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.