If you're taking this course, you probably have an inquiring mind and you want to know why you should study this particular area where computer science and life science intersect. Why do we want to study how computer science is applied to analyzing DNA sequencing data? Well, let me tell you why I think this is such an important and timely area of study. So first of all, DNA sequencing has become so inexpensive, and so good at delivering lots of data very quickly, that sequencers are now being used all over the field of life science. You can learn a lot about this just by reading the newspaper. So almost weekly, you'll see articles describing new ways in which scientists are applying and refining sequencing to study people's genomes. So for example, scientists are using sequencing to study rare genetic diseases in children. So sequence the genomes of ancient humans to learn more about human origins and evolution and patterns of migration. They're using it to study tumors of people who have cancer to try to better figure out how to treat that patient's cancer. They're using it to study the vast number of microbes, bacteria and such that live inside our bodies, especially in our guts, and help us do things like digest food. Or to simply study the basic ways in which genomes work. To answer basic questions, like what does all the DNA and the genome do? How does the genome work? Sequencing is used absolutely everywhere in life science and medicine these days. So in this sense, sequencing is a little bit like computing. Now that the technology is really cheap and good, it's used for many things. And some of them are the sorts of things that you might expect, and some of them are actually pretty clever and surprising uses of this technology. 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. Another reason is that what's been, knowing about what's been done is the first step to figuring out how to do it better. This is a very active area of research. It's being pursued in academia, and in industry, and in research labs all over the world. There are lots of people who would like to get their hands on better methods for analyzing large quantities of DNA sequencing data. There's really no large scale project in the field of genomics that doesn't employ computer scientists or other computational experts. And there's no effort, large or small, in genomics, that doesn't depend crucially on algorithms that were developed by computer scientists. So this is an active frontier of research. And this could be, for you, a step toward making your own contributions. And finally, if you enjoy computer science and you enjoy studying and thinking about algorithms and data structures, then this is a particularly exciting application area to explore. Where there are many interesting applications of several really important data structures and algorithms. Pretty much every algorithm and data structure that you learn about here also has applications outside of genomics, in areas like information retrieval, or natural language, or pretty much anywhere where you have to deal with very large quantities of text.
