Using machine learning, a team of Western computer scientists and biologists have identified an underlying genomic signature for 29 different COVID-19 DNA sequences.
This new data discovery tool will allow researchers to quickly and easily classify a deadly virus like COVID-19 in just minutes – a process and pace of high importance for strategic planning and mobilizing medical needs during a pandemic.
The study also supports the scientific hypothesis that COVID-19 (SARS-CoV-2) has its origin in bats as Sarbecovirus, a subgroup of Betacoronavirus.
The findings, Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study, were published recently in PLOS ONE.
The “ultra-fast, scalable, and highly accurate” classification system uses a new graphic-based, specialized software and decision-tree approach to illustrate the classification and arrive at the best choice out of all possible outcomes.
Biology professor Kathleen Hill co-led the study with Western collaborators in Computer Science and Statistical and Actuarial Sciences, along with others in the University of Waterloo’s Department of Computer Science.
The machine-learning method achieves 100 percent accurate classification of the COVID-19 sequences and more importantly, discovers the most relevant relationships among more than 5,000 viral genomes again within minutes.
“All we needed was the COVID-19 DNA sequence to discover its own intrinsic sequence pattern. We used that signature pattern and a logical approach to match that pattern as close as possible to other viruses and achieved a fine level of classification in minutes – not days, not hours but minutes,” Hill said.
This classification tool has already been used to analyze more than 5,000 unique viral genomic sequences, including the 29 COVID-19 sequences available on January 27.
Hill believes the tool, which is able to classify any newly discovered virus sequence COVID-19 or otherwise, will be an essential component in the toolkit for vaccine and drug developers, front-line health-care workers, researchers and scientists during this global pandemic and beyond.
Reference: “Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study” by Gurjit S. Randhawa, Maximillian P. M. Soltysiak, Hadi El Roz, Camila P. E. de Souza, Kathleen A. Hill and Lila Kari, 24 April 2020, PLOS ONE.
DOI: 10.1371/journal.pone.0232391
Never miss a breakthrough: Join the SciTechDaily newsletter.
Follow us on Google and Google News.