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Finding Mutations in DNA and Proteins (Bioinformatics VI)

Bioinformatics,

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.

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Rating 4.6★ based on 12 ratings
Length 7 weeks
Starts Nov 14 (19 weeks ago)
Cost $79
From University of California San Diego via Coursera
Instructors Pavel Pevzner, Phillip E. C. Compeau, Phillip Compeau
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Science
Tags Computer Science Life Sciences Algorithms Bioinformatics Health Informatics

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What people are saying

computational biologists routinely use

While it adds to the workload, this change is very worthwhile -- it exposes you to databases and tools that computational biologists routinely use in the areas covered by the course.

from similar proteins despite

This final installment of the UCSD bioinformatics series covers two very important classes of tool (algorithms for read mapping, including the Burrows-Wheeler Transform, and hidden Markov models) that address two fascinating questions in computational biology: how do we locate disease-causing DNA mutations and how can we identify the function of a protein from similar proteins despite the cumulative effects of evolution?

locate disease-causing dna mutations

sessions start every month

Another change is that the course is now fully self-paced; although you sign up for a particular session, new sessions start every month and your work will carry over through multiple sessions.

much more difficult

Much more difficult than 1-5 or 7!

difficult than 1-5

eventually completed all

Although I found the course to be a superb learning experience and eventually completed all of the programming and peer-graded exercises, there was no way I could have done so in one six-week session with a full-time job.

genetic data analysis

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

markov model unit

The programming problems, particularly the later ones in the course, are pretty challenging, and no online textbook materials are provided for the hidden Markov model unit -- you have to rely solely on the lectures, which for me were sometimes hard to follow.

very interesting material

Very interesting material.

about suffix trees/arrays

areas covered by

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Rating 4.6★ based on 12 ratings
Length 7 weeks
Starts Nov 14 (19 weeks ago)
Cost $79
From University of California San Diego via Coursera
Instructors Pavel Pevzner, Phillip E. C. Compeau, Phillip Compeau
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Science
Tags Computer Science Life Sciences Algorithms Bioinformatics Health Informatics

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