Jenkins, Jeremy (2016) What is the key best practice for collaborating with a computational biologist? Cell systems., 3 (1). pp. 7-11. ISSN 24054712

Abstract

“This might be a naïve question, but…” Trust is often built on such humble statements. Nobody has mastered all of science; collaborations between “wet” and “dry” lab scientists flourish when each person understands enough of both disciplines to be conversant, yet leans on each other’s expertise. New hypotheses will arise by bleeding over into each other’s worlds through shared perspectives. To do so requires that the computational biologists are treated as collaborators, not just as project support. Data scientists are in high demand with many options for projects. It is therefore good practice to cultivate a partnership with a computational biologist where there are common interests and mutual co-education, with scientific credit flowing freely between.
The computational collaborator should be involved early so that experimental design effectively frames the resulting analysis. Sit together in front of a computer, iterating on data interpretation in order to create visualizations that tell a narrative. File formats that are primped for humans to read may not be good for computers. Consider the heavy burden of file munging, or reformatting of data for integration. Standardizing results and experimental metadata terms (with controlled vocabulary or reference ontologies) will save labor and time and facilitate future meta-analyses across projects. And finally, try to resist referring to computational analysis as “waving a wand”- it is science, not magic, after all!

Item Type:	Article
Date Deposited:	28 Sep 2016 00:45
Last Modified:	28 Sep 2016 00:45
URI:	https://oak.novartis.com/id/eprint/30115