Please tell us about yourself. How did you end up in such an offbeat, unconventional and interesting career?
My journey to become a bioinformatician actually began at Accenture, where I worked as a software engineer after completing my engineering, helping our large pharmaceutical clients mine and manage their large article databases. Working with pharma clients piqued my interest for working with biological data types and I jumped at the opportunity to pursue a masters in Bioinformatics at San Diego State University when relocating to California. During my masters studies, I built a pipeline, which incorporated tools from the Broad Institute GATK toolkit, to analyze genomic data from 400 samples of microbacteria with the goal of finding mutations that correlate with resistance to tuberculosis (TB) treatment.
As a bioinformatician, what types of problems have you been tasked with investigating? What data types have you explored?
In a recent experience, I worked at Gensignia alongside a team of bioinformaticians to develop a multi-marker diagnostic test that combines the measure of cell-free miRNA in plasma with a computational algorithm to improve early detection of lung cancer. In an earlier role at La Jolla Institute for Allergy and Immunology, I worked with a variety of data types such as RNAseq and ChIPseq data with the goal of understanding and exploring different human diseases. One central role for me as a bioinformation has been to build computational pipelines for biomarker discovery. This project with Cytobank was unique because I hadn’t previously worked with mass cytometry data; however, the goal of building a pipeline for biomarker discovery was similar to previous projects.
What types of tools/methods have you used to mine data?
How did this help you solve the research problems you and your team were investigating?
Tools and methods to mine the data have depended on the data type. For example, I have built databases to store data for some projects; whereas, other projects did not require this step. The key to each project is understanding and defining the project goals before deciding which tools will best solve the given problem (such as understanding the mutations in mycobacteria that cause resistance to TB drugs). Also the time one may take to write a software program varies with each project goal. Sometimes it may take just a few minutes if the underlying algorithm is sufficiently clear. On other occasions, it may take weeks or days to design the algorithm and write the software program. Furthermore, building these pipelines permits more flexibility in adjusting parameters, which can save time and help the team find results faster.
How do you see the field of bioinformatics augmenting decision making in the clinic and/or improving understanding of human disease?
I believe strongly in bioinformatics and its ability to provide insights in human health. Bioinformatics is a tool powered to help us diagnose disease earlier while curative treatment options are still available by mining large datasets for insights. If we can diagnose disease earlier, we can improve survival rates. I’m very much looking forward to discoveries in the field that continue to advance personalized medicine.