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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.

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.

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.

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.

Genome sequencing of a unique frog (Glandirana rugosa) having XY/ZW systems within the species and comparative genomics with other six frogs were performed using a batch-learning self-organizing map, which is unsupervised AI for oligonucleotide compositions, to clarify its genome characteristics., The Japanese wrinkled frog (Glandirana rugosa) 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 G. rugosa using a batch-learning self-organizing map which is unsupervised artificial intelligence for oligonucleotide compositions. To clarify genome characteristics of G. rugosa, we compared its short oligonucleotide compositions in all 1-Mb genomic fragments with those of other six frog species (Pyxicephalus adspersus, Rhinella marina, Spea multiplicata, Leptobrachium leishanense, Xenopus laevis, and Xenopus tropicalis). In G. rugosa, we found an Mb-level large size of repeat sequences having a high identity with the W chromosome of the African bullfrog (P. adspersus). Our study concluded that G. rugosa has unique genome characteristics with a high CG frequency, and its genome is assumed to heterochromatinize a large size of genome via methylataion of CG.

BackgroundWhen 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.ResultsTime-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.ConclusionsClade-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.

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. 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. The BLSOM has powerful image display capabilities and enables efficient knowledge discovery about viral evolutionary processes.

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.

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. 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. The BLSOM has powerful image display capabilities and enables efficient knowledge discovery about viral evolutionary processes.

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.

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., Highlights • Time-series change in mono- and dinucleotide compositions in SARS-CoV-2 genome • The time-series change found for SARS-CoV-2 differed from that of Zaire ebolavirus. • Sequence alignment-free method to search advantageous mutation in viral genomes • Seven mutations of SARS-CoV-2 rapidly expanding their population frequency • A method other than phylogenetic tree construction for viral evolutionary study

Transfer RNA genes (tDNAs) are essential genes that encode tRNAs in all species. To understand new functions of tDNAs, other than that of encoding tRNAs, we used ENCODE data to examine binding characteristics of transcription factors (TFs) for all tDNA regions (489 loci) in the human genome. We divided the tDNAs into three groups based on the number of TFs that bound to them. At the two extremes were tDNAs to which many TFs bound (Group 1) and those to which no TFs bound (Group 3). Several TFs involved in chromatin remodeling such as ATF3, EP300 and TBL1XR1 bound to almost all Group 1 tDNAs. Furthermore, almost all Group 1 tDNAs included DNase I hypersensitivity sites and may thus interact with other chromatin regions through their bound TFs, and they showed highly conserved synteny across tetrapods. In contrast, Group 3 tDNAs did not possess these characteristics. These data suggest the presence of a previously uncharacterized function of these tDNAs. We also examined binding of CTCF to tDNAs and their involvement in topologically associating domains (TADs) and lamina-associated domains (LADs), which suggest a new perspective on the evolution and function of tDNAs.

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. For metagenome sequences, however, high-density sequencing does not necessarily increase the sequence quality because multiple and unknown genomes, including those of closely related species, are likely to exist in the sample. Therefore, a function prediction method that is robust against sequence errors becomes an increased need. Here, we present a method for predicting protein gene function that does not depend on sequence similarity searches. Using an unsupervised machine learning method called BLSOM (batch-learning self-organizing map) for short oligopeptide frequencies, we previously developed a sequence alignment-free method for clustering bacterial protein genes according to clusters of orthologous groups of proteins (COGs), without using information from COGs during machine learning. This allows function-unknown proteins to cluster with function-known proteins, based solely on similarity with respect to oligopeptide frequency, although the method required high-performance supercomputers (HPCs). Based on a wide range of knowledge obtained with HPCs, we have now developed a strategy to correlate function-unknown proteins with COG categories, using only oligopeptide frequency distances (OPDs), which can be conducted with PC-level computers. The OPD strategy is suitable for predicting the functions of proteins with low sequence similarity and is applied here to predict the functions of a large number of gene candidates discovered using metagenome sequencing.

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. To study molecular mechanisms of cell type-dependent chromocenter formation, we analyzed distribution patterns of occurrence per Mb of hexa- and heptanucleotide TFBSs, which have been compiled by the SwissRegulon Portal, and of CpG-containing oligonucleotides. We found Mb-level islands enriched for TFBSs and CpG-containing oligonucleotides in centromeric and pericentromeric regions on all human chromosomes except chrY. Considering molecular mechanisms for cell type-dependent centromere clustering, the chromosome-dependent enrichment of a set of TFBSs and CpG-containing oligonucleotides is of particular interest, since the cellular content of TFs and methyl-CpG-binding proteins exhibits cell type-dependent regulation. A newly introduced BLSOM, which analyzed occurrences of a total of 3,946 octanucleotide TFBSs compiled by the SwissRegulon Portal, has self-organized (separated) the sequences that are characteristically enriched in TFBSs and shown that these sequences are derived primarily from centromeric and pericentromeric constitutive heterochromatin regions. Furthermore, the BLSOM identified and visualized characteristic TFBSs that are enriched in these regions. By analyzing Hi-C data for interchromosomal interactions, the present study showed that the chromatin segments supporting the interchromosomal interactions locate primarily in Mb-level TFBS and CpG islands and are thus enriched for a wide variety of TFBSs and CG-containing oligonucleotides.

Oligonucleotides are key elements of nucleic acid therapeutics such as small interfering RNAs (siRNAs). Influenza and Ebolaviruses are zoonotic RNA viruses mutating very rapidly, and their sequence changes must be characterized intensively to design therapeutic oligonucleotides with long utility. Focusing on a total of 182 experimentally validated siRNAs for influenza A, B and Ebolaviruses compiled by the siRNA database, we conducted time-series analyses of occurrences of siRNA targets in these viral genomes. Reflecting their high mutation rates, occurrences of target oligonucleotides evidently fluctuate in viral populations and often disappear. Time-series analysis of the one-base changed sequences derived from each original target identified the oligonucleotide that shows a compensatory increase and will potentially become the 'awaiting-type oligonucleotide'; the combined use of this oligonucleotide with the original can provide therapeutics with long utility. This strategy is also useful for assigning diagnostic reverse transcription-PCR primers with long utility.

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. We have developed Self-Compressing BLSOM (SC-BLSOM) for reduction of computation time, which allows us to carry out comprehensive analysis of big sequence data without the use of high-performance supercomputers. The strategy of SC-BLSOM is to hierarchically construct BLSOMs according to data class, such as phylotype. The first-layer BLSOM was constructed with each of the divided input data pieces that represents the data subclass, such as phylotype division, resulting in compression of the number of data pieces. The second BLSOM was constructed with a total of weight vectors obtained in the first-layer BLSOMs. We compared SC-BLSOM with the conventional BLSOM by analyzing bacterial genome sequences. SC-BLSOM could be constructed faster than BLSOM and cluster the sequences according to phylotype with high accuracy, showing the method’s suitability for efficient knowledge discovery from 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. This unsupervised clustering can reveal combinatorial oligonucleotide motifs that are responsible for the amino acid-dependent clustering, as well as other functionally and structurally important consensus motifs, which have been evolutionarily conserved. BLSOM is also useful for identifying tDNAs as phylogenetic markers for special phylotypes. When we constructed BLSOM with 'species-unknown' tDNAs from metagenomic sequences plus 'species-known' microbial tDNAs, a large portion of metagenomic tDNAs self-organized with species-known tDNAs, yielding information on microbial communities in environmental samples. BLSOM can also enhance accuracy in the tDNA database obtained from big sequence data. This unsupervised data mining should become important for studying numerous functionally unclear RNAs obtained from a wide range of organisms.

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. In the present study, we constructed BLSOMs for oligonucleotide compositions in fragment sequences (e.g. 100 kb) from a wide range of vertebrates, including coelacanth, and found that the sequences were clustered primarily according to species without species information. As one of the nearest living relatives of tetrapod ancestors, coelacanth is believed to provide access to the phenotypic and genomic transitions leading to the emergence of tetrapods. The characteristic oligonucleotide composition found for coelacanth was connected with the lowest dinucleotide CG occurrence (i.e. the highest CG suppression) among fishes, which was rather equivalent to that of tetrapods. This evident CG suppression in coelacanth should reflect molecular evolutionary processes of epigenetic systems including DNA methylation during vertebrate evolution. Sequence of a de novo DNA methylase (Dntm3a) of coelacanth was found to be more closely related to that of tetrapods than that of other fishes.

Since oligonucleotide composition in the genome sequence varies significantly among species even among those possessing the same genome G + C%, the composition has been used to distinguish a wide range of genomes and called as “genome signature”. Oligonucleotides often represent motif sequences responsible for sequence-specific protein binding (e.g., transcription-factor binding). Occurrences of such motif oligonucleotides in the genome should be biased compared to those observed in random sequences and may differ among genomes and genomic portions. Self-Organizing Map (SOM) is a powerful tool for clustering high-dimensional data such as oligonucleotide composition on one plane. We previously modified the conventional SOM for genome informatics to batch learning SOM or “BLSOM”. When we constructed BLSOMs to analyze pentanucleotide composition in 20-, 50-, and 100-kb sequences derived from the human genome, BLSOMs did not classify human sequences according to chromosome but revealed several specific zones composed primarily of sequences derived from pericentric regions. Interestingly, various transcription-factor-binding motifs were characteristically overrepresented in pericentric regions but underrepresented in most genomic sequences. When we focused on much shorter sequences (e.g., 1 kb), the clustering of transcription-factor-binding motifs was evident in pericentric, subtelomeric and sex chromosome pseudoautosomal regions. The biological significance of the clustering in these regions was discussed in connection with cell-type and -stage-dependent chromocenter formation and nuclear organization. Electronic supplementary material The online version of this article (doi:10.1007/s10577-013-9371-y) contains supplementary material, which is available to authorized users.

Biology is increasingly becoming a data-intensive science with the recent progress of the omics fields, e.g. genomics, transcriptomics, proteomics and metabolomics. The species-metabolite relationship database, KNApSAcK Core, has been widely utilized and cited in metabolomics research, and chronological analysis of that research work has helped to reveal recent trends in metabolomics research. To meet the needs of these trends, the KNApSAcK database has been extended by incorporating a secondary metabolic pathway database called Motorcycle DB. We examined the enzyme sequence diversity related to secondary metabolism by means of batch-learning self-organizing maps (BL-SOMs). Initially, we constructed a map by using a big data matrix consisting of the frequencies of all possible dipeptides in the protein sequence segments of plants and bacteria. The enzyme sequence diversity of the secondary metabolic pathways was examined by identifying clusters of segments associated with certain enzyme groups in the resulting map. The extent of diversity of 15 secondary metabolic enzyme groups is discussed. Data-intensive approaches such as BL-SOM applied to big data matrices are needed for systematizing protein sequences. Handling big data has become an inevitable part of biology.

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. In the recent West Africa Ebola outbreak, directional time-series changes in a wide range of oligonucleotides were observed in common for three geographic areas, and the directional changes were observed also for the recent MERS coronavirus epidemics starting in the Middle East. In addition, common directional changes in human influenza A viruses were observed for three subtypes, whose epidemics started independently. Long oligonucleotides that showed an evident directional change observed in common for the three subtypes corresponded to some of influenza A siRNAs, whose activities have been experimentally proven. Predicting directional and reoccurring changes in oligonucleotide composition should become important for designing diagnostic RT-PCR primers and therapeutic oligonucleotides with long effectiveness.

Influenza virus poses a significant threat to public health, as exemplified by the recent introduction of the new pandemic strain H1N1/09 into human populations. Pandemics have been initiated by the occurrence of novel changes in animal sources that eventually adapt to human. One important issue in studies of viral genomes, particularly those of influenza virus, is to predict possible changes in genomic sequence that will become hazardous. We previously established a clustering method termed ‘BLSOM’ (batch-learning self-organizing map) that does not depend on sequence alignment and can characterize and compare even 1 million genomic sequences in one run. Strategies for comparing a vast number of genomic sequences simultaneously become increasingly important in genome studies because of remarkable progresses in nucleotide sequencing. In this study, we have constructed BLSOMs based on the oligonucleotide and codon composition of all influenza A viral strains available. Without prior information with regard to their hosts, sequences derived from strains isolated from avian or human sources were successfully clustered according to the hosts. Notably, the pandemic H1N1/09 strains have oligonucleotide and codon compositions that are clearly different from those of human seasonal influenza A strains. This enables us to infer future directional changes in the influenza A viral genome.

As a result of remarkable progresses of DNA sequencing technology, vast quantities of genomic sequences have been decoded. Homology search for amino acid sequences, such as BLAST, has become a basic tool for assigning functions of genes/proteins when genomic sequences are decoded. Although the homology search has clearly been a powerful and irreplaceable method, the functions of only 50% or fewer of genes can be predicted when a novel genome is decoded. A prediction method independent of the homology search is urgently needed. By analyzing oligonucleotide compositions in genomic sequences, we previously developed a modified Self-Organizing Map ‘BLSOM’ that clustered genomic fragments according to phylotype with no advance knowledge of phylotype. Using BLSOM for di-, tri- and tetrapeptide compositions, we developed a system to enable separation (self-organization) of proteins by function. Analyzing oligopeptide frequencies in proteins previously classified into COGs (clusters of orthologous groups of proteins), BLSOMs could faithfully reproduce the COG classifications. This indicated that proteins, whose functions are unknown because of lack of significant sequence similarity with function-known proteins, can be related to function-known proteins based on similarity in oligopeptide composition. BLSOM was applied to predict functions of vast quantities of proteins derived from mixed genomes in environmental samples.

In clinical practice, glucocorticoids are often used with the aim of modulating the efficacy and toxicity of chemotherapeutic agents. However, how glucocorticoids modulate the pharmacological action of chemotherapeutic agents remains to be clarified. In this study, we generated glucocorticoid receptor (GR)-deficient rat-1 cells to investigate the role of GR in the regulation of cellular sensitivity to irinotecan hydrochloride (CPT-11). Treatment of wild-type rat-1 cells with dexamethasone (DEX) significantly enhanced the cytotoxic effect of CPT-11, whereas the treatment had little effect on the cytotoxicity of CPT-11 in GR-deficient cells. Topoisomerase-I activity in wild-type cells after concomitant treatment with DEX and CPT-11 was significantly lower than that after treatment with CPT-11 alone. DEX treatment also enhanced the inhibitory action of CPT-11 on the phosphatidylinositol 3-kinase–Akt signaling pathway in wild-type cells, accompanied by facilitating caspase-3 activity. These modulatory effects of DEX on the CPT-11–induced cytotoxicity were not observed in GR-deficient cells. Our present findings reveal the underlying mechanism by which GCs enhance the chemotherapeutic effect of CPT-11 and indicate the possibility that the dosage of CPT-11 could be reduced by the combination treatment with GCs, which may attenuate the adverse effect without decreasing anti-tumor activity.[Supplementary Figures: available only at http://dx.doi.org/10.1254/jphs.08219FP] Keywords:: glucocorticoid receptor, dexamethasone, CPT-11, topoisomerase-I, phosphatidylinositol 3-kinase [PI(3)K]–Akt signaling

Novel tools are needed for comprehensive comparisons of the inter- and intraspecies characteristics of a large amounts of available genome sequences. An unsupervised neural network algorithm, Kohonen's Self-Organizing Map (SOM), is an effective tool for clustering and visualizing high-dimensional complex data on a single map. We modified the conventional SOM for genome informatics on the basis of Batch Learning SOM (BLSOM), making the resulting map independent of the order of data input. We generated BLSOMs for oligonucleotide frequencies in fragment sequences (e.g. 10-kb) from 13 plant genomes for which almost complete genome sequences are available. BLSOM recognized species- specific characteristics (key combinations of oligonucleotide frequencies) in most of the fragment sequences, permitting classification (self-organization) of sequences according to species without any information regarding the species during computation. To disclose sequence characteristics of a single genome independently of other genomes, we constructed BLSOMs for sequence fragments from one genome plus computer-generated random sequences. Genomic sequences were clearly separated from random sequences, revealing the oligonucleotides with characteristic occurrence levels in the genomic sequences. We discussed these oligonucleotides diagnostic for genomic sequences, in connection with genetic signal sequences. Because the classification and visualization power is very high, BLSOM is thought to be an efficient and powerful tool for extracting a wide range of genomic information.