Bioinformatics Algorithms (Part 1) (Coursera)

Bioinformatics Algorithms (Part 1) (Coursera)
Free Course
Categories
Effort
Certification
Languages
You should know the basics of programming in any language that you choose. If you don’t know how to program, working introductory problems on Rosalind will help introduce you to Python (approximately 10 hours of work).
Misc
Bioinformatics Algorithms (Part 1) (Coursera)
This course will cover some of the common algorithms underlying the following fundamental topics in bioinformatics: assembling genomes, comparing DNA and protein sequences, finding regulatory motifs, analyzing genome rearrangements, identifying proteins, and many other topics.

The sequencing of the human genome a decade ago fueled a computational revolution in biology, which has arguably been an impetus for more new algorithms than any other fundamental realm of science. The newly formed links between computer science and biology affect the way we teach computational ideas to biologists, as well as how applied algorithms are taught to computer scientists.

Genome sequencing is just one of hundreds of biological problems that have become inextricable from the computational methods required to solve them. In this course, we will take a look at some of the algorithmic ideas that are fundamental to an understanding of modern biology. Computational concepts like dynamic programming and graph theory will help us explore algorithms applied to a wide range of biological topics, from finding regulatory motifs to reconstructing the tree of life. Throughout the process, we will apply real bioinformatics algorithms to real genetic data.

We have created an interactive textbook for this course, which allows you to learn and solve problems concurrently. This textbook will be hosted on Stepic, a new resource for learning interactively.



Free Course
You should know the basics of programming in any language that you choose. If you don’t know how to program, working introductory problems on Rosalind will help introduce you to Python (approximately 10 hours of work).