New York, April 8 (IANS) Scientists have developed a new technology that enables them to read and interpret the human genome, opening the door to identifying new drug targets to treat many genetic diseases.
The technology called TargetFinder helps researchers connect mutations in the so-called genomic “dark matter” with the genes they affect, potentially revealing new therapeutic targets for genetic disorders.
“Most genetic mutations that are associated with disease occur in enhancers, making them an incredibly important area of study,” said senior study author Katherine Pollard from the Gladstone Institutes in San Francisco.
Scientists originally believed that enhancers mostly affect the gene nearest to them. However, the new study revealed that, on a strand of DNA, enhancers can be millions of letters away from the gene they influence, skipping over the genes in between.
When an enhancer is far away from the gene it affects, the two connect by forming a three-dimensional loop, like a bow on the genome.
Using the new learning technology, which was described recently in the journal Nature Genetics, the researchers were able to analyse hundreds of existing datasets from six different cell types to look for patterns in the genome that identify where a gene and enhancer interact.
They discovered several patterns that exist on the loops that connect enhancers to genes. This pattern accurately predicted whether a gene-enhancer interaction occurred 85 percent of the time.
“It’s remarkable that we can predict complex three-dimensional interactions from relatively simple data,” said led author Sean Whalen from Gladstone.
“No one had looked at the information stored on loops before, and we were surprised to discover how important that information is,” Whalen added.
Performing experiments in the lab to identify all of these gene-enhancer interactions can take millions of dollars and years of research.
The new computational approach is a much cheaper and less time-consuming way to identify gene-enhancer connections in the genome.