Unsupervised AI reveals insect species-specific genome signatures.

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).

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

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.

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

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.

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

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.

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

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.

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.

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.

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.

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.

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

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.

Comparative genomics of using unsupervised AI reveals a high CG frequency.

The Japanese wrinkled frog () is unique in having both XX-XY and ZZ-ZW types of sex chromosomes within the species. The genome sequencing and comparative genomics with other frogs should be important to understand mechanisms of turnover of sex chromosomes within one species or during a short period. In this study, we analyzed the newly sequenced genome of using a batch-learning self-organizing map which is unsupervised artificial intelligence for oligonucleotide compositions.

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.

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.

Heterogeneity of synonymous substitution rates in the Xenopus frog genome.

With the increasing availability of high quality genomic data, there is opportunity to deeply explore the genealogical relationships of different gene loci between closely related species. In this study, we utilized genomes of Xenopus laevis (XLA, a tetraploid species with (L) and (S) sub-genomes) and X. tropicalis (XTR, a diploid species) to investigate whether synonymous substitution rates among orthologous or homoeologous genes displayed any heterogeneity.

A strategy for predicting gene functions from genome and metagenome sequences on the basis of oligopeptide frequency distance.

As a result of the extensive decoding of a massive amount of genomic and metagenomic sequence data, a large number of genes whose functions cannot be predicted by sequence similarity searches are accumulating, and such genes are of little use to science or industry. Current genome and metagenome sequencing largely depend on high-throughput and low-cost methods. In the case of genome sequencing for a single species, high-density sequencing can reduce sequencing errors.

Mb-level CpG and TFBS islands visualized by AI and their roles in the nuclear organization of the human genome.

Unsupervised machine learning that can discover novel knowledge from big sequence data without prior knowledge or particular models is highly desirable for current genome study. We previously established a batch-learning self-organizing map (BLSOM) for oligonucleotide compositions, which can reveal various novel genome characteristics from big sequence data, and found that transcription factor binding sequences (TFBSs) and CpG-containing oligonucleotides are enriched in human centromeric and pericentromeric regions, which support centromere clustering and form the condensed heterochromatin "chromocenter" in interphase nuclei. The number and size of chromocenters, as well as the type of centromeres gathered in individual chromocenters, vary depending on cell type.

An artificial intelligence approach fit for tRNA gene studies in the era of big sequence data.

Unsupervised data mining capable of extracting a wide range of knowledge from big data without prior knowledge or particular models is a timely application in the era of big sequence data accumulation in genome research. By handling oligonucleotide compositions as high-dimensional data, we have previously modified the conventional self-organizing map (SOM) for genome informatics and established BLSOM, which can analyze more than ten million sequences simultaneously. Here, we develop BLSOM specialized for tRNA genes (tDNAs) that can cluster (self-organize) more than one million microbial tDNAs according to their cognate amino acid solely depending on tetra- and pentanucleotide compositions.

Directional and reoccurring sequence change in zoonotic RNA virus genomes visualized by time-series word count.

Ebolavirus, MERS coronavirus and influenza virus are zoonotic RNA viruses, which mutate very rapidly. Viral growth depends on many host factors, but human cells may not provide the ideal growth conditions for viruses invading from nonhuman hosts. The present time-series analyses of short and long oligonucleotide compositions in these genomes showed directional changes in their composition after invasion from a nonhuman host, which are thought to recur after future invasions.

Development of self-compressing BLSOM for comprehensive analysis of big sequence data.

With the remarkable increase in genomic sequence data from various organisms, novel tools are needed for comprehensive analyses of available big sequence data. We previously developed a Batch-Learning Self-Organizing Map (BLSOM), which can cluster genomic fragment sequences according to phylotype solely dependent on oligonucleotide composition and applied to genome and metagenomic studies. BLSOM is suitable for high-performance parallel-computing and can analyze big data simultaneously, but a large-scale BLSOM needs a large computational resource.

CG-containing oligonucleotides and transcription factor-binding motifs are enriched in human pericentric regions.

Unsupervised data mining capable of extracting a wide range of information from big sequence data without prior knowledge or particular models is highly desirable in an era of big data accumulation for research on genes, genomes and genetic systems. By handling oligonucleotide compositions in genomic sequences as high-dimensional data, we have previously modified the conventional SOM (self-organizing map) for genome informatics and established BLSOM for oligonucleotide composition, which can analyze more than ten million sequences simultaneously and is thus suitable for big data analyses. Oligonucleotides often represent motif sequences responsible for sequence-specific binding of proteins such as transcription factors.

tRNADB-CE: tRNA gene database well-timed in the era of big sequence data.

The tRNA gene data base curated by experts "tRNADB-CE" (http://trna.ie.niigata-u.

A novel bioinformatics method for efficient knowledge discovery by BLSOM from big genomic sequence data.

With remarkable increase of genomic sequence data of a wide range of species, novel tools are needed for comprehensive analyses of the big sequence data. Self-Organizing Map (SOM) is an effective tool for clustering and visualizing high-dimensional data such as oligonucleotide composition on one map. By modifying the conventional SOM, we have previously developed Batch-Learning SOM (BLSOM), which allows classification of sequence fragments according to species, solely depending on the oligonucleotide composition.

Evolutionary changes in vertebrate genome signatures with special focus on coelacanth.

With a remarkable increase in genomic sequence data of a wide range of species, novel tools are needed for comprehensive analyses of the big sequence data. Self-organizing map (SOM) is a powerful tool for clustering high-dimensional data on one plane. For oligonucleotide compositions handled as high-dimensional data, we have previously modified the conventional SOM for genome informatics: BLSOM.

Visualization of genome signatures of eukaryote genomes by batch-learning self-organizing map with a special emphasis on Drosophila genomes.

A strategy of evolutionary studies that can compare vast numbers of genome sequences is becoming increasingly important with the remarkable progress of high-throughput DNA sequencing methods. We previously established a sequence alignment-free clustering method "BLSOM" for di-, tri-, and tetranucleotide compositions in genome sequences, which can characterize sequence characteristics (genome signatures) of a wide range of species. In the present study, we generated BLSOMs for tetra- and pentanucleotide compositions in approximately one million sequence fragments derived from 101 eukaryotes, for which almost complete genome sequences were available.

8-oxoguanine causes spontaneous de novo germline mutations in mice.

Spontaneous germline mutations generate genetic diversity in populations of sexually reproductive organisms, and are thus regarded as a driving force of evolution. However, the cause and mechanism remain unclear. 8-oxoguanine (8-oxoG) is a candidate molecule that causes germline mutations, because it makes DNA more prone to mutation and is constantly generated by reactive oxygen species in vivo.

A Novel Bioinformatics Strategy to Analyze Microbial Big Sequence Data for Efficient Knowledge Discovery: Batch-Learning Self-Organizing Map (BLSOM).

With the remarkable increase of genomic sequence data of microorganisms, novel tools are needed for comprehensive analyses of the big sequence data available. The self-organizing map (SOM) is an effective tool for clustering and visualizing high-dimensional data, such as oligonucleotide composition on one map. By modifying the conventional SOM, we developed batch-learning SOM (BLSOM), which allowed classification of sequence fragments (e.

Novel bioinformatics strategies for prediction of directional sequence changes in influenza virus genomes and for surveillance of potentially hazardous strains.

Background: With the remarkable increase of microbial and viral sequence data obtained from high-throughput DNA sequencers, novel tools are needed for comprehensive analysis of the big sequence data. We have developed "Batch-Learning Self-Organizing Map (BLSOM)" which can characterize very many, even millions of, genomic sequences on one plane. Influenza virus is one of zoonotic viruses and shows clear host tropism.