Finding Mutations in DNA and Proteins (Bioinformatics VI)

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Finding Mutations in DNA and Proteins (Bioinformatics VI)

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FREE,
Add a Verified Certificate for $79

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Coursera online courses
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with yo...
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with your coursework. Coursera also partners with the US State Department to create “learning hubs” around the world. Students can get internet access, take courses, and participate in weekly in-person study groups to make learning even more collaborative. Begin your journey into the mysteries of the human brain by taking courses in neuroscience. Learn how to navigate the data infrastructures that multinational corporations use when you discover the world of data analysis. Follow one of Coursera’s “Skill Tracks”. Or try any one of its more than 560 available courses to help you achieve your academic and professional goals.

Provider Subject Specialization
Humanities
Sciences & Technology
4715 reviews

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 researcher... 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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Reviews 9/10 stars
1 Review for Finding Mutations in DNA and Proteins (Bioinformatics VI)

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Steven Frank profile image
Steven Frank profile image
10/10 starsCompleted
  • 59 reviews
  • 57 completed
2 years, 11 months ago
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? The six-course UCSD bioinformatics series is now offered under Coursera's new platform, and the courses have been reformatted a bit. The biggest change is that those following the "hacker track" (now called "honors track") and tackling the programming assignments will also have to complete a peer-graded set of "bioinformatics challenge" exercises at the end of the course; in previous sessions, only those following the no-programming "biologist track" were required to complete the challenge exercises. While it add... 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? The six-course UCSD bioinformatics series is now offered under Coursera's new platform, and the courses have been reformatted a bit. The biggest change is that those following the "hacker track" (now called "honors track") and tackling the programming assignments will also have to complete a peer-graded set of "bioinformatics challenge" exercises at the end of the course; in previous sessions, only those following the no-programming "biologist track" were required to complete the challenge exercises. 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. 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. The idea is to try to place you with a cohort of learners for grading and discussion purposes. One consequence, however, is that course staffing is sporadic; staff may be available to respond to your questions or may not be. 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. The instructors' excellent textbook is really helpful here. 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.
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