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University of California, San Diego

Finding Hidden Messages in DNA (Bioinformatics I)

University of California, San Diego via Coursera


Named a top 50 MOOC of all time by Class Central! 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. In the first half of the course, we investigate DNA replication, and ask the question, where in the genome does DNA replication begin? We will see that we can answer this question for many bacteria using only some straightforward algorithms to look for hidden messages in the genome. In the second half of the course, we examine a different biological question, when we ask which DNA patterns play the role of molecular clocks. The cells in your body manage to maintain a circadian rhythm, but how is this achieved on the level of DNA? Once again, we will see that by knowing which hidden messages to look for, we can start to understand the amazingly complex language of DNA. Perhaps surprisingly, we will apply randomized algorithms, which roll dice and flip coins in order to solve problems. Finally, you will get your hands dirty and apply existing software tools to find recurring biological motifs within genes that are responsible for helping Mycobacterium tuberculosis go "dormant" within a host for many years before causing an active infection.


  • Week 1: Welcome!
    • 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:

      1. Weeks 1-2: Where in the Genome Does DNA Replication Begin? (Algorithmic Warmup)
      2. Weeks 3-4: Which DNA Patterns Play the Role of Molecular Clocks? (Randomized Algorithms)

      Week 5 will consist of a Bioinformatics Application Challenge in which you will get to apply software for finding DNA motifs to a real biological dataset.

      Each of the two chapters in the course is accompanied by a Bioinformatics Cartoon created by 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 cryptic message leading to buried treasure have to do with biology? We hope you will join us to find out!

      Phillip and Pavel

  • Week 2: Finding Replication Origins
    • Welcome to Week 2 of class!

      This week, we will examine the biological details of how DNA replication is carried out in the cell. We will then see how to use these details to help us design an intelligent algorithmic approach looking for the replication origin in a bacterial genome.

  • Week 3: Hunting for Regulatory Motifs
    • Welcome to Week 3 of class!

      This week, we begin a new chapter, titled "Which DNA Patterns Play the Role of Molecular Clocks?" At the bottom of this message is this week's Bioinformatics Cartoon.  What does a late night casino trip with two 18th Century French mathematicians have in common with finding molecular clocks?  Start learning to find out...

  • Week 4: How Rolling Dice Helps Us Find Regulatory Motifs
    • Welcome to Week 4 of class!

      Last week, we encountered a few introductory motif-finding algorithms. This week, we will see how to improve upon these motif-finding approaches by designing randomized algorithms that can "roll dice" to find motifs.

  • Week 5: Bioinformatics Application Challenge
    • Welcome to week 5 of the class! This week, we will apply popular motif-finding software in order to hunt for motifs in a real biological dataset.

Taught by

Pavel Pevzner and Phillip Compeau


4.5 rating, based on 17 Class Central reviews

4.3 rating at Coursera based on 997 ratings

Start your review of Finding Hidden Messages in DNA (Bioinformatics I)

  • Profile image for Jeff Lam
    Jeff Lam
    For someone like me with a background in computer science/engineering, this course had great content introducing biological concepts and the mathematical/algorithmic ideas used in computation for problems in biology. They certainly provide plenty of step-by-step instructions via video and and text. However, I was able to get a good grade (98%) without them checking for a deep enough understanding of the concepts & principles. Much of it felt like simply translating their pseudocode into your programming language of choice. I'll have to review everything if I ever get a job in bioinformatics...
  • Anonymous
    A very good course that uses on online textbook, with built-in programming assignments using real biological data. I have a biology background, but limited programming experience. The assignments start off easy enough, but develop to more advanced algorithms that are explained somewhat vaguely, making the implementation more challenging than it needed to be. I learnt alot though and will certainly be taking the next course in the series.
  • Anonymous
    A very good course. It's a little difficult if you don't have a CS background, as the informatics part is a lot harder and counts more than the biology part. For me, it has been a great way to learn new things that I did not know and specially to discover a new exciting field.
  • Anonymous
    I have PhD in bioinformatics (BSs in CS.). And I'm enjoying this course a lot! The algorithms are explained in very user-friendly way. So are the biological concepts. My big gratitutes to creators of the course content and the tools.
  • Anonymous
    Seems harder than it needs to be. Some of the instructions are very vague. Sometimes it is easier to come up with my own code than to follow their suggested code in many of the exercises,
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