Unsupervised explainable AI for the collective analysis of a massive number of genome sequences: various examples from the small genome of pandemic SARS-CoV-2 to the human genome

Abstract

In genetics and related fields, huge amounts of data, such as genome sequences, are accumulating, and the use of artificial intelligence (AI) suitable for big data analysis has become increasingly important. Unsupervised AI that can reveal novel knowledge from big data without prior knowledge or particular models is highly desirable for analyses of genome sequences, particularly for obtaining unexpected insights. We have developed a batch-learning self-organizing map (BLSOM) for oligonucleotide compositions that can reveal various novel genome characteristics. Here, we explain the data mining by the BLSOM: unsupervised and explainable AI. As a specific target, we first selected SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) because a large number of the viral genome sequences have been accumulated via worldwide efforts. We analyzed more than 0.6 million sequences collected primarily in the first year of the pandemic. BLSOMs for short oligonucleotides (e.g., 4~6-mers) allowed separation into known clades, but longer oligonucleotides further increased the separation ability and revealed subgrouping within known clades. In the case of 15-mers, there is mostly one copy in the genome; thus, 15-mers appeared after the epidemic start could be connected to mutations. Because BLSOM is an explainable AI, BLSOM for 15-mers revealed the mutations that contributed to separation into known clades and their subgroups. After introducing the detailed methodological strategies, we explained BLSOMs for various topics. The tetranucleotide BLSOM for over 5 million 5-kb fragment sequences derived from almost all microorganisms currently available and its use in metagenome studies. We also explained BLSOMs for various eukaryotes, such as fishes, frogs and Drosophila species, and found a high separation ability among closely related species. When analyzing the human genome, we found evident enrichments in transcription factor-binding sequences (TFBSs) in centromeric and pericentromeric heterochromatin regions. The tDNAs (tRNA genes) were separated by the corresponding amino acid.

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

“…When functionally advantageous mutations including synonymous ones occur, they are thought to lead to the emergence of a new clade and BLSOM separation. At this time, we do not have a clear answer to the actual molecular mechanisms of the possible advantageous mutations, but we are analyzing them as separate studies (Wada et al 2020;Ikemura et al 2021). In a previous study, we assigned mutations that contribute to the separation on BLSOM; these diagnostic mutations including synonymous ones are found not only in the spike protein gene but also in many other genes (Ikemura et al 2020(Ikemura et al & 2021.…”