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Article ; Online: Oligonucleotide usage in coronavirus genomes mimics that in exon regions in host genomes.

Iwasaki, Yuki / Abe, Takashi / Ikemura, Toshimichi

2023 Volume 20, Issue 1, Page(s) 39

Abstract: Background: Viruses use various host factors for their growth, and efficient growth requires efficient use of these factors. Our previous study revealed that the occurrence frequency of oligonucleotides in the influenza virus genome is distinctly ... ...

Abstract	Background: Viruses use various host factors for their growth, and efficient growth requires efficient use of these factors. Our previous study revealed that the occurrence frequency of oligonucleotides in the influenza virus genome is distinctly different among derived hosts, and the frequency tends to adapt to the host cells in which they grow. We aimed to study the adaptation mechanisms of a zoonotic virus to host cells. Methods: Herein, we compared the frequency of oligonucleotides in the genome of alpha- and betacoronavirus with those in the genomes of humans and bats, which are typical hosts of the viruses. Results: By comparing the oligonucleotide frequency in coronaviruses and their host genomes, we found a statistically tested positive correlation between the frequency of coronaviruses and that of the exon regions of the host from which the virus is derived. To examine the characteristics of early-stage changes in the viral genome, which are assumed to accompany the host change from non-humans to humans, we compared the oligonucleotide frequency between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the beginning of the pandemic and the prevalent variants thereafter, and found changes towards the frequency of the host exon regions. Conclusions: In alpha- and betacoronaviruses, the genome oligonucleotide frequency is thought to change in response to the cellular environment in which the virus is replicating, and actually the frequency has approached the frequency in exon regions in the host.
MeSH term(s)	Animals ; COVID-19 ; SARS-CoV-2 ; Exons ; Genome, Viral ; Chiroptera ; Oligonucleotides
Chemical Substances	Oligonucleotides
Language	English
Publishing date	2023-03-01
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2160640-7
ISSN	1743-422X ; 1743-422X
ISSN (online)	1743-422X
ISSN	1743-422X
DOI	10.1186/s12985-023-01995-3
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Oligonucleotide usage in coronavirus genomes mimics that in exon regions in host genomes

Iwasaki, Yuki / Abe, Takashi / Ikemura, Toshimichi

Virol J. 2023 Dec., v. 20, no. 1 p.39-39

2023

Abstract: BACKGROUND: Viruses use various host factors for their growth, and efficient growth requires efficient use of these factors. Our previous study revealed that the occurrence frequency of oligonucleotides in the influenza virus genome is distinctly ... ...

Abstract	BACKGROUND: Viruses use various host factors for their growth, and efficient growth requires efficient use of these factors. Our previous study revealed that the occurrence frequency of oligonucleotides in the influenza virus genome is distinctly different among derived hosts, and the frequency tends to adapt to the host cells in which they grow. We aimed to study the adaptation mechanisms of a zoonotic virus to host cells. METHODS: Herein, we compared the frequency of oligonucleotides in the genome of alpha- and betacoronavirus with those in the genomes of humans and bats, which are typical hosts of the viruses. RESULTS: By comparing the oligonucleotide frequency in coronaviruses and their host genomes, we found a statistically tested positive correlation between the frequency of coronaviruses and that of the exon regions of the host from which the virus is derived. To examine the characteristics of early-stage changes in the viral genome, which are assumed to accompany the host change from non-humans to humans, we compared the oligonucleotide frequency between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the beginning of the pandemic and the prevalent variants thereafter, and found changes towards the frequency of the host exon regions. CONCLUSIONS: In alpha- and betacoronaviruses, the genome oligonucleotide frequency is thought to change in response to the cellular environment in which the virus is replicating, and actually the frequency has approached the frequency in exon regions in the host.
Keywords	Orthomyxoviridae ; Severe acute respiratory syndrome coronavirus 2 ; exons ; oligonucleotides ; pandemic ; viral genome ; viruses
Language	English
Dates of publication	2023-12
Size	p. 39.
Publishing place	BioMed Central
Document type	Article ; Online
ZDB-ID	2160640-7
ISSN	1743-422X
ISSN	1743-422X
DOI	10.1186/s12985-023-01995-3
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Unsupervised AI reveals insect species-specific genome signatures.

Sawada, Yui / Minei, Ryuhei / Tabata, Hiromasa / Ikemura, Toshimichi / Wada, Kennosuke / Wada, Yoshiko / Nagata, Hiroshi / Iwasaki, Yuki

PeerJ

2024 Volume 12, Page(s) e17025

Abstract: Insects are a highly diverse phylogeny and possess a wide variety of traits, including the presence or absence of wings and metamorphosis. These diverse traits are of great interest for studying genome evolution, and numerous comparative genomic studies ... ...

Abstract	Insects are a highly diverse phylogeny and possess a wide variety of traits, including the presence or absence of wings and metamorphosis. These diverse traits are of great interest for studying genome evolution, and numerous comparative genomic studies have examined a wide phylogenetic range of insects. Here, we analyzed 22 insects belonging to a wide phylogenetic range (Endopterygota, Paraneoptera, Polyneoptera, Palaeoptera, and other insects) by using a batch-learning self-organizing map (BLSOM) for oligonucleotide compositions in their genomic fragments (100-kb or 1-Mb sequences), which is an unsupervised machine learning algorithm that can extract species-specific characteristics of the oligonucleotide compositions (genome signatures). The genome signature is of particular interest in terms of the mechanisms and biological significance that have caused the species-specific difference, and can be used as a powerful search needle to explore the various roles of genome sequences other than protein coding, and can be used to unveil mysteries hidden in the genome sequence. Since BLSOM is an unsupervised clustering method, the clustering of sequences was performed based on the oligonucleotide composition alone, without providing information about the species from which each fragment sequence was derived. Therefore, not only the interspecies separation, but also the intraspecies separation can be achieved. Here, we have revealed the specific genomic regions with oligonucleotide compositions distinct from the usual sequences of each insect genome,
MeSH term(s)	Animals ; Humans ; Phylogeny ; Genome, Human ; Genome, Insect/genetics ; Oligonucleotides/genetics ; Artificial Intelligence
Chemical Substances	Oligonucleotides
Language	English
Publishing date	2024-03-06
Publishing country	United States
Document type	Journal Article
ZDB-ID	2703241-3
ISSN	2167-8359 ; 2167-8359
ISSN (online)	2167-8359
ISSN	2167-8359
DOI	10.7717/peerj.17025
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Unsupervised explainable AI for molecular evolutionary study of forty thousand SARS-CoV-2 genomes.

Iwasaki, Yuki / Abe, Takashi / Wada, Kennosuke / Wada, Yoshiko / Ikemura, Toshimichi

BMC microbiology

2022 Volume 22, Issue 1, Page(s) 73

Abstract: Background: Unsupervised AI (artificial intelligence) can obtain novel knowledge from big data without particular models or prior knowledge and is highly desirable for unveiling hidden features in big data. SARS-CoV-2 poses a serious threat to public ... ...

Abstract	Background: Unsupervised AI (artificial intelligence) can obtain novel knowledge from big data without particular models or prior knowledge and is highly desirable for unveiling hidden features in big data. SARS-CoV-2 poses a serious threat to public health and one important issue in characterizing this fast-evolving virus is to elucidate various aspects of their genome sequence changes. We previously established unsupervised AI, a BLSOM (batch-learning SOM), which can analyze five million genomic sequences simultaneously. The present study applied the BLSOM to the oligonucleotide compositions of forty thousand SARS-CoV-2 genomes. Results: While only the oligonucleotide composition was given, the obtained clusters of genomes corresponded primarily to known main clades and internal divisions in the main clades. Since the BLSOM is explainable AI, it reveals which features of the oligonucleotide composition are responsible for clade clustering. Additionally, BLSOM also provided information concerning the special genomic region possibly undergoing RNA modifications. Conclusions: The BLSOM has powerful image display capabilities and enables efficient knowledge discovery about viral evolutionary processes, and it can complement phylogenetic methods based on sequence alignment.
MeSH term(s)	Artificial Intelligence ; COVID-19 ; Evolution, Molecular ; Humans ; Phylogeny ; SARS-CoV-2/genetics
Language	English
Publishing date	2022-03-10
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2041505-9
ISSN	1471-2180 ; 1471-2180
ISSN (online)	1471-2180
ISSN	1471-2180
DOI	10.1186/s12866-022-02484-3
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: AI-based search for convergently expanding, advantageous mutations in SARS-CoV-2 by focusing on oligonucleotide frequencies.

Ikemura, Toshimichi / Iwasaki, Yuki / Wada, Kennosuke / Wada, Yoshiko / Abe, Takashi

PloS one

2022 Volume 17, Issue 8, Page(s) e0273860

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 ... ...

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.
MeSH term(s)	Artificial Intelligence ; COVID-19/genetics ; Genome, Viral ; Humans ; Machine Learning ; Mutation ; Oligonucleotides/genetics ; Phylogeny ; SARS-CoV-2/genetics ; Spike Glycoprotein, Coronavirus/genetics
Chemical Substances	Oligonucleotides ; Spike Glycoprotein, Coronavirus ; spike protein, SARS-CoV-2
Language	English
Publishing date	2022-08-31
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0273860
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Human cell-dependent, directional, time-dependent changes in the mono- and oligonucleotide compositions of SARS-CoV-2 genomes.

Iwasaki, Yuki / Abe, Takashi / Ikemura, Toshimichi

BMC microbiology

2021 Volume 21, Issue 1, Page(s) 89

Abstract: Background: When a virus that has grown in a nonhuman host starts an epidemic in the human population, human cells may not provide growth conditions ideal for the virus. Therefore, the invasion of severe acute respiratory syndrome coronavirus-2 (SARS- ... ...

Abstract	Background: When a virus that has grown in a nonhuman host starts an epidemic in the human population, human cells may not provide growth conditions ideal for the virus. Therefore, the invasion of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which is usually prevalent in the bat population, into the human population is thought to have necessitated changes in the viral genome for efficient growth in the new environment. In the present study, to understand host-dependent changes in coronavirus genomes, we focused on the mono- and oligonucleotide compositions of SARS-CoV-2 genomes and investigated how these compositions changed time-dependently in the human cellular environment. We also compared the oligonucleotide compositions of SARS-CoV-2 and other coronaviruses prevalent in humans or bats to investigate the causes of changes in the host environment. Results: Time-series analyses of changes in the nucleotide compositions of SARS-CoV-2 genomes revealed a group of mono- and oligonucleotides whose compositions changed in a common direction for all clades, even though viruses belonging to different clades should evolve independently. Interestingly, the compositions of these oligonucleotides changed towards those of coronaviruses that have been prevalent in humans for a long period and away from those of bat coronaviruses. Conclusions: Clade-independent, time-dependent changes are thought to have biological significance and should relate to viral adaptation to a new host environment, providing important clues for understanding viral host adaptation mechanisms.
MeSH term(s)	Animals ; Base Composition ; Chiroptera/virology ; Evolution, Molecular ; Genome, Viral ; Humans ; Oligonucleotides ; SARS-CoV-2/genetics
Chemical Substances	Oligonucleotides
Language	English
Publishing date	2021-03-23
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1471-2180
ISSN (online)	1471-2180
DOI	10.1186/s12866-021-02158-6
Database	MEDical Literature Analysis and Retrieval System OnLINE

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

Iwasaki, Yuki / Ikemura, Toshimichi / Wada, Kennosuke / Wada, Yoshiko / Abe, Takashi

BMC genomics

2022 Volume 23, Issue 1, Page(s) 497

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Animals ; COVID-19/transmission ; Chiroptera/genetics ; Chiroptera/virology ; CpG Islands ; Genome, Human/genetics ; Genomics/methods ; Heterochromatin/chemistry ; Heterochromatin/genetics ; Humans ; Molecular Conformation ; Oligonucleotides/chemistry ; SARS-CoV-2/physiology ; Unsupervised Machine Learning
Chemical Substances	Heterochromatin ; Oligonucleotides
Language	English
Publishing date	2022-07-08
Publishing country	England
Document type	Comparative Study ; Journal Article
ZDB-ID	2041499-7
ISSN	1471-2164 ; 1471-2164
ISSN (online)	1471-2164
ISSN	1471-2164
DOI	10.1186/s12864-022-08664-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: AI-based search for convergently expanding, advantageous mutations in SARS-CoV-2 by focusing on oligonucleotide frequencies.

Toshimichi Ikemura / Yuki Iwasaki / Kennosuke Wada / Yoshiko Wada / Takashi Abe

PLoS ONE, Vol 17, Iss 8, p e

2022 Volume 0273860

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.
Keywords	Medicine ; R ; Science ; Q
Subject code	570
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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

Takashi Abe / Ryuki Furukawa / Yuki Iwasaki / Toshimichi Ikemura

Data Science Journal, Vol 20, Iss

2021 Volume 1

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 ... ...

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.
Keywords	covid-19 ; sars-cov-2 ; oligonucleotide composition ; batch-learning self-organizing map (blsom) ; unsupervised explainable machine learning ; time-series trend ; Science (General) ; Q1-390
Subject code	006
Language	English
Publishing date	2021-09-01T00:00:00Z
Publisher	Ubiquity Press
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Time-series analyses of directional sequence changes in SARS-CoV-2 genomes and an efficient search method for candidates for advantageous mutations for growth in human cells.

Wada, Kennosuke / Wada, Yoshiko / Ikemura, Toshimichi

Gene

2020 Volume 763S, Page(s) 100038

Abstract: We first conducted time-series analysis of mono- and dinucleotide composition for over 10,000 SARS-CoV-2 genomes, as well as over 1500 Zaire ebolavirus genomes, and found clear time-series changes in the compositions on a monthly basis, which should ... ...

Abstract	We first conducted time-series analysis of mono- and dinucleotide composition for over 10,000 SARS-CoV-2 genomes, as well as over 1500 Zaire ebolavirus genomes, and found clear time-series changes in the compositions on a monthly basis, which should reflect viral adaptations for efficient growth in human cells. We next developed a sequence alignment free method that extensively searches for advantageous mutations and rank them in an increase level for their intrapopulation frequency. Time-series analysis of occurrences of oligonucleotides of diverse lengths for SARS-CoV-2 genomes revealed seven distinctive mutations that rapidly expanded their intrapopulation frequency and are thought to be candidates of advantageous mutations for the efficient growth in human cells.
Language	English
Publishing date	2020-08-06
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	391792-7
ISSN	1879-0038 ; 0378-1119
ISSN (online)	1879-0038
ISSN	0378-1119
DOI	10.1016/j.gene.2020.100038
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