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Guest post: It’s an exciting time to be studying human genetics

This is a guest post by Mark Kaganovich, PhD candidate in Genetics at Stanford University and Founder of SolveBio (full bio below). 

It’s an exciting time to be studying human genetics.  Advances in genomic technologies mean that we are rapidly accumulating information about the genetic differences between individuals and across populations. But, how do those differences in A’s, C’s, T’s and G’s become the differences we see in physical traits or susceptibility to disease? The more we learn about modifications and regulation of DNA, RNA and proteins, the more complicated this question is to answer.

I am a doctoral student at Stanford University – I approach the study of genetic variation by integrating computational data analysis with experimental genomics. Essentially, that means we look across genomes, transcriptomes, and proteomes for patterns in the data, form hypotheses about what those patterns might mean functionally for a cell – and ultimately an organism – and then set out to test those hypotheses. The advent of technologies that generate large, informative data sets and the computational infrastructure to learn from the data means that we can generate meaningful hypotheses quickly. The pace of research depends on our ability to test our hunches and move forward to better understand the cellular mechanisms underlying our genomic/proteomic observations.

Setting up high-throughput experiments to confirm or reject computational predictions usually requires specialized equipment and expertise in scaling up individual experiments that no single laboratory can be expected to afford or master in a reasonable time frame.  So for us, collaboration is a natural avenue to explore. We have looked at working with core facilities and companies to do cell culture, microarray work, and sequencing when in-house lab or core facility capacity and scale cannot meet our needs. There are also methods that we are well set up to perform, but often need results sooner rather than later, so a company or core facility can help.

But, how do we find and evaluate opportunities to work with companies or core facilities? In my case, and in the case of colleagues in the lab, we often don’t know the range of possible services that are out there than can help with our projects. The mechanism by which Science Exchange allows service providers to generate ideas about how to solve your problem, once you post your project description, is a very useful way to get a handle on what kinds of collaborations are possible. This way, even if you want something done that is really hard to find, you can get ideas for how to proceed from the bids that facilities or companies submit. It means you don’t have to figure out everything in advance, but can take a more collaborative approach up front of matching your needs with the services that are out there. Because when you are looking at something as complex as human genetic variation, there’s no point in believing you can think of, let alone tackle, every cutting-edge approach to best address your particular problem.

[about_box image=””]Mark Kaganovich is a doctoral candidate in Genetics at Stanford University and CEO of SolveBio (Twitter: @solvebio), a computational biology app and data platform in the cloud.  He has a bachelor’s degree in Biochemistry and Computer Science from Harvard University.[/about_box]


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