Finding Mutations in DNA and Proteins (Bioinformatics VI)

5 Google Cloud Platform " Google Cloud Platform A" Cloud Dataproc A Spark ML API A" Cloud Dataflow A" Google BigQuery A" AAA TensorFlow Cloud ML A" " " " AA" A>>>Qwiklabs'FAQhttps://qwiklabs.com/terms_of_service <<<

Created by: Pavel Pevzner

icon
Quality Score

Content Quality
/
Video Quality
/
Qualified Instructor
/
Course Pace
/
Course Depth & Coverage
/

Overall Score : 88 / 100

icon
Live Chat with CourseDuck's Co-Founder for Help

Need help deciding on a machine learning course? Or looking for more detail on Pavel Pevzner's Finding Mutations in DNA and Proteins (Bioinformatics VI)? Feel free to chat below.
Join CourseDuck's Online Learning Discord Community

icon
Course Description

In previous courses in the Specialization, we have discussed how to sequence and compare genomes. This course will cover advanced topics in finding mutations lurking within DNA and proteins.In the first half of the course, we would like to ask how an individual's genome differs from the "reference genome" of the species. Our goal is to take small fragments of DNA from the individual and "map" them to the reference genome. We will see that the combinatorial pattern matching algorithms solving this problem are elegant and extremely efficient, requiring a surprisingly small amount of runtime and memory.In the second half of the course, we will learn how to identify the function of a protein even if it has been bombarded by so many mutations compared to similar proteins with known functions that it has become barely recognizable. This is the case, for example, in HIV studies, since the virus often mutates so quickly that researchers can struggle to study it. The approach we will use is based on a powerful machine learning tool called a hidden Markov model.Finally, you will learn how to apply popular bioinformatics software tools applying hidden Markov models to compare a protein against a related family of proteins.

icon
Instructor Details

Pavel Pevzner

Pavel Pevzner (https://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 informatics Algorithms course for the last 12 years. In 2006, he was named a Howard Hughes Medical Institute Professor. In 2011, he founded the Algorithmic logy Laboratory in St. Petersburg, Russia, which develops online bioinformatics platform Rosalind (https://rosalind.info). His research concerns the creation of bioinformatics algorithms for analyzing genome rearrangements, DNA sequencing, and computational proteomics. He authored Computational Molecular logy (The MIT Press, 2000), co-authored (jointly with Neil Jones) An Introduction to informatics Algorithms (The MIT Press, 2004), and co-edited (with Ron Shamir) informatics for logists (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 logy (ISCB).

icon
Reviews

4.4

7 total reviews

5 star 4 star 3 star 2 star 1 star
% Complete
% Complete
% Complete
% Complete
% Complete

By Dr S C on 16-Sep-18

Really enjoyed this course. It was great to get to build on work from previous courses.

By Tamas K on 29-Jun-16

One of the best specialization on Coursera. Highly recommended for anyone who wants to apply his/her programming skills to fascinating real-world problems.

By Zack X on 21-Jul-19

In depth and comprehensive coverage of the topics in genetic data analysis.

By Thomas G on 20-Dec-18

Much more difficult than 1-5 or 7!

By Thomas L on 17-Jul-17

Very interesting material. The only reason I marked this course as 4 stars instead of 5 stars is by comparison to the other courses in this specialization. This course does not have the online textbook which means that the student needs to work through the lecture videos very closely in order to be able to solve the programming assignments.

By Weidong X on 27-Feb-17

good

By Mohamed A on 14-Aug-17

Good course. I did this course after the course "Algorithms for DNA Sequencing" of Ben Langmead. Since Ben Langmead was excellent in his explanations, my expectations were the same for this course but I felt that it is not as good as Ben Langmead course maybe because there are no practical videos about programming ideas.