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Researchers from the Hebrew University and other institutions have published a paper titled "Fast and accurate taxonomic domain assignment of short metagenomic reads using BBERT," proposing the BBERT model. This model can identify bacterial sequences in DNA fragments as short as 100 bases; in benchmark tests, its accuracy in distinguishing bacterial from non-bacterial DNA is about 90%, its accuracy in identifying protein-coding fragments is about 88%, and its accuracy in determining the reading frame is about 96%. In simulated mixed samples, filtering with BBERT first reduced the average assembly time from about 66 hours to 25 hours while retaining about 96% average bacterial genome coverage. The research has currently been published as a paper, and no follow-up progress has been reported.
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Oct 29
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BBERT uses AI to identify hidden bacterial DNA in soil samples
Researchers from the Hebrew University and other institutions have developed BBERT, which can identify bacterial sequences in DNA fragments as short as 100 bases, achieving about 90% accuracy in distinguishing bacterial from non-bacterial DNA in benchmark tests. The model is based on BERT and learns the context of nucleotide sequences, achieving about 88% accuracy in identifying protein-coding fragments and about 96% accuracy in determining reading frames. In simulated mixed samples, filtering with BBERT first reduced the average assembly time from about 66 hours to 25 hours while retaining about 96% average bacterial genome coverage.