Molecular Evolution (Bioinformatics IV)

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Molecular Evolution (Bioinformatics IV)

Course Details

Cost

FREE

Upcoming Schedule

  • On demand

Course Provider

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
4699 reviews

Course Description

In this course, we will see how evolutionary trees resolve quandaries from finding the origin of a deadly virus to locating the birthplace of modern humans. We will then use methods from computational proteomics to test whether we can reconstruct Tyrannosaurus rex proteins and prove that birds evolved from dinosaurs.
Reviews 9/10 stars
1 Review for Molecular Evolution (Bioinformatics IV)

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Steven Frank profile image
Steven Frank profile image
10/10 starsCompleted
  • 59 reviews
  • 57 completed
3 years, 2 months ago
This is, by far, the most difficult segment I've encountered in this course series (having taken parts I through V). It addresses two fascinating high-level questions in biology: how do we know what evolved from what, and why does T. rex look so much like a chicken? That first question has very practical implications in identifying the animal source of viruses such as the SARS pathogen, but answering it requires a deep dive into the challenging topics of phylogenies and evolutionary trees. Here the hacker track offers up some pretty serious ruts and bumps, with problems that may have you forgetting about the biological dimension altogether as you ponder leaves and limbs and other rarefied artifacts of graph theory that can be pretty tough to turn into working code. Then it's a sudden turn into the world of computational proteomics, which re-introduces mass spectrometry (originally covered in part I) but with newer and hairier t... This is, by far, the most difficult segment I've encountered in this course series (having taken parts I through V). It addresses two fascinating high-level questions in biology: how do we know what evolved from what, and why does T. rex look so much like a chicken? That first question has very practical implications in identifying the animal source of viruses such as the SARS pathogen, but answering it requires a deep dive into the challenging topics of phylogenies and evolutionary trees. Here the hacker track offers up some pretty serious ruts and bumps, with problems that may have you forgetting about the biological dimension altogether as you ponder leaves and limbs and other rarefied artifacts of graph theory that can be pretty tough to turn into working code. Then it's a sudden turn into the world of computational proteomics, which re-introduces mass spectrometry (originally covered in part I) but with newer and hairier twists. Once again the broader biological questions dissolve somewhat in the technical details of analyzing spectra (and more graph theory), but spectrometry is itself such a basic analytical tool for quantitative biologists that you never feel too far removed from the science. Overall, this course segment is tough, but also rewarding once you bend all that graph theory to your will -- and forewarned is forearmed.
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