Learning Bioinformatics

In today’s data-heavy research environment, wet-lab scientists can benefit from new computational skills.

Written byEsther Landhuis
| 8 min read

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© ISTOCK.COM/NICOLASAdelaide Rhodes had no idea a tiny crustacean would fuel such a big career shift. About a decade ago, as a postdoc at the University of Washington, she was researching copepods—microscopic organisms that convert unsaturated fatty acids into the omega-3 fats that make salmon a healthy meal. They’re “what fish eat to get fat,” Rhodes says. During an aquaculture boom, she began hunting for genes involved in the fat-converting process. Trouble was, very few researchers studied copepod genetics. Back in 2005, Rhodes’s searches for “copepod and lipids” on the DNA Data Bank of Japan, European Nucleotide Archive, and GenBank yielded no results. When she searched “crustacean,” she got a list of some 50 genes, but none were related to lipid metabolism.

Undeterred, Rhodes broadened her search to include insect genes, then designed primer sets and ran countless PCR assays to check if those same genes were found in copepods. She also went around at meetings asking other researchers if they had copepod data or sequences to share. Rhodes eventually identified two potential copepod desaturases—enzymes that introduce double bonds into fatty acid chains. However, she couldn’t confirm whether those genes are specific to copepods, because there weren’t enough publicly available crustacean genomes for comparison.

These days, she wouldn’t have that problem. When researchers identify a new genomic sequence, they can use modern computing and bioinformatics tools to check for its presence in related species’ genomes with just a few keystrokes. And as technical advances yield unmanageable amounts ...

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