University of California San Diego
Comparing Genes, Proteins, and Genomes (Bioinformatics III)
University of California San Diego

Comparing Genes, Proteins, and Genomes (Bioinformatics III)

This course is part of Bioinformatics Specialization

Taught in English

Some content may not be translated

Pavel  Pevzner
Phillip Compeau
Nikolay Vyahhi

Instructors: Pavel Pevzner

20,998 already enrolled

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

4.7

(130 reviews)

Intermediate level
Some related experience required
22 hours (approximately)
Flexible schedule
Learn at your own pace

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

5 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.7

(130 reviews)

Intermediate level
Some related experience required
22 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

Placeholder

Build your subject-matter expertise

This course is part of the Bioinformatics Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
Placeholder
Placeholder

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

Placeholder

There are 6 modules in this course

<p>Welcome to class!</p><p>If you joined us in the previous course in this Specialization, then you became an expert at <em>assembling</em> genomes and sequencing antibiotics. The next natural question to ask is how to compare DNA and amino acid sequences. This question will motivate this week's discussion of <strong>sequence alignment</strong>, which is the first of two questions that we will ask in this class (the algorithmic methods used to answer them are shown in parentheses):</p><ol><li>How Do We Compare DNA Sequences? (<em>Dynamic Programming</em>)</li><li>Are There Fragile Regions in the Human Genome? (<em>Combinatorial Algorithms</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. Why have taxis suddenly become free of charge in Manhattan? Where did Pavel get so much spare change? And how should you get dressed in the morning so that you aren't late to your job as a crime-stopping superhero? Answers to these questions, and many more, in this week's installment of the course.</p><p><img src="http://bioinformaticsalgorithms.com/images/cover/alignment_cropped.jpg" width="528"></p>

What's included

7 videos2 readings1 quiz2 app items

<p>Welcome to Week 2 of the class!</p> <p>Last week, we saw how touring around Manhattan and making change in a Roman shop help us find a longest common subsequence of two DNA or protein strings.</p> <p>This week, we will study how to find a highest scoring alignment of two strings. We will see that regardless of the underlying assumptions that we make regarding how the strings should be aligned, we will be able to phrase our alignment problem as an instance of finding the longest path in a directed acyclic graph.</p>

What's included

1 video1 reading1 quiz2 app items

<p>Welcome to Week 3 of the class!</p> <p>Last week, we saw how a variety of different applications of sequence alignment can all be reduced to finding the longest path in a Manhattan-like graph.</p> <p>This week, we will conclude the current chapter by considering a few advanced topics in sequence alignment. For example, if we need to align long strings, our current algorithm will consume a huge amount of memory. Can we find a more memory-efficient approach? And what should we do when we move from aligning just two strings at a time to aligning many strings?</p>

What's included

3 videos1 reading1 quiz2 app items

<p>Welcome to Week 4 of the class!</p> <p>You now know how to compare two DNA (or protein) strings. &nbsp;But what if we wanted to compare entire genomes? When we "zoom out" to the genome level, we find that substitutions, insertions, and deletions don't tell the whole story of evolution: we need to model more dramatic evolutionary events known as <strong>genome rearrangements</strong>, which wrench apart chromosomes and put them back together in a new order. A natural question to ask is whether there are "fragile regions" hidden in your genome where chromosome breakage has occurred more often over millions of years. This week, we will begin addressing this question by asking how we can compute the number of rearrangements on the evolutionary path connecting two species.</p> <p>You can find this week's Bioinformatics Cartoon from Randall Christopher at the bottom of this E-mail. What do earthquakes and a stack of pancakes have to do with species evolution? Keep learning to find out!</p> <p><img width="528" src="http://bioinformaticsalgorithms.com/images/cover/rearrangements_cropped.jpg"></p>

What's included

5 videos1 reading1 quiz2 app items

<p>Last week, we asked whether there are fragile regions in the human genome. Then, we took a lengthy detour to see how to compute a distance between species genomes, a discussion that we will continue this week.</p> <p>It is probably unclear how computing the&nbsp;<em>distance</em> between two genomes can help us understand whether <em>fragile regions</em> exist. If so, please stay tuned -- we will see that the connection between these two concepts will yield a surprising conclusion to the class.</p>

What's included

4 videos1 reading1 quiz2 app items

In the sixth and final week of the course, we will apply sequence alignment algorithms to infer the non-ribosomal code.

What's included

1 peer review

Instructors

Instructor ratings
4.9 (9 ratings)
Pavel  Pevzner
University of California San Diego
16 Courses805,647 learners
Phillip Compeau
University of California San Diego
8 Courses273,813 learners
Nikolay Vyahhi
University of California San Diego
1 Course20,998 learners

Offered by

Recommended if you're interested in Health Informatics

Why people choose Coursera for their career

Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Learner reviews

Showing 3 of 130

4.7

130 reviews

  • 5 stars

    75.38%

  • 4 stars

    19.23%

  • 3 stars

    3.07%

  • 2 stars

    2.30%

  • 1 star

    0%

LZ
4

Reviewed on Jul 9, 2017

YW
5

Reviewed on Oct 1, 2016

RS
5

Reviewed on Nov 10, 2016

New to Health Informatics? Start here.

Placeholder

Open new doors with Coursera Plus

Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Frequently asked questions