Finding Mutations in DNA and Proteins (Bioinformatics VI)

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Opleiderscore: starstarstarstar_borderstar_border 6,3 Coursera heeft een gemiddelde beoordeling van 6,3 (uit 4 ervaringen)

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Beschrijving

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About 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…

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Nog niet gevonden wat je zocht? Bekijk deze onderwerpen: Laboratoriumtechniek, Biologie, Scheikunde, Chemie en Good Clinical Practice.

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About 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.

Created by:  University of California, San Diego
  • Taught by:  Pavel Pevzner, Professor

    Department of Computer Science and Engineering
  • Taught by:  Phillip Compeau, Visiting Researcher

    Department of Computer Science & Engineering
Basic Info Course 6 of 7 in the Bioinformatics Specialization Level Beginner Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.6 stars Average User Rating 4.6See what learners said Trabajo del curso

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Syllabus


WEEK 1


Week 1: Introduction to Read Mapping



<p>Welcome to our class! We are glad that you decided to join us.</p><p>In this class, we will consider the following two central biological&nbsp;questions (the computational approaches needed to solve them are shown in parentheses):</p><ol><li>How Do We Locate Disease-Causing Mutations? (<em>Combinatorial Pattern Matching</em>)</li><li>Why Have Biologists Still Not Developed an HIV Vaccine?&nbsp;(<em>Hidden Markov Models</em>)</li></ol><p>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. </p><p><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"></p>


4 videos, 2 readings expand


  1. Video: (Check Out Our Wacky Course Intro Video!)
  2. Leyendo: Course Details
  3. Elemento LTI: Stepik Interactive Text for Week 1
  4. Video: Why Do We Map Reads?
  5. Video: Using the Trie
  6. Video: From a Trie to a Suffix Tree
  7. Leyendo: Week 1 FAQs (Optional)

Graded: How Do We Find Disease-Causing Mutations? (Week 1)
Graded: Open in order to Sync Your Progress: Stepik Interactive Text for Week 1

WEEK 2


Week 2: The Burrows-Wheeler Transform



<p>Welcome to week 2 of the class!</p> <p>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.</p>


3 videos, 1 reading expand


  1. Elemento LTI: Stepik Interactive Text for Week 2
  2. Video: String Compression and the Burrows-Wheeler Transform
  3. Video: Inverting Burrows-Wheeler
  4. Video: Using Burrows-Wheeler for Pattern Matching
  5. Leyendo: Week 2 FAQs (Optional)

Graded: Open in order to Sync Your Progress: Stepik Interactive Text for Week 2

WEEK 3


Week 3: Speeding Up Burrows-Wheeler Read Mapping



<p>Welcome to week 3 of class!</p> <p>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.</p>


4 videos, 1 reading expand


  1. Elemento LTI: Stepik Interactive Text for Week 3
  2. Video: Finding the Matched Patterns
  3. Video: Setting Up Checkpoints
  4. Video: Inexact Matching
  5. Video: Further Applications of Read Mapping
  6. Leyendo: Week 3 FAQs (Optional)

Graded: How Do We Find Disease-Causing Mutations? (Weeks 2-3)
Graded: Open in order to Sync Your Progress: Stepik Interactive Text for Week 3

WEEK 4


Week 4: Introduction to Hidden Markov Models



<p>Welcome to week 4 of class!</p> <p>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.</p> <p>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.</p>


5 videos, 1 reading expand


  1. Leyendo: Note on This Week's Content
  2. Video: Classifying HIV Phenotypes
  3. Video: Gambling with Yakuza
  4. Video: From a Crooked Casino to a Hidden Markov Model
  5. Video: The Decoding Problem
  6. Video: The Viterbi Algorithm

Graded: Stepik Code Challenges for Week 4

WEEK 5


Week 5: Profile HMMs for Sequence Alignment



<p>Welcome to week 5 of class!</p> <p>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.</p>


5 videos, 2 readings expand


  1. Leyendo: Note on This Week's Content
  2. Video: Profile HMMs for Sequence Alignment
  3. Video: Classifying Proteins with Profile HMMs
  4. Video: Viterbi Learning
  5. Video: Soft Decoding Problem
  6. Video: Baum-Welch Learning
  7. Leyendo: Week 5 FAQs (Optional)

Graded: Why Have Biologists Still Not Developed an HIV Vaccine? (Weeks 4-5)
Graded: Stepik Code Challenges for Week 5

WEEK 6


Week 6: Bioinformatics Application Challenge
<p>Welcome to the sixth and final week of class!</p> <p>This week brings our Application Challenge, in which we apply the HMM sequence alignment algorithms that we have developed.</p>




    Graded: Bioinformatics Application Challenge

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