Here in this post, I am continuing from my last post where I mentioned how I ended up deciding on cancer study during my PhD. Cancer study was in itself a very broad field and was for sure not enough to be my thesis topic. This meant that I was yet not over with answering questions in my PhD voyage.
I came from a background with higher interest in biological sciences. Hence, I knew that the option for being on consumer side and using other people’s tool to study basic biology was an easier route for me. And, the only reason I had more interest in biological sciences was I think because of my longer journey with it. I started studying programming only in my undergrad, and now I realize that I have equal interests in both. So to say, you should not stick to something too early without trying out different options.
Then, to do bioinformatics’ analysis in Cancer, I asked myself if I want to focus on proteomics or genomics? Well, in order to decide on this, I had to not only consider my personal interests but also which one deserves to be studied first in my understanding and was easier in terms of resources in the current research community.
Among genomics and proteomics, few scientists argue that proteomics is a better predictor of disease than genomics. I knew that proteins are functional units of a cell. I was also convinced that a difference in protein content would definitely exert a functional difference at the level of a cell between a diseased and normal but a difference in DNA may not contribute greatly to cell function and metabolism. On top of that, protein binding and docking studies are interesting and directly applicable to drug discovery and treatment. Apart from proteomics, there is another level of lipidomics studying lipid composition of cells that allured me a lot. Scientists predicted it to be coming in near future. They show their role in compartmentalization, cycling of proteins, signaling, and possibly in pathogenesis.
Studying proteins or any higher level might be a better predictor of disease than genomics in future but we should not forget that that still genomics is the basic building block. Before understanding proteins and their network in human body, I believe we should understand our genetic makeup more deeply. There will always be levels of complexity or components to consider in cells and disease states. And, study of all -omics will be needed in order to fully understand but going upwards in the hierarchy of complexity would always be my way.
My reasoning fell just right at timing when next-generation sequencing (NGS) technology was blooming in full. It was less than a decade since the first next-gen sequencers hit the market and the technology had already transformed nearly every field of biological sciences. Last several years had seen revolutionary advances in DNA sequencing technologies with the advent of NGS techniques. NGS methods now allowed millions of bases to be sequenced in one round, at a fraction of the cost relative to traditional Sanger sequencing. As costs and capabilities of these technologies continued to improve, we were only beginning to see the possibilities of NGS platforms, which were developing in parallel with online availability of a wide range of biological data sets and scientific publications and allowing us to address a variety of questions not possible before.
I was trying to read more and scientific journal to see the latest research topics and the scientific literature was brimming with NGS-related studies. The pace of scientific discovery was largely driven by innovative applications and an astonishing rapid evolution of the technology.
I could easily see the new era in next-gen sequencing, one in which NGS technologies was not only being used for discovery, but already integrated into clinical care. Along with that, in my future job prospective, I could see a greater need for specialists who could make sense of the mountains of information in such a way that is meaningful for scientists and clinicians, and ultimately beneficial to customers and patients.
Hence, deciding on genomics and NGS was pretty simple for me at that time.
Also, its hype was one big reason for redirecting my focus from tool development to NGS analysis.