing is just one of hundreds of biological problems that have become inextricable from the computational methods required to solve them. In this course, we will uncover some of the algorithmic ideas that are fundamental to an understanding of modern biology.  Computational concepts like dynamic programming and graph theory will help us explore algorithms applied to a wide range of biological topics, from finding regulatory motifs to determining whether the human genome has "fragile" regions.  Throughout the process, we will apply bioinformatics algorithms to real genetic data.

Each chapter in the course textbook covers a single biological question and slowly builds the algorithmic knowledge required to address this challenge.  Along the way, coding challenges and exercises (many of which ask you to apply your skills to real genetic data) will be directly integrated into the text at the exact moment they are needed.

FAQ

  • Will I get a statement of accomplishment after completing this class?

Yes. Students who successfully complete the class will receive a statement of accomplishment signed by the instructor.

Syllabus

The course will be based on six central questions, with the algorithmic ideas that we will use to solve them in parentheses:
  • Where Does DNA Replication Begin? (Algorithmic Warm-up)
  • How Do We Sequence Antibiotics? (Brute Force Algorithms)
  • Which DNA Patterns Act As Cellular Clocks? (Greedy and Randomized Algorithms)
  • How Do We Assemble Genomes? (Graph Algorithms)
  • How Do We Compare Biological Sequences? (Dynamic Programming Algorithms)
  • Are There Fragile Regions in the Human Genome? (Combinatorial Algorithms)
The grading for the course will be based on several weekly programming challenges. Bioinformatics Algorithms (Part 2) will be based around the following questions: 
  • Which Animal Gave Us SARS? (Evolutionary Trees)
  • How Do We Locate Disease-Causing Mutations? (Combinatorial Pattern Matching)
  • How Did Yeast Become Such a Good Wine Brewer? (Clustering Algorithms)
  • Why Do We Still Not Have an HIV Vaccine? (Hldden Markov Models)
  • Was T-Rex Just a Big Chicken? (Computational Proteomics)
  • What Genetic Characteristics Do Human Populations Share? (Principal Components Analysis)

Format

The class will offer two ways of learning the material.  In addition to a collection of lecture videos, the primary content for the course is the textbook Bioinformatics Algorithms: An Active-Learning Approach, by Phillip Compeau & Pavel Pevzner.

Suggested Reading

Bioinformatics Algorithms: An Active-Learning Approach, by Compeau & Pevzner.

Course Workload

8-12 hours/week

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