Modern research incorporates a variety of scientific and mathematical disciplines. Are you fascinated by the interplay between biology, computer science, and statistics? Recently, we’ve been spotlighting non-traditional and uncommon careers within life science industries. We interviewed Gaius Augustus, a PhD candidate at the University of Arizona, who is a participant in their Cancer Biology Graduate Interdisciplinary Program. He is also a bioinformatician, who regularly incorporates epidemiological data into his work. Gaius clearly describes the complex field of bioinformatics along with its practical applications.
1. Can you tell us a little bit about your background, before beginning your focus on bioinformatics and epidemiology?
I actually started my career as an artist. I was trained in the fine arts and video production. I later decided to return to school for science, and worked in two labs during my undergrad. My research there focused on evolutionary development and plant population genetics. My focus has always been on integration of different fields of research, and my bachelor’s degree is actually in integrative studies with a focus on chemistry and biology.
2. How does the study of bioinformatics and epidemiology together impact biological research and outcomes?
Science at its core is the study of patterns in nature. What I love about bioinformatics is that it allows us to look at patterns that are hidden within huge amounts of data. We have 3 billion base pairs in our genomes, and that information is incredibly complex. It’s impossible to process that data manually. Bioinformatics allows us to try to make sense of it, but it becomes even more powerful when you can connect it to community data through epidemiology. For example, if you have both incidence reports and access to patient tissue, you can ask questions about longitudinal trends that you can’t with just one set of data.
3. What are some of the top benefits to having a focus in bioinformatics and epidemiology?
The field of bioinformatics is incredibly important right now because there are huge amounts of genomic data that has not been fully analyzed. In my PhD, I did almost no data collection, as the data was already available. It just needed to be processed and analyzed. Being able to incorporate epidemiological, clinical and demographic data into a bioinformatic analysis can strengthen our ability to find trends that we wouldn’t see otherwise.
4. Have you noticed any new trends related to the cross-section of bioinformatics and epidemiology?
It’s becoming more common to incorporate some environmental and demographic information into genomic studies, or vice versa. The best example is probably the studies that incorporate Ancestry Informative Markers (AIMs) into their study, which allows a less biased approach to determining ancestry than self-report. But we could still do better. I hope that in the future, we’ll see studies that incorporate diet, environmental exposures, socioeconomic status and zip code into the data collection process.
5. How competitive is it to enter your field?
Bioinformatics is an interesting field because we are in need of people to analyze all this data that’s being and has been collected. A bioinformatician is a biologist, a computer scientist and a statistician, so there’s this steep learning curve that many struggle to get through. For this reason, my experience has been that if you can get over that hurdle and learn to do all three competently, you won’t find it difficult to enter the field. The hard part is the upfront learning and integration of that knowledge. If you also have the foresight to incorporate epidemiological, demographic or clinical data into your bioinformatics analyses, you’ll find an even broader range of applicable work.
6. What advice would you give to aspiring researchers interested in studying bioinformatics and epidemiology?
Get started today. You can begin learning the bioinformatic and statistical basics relatively inexpensively. Learn to code proficiently in the command line, R and Python. Read lots of studies and learn how to identify gaps in knowledge. Find public data sets and start playing around with them, both getting the data in different formats as well as asking and answering questions about trends you see in the data. I also think that machine learning is quickly becoming an essential skill in bioinformatics, so add that tool to your skillset once you’ve got the basics down.
A Bioinformatician utilizes different fields of science and mathematics to study diseases and their demographical aspects. If you have an interest in epidemiology, computer science, and statistics, this career might be worth exploring. Analysis of data is at the core of a Bioinformatician’s role, and other scientists rely on them to help explain the answers to complex questions. Does a position solving scientific puzzles sound like a fit for you? If so, think about speaking to a Bioinformatician for more details.
Porschia Parker is a Certified Coach, Professional Resume Writer, and Founder of Fly High Coaching. (https://www.fly-highcoaching.com) She empowers ambitious professionals and motivated executives to add $10K on average to their salaries.
Gaius J. Augustus is a Bioinformatician who incorporates epidemiological data into his analyses. He currently studies cancer health disparities at the University of Arizona, focusing on colorectal cancer in African Americans.