Full Introduction to Computational Biology Course

“How COVID unlocked the power of RNA vaccines” - Nature
“25 Down And 71,632 To Go: Scientists Seek Genomes Of All Critters With A Backbone” - NPR
“The DNA Data Deluge - Fast, efficient genome sequencing machines are spewing out more data than geneticists can analyze” - IEEE Spectrum

In April 2003, scientists working with the National Institutes of Health completed the first draft of the human genome. As a result of one of the greatest scientific advances ever made, scientists have been able to create new therapies and health technologies, such as the mRNA COVID-19 vaccine, by studying the DNA - the “biological code” - of humans, animals, viruses, and living organisms. In line with this falling cost of DNA sequencing, the field of bioinformatics has emerged, and progress in this field has not been able to keep up with the growing volume of DNA data produced by sequencing machines.

Now, this leads to the question - what exactly is the field of bioinformatics/computational biology? 

Bioinformatics is the study of biological data using the tools of modern computer science and data analysis, including machine learning and graph theory. It helps scientists to process genomic data and can be used in applications like predicting how potential medicines/vaccines will interact with the human genome of a potential patient (this is called precision medicine).  As bioinformaticians, we study the human code, written of the four nucleotides: A, T, C, and G. Although this may sound complicated, we will be starting with the basics of computer science and genetics - the only prerequisite for your student is having taken an algebra class (to understand how variables are used).

This course is offered as a multi-week continuation course for those who have taken the one-time “Introduction to Computational Biology Seminar”. However, you do not need to have taken the seminar if you have met the aforementioned prerequisites and are interested in the subject. Through this multi-week course, we will discuss algorithmic approaches to solving problems in computational biology on the Rosalind learning platform (http://rosalind.info/problems/list-view/). After completing this course, you should be familiar with how to apply Python to biological data sets to solve common (and potentially novel) problems in computational genetics.

In addition to covering computational approaches, we will also be covering the history of the field and of the Human Genome Project, and where we hope the field may go in the future. We will also be discussing in detail the different career paths that students can consider in the future in the field of computational biology. Students will also need to sign up under my classroom on the Rosalind platform after they enroll in the Outschool classroom (Listed under the section “Supply List”).

Prerequisite: Introduction to Computational Biology Seminar

COURSE SCHEDULE
- Week 1: Introduction to Genetics and Python
- Week 2: Computational Biology Algorithms I
- Week 3: Computational Biology Data Science
- Week 4: Computational Biology Algorithms II
- Week 5: Applied Computational Biology and Final Project

Hope to explore the wonders of the human genome with your student!
Steffen C.