On the back of getting a large contract with some of the major pharmaceutical company, PetaGene, the Cambridge UK based genomics data compression company has now secured $2.1 Million in the funding round.
“PetaGene offers powerful solutions for a growing industry, and we’re delighted to support them as they play their part in democratizing personalized medicine.”
Romulus capital led the investment round with the collaboration from some other investors based in Silicon Valley and London, which includes the Entrepreneur First Backed by Greylock Partners. The latest help to bring PetaGene’s total funding to $3.2 Million. The new funding will help the company to grow its technical team based at in Cambridge, its global sales team and further expand PetaGene product offerings.
Romulus Capital is an early stage Venture Capital fund founded by the pool of MIT in 2008. It focuses on building technology, and science enabled companies. It seeks to invest in the companies which provide the selling technology that solves the real problems in some of the large traditional industries or the deep latest technology companies that have spun out of top-tier research groups.
Krishna K. Gupta, founder of Romulus Capital commented “I was impressed by what part of the genomics value chain PetaGene which was also targeting with its best-in-class compression software that which we made our initial investment in the year 2017. Since then, there is also a sort of ability to successfully develop their product for the purpose of cloud and the strong interest from some of the potential customers have only served to reinforce our own set of view.” He went on to say that the company “PetaGene offer some of the powerful solutions for a growing industry and we are delighted to support them as they play their part in democratizing some of the more personalized medicine.”
PetaGene was founded in Cambridge, the birthplace of genomics, to address the rapidly growing data management problems of the genomics industry. PetaGene’s software enables the compression of a huge amount of genomics data without compromising on data quality.
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