Bioinformatics Algorithms (Part 1)

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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
4680 reviews

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.

This course has now been split into three smaller pieces:

  • Finding Hidden Messages in DNA: This course begins a series of classes illustrating the power of computing in modern biology. Please join us on the frontier of bioinformatics to look for hidden messages in DNA without ever needing to put on a lab coat. After warming up our algorithmic muscles, we will learn how randomized algorithms can be used to solve problems in bioinformatics.
  • Assembling Genomes and Sequencing Antibiotics: Biologists still cannot read the nucleotides of an entire genome or the amino acids of an antib...

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.

This course has now been split into three smaller pieces:

  • Finding Hidden Messages in DNA: This course begins a series of classes illustrating the power of computing in modern biology. Please join us on the frontier of bioinformatics to look for hidden messages in DNA without ever needing to put on a lab coat. After warming up our algorithmic muscles, we will learn how randomized algorithms can be used to solve problems in bioinformatics.
  • Assembling Genomes and Sequencing Antibiotics: Biologists still cannot read the nucleotides of an entire genome or the amino acids of an antibiotic as you would read a book from beginning to end. However, they can read short pieces of DNA and weigh small antibiotic fragments. In this course, we will see how graph theory and brute force algorithms can be used to reconstruct genomes and antibiotics.
  • Comparing Genes, Proteins, and Genomes: After sequencing genomes, we would like to compare them. We will see that dynamic programming is a powerful algorithmic tool when we compare two genes or two proteins. When we "zoom out" to compare entire genomes, we will employ combinatorial algorithms.

Each course parallels two chapters from a textbook covering 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.

Reviews 9/10 stars
32 Reviews for Bioinformatics Algorithms (Part 1)

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10/10 starsTaking Now
5 years, 5 months ago
Bioinformatics Algorithms includes multiple amazing resources to enhance your class experience. First, Rosalind, a programming resource, offers an introduction to Python as well as complex bioinformatics problems, along with explanations. The second resource is an online textbook called Stepic. It teaches the material and gives you problems at various points in the text (they account for your grade). You have infinite attempts on each problem, but the programming runtime cannot exceed 5 minutes. Finally, the instructors have created several MOORs, or Massive Open Online Research projects to expand on the topics in the textbook.
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Darya Prokurat profile image
Darya Prokurat profile image
10/10 starsCompleted
  • 8 reviews
  • 6 completed
5 years, 2 months ago
Сourse is very friendly to complete beginners in biology. but without knowledge of the algorithms can be hard. Many tasks for the implementation of algorithms. They can be done in any language, including application packages like mathematica. The input data are given to you, and you have to write an answer. Where and how you get it - your business. You can calculate it by hands, if you want. But it is unlikely you will have time to time: Limit - 5 minutes. (BS in Computer Science, No biology at all)
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