So the next question is, why study computational genomics?

Why should we study the computational underpinning s of these methods?

First of all, understanding these algorithms is key

to understanding where they will succeed and where they'll fail.

For example, you might know that starting in the late 90s or so,

there were two parallel efforts to sequence the human genome.

They both wanted to be the first to finish the sequence of the human genome.

And these parallel efforts started out using different sets of approaches,

each of the teams believing that their approaches were

the most practical way to complete the project quickly.

And a point they disagreed on was the practicality of solving a particular

computational problem known as the de novo shotgun assembly problem.

We'll talk about exactly that problem later in this course.

One project thought that the problem simply couldn't be solved in practice,

while the other project thought, sure it'll be hard, but with a big enough

computer we can basically solve this problem in enough time.

And it turned out that the latter view, the second team's viewpoint, was more or

less correct.

And that by successfully tackling this computational problem, that

team was able to move very quickly toward their goal of assembling the human genome.

So understanding algorithms helps us to understand what they can do,

what's possible, what's practical.