DNA mutations can broadly be divided into two categories. Point mutations, in which a single nucleotide (A, C, G, T) is altered, inserted, or deleted, are comparable to erosion slowly changing the shape of a boulder. Much of human differentiation is attributable to the accumulation of point mutations.
The other type of mutation is extremely rare and can cause dramatic effects on the scale of species evolution. In genome rearrangements, huge blocks of DNA are heaved around, often from one chromosome to another. These mutations are comparable to earthquakes, which hoist up mountains and wrench apart continents.
When we compare two relatively short pieces of DNA that have not been affected by genome rearrangements (say, two genes taken from individuals from the same species), our goal is to identify a “path of least resistance” connecting these two genes via point mutations. We can find such a path using a powerful algorithmic paradigm called dynamic programming.
On the other hand, when we zoom out to the compare entire genomes taken from different species that diverged millions of years ago (such as humans and mice), the effects of genome rearrangements become more pronounced. To determine how far diverged these genomes are, we will need completely different combinatorial algorithms that will help us answer questions about the patterns of genome rearrangements. For example, in order to move around large blocks of DNA, a genome rearrangement must “break” the genome in at least two places. We know that there are fault lines on the earth’s surface where earthquakes are more likely; are there analogous “fragile regions” in the human genome where breakage has been more likely to occur during a genome rearrangement?
How Do We Compare Biological Sequences? (Dynamic Programming)
Cracking the non-ribosomal code
Introduction to sequence alignment
The Manhattan Tourist Problem
Sequence alignment is the Manhattan Tourist Problem in disguise
An introduction to dynamic programming: The Change Problem
The Manhattan Tourist Problem revisited
From Manhattan to an arbitrary DAG
Backtracking in the alignment graph
From global to local alignment
The changing faces of sequence alignment
Penalizing insertions and deletions in sequence alignments
Space-efficient sequence alignment
Epilogue: Multiple sequence alignment
Are There Fragile Regions in the Human Genome? (Combinatorial Algorithms)
Of mice and men
The Random Breakage Model of chromosome evolution
Sorting by reversals
A greedy algorithm for sorting by reversals
Rearrangements in tumor genomes
From unichromosomal to multichromosomal genomes
Computing the 2-break distance
Rearrangement hotspots in the human genome
Epilogue: Synteny block construction
“Assembling DNA and Proteins” is the suggested prerequisite for taking this course, but it is not a strict prerequisite, especially if you have some programming experience.
The programming assignments in this class can be solved using any programming language.
The printed course companion is Bioinformatics Algorithms: An Active-Learning Approach, by Compeau & Pevzner.
The majority of assessments for the course will consist of exercises and programming assignments. This course covers two chapters taken from Bioinformatics Algorithms: An Active Learning Approach, by Compeau & Pevzner.
Each chapter is also accompanied by a summary quiz and lecture videos.
Q: Will I get a statement of accomplishment after completing this class?
Yes. Students who successfully complete the class will receive a statement of accomplishment signed by the instructor.
Q: Can I receive a verified certificate for this course?
Yes. Students who would like a verified certificate can sign up for the course’s Signature Track option.
Q: I remember this course used to be part of the larger “Bioinformatics Algorithms (Part 1) course. Why was it split into three courses?
Based on survey feedback, completion data, and studies of other courses, we realized that having shorter courses gives our students more flexibility around their busy schedules. Even though the courses have been split, the overall content remains the same, so we feel confident that we’re maintaining learning standards of our material.
Q: What if I earned a voucher for retaking the old course? Can I use it in this course?
Vouchers from the older course will be valid for the newer courses. If you took the original course and earned a voucher, you will be issued a voucher for this course as well as for “Finding Hidden Messages in DNA” and “Assembling Genomes and Sequencing Antibiotics” (three vouchers total).
Q: Does this mean that the overall cost for earning Verified Certificates in the course is greater now?
Yes. Since there are more courses now, the overall cost for Verified Certificates is greater than before. Coursera offers a Financial Aid program for learners who would face a serious hardship paying for our courses. Plus, if you just want to join and check out our course content, it’s still free and available to everyone.