A latest survey report in Nature Biotechnology highlights some of notable computational biology advances in year 2010. Featured computational biology breakthroughs falls broadly in four categories,
- Emergence of next-generation computational methods for Next-generation sequence analysis such as de novo transcriptome assembly of short reads.
- Best use cases using exisitng data repositorie such as PheWAS (for phenomewide association scans).
- High-thogughtput images analysis algorithms based on machine learning for automation of tasks such as classifying whether a pattern of fluorescent staining represents localization to one subcellular organelle or to a mixture of locations.
- Nontraditional computaional biology advances such as FoldIt and emerging web 2.0 trends.
Above all, survey suggests that a particular type of computational biology researchers, cross-functional individuals who are interested in multiple domains simultaneously, are driving much of cutting-edge computational biology.
Tools for computational analyses permeate biological research according to three stages: first, a crossfunctional individual sees a problem and devises a solution good enough to demonstrate the feasibility of a type of analysis; second, robust tools are created, often utilizing the specialized knowledge of formally trained computer scientists; and third, the tools reach biologists focused on understanding specific phenomena, who incorporate the tools into everyday use. These stages echo existing broader literature on disruptive innovations and technology-adoption life cycles, which may suggest how breakthroughs in computational biology can be nurtured.
As one of the former member of the George Church Lab, Greg Porreca suggests,
I’ve found that many advances in computational biology start with simple solutions written by crossfunctional individuals to accomplish simple tasks. Bigger problems are hard to address with those rudimentary algorithms, so folks with classical training in computer science step in and devise highly optimized solutions that are faster and more flexible.
I could not agree more, I guess in early stage the focus should be on problem solving, coming up with right questions and may be toy solutions to answer them. In short, a successful computational biologists is like multi-tasking hacker who prioritize the problem solving and getting things done.
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