Meet Dr. Jason Ernst

Why did you decide to go into the computational biology cross discipline?

I was always interested in research. I did my undergraduate work in computer science and math at the University of Maryland. Then, I went to graduate school at Carnegie Mellon University thinking I would work in machine learning.

At that time, when I was looking for an area to focus in on my thesis research, there was a new faculty who was applying a lot of machine learning to biology. This was soon after the Human Genome Project. There was a lot of excitement in that space due to the opportunities for computer science and machine learning to impact biology with all of the new genomic data. I figured it would be a good opportunity to have an impact, so I went into that direction. My PhD focused on applying machine learning to problems in biology. After my graduate work, I did a postdoc at MIT, where I went further into the field. That led to a faculty position being a professor at UCLA.

What are some of the research projects your lab is working on?

The projects we work on aim to understand the genome. The human genome contains 3 billion pairs of bases and only about 1% codes for protein. There’s this vast majority of the genome which doesn’t code for proteins and is less well understood but can be very important to disease. A lot of genetic variation associated with disease falls in these regions, and there is a lot of interest in understanding these parts of the genome involving regulating genes.

My work has a lot to do with methods to interpret the non-coding genome and looking at epigenomic data, which is a type of data that looks at chemical markers on top of the DNA or the proteins around which the DNA is packaged. In my group, we are developing and applying machine learning methods to take advantage of the high throughput biological data that’s emerging.

More specifically, one project looked at comparing humans and mice at the epigenetic level. There might be thousands of data sets collected in humans, which involves mapping different chemical markers in different cells and tissue types. This is similar in mice, which are model organisms for a lot of human research. Researchers would like to be able to say if this human region, in some sense, is similar to this mouse region.

Traditionally, there are systematic ways to do this at the sequence level, but we came up with a computational strategy that could score this at an epigenomic level by integrating information from lots of different datasets from humans and mice. It’s based on machine learning that automatically finds the relevant patterns to classify two regions that have evidence of this conservation across species. This is a project that was recently completed.

Other work we do involves trying to understand whole genome sequencing data– particularly, rare non-coding variation in psychiatric disorders, for example.

What are the benefits of working in an interdisciplinary field?

The field that I work in is inherently interdisciplinary. When you have these large biological datasets, it needs both the computational researchers who are familiar with computer science, statistics, and approaches to best analyze the data, and also the experimental researchers who generate the data and focus on the biological questions.

What does a typical workday look like for you?

On a typical day, I usually meet with a few of my graduate students or attend a research seminar. I also might spend some time working on a manuscript, like going over a draft a student has sent me. I might spend time reviewing a paper that a journal asked me to review. I might be having meetings with other faculty on a committee. I might have collaboration calls with colleagues outside the university, and there are emails to handle as well.

Is there anything that surprised you about your current role or field?

When I started, what was potentially the biggest surprise was how much impact I could have on biologists while knowing so little biology at the time: I actually hadn’t taken biology since high school. When I started in this field as a graduate student, my first project was already having a large audience among biologists. Although I wasn’t specifically a focused biologist, I could still have a lot of impact.

Similarly, when I was faculty later on, I was hired as my primary appointment into a biology-type department, even though my whole training wasn’t through biology departments.

What is the most rewarding aspect of your job?

The most rewarding part is when you see the impact of your research. What could start as an idea that you’re working out yourself or with a few collaborators, and then you start seeing it being used by hundreds of researchers who are studying a wide range of different diseases and biomedical research problems. That type of impact can be quite gratifying.

What is the most challenging aspect of your job?

The most challenging aspect is juggling everything that’s going on because as a faculty, you need to play a lot of different types of roles. You’re spending time on teaching, writing grants, mentoring students, writing papers, coming up with research, giving talks, and reviewing papers. There are many tasks, so the most challenging aspect is keeping up with everything one needs to be able to do.

What skills would you say are important for a role like yours?

If you’re working specifically on the computational side, then a computer science, statistics, and math background is important. If you’re working in an interdisciplinary field, having knowledge on the field you’re applying your problems to, having the ability to recognize what problems are important to work on, and being able to effectively interact with biologists are all important skills.

In terms of soft skills, it’s important to be able to write effectively and present effectively. When you’re working in this highly collaborative field, having interpersonal skills is important as well.

What advancements do you foresee happening in the future of genomics?

There’s an overall shift in genomics to have more of an impact on health. Now, you hear people using terms like precision medicine, where there’s more personalized health care to individuals based on genomic profiling. I think as we have a better understanding of the genome, which still requires a lot of more basic work, it provides the foundation for more of that translation to healthcare.

I also think, in general, the accumulation of large datasets is a large advancement. There are new assays continuing to be developed that allow us to probe biological systems in ways we haven’t been able to before at larger scales. Each of these new assays often leads to new computational challenges and opportunities. In the genetics space, there is a large impact in the move to large scale biobanks, where they collect genetic data on large cohorts of individuals and then phenotype them for a large range of phenotypes all at once.

What advice would you give to a student who is interested in computational biology?

If you obtain a strong foundation in some of these quantitative areas like computer science, statistics, and math, that can give you a foundation to then move into a lot of different scientific areas. If you know specifically what you would want to do, like applying the quantitative approaches to biology, then it’s good to have some sort of work in that applied domain you’re interested in as well. Also, get involved in research opportunities.

Do you have resource recommendations for students who are interested in STEM?

At the undergraduate level at UCLA, we have a summer program called Bruins In Genomics, where we bring in undergraduates from all over the country for a research opportunity. It’s an eight-week program where participants get embedded in research labs.

In terms of resources, there is a large movement towards videos, seminars, and course material that has been put online. There is a lot of educational material at the introductory level. You can also experience the actual state of art knowledge through research seminar videos that are often accessible online.

In terms of reading materials, textbooks can often give you a good overview of some areas, but they can tend to be out of date for fast-moving fields. If you look at review papers or primary research articles, you can get the state of the art of what’s going on in the field.

Why do you love working in STEM?

Thinking about problems that are intellectually stimulating and seeing the impact that you can have on important societal problems are rewarding to me!

Learn More:

• Bruins In Genomics UCLA Summer Program
• Jason Ernst Lab
• Computational Biology Course
• Biomedical Big Data Revolution- TedX Talk