Israel’s NRGene identifies mutation that causes colon cancer
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Israel’s NRGene identifies mutation that causes colon cancer

Startup's software can accurately and efficiently find genetic mutations in humans, says CEO Gil Ronen

Human DNA strand (iStock by Getty Images)
Human DNA strand (iStock by Getty Images)

Israel’s NRGene — the only company in the world to map the genome for bread, pasta and wild emmer wheat — has successfully identified a mutation that causes colon cancer, by means of a full analysis of a human family’s genomic profile, and has

“We have shown that our software is able to accurately, quickly and in a cheaper way identify genetic abnormalities and mutations in humans,” said Gil Ronen, the CEO and co-founder of NRGene, in a phone interview. “It can now be used to detect other genetic-borne diseases, from a peanuts allergy to cancer, to get early detection and hopefully prevention.”

After successfully and quickly mapping a number of genomes — of wild emmer wheat, the ancestor of commercial wheat known for its ability to withstand harsh environments and diseases, and of bread wheat and other crops and animals, the company is turning its focus to the human genome and human healthcare, which is a much bigger market, Ronen said. This is the first time NRGene’s technology has been used for human genome analysis.

The study, conducted together with Dr. Henle Ji, associate professor of Medicine (Oncology) at Stanford University, surveyed the genetic composition of a family with inherited colon cancer. DNA samples were collected from the grandparents, parents and children in the family, and NRGene’s software looked for the inherited mutation that causes the cancer. The presence of the mutation causes colon cancer in 60 percent of its hosts, Ronen said.

NRGene’s CEO Gil Ronen (Courtesy)

“Stanford had already successfully identified the mutation, but wanted to compare our system with the alternative one they used,” said Ronen. “Usually genetic screening to find mutations can take years and the success rate is low, as you can find several suspect mutations. Uncovering the right mutation takes painstaking work. Our system did it in one week and we got one answer, and it was the right one. It was a perfect match.”

Dr. Ji presented the results of the collaboration with NRGene at the American Society of Human Genetics Conference in Orlando on Wednesday.

NRGene’s GenoMagic software is used generally to trawl through vast amounts of genetic data of plants, crops and animals to identify the stronger breeds and help create new and healthier varieties. But the wheat genomes is five times the size of the human genome, said Ronen. NRGene’s methods are one-third the price and ten times faster than existing systems, Ronen added.

The Human Genome Project produced the first complete sequences of individual human genomes some 17 years ago. However, big data software analysis is needed to be able to compare vast amounts of genetic information of a variety of populations, and that is where NRGene can play a part, in the same way it has been playing a part in agriculture.

“It is a big data challenge to do the sequence analysis accurately, quickly and cheaply and find the different characteristics and the sick genes by comparing thousands of patients,” said Ronen. “Our system is the only one that meets this challenge. We are already doing it for millions of plants. The more data there is, the bigger advantage we have over competitors.”

Peppers growing in NRGene’s Indian chile pepper field (Courtesy)

The GenoMagic software can be used by researchers and medical professionals for their patients, as well as by big pharma companies who are developing personalized medications and looking to better match their drugs to the genetic makeup of individuals, Ronen said.

Founded by Ronen and Guy Kol in 2010, NRGene set out to develop software to speed up the breeding of elite varieties of crops. They enlisted code crackers from the Israeli Defense Forces’ elite 8200 unit to write algorithms that would do the job. These ex-army decoders worked intensively for about four years with a team of software engineers and bioinformaticians. In the end, a set of computational tools emerged that allowed NRGene to map even the most complex genomes quickly and accurately.

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