Ekta Khurana: Data Comes Together
Associate research scientist, Gerstein group, Yale University
Recommended by Mark Gerstein, Yale University
Omic techniques are producing torrents of data, and Ekta Khurana is harnessing that flood to figure out what certain noncoding variants do.
"With so much genomic data being generated, bioinformatics is inevitable — we need computer programs and mathematical models to make sense of all the data," she said.
Khurana's work focuses on pulling together different streams of data and combining them into integrative models. In particular, she is searching for noncoding mutations in cancer that affect gene expression and the development of the disease. For example, she and her colleagues have developed a computation tool called FunSeq that filters and prioritizes gene variants. They have then applied that tool to some 90 cancer genomes to uncover about a hundred candidate noncoding drivers of disease.
But getting access to all those different bits of data can be a challenge, Khurana noted. Different kinds of data are being generated in different labs worldwide. "I would benefit a lot if I could have easy access to all this data in an organized and effective manner," she said. She added, though, that there are efforts underway to address this problem.
Paper of note
In October, Khurana and her colleagues with the 1000 Genomes Project Consortium and elsewhere reported in Science on the development and application of FunSeq to examine noncoding variants in a number of cancer types.
"We integrated data from many different resources — a lot of large-scale data — to learn biology that you could not learn from one of these and we developed this novel approach," Khurana said.
The researchers drew upon 1000 Genome Project gene variant data and combined it with ENCODE project information on noncoding regions to find spots in the genome that don't code for proteins, but that are sensitive or ultrasensitive to changes and are under purifying selection. They took that knowledge and predictions about the knock-on effects of changes to those sensitive regions and fed them into their FunSeq tool.
Khurana and her colleagues applied the tool to a set of 90 cancer genomes, including breast, prostate, and medulloblastoma samples. From this, they identified about a hundred noncoding regions that may drive disease. The approach, they noted in Science, may help researchers to determine which variants to follow up on first.
"People are already using it to prioritize noncoding variants in their studies," Khurana added.
Looking ahead
In the future, Khurana sees precision medicine playing an increasing role as individual treatments are tailored with patients' genetic makeup in mind, particularly for cancer patients. "Once you know the genetic makeup of the tumor, then there is a treatment specific for that," she added.
And the Nobel goes to…
Khurana said if she were to win the Nobel Prize, she'd want it to be for discovering novel targets for cancer treatments that then could be developed into therapies or even into preventive measures.