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European Biopharmaceutical Review

Down the Rabbit Hole: Navigating the Functional Genomics Funnel

For all its many advantages, functional genomics screening retains the sizeable restriction of requiring to be carried out in cellular models that cannot represent human pathology with perfect accuracy. In large part, screening has upended the way we do science, allowing us to approach scientific questions with a very loose hypothesis, and use these techniques to explore that hypothesis in an unbiased way. While this has led to discoveries that would likely have been missed otherwise, the very nature of functional genomics screening is statistical, perhaps even imprecise. An absolute requisite to the discovery process is the careful and methodical follow-up of the hits derived from a functional genomics experiment. Functional genomics screening has outpaced its expected growth trajectory, with new methodologies and technologies extensively applied across a range of disciplines. With these rapid advancements comes the pressing need to make sense of the data and results derived from such screens. Given that current screens often survey tens of thousands of genes or gene interactions, this is far from trivial, but it is an absolutely, if not the critical component of deriving useful biological data from functional genomics screens (1).

Data Analysis – Finding the Needle in a Haystack

Initial evaluation of functional genomics experiments is most often a computational exercise. One sorts the good data from the bad and corrects, as best one can, for the biological variation in their system. Most state-of-the-art algorithms for analysing the massive quantities of data derived from a functional genomics screen rely on advanced computational methods like machine learning to map reads back to a pre-established genomic background, assemble reads into full genes, and distinguish between over- and underrepresented phenotypes. These tools can seem inaccessible to the average biologist; however, numerous providers of bioinformatics suites, such as Benchling, Desktop Genetics, and Geneious, are beginning to work these computational approaches into more user-friendly graphical interfaces.

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Dr Benjamin Borgo currently leads a team of forward-thinking product and service managers within Merck’s Genome Engineering and Modulation franchise. His first exposure to CRISPR-based genome editing was as a graduate student, where he attempted to engineer novel protein variants. Though he met little success, it inspired a passion for CRISPR technology, which he has brought with him to his current role within a company that is laser focused on developing and utilising revolutionary genome editing technologies. Prior to joining Merck, he worked in various commercial and technical roles at Berkeley Light Inc, Agilent Technologies, Nanopore Diagnostics, and several small startups focused on software applications. Ben has a PhD in Computational Biology from Washington University in St Louis, US.
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Dr Benjamin Borgo
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