AI-based search for convergently expanding, advantageous mutations in SARS-CoV-2 by focusing on oligonucleotide frequencies

Abstract

Among mutations that occur in SARS-CoV-2, efficient identification of mutations advantageous for viral replication and transmission is important to characterize and defeat this rampant virus. Mutations rapidly expanding frequency in a viral population are candidates for advantageous mutations, but neutral mutations hitchhiking with advantageous mutations are also likely to be included. To distinguish these, we focus on mutations that appear to occur independently in different lineages and expand in frequency in a convergent evolutionary manner. Batch-learning SOM (BLSOM) can separate SARS-CoV-2 genome sequences according by lineage from only providing the oligonucleotide composition. Focusing on remarkably expanding 20-mers, each of which is only represented by one copy in the viral genome, allows us to correlate the expanding 20-mers to mutations. Using visualization functions in BLSOM, we can efficiently identify mutations that have expanded remarkably both in the Omicron lineage, which is phylogenetically distinct from other lineages, and in other lineages. Most of these mutations involved changes in amino acids, but there were a few that did not, such as an intergenic mutation.

Oligonucleotide usage in coronavirus genomes mimics that in exon regions in host genomes

“…Independently of the present study, our group have shown that variant-specific clustering, and thus variant-specific feature extraction, is possible by using an unsupervised AI suitable for analysis of a large number of variables and thus of long oligonucleotides [ 22 , 23 , 35 ]. By analyzing the usage of long oligonucleotides (e.g.,20-mers) in Omicron and other variants, we have recently characterized advantageous mutations that spread convergently in multiple lineages [ 36 ].…”