Comparative genomics of <i>Glandirana rugosa</i> using unsupervised AI reveals a high CG frequency

Katsura, Yoshiteru; Ikemura, Toshimichi; Kajitani, Rei; Toyoda, Atsushi; Itoh, Takehiko; Ogata, Mitsuaki; Miura, Ikuo; Wada, Kennosuke; Wada, Yasuhiro; Satta, Yoko

doi:10.26508/lsa.202000905

Cited by 10 publications

(9 citation statements)

References 41 publications

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“…4E) (Abe et al, 2006c), 12 Drosophila species (Fig. 4F) , eight frogs (Katsura et al, 2021) and 13 plant species and found high separation ability even for closely related species.…”

Section: Analyses Of Various Eukaryotic Genomesmentioning

confidence: 88%

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

et al. 2021

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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: an unsupervised AI. As a specific target, we first selected SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) because a large number of 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 that appeared after the epidemic started could be connected to mutations, and the BLSOM for 15-mers revealed the mutations that contributed to separation into known clades and their subgroups. After introducing the detailed methodological strategies, we explain BLSOMs for various topics, such as 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 explain BLSOMs for various eukaryotes, including fishes, frogs and Drosophila species, and found a high separation ability among closely related species. When analyzing the human genome, we found enrichments in transcription factor-binding sequences in centromeric and pericentromeric heterochromatin regions. The tDNAs (tRNA genes) could be separated according to their corresponding amino acid.

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“…4E) (Abe et al, 2006c), 12 Drosophila species (Fig. 4F) , eight frogs (Katsura et al, 2021) and 13 plant species and found high separation ability even for closely related species.…”

Section: Analyses Of Various Eukaryotic Genomesmentioning

confidence: 88%

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

et al. 2021

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“…S8, we show examples of the three longest scaffolds of all three bats, and evident terminal peaks and internal peaks are observed, although their height and thickness vary among species. It should also be noted that we have previously observed Mb-level CpG islands near the ends of frog chromosomes [33], and the evolutionary processes that formed the Mb-level CpG islands are therefore of interest.…”

Section: Analyses Of Cg-containing Degepentas In Three Batsmentioning

confidence: 81%

Comparative genomic analysis of the human genome and six bat genomes using unsupervised machine learning: Mb-level CpG and TFBS islands

et al. 2022

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Background Emerging infectious disease-causing RNA viruses, such as the SARS-CoV-2 and Ebola viruses, are thought to rely on bats as natural reservoir hosts. Since these zoonotic viruses pose a great threat to humans, it is important to characterize the bat genome from multiple perspectives. Unsupervised machine learning methods for extracting novel information from big sequence data without prior knowledge or particular models are highly desirable for obtaining unexpected insights. We previously established a batch-learning self-organizing map (BLSOM) of the oligonucleotide composition that reveals novel genome characteristics from big sequence data. Results In this study, using the oligonucleotide BLSOM, we conducted a comparative genomic study of humans and six bat species. BLSOM is an explainable-type machine learning algorithm that reveals the diagnostic oligonucleotides contributing to sequence clustering (self-organization). When unsupervised machine learning reveals unexpected and/or characteristic features, these features can be studied in more detail via the much simpler and more direct standard distribution map method. Based on this combined strategy, we identified the Mb-level enrichment of CG dinucleotide (Mb-level CpG islands) around the termini of bat long-scaffold sequences. In addition, a class of CG-containing oligonucleotides were enriched in the centromeric and pericentromeric regions of human chromosomes. Oligonucleotides longer than tetranucleotides often represent binding motifs for a wide variety of proteins (e.g., transcription factor binding sequences (TFBSs)). By analyzing the penta- and hexanucleotide composition, we observed the evident enrichment of a wide range of hexanucleotide TFBSs in centromeric and pericentromeric heterochromatin regions on all human chromosomes. Conclusion Function of transcription factors (TFs) beyond their known regulation of gene expression (e.g., TF-mediated looping interactions between two different genomic regions) has received wide attention. The Mb-level TFBS and CpG islands are thought to be involved in the large-scale nuclear organization, such as centromere and telomere clustering. TFBSs, which are enriched in centromeric and pericentromeric heterochromatin regions, are thought to play an important role in the formation of nuclear 3D structures. Our machine learning-based analysis will help us to understand the differential features of nuclear 3D structures in the human and bat genomes.

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“…S8, we show examples of the three longest scaffolds of all three bats, and evident terminal peaks and internal peaks are observed, although their height and thickness vary among species. It should also be noted that we have previously observed the Mb-level CpG islands near the ends of frog chromosomes [27], and therefore, the evolutionary processes that formed the Mb-level CpG islands are of interest.…”

Section: Analyses Of Cg-containing Degepenta In the 3 Batsmentioning

confidence: 86%

Comparative genomics of the human genome and six bat genomes using AI: Mb-level CpG and TFBS islands

Iwasaki

Ikemura

Wada

et al. 2022

Preprint

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BackgroundEmerging infectious disease RNA viruses, such as the SARS-CoV-2 and Ebola viruses, are thought to rely on bats as their natural reservoir hosts. Since these zoonotic viruses pose a great threat to humans, it should be important to characterize the bat genome from multiple perspectives. Unsupervised artificial intelligence (AI) methods extracting novel information from big sequence data without prior knowledge or particular models are highly desirable for obtaining unexpected insights. We previously established a batch-learning self-organizing map (BLSOM) of the oligonucleotide composition that reveals novel genome characteristics from big sequence data.ResultsIn this study, using the oligonucleotide BLSOM, we conducted a comparative genome study of humans and bats. BLSOM is an explainable-type AI that reveals the diagnostic oligonucleotides contributing to sequence clustering (self-organization). When the unsupervised AI reveals unexpected and/or characteristic features, these features can be studied in more detail via the much simpler and more direct standard distribution map method. Based on this combined strategy, we identified the Mb-level enrichment of CG (Mb-level CpG islands) around the termini of bat long-scaffold sequences. In addition, a class of CG-containing oligonucleotides were enriched also in the centromeric and pericentromeric regions of human chromosomes. Oligonucleotides longer than tetranucleotides often represent binding motifs for a wide variety of proteins, e.g., transcription factor binding sequences (TFBSs). By analyzing penta- and hexanucleotide compositions, we observed the evident enrichment of a wide range of hexanucleotide TFBSs in centromeric and pericentromeric heterochromatin regions on all human chromosomes.ConclusionTFBSs which are enriched in centromere and pericentromeric heterochromatin regions, are thought to play an important role in the formation of nuclear 3D structures. Our AI-based analysis should help us to understand differential features of nuclear 3D structures of the human and bat genomes.

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Comparative genomics of Glandirana rugosa using unsupervised AI reveals a high CG frequency

Cited by 10 publications

References 41 publications

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

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

Comparative genomic analysis of the human genome and six bat genomes using unsupervised machine learning: Mb-level CpG and TFBS islands

Comparative genomics of the human genome and six bat genomes using AI: Mb-level CpG and TFBS islands

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