Time-Series Trend of Pandemic SARS-CoV-2 Variants Visualized Using Batch-Learning Self-Organizing Map for Oligonucleotide Compositions

Abstract

To confront the global threat of coronavirus disease 2019, a massive number of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome sequences have been decoded, with the results promptly released through the GISAID database. Based on variant types, eight clades have already been defined in GISAID, but the diversity can be far greater. Owing to the explosive increase in available sequences, it is important to develop new technologies that can easily grasp the whole picture of the big-sequence data and support efficient knowledge discovery. An ability to efficiently clarify the detailed time-series changes in genome-wide mutation patterns will enable us to promptly identify and characterize dangerous variants that rapidly increase their population frequency. Here, we collectively analyzed over 150,000 SARS-CoV-2 genomes to understand their overall features and time-dependent changes using a batch-learning self-organizing map (BLSOM) for oligonucleotide composition, which is an unsupervised machine learning method. BLSOM can separate clades defined by GISAID with high precision, and each clade is subdivided into clusters, which shows a differential increase/decrease pattern based on geographic region and time. This allowed us to identify prevalent strains in each region and to show the commonality and diversity of the prevalent strains. Comprehensive characterization of the oligonucleotide composition of SARS-CoV-2 and elucidation of time-series trends of the population frequency of variants can clarify the viral adaptation processes after invasion into the human population and the time-dependent trend of prevalent epidemic strains across various regions, such as continents.

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

“…It should be noted that when nodes that have sequences with distinctly different frequencies are located nearby, empty nodes, to which no sequences are attributed after the learning, often appear between them, and these nodes are left blank (no color) on the BLSOM [12,13]. As in our previous BLSOM analysis of SARS-CoV-2 strains [13,29,30], which were mainly isolated within the first year of the pandemic, good clustering by lineage was obtained, and Omicron strains formed their own territory (red) on the right side of the map (Fig 2A).…”

“…By analyzing oligonucleotide composition to study the viral adaptation, we previously found time-series directional changes (i.e., monotonic increases or decreases) in mono-and oligonucleotide composition of four zoonotic RNA viruses (influenza virus [23][24][25][26], Zaire ebolavirus [25,26], MERS coronavirus [25] and SARS-CoV-2 [27][28][29][30], which were detectable even on a monthly basis. In the present study, we used a similar approach to analyze the recently prevalent Omicron subvariants and compare them with previously prevalent lineages.…”

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

“…When focusing on long oligonucleotides, the relationship with functions may become clearer while the types of target variables become large, and variant-specific changes can be characterized. 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 ].…”

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

“…Zaire ebolavirus [21,22], MERS coronavirus [21] and SARS-CoV-2 [23][24][25], which were detectable even on a monthly basis.…”

“…It is thus important to compile examples of remarkably expanded mutations, even outside the S gene, for the development of antiviral drugs, e.g., oligonucleotide drugs. By analyzing oligonucleotide composition to study the viral adaptation, we previously found time-series directional changes (i.e., monotonic increases or decreases) in mono- and oligonucleotide composition of four zoonotic RNA viruses (influenza virus [19-22], Zaire ebolavirus [21,22], MERS coronavirus [21] and SARS-CoV-2 [23-25], which were detectable even on a monthly basis.…”