First week in the HHRF Program: I was introduced as the only undergrad to a group of welcoming ph.D students in Duke CBB (Computational Biology and Bioinformatics) Program, only then to be completely overwhelmed by their presentations during group meeting. Machine learning, data mining, Hidden Markov Model, fancy statistics graphs and “alphabet soup” of abbreviations…first day, I was already bombarded with things I’m not even remotely familiar with. Then comes reading scientific papers– I felf so lost that I wouldn’t notice if I skipped a line. Wikipedia has become my best friend since day one and I immediately fell into the infinite loop of looking up technical terms and more keep popping up. It wouldn’t be long before my brain reports a “stackOverFlow” error.
My fantasy of research, or what I thought as research, being standing in front a bench in white coats and latex gloves, pipetting or designing primers, and using advanced biotech machines is torn by the almost painful fact that there’re way more commitments “behind the scene”. I realized that research, especially in computational biology, is also (or even exclusively) sitting down in front of a computer, making sense of data and trying to figure out why the model, for example, works for some transcription factors in some cell type under certain condition but not others. Sometimes the work can be tedious, as (quote my mentor) you could spend the entire day making a graph and to come up with some significance of the results; but this is also the fascinating part: models give us the power to predict, which is extremely useful when the high-cost and time-consuming experiments can not be conducted. Applying algorithms and theories in statistical sciences into genomic studies also enables to handle huge amounts of data. Without algorithms behind sequence assembly and genome alignment, for example, shotgun sequencing and next-gen sequencing technologies wouldn’t have been possible.
One week has passed sitting in front of a computer reading literature and experimenting with Rstudio. I’m glad, or even proud, to say that I’m feeling a little less disoriented than when I first started. I can finally explain to my friends the project I’ll be working on. I’ve discovered a lot of cool softwares and tools for dealing with data and also for making fancy-looking graphs. I’ve filled myself with a little more statistics and biology background. I’ve also come to know my extremely nice mentor and other computer scientists (who seem to work opposite time zone…).
It’s not exactly the great start like I’ve always pictured. However, that’s the point of this program and part of my expectations: to know and experience what’s it like to be ‘doing science’, to learn about life of a research scientist on a daily basis, and ideally, to make some contributions and to add some knowledge to the bigger picture.
More specifically (or personally), I wish to learn how to ask questions, since that’s where we would start from when developing our own interesting research projects and how science is pushed forward; I wish to learn to reach out and take initiatives, as eventually (quote Dr. Mohamed Noor) we would be at the “all for yourself” end of the continuum; and most importantly, to learn to be consistently happy and persistently passionate even when the outcomes fail me, because that’s going to happen a lot, not only in science, but also in life.
I still might not be able to say if research is for me by the end of this summer as there’re still a whole lot more to experience, the eight weeks of the program, however, would definitely be a solid first step of the journey.
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