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Finding Mutations in DNA and Proteins (Bioinformatics VI)

Finding Mutations in DNA and Proteins (Bioinformatics VI)

This course is part of Bioinformatics Specialization

Instructors: Pavel Pevzner

17,292 already enrolled

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6 modules

Gain insight into a topic and learn the fundamentals.

61 reviews

Intermediate level

Some related experience required

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

6 modules

Gain insight into a topic and learn the fundamentals.

61 reviews

Intermediate level

Some related experience required

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

3 assignments¹

AI Graded see disclaimer

Taught in English

91% of learners achieved a positive career outcome

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This course is part of the Bioinformatics Specialization

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There are 6 modules in this course

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.

Module details

Welcome to our class! We are glad that you decided to join us.In this class, we will consider the following two central biological questions (the computational approaches needed to solve them are shown in parentheses):<ol><li>How Do We Locate Disease-Causing Mutations? (Combinatorial Pattern Matching)</li><li>Why Have Biologists Still Not Developed an HIV Vaccine? (Hidden Markov Models)</li></ol>As in previous courses, each of these two chapters is accompanied by a Bioinformatics Cartoon created by talented artist Randall Christopher and serving as a chapter header in the Specialization's bestselling <a href="http://bioinformaticsalgorithms.com" target="_blank">print companion</a>. You can find the first chapter's cartoon at the bottom of this message. <img src="https://stepic.org/media/attachments/lessons/292/chapter7_cropped.jpg" title="Image: https://stepic.org/media/attachments/lessons/292/chapter7_cropped.jpg" width="528">

What's included

4 videos2 readings1 assignment2 app items

4 videosTotal 33 minutes

(Check Out Our Wacky Course Intro Video!)4 minutes
Why Do We Map Reads? 8 minutes
Using the Trie 10 minutes
From a Trie to a Suffix Tree 12 minutes

2 readingsTotal 10 minutes

Course Details10 minutes
Week 1 FAQs (Optional)0 minutes

1 assignmentTotal 15 minutes

How Do We Find Disease-Causing Mutations? (Week 1)15 minutes

2 app itemsTotal 210 minutes

Interactive Text for Week 1210 minutes
Open in order to Sync Your Progress: Interactive Text for Week 10 minutes

Welcome to week 2 of the class! This week, we will introduce a paradigm called the Burrows-Wheeler transform; after seeing how it can be used in string compression, we will demonstrate that it is also the foundation of modern read-mapping algorithms.

What's included

3 videos1 reading2 app items

Welcome to week 3 of class! Last week, we saw how the Burrows-Wheeler transform could be applied to multiple pattern matching. This week, we will speed up our algorithm and generalize it to the case that patterns have errors, which models the biological problem of mapping reads with errors to a reference genome.

What's included

4 videos1 reading1 assignment2 app items

4 videosTotal 22 minutes

Finding the Matched Patterns 4 minutes
Setting Up Checkpoints 9 minutes
Inexact Matching 7 minutes
Further Applications of Read Mapping 3 minutes

1 reading

Week 3 FAQs (Optional)0 minutes

1 assignmentTotal 20 minutes

How Do We Find Disease-Causing Mutations? (Weeks 2-3)20 minutes

2 app itemsTotal 180 minutes

Interactive Text for Week 3180 minutes
Open in order to Sync Your Progress: Interactive Text for Week 30 minutes

Welcome to week 4 of class! This week, we will start examining the case of aligning sequences with many mutations -- such as related genes from different HIV strains -- and see that our problem formulation for sequence alignment is not adequate for highly diverged sequences. To improve our algorithms, we will introduce a machine-learning paradigm called a hidden Markov model and see how dynamic programming helps us answer questions about these models.

What's included

5 videos2 app items

Welcome to week 5 of class! Last week, we introduced hidden Markov models. This week, we will see how hidden Markov models can be applied to sequence alignment with a profile HMM. We will then consider some advanced topics in this area, which are related to advanced methods that we considered in a previous course for clustering.

What's included

5 videos1 reading1 assignment2 app items

5 videosTotal 37 minutes

Profile HMMs for Sequence Alignment 9 minutes
Classifying Proteins with Profile HMMs 6 minutes
Viterbi Learning 5 minutes
Soft Decoding Problem 7 minutes
Baum-Welch Learning 10 minutes

1 reading

Week 5 FAQs (Optional)0 minutes

1 assignmentTotal 30 minutes

Why Have Biologists Still Not Developed an HIV Vaccine? (Weeks 4-5)30 minutes

2 app itemsTotal 180 minutes

Interactive Text for Week 5180 minutes
Code Challenges for Week 50 minutes

Welcome to the sixth and final week of class! This week brings our Application Challenge, in which we apply the HMM sequence alignment algorithms that we have developed.