Scientists discover trove of over 5,500 new viruses hidden in the ocean — Analysis

The diversity and new species are captured in the creation of new classifications

At least 5,500 new virus species have reportedly been found across the world’s oceans in a major new discovery published by an international team of researchers in the Science journal on Thursday.

They claimed that this breakthrough was possible by using machine learning analyses along with traditional evolutionary trees in order to analyze 35,000 water samples around the globe.

This group was looking for viruses that contain RNA genetic material. The scientists claim that there is very little information available about this type of virus in the larger world.

“Whereas DNA viruses are known to be abundant, diverse, and commonly key ecosystem players, RNA viruses are insufficiently studied outside disease settings,” reads the report’s abstract. “Using new approaches to optimize discovery and classification, we identified RNA viruses that necessitate substantive revisions of taxonomy (doubling phyla and adding >50% new classes) and evolutionary understanding.”

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This report reveals that the most abundant collection, or phylum as scientists call it, of recently identified species belonged to a new classification. “Taraviricota,”The Tara Oceans Consortium is a tribute to the source for the 35,000 samples of water.

“There’s so much new diversity here – and an entire phylum, the Taraviricota, were found all over the oceans, which suggests they’re ecologically important,”Matthew Sullivan from Ohio State University was the lead author of this study, according to a statement.

According to scientists, the new discovered “Taraviricota”The phyla could represent the missing link in the early evolution of RNA viruses over billions of years. Another new phyla is the “Arctiviricot,”These species are highly prevalent and predominant in the oceans. Research on them will be a huge help. “provide foundational knowledge critical to integrating RNA viruses into ecological and epidemiological models.”

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