Predict the origins of mysterious outbreaks using viral RNA
Predict the origins of mysterious outbreaks using viral RNA
Predict the origins of mysterious outbreaks using viral RNA
Automatic learning allows guests and vectors of mysterious viral infections to use their genomic sequences, which offers a quick way to reduce the delay between virus discovery, research and response during outbreaks.
More than 200 species of RNA viruses are known to infect humans and are responsible for a wide variety of diseases, ranging from the common cold to Ebola, discovering new species each year. Outbreaks of infectious diseases caused by unknown viruses have the potential to spread rapidly and become serious public health crises.
Understanding the natural vectors and hosts of the virus (the animals in which they originated, such as rodents) and how they are transmitted to humans, such as by the bite of an infected flea, can help identify which populations present an increased risk of infection, offering an effective response by those responsible for public health.
However, the identification of the animal origins of some pathogens may require many years of field and laboratory studies, which greatly limits the efforts towards rapid control and prevention, especially under emergency conditions. While understanding the biology of an unknown virus may remain dark for years, its genome can be quickly obtained.
Simon Babayan and his colleagues gathered a data set containing the genomic sequences of more than 500 single-stranded RNA viruses and used it to create a model capable of predicting viral hosts and vectors by leveraging machine learning algorithms. According to Babayan et al., Closely related viruses often present closely related hosts and the viral genome composition traits can inform about the host-virus relationships.
His model used machine learning to extract coevolutive signals between viral genomes genetically related to known hosts, identifying the genomic traits that discriminate the host and the type of vector. The authors demonstrate their ability to predict these factors by identifying a potential hoofed mammal host and a fly vector for the poorly known Bas-Congo virus. In a related Perspective article, Mark Woolhouse discusses the limitations of the Mimica et al. Model, though he notes that the study "is a valuable step and, hopefully, portends further advances in our ability to extract valuable information for public health. directly from genome sequences of the virus. " (Source: AAAS)
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