Batch-Learning Self-Organizing Map Research Articles

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). 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, e.g., Mb-level structures found for a grasshopper Schistocerca americana. One aim of this study was to compare the genome characteristics of insects with those of vertebrates, especially humans, which are phylogenetically distant from insects. Recently, humans seem to be the "model organism" for which a large amount of information has been accumulated using a variety of cutting-edge and high-throughput technologies. Therefore, it is reasonable to use the abundant information from humans to study insect lineages. The specific regions of Mb length with distinct oligonucleotide compositions have also been previously observed in the human genome. These regions were enriched by transcription factor binding motifs (TFBSs) and hypothesized to be involved in the three-dimensional arrangement of chromosomal DNA in interphase nuclei. The present study characterized the species-specific oligonucleotide compositions (i.e., genome signatures) in insect genomes and identified specific genomic regions with distinct oligonucleotide compositions.

Oligonucleotide skews enable comprehensive and insightful characterization of GC- and TA-skew properties observed throughout the human genome with support of unsupervised AI with reference to gene- and Alu-polarity skews

GC and TA skew are fundamental genomic features observed widely from bacteria to humans. They have been described as replication- and transcription-coupled skews because they are mainly caused by differences in mutation and repair biases between complementary DNA strands in replication and transcription processes. The skews are clearly observed in bacteria, plants and birds, but are more difficult to accurately identify in mammals, including humans. The main reason for this difficulty is that the correlation between GC and TA skew is not high for their variation in the mammalian genomes. In this study, we focused not only on these mononucleotide skews but also on di- and trinucleotide skews, first in 100-kb sequences of two short human chromosomes (chr21 and 22), and by using the conventional distribution map analysis, we clearly observed these skews over these chromosomes, except for centromeric heterochromatin regions where diverse repetitive sequences are densely clustered. Then, by using a batch learning self-organizing map (BLSOM), which can display large amounts of sequence data at once, we detected the oligonucleotide skews across nearly the entire human genome. Next, to understand skews within centromeric heterochromatin regions, we focused on Alu sequences ubiquitously distributed in the human genome and analyzed mono-, di-, and trinucleotide skews by referring to the polarity of Alu sequences. Since this polarity is unevenly distributed in the human genome, a clear TA skew was observed by reflecting the evident A-richness to T of Alu elements. However, due to some degree of their G-richness to C, TA and GC skews showed inversely correlated variations, when analyzing Alu sequences alone. Notably, when we analyzed genome sequences after excluding Alu sequences, the concordant correlation between GC- and TA-skew variations increased, indicating that a large amount of Alu sequences has made the detection of these mononucleotide skews in the human genome more difficult than in other organisms. Analysis of oligonucleotide skews using BLSOM is thought to be suitable for characterizing sequence features in skew transition regions.

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

BackgroundEmerging 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.ResultsIn 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.ConclusionFunction 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.

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

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.

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. 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.

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

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.

Batch-Learning Self-Organizing Map Identifies Horizontal Gene Transfer Candidates and Their Origins in Entire Genomes.

Horizontal gene transfer (HGT) has been widely suggested to play a critical role in the environmental adaptation of microbes; however, the number and origin of the genes in microbial genomes obtained through HGT remain unknown as the frequency of detected HGT events is generally underestimated, particularly in the absence of information on donor sequences. As an alternative to phylogeny-based methods that rely on sequence alignments, we have developed an alignment-free clustering method on the basis of an unsupervised neural network “Batch-Learning Self-Organizing Map (BLSOM)” in which sequence fragments are clustered based solely on oligonucleotide similarity without taxonomical information, to detect HGT candidates and their origin in entire genomes. By mapping the microbial genomic sequences on large-scale BLSOMs constructed with nearly all prokaryotic genomes, HGT candidates can be identified, and their origin assigned comprehensively, even for microbial genomes that exhibit high novelty. By focusing on two types of Alphaproteobacteria, specifically psychrotolerant Sphingomonas strains from an Antarctic lake, we detected HGT candidates using BLSOM and found higher proportions of HGT candidates from organisms belonging to Betaproteobacteria in the genomes of these two Antarctic strains compared with those of continental strains. Further, an origin difference was noted in the HGT candidates found in the two Antarctic strains. Although their origins were highly diversified, gene functions related to the cell wall or membrane biogenesis were shared among the HGT candidates. Moreover, analyses of amino acid frequency suggested that housekeeping genes and some HGT candidates of the Antarctic strains exhibited different characteristics to other continental strains. Lys, Ser, Thr, and Val were the amino acids found to be increased in the Antarctic strains, whereas Ala, Arg, Glu, and Leu were decreased. Our findings strongly suggest a low-temperature adaptation process for microbes that may have arisen convergently as an independent evolutionary strategy in each Antarctic strain. Hence, BLSOM analysis could serve as a powerful tool in not only detecting HGT candidates and their origins in entire genomes, but also in providing novel perspectives into the environmental adaptations of microbes.

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. 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.

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. 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.

Viral population analysis of the taiga tick, Ixodes persulcatus, by using Batch Learning Self-Organizing Maps and BLAST search.

Ticks transmit a wide range of viral, bacterial, and protozoal pathogens, which are often zoonotic. Several novel tick-borne viral pathogens have been reported during the past few years. The aim of this study was to investigate a diversity of tick viral populations, which may contain as-yet unidentified viruses, using a combination of high throughput pyrosequencing and Batch Learning Self-Organizing Map (BLSOM) program, which enables phylogenetic estimation based on the similarity of oligonucleotide frequencies. DNA/cDNA prepared from virus-enriched fractions obtained from Ixodes persulcatus ticks was pyrosequenced. After de novo assembly, contigs were cataloged by the BLSOM program. In total 41 different viral families and order including those previously associated with human and animal diseases such as Bunyavirales, Flaviviridae, and Reoviridae, were detected. Therefore, our strategy is applicable for viral population analysis of other arthropods of medical and veterinary importance, such as mosquitos and lice. The results lead to the contribution to the prediction of emerging tick-borne viral diseases. A sufficient understanding of tick viral populations will also empower to analyze and understand tick biology including vector competency and interactions with other pathogens.

Phytochemical profiles of Brassicaceae vegetables and their multivariate characterization using chemometrics

Twenty-eight metabolites were extracted from nine Brassicaceae of Korean origin (broccoli, Brussels sprouts, cabbage, Chinese cabbage, kale, kohlrabi, pak choi, radish sprouts, and red cabbage) and analyzed using gas chromatography–mass spectrometry and high-performance liquid chromatography. Principal components analysis (PCA), orthogonal projection to latent structure-discriminant analysis (OPLS-DA), Pearson’s correlation analysis, hierarchical clustering analysis (HCA), and batch learning self-organizing map analysis (BL-SOM) were used to visualize metabolite pattern differences among Brassicaceae samples. The PCA score plots from the metabolic data sets provided a clear distinction between Brassica species and radish sprouts (genus Raphanus L.). Additionally, B. oleracea L. varieties were differentiated from B. rapa L. varieties by PCA and OPLS-DA score plots. HCA and BL-SOM of these metabolites clustered metabolites that are metabolically related. This study demonstrates that plants’ characterization by multivariate statistical analysis using metabolic profiling allows distinguishing their phenotypes and identifying desired characteristics.

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. In the present study, we introduce the oligonucleotide BLSOM used for characterization of vertebrate genome sequences. We first analyzed pentanucleotide compositions in 100 kb sequences derived from a wide range of vertebrate genomes and then the compositions in the human and mouse genomes in order to investigate an efficient method for detecting differences between the closely related genomes. BLSOM can recognize the species-specific key combination of oligonucleotide frequencies in each genome, which is called a “genome signature,” and the specific regions specifically enriched in transcription-factor-binding sequences. Because the classification and visualization power is very high, BLSOM is an efficient powerful tool for extracting a wide range of information from massive amounts of genomic sequences (i.e., big sequence data).

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

BackgroundWith 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. Important issues for bioinformatics studies of influenza viruses are prediction of genomic sequence changes in the near future and surveillance of potentially hazardous strains.MethodsTo characterize sequence changes in influenza virus genomes after invasion into humans from other animal hosts, we applied BLSOMs to analyses of mono-, di-, tri-, and tetranucleotide compositions in all genome sequences of influenza A and B viruses and found clear host-dependent clustering (self-organization) of the sequences.ResultsViruses isolated from humans and birds differed in mononucleotide composition from each other. In addition, host-dependent oligonucleotide compositions that could not be explained with the host-dependent mononucleotide composition were revealed by oligonucleotide BLSOMs. Retrospective time-dependent directional changes of mono- and oligonucleotide compositions, which were visualized for human strains on BLSOMs, could provide predictive information about sequence changes in newly invaded viruses from other animal hosts (e.g. the swine-derived pandemic H1N1/09).ConclusionsBasing on the host-dependent oligonucleotide composition, we proposed a strategy for prediction of directional changes of virus sequences and for surveillance of potentially hazardous strains when introduced into human populations from non-human sources. Millions of genomic sequences from infectious microbes and viruses have become available because of their medical and social importance, and BLSOM can characterize the big data and support efficient knowledge discovery.

Notable clustering of transcription-factor-binding motifs in human pericentric regions and its biological significance

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 materialThe online version of this article (doi:10.1007/s10577-013-9371-y) contains supplementary material, which is available to authorized users.

Exploratory analysis of high-throughput metabolomic data

In order to make sense of the sheer volume of metabolomic data that can be generated using current technology, robust data analysis tools are essential. We propose the use of the growing self-organizing map (GSOM) algorithm and by doing so demonstrate that a deeper analysis of metabolomics data is possible in comparison to the widely used batch-learning self-organizing map, hierarchical cluster analysis and partitioning around medoids algorithms on simulated and real-world time-course metabolomic datasets. We then applied GSOM to a recently published dataset representing metabolome response patterns of three wheat cultivars subject to a field simulated cyclic drought stress. This novel and information rich analysis provided by the proposed GSOM framework can be easily extended to other high-throughput metabolomics studies.

A novel approach, based on BLSOMs (Batch Learning Self-Organizing Maps), to the microbiome analysis of ticks

Ticks transmit a variety of viral, bacterial and protozoal pathogens, which are often zoonotic. The aim of this study was to identify diverse tick microbiomes, which may contain as-yet unidentified pathogens, using a metagenomic approach. DNA prepared from bacteria/archaea-enriched fractions obtained from seven tick species, namely Amblyomma testudinarium, Amblyomma variegatum, Haemaphysalis formosensis, Haemaphysalis longicornis, Ixodes ovatus, Ixodes persulcatus and Ixodes ricinus, was subjected to pyrosequencing after whole-genome amplification. The resulting sequence reads were phylotyped using a Batch Learning Self-Organizing Map (BLSOM) program, which allowed phylogenetic estimation based on similarity of oligonucleotide frequencies, and functional annotation by BLASTX similarity searches. In addition to bacteria previously associated with human/animal diseases, such as Anaplasma, Bartonella, Borrelia, Ehrlichia, Francisella and Rickettsia, BLSOM analysis detected microorganisms belonging to the phylum Chlamydiae in some tick species. This was confirmed by pan-Chlamydia PCR and sequencing analysis. Gene sequences associated with bacterial pathogenesis were also identified, some of which were suspected to originate from horizontal gene transfer. These efforts to construct a database of tick microbes may lead to the ability to predict emerging tick-borne diseases. Furthermore, a comprehensive understanding of tick microbiomes will be useful for understanding tick biology, including vector competency and interactions with pathogens and symbionts.

Prediction of Directional Changes of Influenza A Virus Genome Sequences with Emphasis on Pandemic H1N1/09 as a Model Case

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.

Metabolomics of Medicinal Plants: The Importance of Multivariate Analysis of Analytical Chemistry Data

Metabolomics, the comprehensive and global analysis of diverse metabolites produced in cells and organisms, has greatly expanded metabolite fingerprinting and profiling as well as the selection and identification of marker metabolites. The methodology typically employs multivariate analysis to statistically process the massive amount of analytical chemistry data resulting from high-throughput and simultaneous metabolite analysis. Although the technology of plant metabolomics has mainly developed with other post-genomics in systems biology and functional genomics, it is independently applied to the evaluation of the qualities of medicinal plants, based on the diversity of metabolite fingerprints resulting from multivariate analysis of non-targeted or widely targeted metabolite analysis. One advantage of applying metabolomics is that medicinal plants are evaluated based not only on the limited number of metabolites that are pharmacologically important chemicals, but also on the fingerprints of minor metabolites and bioactive chemicals. In particular, score plot and loading plot analyses e.g. principal component analysis (PCA), partial-least-squares discriminant analysis (PLS-DA), and discrimination map analysis such as batch-learning self-organizing map (BL-SOM) analysis, are often employed for the reduction of a metabolite fingerprint and the classification of analyzed samples. Based on recent studies, we now understand that metabolomics can be an effective approach for comprehensive evaluation of the qualities of medicinal plants. In this review, we describe practical cases in which metabolomic study was performed on medicinal plants, and discuss the utility of metabolomics for this research field, with focus on multivariate analysis.

Novel bioinformatics for inter- and intraspecies comparison of genome signatures in plant genomes

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.

Widely Targeted Metabolomics Based on Large-Scale MS/MS Data for Elucidating Metabolite Accumulation Patterns in Plants

Metabolomics is an ‘omics’ approach that aims to analyze all metabolites in a biological sample comprehensively. The detailed metabolite profiling of thousands of plant samples has great potential for directly elucidating plant metabolic processes. However, both a comprehensive analysis and a high throughput are difficult to achieve at the same time due to the wide diversity of metabolites in plants. Here, we have established a novel and practical metabolomics methodology for quantifying hundreds of targeted metabolites in a high-throughput manner. Multiple reaction monitoring (MRM) using tandem quadrupole mass spectrometry (TQMS), which monitors both the specific precursor ions and product ions of each metabolite, is a standard technique in targeted metabolomics, as it enables high sensitivity, reproducibility and a broad dynamic range. In this study, we optimized the MRM conditions for specific compounds by performing automated flow injection analyses with TQMS. Based on a total of 61,920 spectra for 860 authentic compounds, the MRM conditions of 497 compounds were successfully optimized. These were applied to high-throughput automated analysis of biological samples using TQMS coupled with ultra performance liquid chromatography (UPLC). By this analysis, approximately 100 metabolites were quantified in each of 14 plant accessions from Brassicaceae, Gramineae and Fabaceae. A hierarchical cluster analysis based on the metabolite accumulation patterns clearly showed differences among the plant families, and family-specific metabolites could be predicted using a batch-learning self-organizing map analysis. Thus, the automated widely targeted metabolomics approach established here should pave the way for large-scale metabolite profiling and comparative metabolomics.