You may have heard a lot about genome sequencing and its potential to usher in an era of personalized medicine, but what does it mean to sequence a genome?
Biologists still cannot read the nucleotides of an entire genome as you would read a book from beginning to end. However, they can read short pieces of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces. We will further learn about brute force algorithms and apply them to sequencing mini-proteins called antibiotics.
In the first half of the course, we will see that biologists cannot read the 3 billion nucleotides of a human genome as you would read a book from beginning to end. However, they can read shorter fragments of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces in what amounts to the largest jigsaw puzzle ever put together.
In the second half of the course, we will discuss antibiotics, a topic of great relevance as antimicrobial-resistant bacteria like MRSA are on the rise. You know antibiotics as drugs, but on the molecular level they are short mini-proteins that have been engineered by bacteria to kill their enemies. Determining the sequence of amino acids making up one of these antibiotics is an important research problem, and one that is similar to that of sequencing a genome by assembling tiny fragments of DNA. We will see how brute force algorithms that try every possible solution are able to identify naturally occurring antibiotics so that they can be synthesized in a lab.
Finally, you will learn how to apply popular bioinformatics software tools to sequence the genome of a deadly Staphylococcus bacterium that has acquired antibiotics resistance.
Week 1: Introduction to Genome Sequencing
Welcome to class!
This course will focus on two questions at the forefront of modern computational biology, along with the algorithmic approaches we will use to solve them in parentheses:
Weeks 1-2: How Do We Assemble Genomes? (Graph Algorithms)
How Do We Sequence Antibiotics? (Brute Force Algorithms)
Each of the two chapters of content in the class is accompanied by a Bioinformatics Cartoon created by talented San Diego artist Randall Christopher and serving as a chapter header in the Specialization's bestselling print companion. You can find the first chapter's cartoon at the bottom of this message. What does a time machine trip to 1735, a stack of newspapers, a jigsaw puzzle, and a giant ant invading a riverside city have to do with putting together a genome? Start learning today to find out!
Week 2: Applying Euler's Theorem to Assemble Genomes
Welcome to Week 2 of class!
This week in class, we will see how a 300 year-old mathematical theorem will help us assemble a genome from millions of tiny pieces of DNA.
Week 3: Sequencing Antibiotics
Welcome to Week 3 of class!
This week, we begin a new chapter, titled "How Do We Sequence Antibiotics?" In this chapter, we will learn how to determine the amino acid sequences making up antibiotics using brute force algorithms.
Below is this week's Bioinformatics Cartoon.
Week 4: From Ideal to Real Spectra for Antibiotics Sequencing
Welcome to Week 4 of class!
Last week, we discussed how to sequence an antibiotic peptide from an ideal spectrum. This week, we will see how to develop more sophisticated algorithms for antibiotic peptide sequencing that are able to handle spectra with many false and missing masses.
Week 5: Bioinformatics Application Challenge!
Welcome to Week 5 of class!
This week, we will see how to apply genome assembly tools to sequencing data from a dangerous pathogenic bacterium.
Start your review of Genome Sequencing (Bioinformatics II)
Anonymous completed this course.
Great training in logic thinking. I was a scientist with almost no programming experience going into the class. It helped me a great deal in understanding and using various data structures and was especially helpful in training me to think about code performance all the time. I struggled a lot at the beginning, but it was a very rewarding experience in the end. I recommend it for any biologist interested in learning programming, not necessarily just for bioinformatics.