Plant Omics Data Center: an integrated web repository for interspecies gene expression networks with NLP-based curation

IPOP: An Integrative Plant Multi-omics Platform for Cross-species Comparison and Evolutionary Study

The advent of high-throughput sequencing technologies has led to the production of a significant amount of omics data in plants, which serves as valuable assets for conducting cross-species multi-omics comparative analysis. Nevertheless, the current dearth of comprehensive platforms providing evolutionary annotation information and multi-species multi-omics data impedes users from systematically and efficiently performing evolutionary and functional analysis on specific genes. In order to establish an advanced plant multi-omics platform that provides timely, accurate, and high-caliber omics information, we collected 7 distinct types of omics data from 6 monocots, 6 dicots, and 1 moss, and reanalyzed these data using standardized pipelines. Additionally, we furnished homology information, duplication events, and phylostratigraphic stages of 13 species to facilitate evolutionary examination. Furthermore, the integrative plant omics platform (IPOP) is bundled with a variety of online analysis tools that aid users in conducting evolutionary and functional analysis. Specifically, the Multi-omics Integration Analysis tool is available to consolidate information from diverse omics sources, while the Transcriptome-wide Association Analysis tool facilitates the linkage of functional analysis with phenotype. To illustrate the application of IPOP, we conducted a case study on the YTH domain gene family, wherein we observed shared functionalities within orthologous groups and discerned variations in evolutionary patterns across these groups. To summarize, the IPOP platform offers valuable evolutionary insights and multi-omics data to the plant sciences community, effectively addressing the need for cross-species comparison and evolutionary research platforms. All data and modules within IPOP are freely accessible for academic purposes (http://omicstudio.cloud:4012/ipod/).

Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom

In higher plants, the complexity of a system and the components within and among species are rapidly dissected by omics technologies. Multi-omics datasets are integrated to infer and enable a comprehensive understanding of the life processes of organisms of interest. Further, growing open-source datasets coupled with the emergence of high-performance computing and development of computational tools for biological sciences have assisted in silico functional prediction of unknown genes, proteins and metabolites, otherwise known as uncharacterized. The systems biology approach includes data collection and filtration, system modelling, experimentation and the establishment of new hypotheses for experimental validation. Informatics technologies add meaningful sense to the output generated by complex bioinformatics algorithms, which are now freely available in a user-friendly graphical user interface. These resources accentuate gene function prediction at a relatively minimal cost and effort. Herein, we present a comprehensive view of relevant approaches available for system-level gene function prediction in the plant kingdom. Together, the most recent applications and sought-after principles for gene mining are discussed to benefit the plant research community. A realistic tabulation of plant genomic resources is included for a less laborious and accurate candidate gene discovery in basic plant research and improvement strategies.

Bioinformatics approaches and applications in plant biotechnology

BACKGROUND

In recent years, major advance in molecular biology and genomic technologies have led to an exponential growth in biological information. As the deluge of genomic information, there is a parallel growth in the demands of tools in the storage and management of data, and the development of software for analysis, visualization, modelling, and prediction of large data set.

MAIN BODY

Particularly in plant biotechnology, the amount of information has multiplied exponentially with a large number of databases available from many individual plant species. Efficient bioinformatics tools and methodologies are also developed to allow rapid genome sequence and the study of plant genome in the 'omics' approach. This review focuses on the various bioinformatic applications in plant biotechnology, and their advantages in improving the outcome in agriculture. The challenges or limitations faced in plant biotechnology in the aspect of bioinformatics approach that explained the low progression in plant genomics than in animal genomics are also reviewed and assessed.

CONCLUSION

There is a critical need for effective bioinformatic tools, which are able to provide longer reads with unbiased coverage in order to overcome the complexity of the plant's genome. The advancement in bioinformatics is not only beneficial to the field of plant biotechnology and agriculture sectors, but will also contribute enormously to the future of humanity.

Nanotechnology and artificial intelligence to enable sustainable and precision agriculture

Climate change, increasing populations, competing demands on land for production of biofuels and declining soil quality are challenging global food security. Finding sustainable solutions requires bold new approaches and integration of knowledge from diverse fields, such as materials science and informatics. The convergence of precision agriculture, in which farmers respond in real time to changes in crop growth with nanotechnology and artificial intelligence, offers exciting opportunities for sustainable food production. Coupling existing models for nutrient cycling and crop productivity with nanoinformatics approaches to optimize targeting, uptake, delivery, nutrient capture and long-term impacts on soil microbial communities will enable design of nanoscale agrochemicals that combine optimal safety and functionality profiles.

Pharmacoinformatics approaches in the discovery of drug-like antimicrobials of plant origin

Medicinal plants have served as an important source for addressing the ailments of humans and animals alike. The emergence of advanced technologies in the field of drug discovery and development has helped in isolating various bioactive phytochemicals and developing them as drugs. Owing to their significant pharmacological benefits and minimum adverse effects, they not only serve as good candidates for therapeutics themselves but also help in the identification and development of related drug like molecules against various metabolic and infectious diseases. The ever-increasing diversity, severity and incidence of infectious diseases has resulted in an exaggerated mortality and morbidity levels. Geno-proteomic mutations in microbes, irrational prescribing of antibiotics, antimicrobial resistance and human population explosion, all call for continuous efforts to discover and develop alternated therapeutic options against the microbes. This review article describes the pharmacoinformatics tools and methods which are currently used in the discovery of bioactive phytochemicals, thus making the process more efficient and effective. The pharmacological aspects of the drug discovery and development process have also been reviewed with reference to the in silico activities. Communicated by Ramaswamy H. Sarma.

BrassicaEDB: A Gene Expression Database for Brassica Crops

The genus Brassica contains several economically important crops, including rapeseed (Brassica napus, 2n = 38, AACC), the second largest source of seed oil and protein meal worldwide. However, research in rapeseed is hampered because it is complicated and time-consuming for researchers to access different types of expression data. We therefore developed the Brassica Expression Database (BrassicaEDB) for the research community. In the current BrassicaEDB, we only focused on the transcriptome level in rapeseed. We conducted RNA sequencing (RNA-Seq) of 103 tissues from rapeseed cultivar ZhongShuang11 (ZS11) at seven developmental stages (seed germination, seedling, bolting, initial flowering, full-bloom, podding, and maturation). We determined the expression patterns of 101,040 genes via FPKM analysis and displayed the results using the eFP browser. We also analyzed transcriptome data for rapeseed from 70 BioProjects in the SRA database and obtained three types of expression level data (FPKM, TPM, and read counts). We used this information to develop the BrassicaEDB, including “eFP”, “Treatment”, “Coexpression”, and “SRA Project” modules based on gene expression profiles and “Gene Feature”, “qPCR Primer”, and “BLAST” modules based on gene sequences. The BrassicaEDB provides comprehensive gene expression profile information and a user-friendly visualization interface for rapeseed researchers. Using this database, researchers can quickly retrieve the expression level data for target genes in different tissues and in response to different treatments to elucidate gene functions and explore the biology of rapeseed at the transcriptome level.

Network aggregation improves gene function prediction of grapevine gene co-expression networks

Aggregation across multiple networks highlights robust co-expression interactions and improves the functional connectivity of grapevine gene co-expression networks. In recent years, the rapid accumulation of transcriptome datasets from diverse experimental conditions has enabled the widespread use of gene co-expression network (GCN) analysis in plants. In grapevine, GCN analysis has shown great promise for gene function prediction, however, measurable progress is currently lacking. Using accumulated microarray datasets from the grapevine whole-genome array (33 experiments, 1359 samples), we explored how meta-analysis through aggregation influences the functional connectivity (performance) of derived networks using guilt-by-association neighbor voting. Two annotation schemes, i.e. MapMan BIN and Pfam, at two sparsity thresholds, i.e. top 100 (stringent) and 300 (relaxed) ranked genes were evaluated. We observed that aggregating across multiple networks improves performance dramatically, with the aggregate outperforming the majority of functional terms across individual networks. Network sparsity and size (i.e. the number of samples and aggregates) were key factors influencing performance while the choice of annotation scheme had little. Systematic comparison with various state-of-the-art microarray and RNA-seq networks was also performed, however, none outperformed the aggregate microarray network despite having good predictive performance. Repeating these series of tests using a functional enrichment-based performance metric also showed remarkably consistent findings with guilt-by-association neighbor voting. To demonstrate its functionality, we explore the function and transcriptional regulation of grapevine EXPANSIN genes. We envisage that network aggregation will offer new and unique opportunities for gene function prediction in future grapevine functional genomics studies. To this end, we make the aggregate networks and associated metadata publicly available at VTC-Agg (https://sites.google.com/view/vtc-agg).

Effect of salt tolerance on biomass production in a large population of sorghum accessions

Salinity causes major reductions in cultivated land area, crop productivity, and crop quality, and salt-tolerant crops have been required to sustain agriculture in salinized areas. The annual C₄ crop plant Sorghum bicolor (L.) Moench is salt tolerant, with large variation among accessions. Sorghum's salt tolerance is often evaluated during early growth, but such evaluations are weakly related to overall performance. Here, we evaluated salt tolerance of 415 sorghum accessions grown in saline soil (0, 50, 100, and 150 mM NaCl) for 3 months. Some accessions produced up to 400 g per plant of biomass and showed no growth inhibition at 50 mM NaCl. Our analysis indicated that the genetic factors that affected biomass production under 100 mM salt stress were more different from those without salt stress, comparing to the differences between those under 50 mM and 100 mM salt stress. A genome-wide association study for salt tolerance identified two single-nucleotide polymorphisms (SNPs) that were significantly associated with biomass production, only at 50 mM NaCl. Additionally, two SNPs were significantly associated with salt tolerance index as an indicator for growth response of each accession to salt stress. Our results offer candidate genetic resources and SNP markers for breeding salt-tolerant sorghum.

TRANSNAP: a web database providing comprehensive information on Japanese pear transcriptome

Japanese pear (Pyrus pyrifolia) is a major fruit tree in the family Rosaceae and is bred for fruit production. To promote the development of breeding strategies and molecular research for Japanese pear, we sequenced the transcripts of Japanese pear variety 'Hosui'. To exhaustively collect information of total gene expression, RNA samples from various organs and stages of Japanese pear were sequenced by three technologies, single-molecule real-time (SMRT) sequencing, 454 pyrosequencing, and Sanger sequencing. Using all those reads, we determined comprehensive reference sequences of Japanese pear. Then, their protein sequences were predicted, and biological functional annotations were assigned. Finally, we developed a web database, TRANSNAP (http://plantomics.mind.meiji.ac.jp/nashi), which is the first web resource of Japanese pear omics information. This database provides highly reliable information via a user-friendly web interface: the reference sequences, gene functional annotations, and gene expression profiles from microarray experiments. In addition, based on sequence comparisons among Japanese, Chinese and European pears, similar protein sequences among the pears and species-specific proteins in Japanese pear can be quickly and efficiently identified. TRANSNAP will aid molecular research and breeding in Japanese pear, and its information is available for comparative analysis among other pear species and families.

Comparison of shape quantification methods for genomic prediction, and genome-wide association study of sorghum seed morphology

Seed shape is an important agronomic trait with continuous variation among genotypes. Therefore, the quantitative evaluation of this variation is highly important. Among geometric morphometrics methods, elliptic Fourier analysis and semi-landmark analysis are often used for the quantification of biological shape variations. Elliptic Fourier analysis is an approximation method to treat contours as a waveform. Semi-landmark analysis is a method of superimposed points in which the differences of multiple contour positions are minimized. However, no detailed comparison of these methods has been undertaken. Moreover, these shape descriptors vary when the scale and direction of the contour and the starting point of the contour trace change. Thus, these methods should be compared with respect to the standardization of the scale and direction of the contour and the starting point of the contour trace. In the present study, we evaluated seed shape variations in a sorghum (Sorghum bicolor Moench) germplasm collection to analyze the association between shape variations and genome-wide single-nucleotide polymorphisms by genomic prediction (GP) and genome-wide association studies (GWAS). In our analysis, we used all possible combinations of three shape description methods and eight standardization procedures for the scale and direction of the contour as well as the starting point of the contour trace; these combinations were compared in terms of GP accuracy and the GWAS results. We compared the shape description methods (elliptic Fourier descriptors and the coordinates of superposed pseudo-landmark points) and found that principal component analysis of their quantitative descriptors yielded similar results. Different scaling and direction standardization procedures caused differences in the principal component scores, average shape, and the results of GP and GWAS.

Complete chloroplast genome sequence and phylogenetic analysis of wasabi (Eutrema japonicum) and its relatives

In Japan, two Eutrema species, wasabi (Eutrema japonicum, the important traditional Japanese condiment) and yuriwasabi (E. tenue), have been recognized as endemic species. We sequenced complete chloroplast (cp) genomes of seven wasabi and yuriwasabi accessions from Japan to study their phylogeny and evolution, using molecular dating of species divergence. Phylogenetic analyses of the complete cp DNA of these two Japanese species and five other Eurasian Eutrema species revealed that wasabi and yuriwasabi did not form a monophyletic group. One yuriwasabi accession (Gifu) formed a clade with E. yunnanense from China, indicating that this accession should be considered as a different species from the other yuriwasabi accessions. We reveal that Japanese Eutrema species diverged from the ‘E. yunnanense–yuriwasabi (Gifu)’ clade approximately 1.3 million years ago (Mya), suggesting that the connection between Japan and the Eurasian continent has existed more recently than the Quaternary period. The abundance of cp sequence data in this study also allowed the detection of genetic differentiation among wasabi cultivars. The two polymorphic sites detected between ‘Fujidaruma’ and ‘Shimane No.3’ were used to develop genotyping markers. The cp genome information provided here will thus inform the evolutionary histories of Japanese Eutrema species and help in genotyping wasabi cultivars.

Comparative co-expression network analysis extracts the SlHSP70 gene affecting to shoot elongation of tomato

Tomato is one of vegetables crops that has the highest value in the world. Thus, researchers are continually improving the agronomical traits of tomato fruits. Auxins and gibberellins regulate plant growth and development. Aux/indole-3-acetic acid 9 (SlIAA9) and the gene encoding the DELLA protein (SlDELLA) are well-known genes that regulate plant growth and development, including fruit set and enlargement by cell division and cell expansion. The absence of tomato SlIAA9 and SlDELLA results in abnormal shoot growth and leaf shape and giving rise to parthenocarpy. To investigate the key regulators that exist up- or downstream of SlIAA9 and SlDELLA signaling pathways for tomato growth and development, we performed gene co-expression network analysis by using publicly available microarray data to extract genes that are directly connected to the SlIAA9 and SlDELLA nodes, respectively. Consequently, we chose a gene in the group of heat-shock protein (HSP)70s that was connected with the SlIAA9 node and SlDELLA node in each co-expression network. To validate the extent of effect of SlHSP70-1 on tomato growth and development, overexpressing lines of the target gene were generated. We found that overexpression of the targeted SlHSP70-1 resulted in internode elongation, but the overexpressing lines did not show abnormal leaf shape, fruit set, or fruit size when compared with that of the wild type. Our study suggests that the targeted SlHSP70-1 is likely to function in shoot growth, like SlIAA9 and SlDELLA, but it does not contribute to parthenocarpy as well as fruit set. Our study also shows that only a single SlHSP70 out of 25 homologous genes could change the shoot length.

Genome sequence analysis of the fairy ring-forming fungus Lepista sordida and gene candidates for interaction with plants

Circular patterns called "fairy rings" in fields are a natural phenomenon that arises through the interaction between basidiomycete fungi and plants. Acceleration or inhibition of plant vegetative growth and the formation of mushroom fruiting bodies are both commonly observed when fairy rings form. The gene of an enzyme involved in the biosynthesis of these regulators was recently isolated in the fairy ring-forming fungus, Lepista sordida. To identify other genes involved in L. sordida fairy ring formation, we used previously generated sequence data to produce a more complete draft genome sequence for this species. Finally, we predicted the metabolic pathways of the plant growth regulators and 29 candidate enzyme-coding genes involved in fairy-ring formation based on gene annotations. Comparisons of protein coding genes among basidiomycete fungi revealed two nitric oxide synthase gene candidates that were uniquely encoded in genomes of fairy ring-forming fungi. These results provide a basis for the discovery of genes involved in fairy ring formation and for understanding the mechanisms involved in the interaction between fungi and plants. We also constructed a new web database F-RINGS ( http://bioinf.mind.meiji.ac.jp/f-rings/ ) to provide the comprehensive genomic information for L. sordida.

Expression of Genes from Paternal Alleles in Rice Zygotes and Involvement of OsASGR-BBML1 in Initiation of Zygotic Development

Upon fertilization in angiosperms, one sperm cell fuses with the egg cell to produce a zygote, and, via karyogamy, the parental genetic information is combined to form the diploid zygotic genome. Recently, analyses with parentally imbalanced rice zygotes indicated that parental genomes are utilized synergistically in zygotes with different functions, and that genes transcribed from the paternal or maternal allele might play important roles in zygotic development. Herein, we first conducted single nucleotide polymorphism-based mRNA-sequencing using intersubspecific rice zygotes. Twenty-three genes, with paternal allele-specific expression in zygotes, were identified, and, surprisingly, their allele dependencies in the globular-like embryo tended to be biallelic. This suggests that the paternal-dependent expression of these genes is temporary, occurring during the early stages of zygote development. Of the 23 genes, we focused on Oryza sativa Apospory-specific Genome Region (ASGR)-BABY-BOOM LIKE (BBML) 1 (OsASGR-BBML1), presumed to encode an AP2-transcription factor, due to its reported role in zygotic development. Interestingly, ectopic expression of OsASGR-BBML1 in egg cells induced nuclear and cell divisions, indicating that exogenously expressed OsASGR-BBML1 converts the proliferation status of the egg cell from quiescent to active. In addition, the suppression of the function of OsASGR-BBML1 and its homologs in zygotes resulted in the developmental arrest, suggesting that OsASGR-BBML1 possesses an important role in initiating zygotic development. Monoallelic or preferential gene expression from the paternal genome in the zygote might be a safety mechanism allowing egg cells to suppress the gene expression cascade toward early embryogenesis that is normally triggered by fusion with a sperm cell.

Repeated inversions within a pannier intron drive diversification of intraspecific colour patterns of ladybird beetles

How genetic information is modified to generate phenotypic variation within a species is one of the central questions in evolutionary biology. Here we focus on the striking intraspecific diversity of >200 aposematic elytral (forewing) colour patterns of the multicoloured Asian ladybird beetle, Harmonia axyridis, which is regulated by a tightly linked genetic locus h. Our loss-of-function analyses, genetic association studies, de novo genome assemblies, and gene expression data reveal that the GATA transcription factor gene pannier is the major regulatory gene located at the h locus, and suggest that repeated inversions and cis-regulatory modifications at pannier led to the expansion of colour pattern variation in H. axyridis. Moreover, we show that the colour-patterning function of pannier is conserved in the seven-spotted ladybird beetle, Coccinella septempunctata, suggesting that H. axyridis’ extraordinary intraspecific variation may have arisen from ancient modifications in conserved elytral colour-patterning mechanisms in ladybird beetles.

The harlequin ladybird beetle, Harmonia axyridis, has remarkable phenotypic diversity, with over 200 colour patterns. Here, Ando et al. show that this patterning is regulated by the transcription factor gene pannier and has diversified by repeated inversions and cis-regulatory modifications of pannier.

Ensemble gene function prediction database reveals genes important for complex I formation in Arabidopsis thaliana

Recent advances in gene function prediction rely on ensemble approaches that integrate results from multiple inference methods to produce superior predictions. Yet, these developments remain largely unexplored in plants. We have explored and compared two methods to integrate 10 gene co-function networks for Arabidopsis thaliana and demonstrate how the integration of these networks produces more accurate gene function predictions for a larger fraction of genes with unknown function. These predictions were used to identify genes involved in mitochondrial complex I formation, and for five of them, we confirmed the predictions experimentally. The ensemble predictions are provided as a user-friendly online database, EnsembleNet. The methods presented here demonstrate that ensemble gene function prediction is a powerful method to boost prediction performance, whereas the EnsembleNet database provides a cutting-edge community tool to guide experimentalists.

Heap: a highly sensitive and accurate SNP detection tool for low-coverage high-throughput sequencing data

Recent availability of large-scale genomic resources enables us to conduct so called genome-wide association studies (GWAS) and genomic prediction (GP) studies, particularly with next-generation sequencing (NGS) data. The effectiveness of GWAS and GP depends on not only their mathematical models, but the quality and quantity of variants employed in the analysis. In NGS single nucleotide polymorphism (SNP) calling, conventional tools ideally require more reads for higher SNP sensitivity and accuracy. In this study, we aimed to develop a tool, Heap, that enables robustly sensitive and accurate calling of SNPs, particularly with a low coverage NGS data, which must be aligned to the reference genome sequences in advance. To reduce false positive SNPs, Heap determines genotypes and calls SNPs at each site except for sites at the both ends of reads or containing a minor allele supported by only one read. Performance comparison with existing tools showed that Heap achieved the highest F-scores with low coverage (7X) restriction-site associated DNA sequencing reads of sorghum and rice individuals. This will facilitate cost-effective GWAS and GP studies in this NGS era. Code and documentation of Heap are freely available from https://github.com/meiji-bioinf/heap (29 March 2017, date last accessed) and our web site (http://bioinf.mind.meiji.ac.jp/lab/en/tools.html (29 March 2017, date last accessed)).

NorWood: a gene expression resource for evo-devo studies of conifer wood development

The secondary xylem of conifers is composed mainly of tracheids that differ anatomically and chemically from angiosperm xylem cells. There is currently no high-spatial-resolution data available profiling gene expression during wood formation for any coniferous species, which limits insight into tracheid development. RNA-sequencing data from replicated, high-spatial-resolution section series throughout the cambial and woody tissues of Picea abies were used to generate the NorWood.conGenIE.org web resource, which facilitates exploration of the associated gene expression profiles and co-expression networks. Integration within PlantGenIE.org enabled a comparative regulomics analysis, revealing divergent co-expression networks between P. abies and the two angiosperm species Arabidopsis thaliana and Populus tremula for the secondary cell wall (SCW) master regulator NAC Class IIB transcription factors. The SCW cellulose synthase genes (CesAs) were located in the neighbourhoods of the NAC factors in A. thaliana and P. tremula, but not in P. abies. The NorWood co-expression network enabled identification of potential SCW CesA regulators in P. abies. The NorWood web resource represents a powerful community tool for generating evo-devo insights into the divergence of wood formation between angiosperms and gymnosperms and for advancing understanding of the regulation of wood development in P. abies.

DES-TOMATO: A Knowledge Exploration System Focused On Tomato Species

Tomato is the most economically important horticultural crop used as a model to study plant biology and particularly fruit development. Knowledge obtained from tomato research initiated improvements in tomato and, being transferrable to other such economically important crops, has led to a surge of tomato-related research and published literature. We developed DES-TOMATO knowledgebase (KB) for exploration of information related to tomato. Information exploration is enabled through terms from 26 dictionaries and combination of these terms. To illustrate the utility of DES-TOMATO, we provide several examples how one can efficiently use this KB to retrieve known or potentially novel information. DES-TOMATO is free for academic and nonprofit users and can be accessed at http://cbrc.kaust.edu.sa/des_tomato/, using any of the mainstream web browsers, including Firefox, Safari and Chrome.

Co-expressed Pathways DataBase for Tomato: a database to predict pathways relevant to a query gene

BACKGROUND

Gene co-expression, the similarity of gene expression profiles under various experimental conditions, has been used as an indicator of functional relationships between genes, and many co-expression databases have been developed for predicting gene functions. These databases usually provide users with a co-expression network and a list of strongly co-expressed genes for a query gene. Several of these databases also provide functional information on a set of strongly co-expressed genes (i.e., provide biological processes and pathways that are enriched in these strongly co-expressed genes), which is generally analyzed via over-representation analysis (ORA). A limitation of this approach may be that users can predict gene functions only based on the strongly co-expressed genes.

RESULTS

In this study, we developed a new co-expression database that enables users to predict the function of tomato genes from the results of functional enrichment analyses of co-expressed genes while considering the genes that are not strongly co-expressed. To achieve this, we used the ORA approach with several thresholds to select co-expressed genes, and performed gene set enrichment analysis (GSEA) applied to a ranked list of genes ordered by the co-expression degree. We found that internal correlation in pathways affected the significance levels of the enrichment analyses. Therefore, we introduced a new measure for evaluating the relationship between the gene and pathway, termed the percentile (p)-score, which enables users to predict functionally relevant pathways without being affected by the internal correlation in pathways. In addition, we evaluated our approaches using receiver operating characteristic curves, which concluded that the p-score could improve the performance of the ORA.

CONCLUSIONS

We developed a new database, named Co-expressed Pathways DataBase for Tomato, which is available at http://cox-path-db.kazusa.or.jp/tomato . The database allows users to predict pathways that are relevant to a query gene, which would help to infer gene functions.

Integrated omics analysis of specialized metabolism in medicinal plants

Medicinal plants are a rich source of highly diverse specialized metabolites with important pharmacological properties. Until recently, plant biologists were limited in their ability to explore the biosynthetic pathways of these metabolites, mainly due to the scarcity of plant genomics resources. However, recent advances in high-throughput large-scale analytical methods have enabled plant biologists to discover biosynthetic pathways for important plant-based medicinal metabolites. The reduced cost of generating omics datasets and the development of computational tools for their analysis and integration have led to the elucidation of biosynthetic pathways of several bioactive metabolites of plant origin. These discoveries have inspired synthetic biology approaches to develop microbial systems to produce bioactive metabolites originating from plants, an alternative sustainable source of medicinally important chemicals. Since the demand for medicinal compounds are increasing with the world's population, understanding the complete biosynthesis of specialized metabolites becomes important to identify or develop reliable sources in the future. Here, we review the contributions of major omics approaches and their integration to our understanding of the biosynthetic pathways of bioactive metabolites. We briefly discuss different approaches for integrating omics datasets to extract biologically relevant knowledge and the application of omics datasets in the construction and reconstruction of metabolic models.

DNApod: DNA polymorphism annotation database from next-generation sequence read archives

With the rapid advances in next-generation sequencing (NGS), datasets for DNA polymorphisms among various species and strains have been produced, stored, and distributed. However, reliability varies among these datasets because the experimental and analytical conditions used differ among assays. Furthermore, such datasets have been frequently distributed from the websites of individual sequencing projects. It is desirable to integrate DNA polymorphism data into one database featuring uniform quality control that is distributed from a single platform at a single place. DNA polymorphism annotation database (DNApod; http://tga.nig.ac.jp/dnapod/) is an integrated database that stores genome-wide DNA polymorphism datasets acquired under uniform analytical conditions, and this includes uniformity in the quality of the raw data, the reference genome version, and evaluation algorithms. DNApod genotypic data are re-analyzed whole-genome shotgun datasets extracted from sequence read archives, and DNApod distributes genome-wide DNA polymorphism datasets and known-gene annotations for each DNA polymorphism. This new database was developed for storing genome-wide DNA polymorphism datasets of plants, with crops being the first priority. Here, we describe our analyzed data for 679, 404, and 66 strains of rice, maize, and sorghum, respectively. The analytical methods are available as a DNApod workflow in an NGS annotation system of the DNA Data Bank of Japan and a virtual machine image. Furthermore, DNApod provides tables of links of identifiers between DNApod genotypic data and public phenotypic data. To advance the sharing of organism knowledge, DNApod offers basic and ubiquitous functions for multiple alignment and phylogenetic tree construction by using orthologous gene information.

TOMATOMICS: A Web Database for Integrated Omics Information in Tomato

Solanum lycopersicum (tomato) is an important agronomic crop and a major model fruit-producing plant. To facilitate basic and applied research, comprehensive experimental resources and omics information on tomato are available following their development. Mutant lines and cDNA clones from a dwarf cultivar, Micro-Tom, are two of these genetic resources. Large-scale sequencing data for ESTs and full-length cDNAs from Micro-Tom continue to be gathered. In conjunction with information on the reference genome sequence of another cultivar, Heinz 1706, the Micro-Tom experimental resources have facilitated comprehensive functional analyses. To enhance the efficiency of acquiring omics information for tomato biology, we have integrated the information on the Micro-Tom experimental resources and the Heinz 1706 genome sequence. We have also inferred gene structure by comparison of sequences between the genome of Heinz 1706 and the transcriptome, which are comprised of Micro-Tom full-length cDNAs and Heinz 1706 RNA-seq data stored in the KaFTom and Sequence Read Archive databases. In order to provide large-scale omics information with streamlined connectivity we have developed and maintain a web database TOMATOMICS (http://bioinf.mind.meiji.ac.jp/tomatomics/). In TOMATOMICS, access to the information on the cDNA clone resources, full-length mRNA sequences, gene structures, expression profiles and functional annotations of genes is available through search functions and the genome browser, which has an intuitive graphical interface.

Practical Utilization of OryzaExpress and Plant Omics Data Center Databases to Explore Gene Expression Networks in Oryza Sativa and Other Plant Species

Analysis of a gene expression network (GEN), which is constructed based on similarity of gene expression profiles, is a widely used approach to gain clues for new biological insights. The recent abundant availability of transcriptome data in public databases is enabling GEN analysis under various experimental conditions, and even comparative GEN analysis across species. To provide a platform to gain biological insights from public transcriptome data, valuable databases have been created and maintained. This chapter introduces the web database OryzaExpress, providing omics information on Oryza sativa (rice). The integrated database Plant Omics Data Center, supporting a wide variety of plant species, is also described to compare omics information among multiple plant species.

CATchUP: A Web Database for Spatiotemporally Regulated Genes

For proper control of biological activity, some key genes are highly expressed in a particular spatiotemporal domain. Mining of such spatiotemporally expressed genes using large-scale gene expression data derived from a broad range of experimental sources facilitates our understanding of genome-scale functional gene networks. However, comprehensive information on spatiotemporally expressed genes is lacking in plants. To collect such information, we devised a new index, Δdmax, which is the maximum difference in relative gene expression levels between sample runs which are neighboring when sorted by the levels. Employing this index, we comprehensively evaluated transcripts using large-scale RNA sequencing (RNA-Seq) data stored in the Sequence Read Archive for eight plant species: Arabidopsis thaliana (Arabidopsis), Solanum lycopersicum (tomato), Solanum tuberosum (potato), Oryza sativa (rice), Sorghum bicolor (sorghum), Vitis vinifera (grape), Medicago truncatula (Medicago), and Glycine max (soybean). Based on the frequency distribution of the Δdmax values, approximately 70,000 transcripts showing 0.3 or larger Δdmax values were extracted for the eight species. Information on these genes including the Δdmax values, functional annotations, conservation among species, and experimental conditions where the genes show high expression levels is provided in a new database, CATchUP (http://plantomics.mind.meiji.ac.jp/CATchUP). The CATchUP database assists in identifying genes specifically expressed under particular conditions with powerful search functions and an intuitive graphical user interface.

PlantExpress: A Database Integrating OryzaExpress and ArthaExpress for Single-species and Cross-species Gene Expression Network Analyses with Microarray-Based Transcriptome Data

Publicly available microarray-based transcriptome data on plants are remarkably valuable in terms of abundance and variation of samples, particularly for Oryza sativa (rice) and Arabidopsis thaliana (Arabidopsis). Here, we introduce the web database PlantExpress (http://plantomics.mind.meiji.ac.jp/PlantExpress/) as a platform for gene expression network (GEN) analysis with the public microarray data of rice and Arabidopsis. PlantExpress has two functional modes. The single-species mode is specialized for GEN analysis within one of the species, while the cross-species mode is optimized for comparative GEN analysis between the species. The single-species mode for rice is the new version of OryzaExpress, which we have maintained since 2006. The single-species mode for Arabidopsis, named ArthaExpress, was newly developed. PlantExpress stores data obtained from three microarrays, the Affymetrix Rice Genome Array, the Agilent Rice Gene Expression 4x44K Microarray, and the Affymetrix Arabidopsis ATH1 Genome Array, with respective totals of 2,678, 1,206, and 10,940 samples. This database employs a ‘MyList’ function with which users may save lists of arbitrary genes and samples (experimental conditions) to use in analyses. In cross-species mode, the MyList function allows performing comparative GEN analysis between rice and Arabidopsis. In addition, the gene lists saved in MyList can be directly exported to the PODC database, which provides information and a platform for comparative GEN analysis based on RNA-seq data and knowledge-based functional annotation of plant genes. PlantExpress will facilitate understanding the biological functions of plant genes.

Identification of reference genes for quantitative expression analysis using large-scale RNA-seq data of Arabidopsis thaliana and model crop plants

In quantitative gene expression analysis, normalization using a reference gene as an internal control is frequently performed for appropriate interpretation of the results. Efforts have been devoted to exploring superior novel reference genes using microarray transcriptomic data and to evaluating commonly used reference genes by targeting analysis. However, because the number of specifically detectable genes is totally dependent on probe design in the microarray analysis, exploration using microarray data may miss some of the best choices for the reference genes. Recently emerging RNA sequencing (RNA-seq) provides an ideal resource for comprehensive exploration of reference genes since this method is capable of detecting all expressed genes, in principle including even unknown genes. We report the results of a comprehensive exploration of reference genes using public RNA-seq data from plants such as Arabidopsis thaliana (Arabidopsis), Glycine max (soybean), Solanum lycopersicum (tomato) and Oryza sativa (rice). To select reference genes suitable for the broadest experimental conditions possible, candidates were surveyed by the following four steps: (1) evaluation of the basal expression level of each gene in each experiment; (2) evaluation of the expression stability of each gene in each experiment; (3) evaluation of the expression stability of each gene across the experiments; and (4) selection of top-ranked genes, after ranking according to the number of experiments in which the gene was expressed stably. Employing this procedure, 13, 10, 12 and 21 top candidates for reference genes were proposed in Arabidopsis, soybean, tomato and rice, respectively. Microarray expression data confirmed that the expression of the proposed reference genes under broad experimental conditions was more stable than that of commonly used reference genes. These novel reference genes will be useful for analyzing gene expression profiles across experiments carried out under various experimental conditions.

Towards integration of population and comparative genomics in forest trees

Contents 338 I. 338 II. 339 III. 340 IV. 342 343 References 343 SUMMARY: The past decade saw the initiation of an ongoing revolution in sequencing technologies that is transforming all fields of biology. This has been driven by the advent and widespread availability of high-throughput, massively parallel short-read sequencing (MPS) platforms. These technologies have enabled previously unimaginable studies, including draft assemblies of the massive genomes of coniferous species and population-scale resequencing. Transcriptomics studies have likewise been transformed, with RNA-sequencing enabling studies in nonmodel organisms, the discovery of previously unannotated genes (novel transcripts), entirely new classes of RNAs and previously unknown regulatory mechanisms. Here we touch upon current developments in the areas of genome assembly, comparative regulomics and population genetics as they relate to studies of forest tree species.

Tools of the trade: studying molecular networks in plants

Driven by recent technological improvements, genes can be now studied in a larger biological context. Genes and their protein products rarely operate as a single entity and large-scale mapping by protein-protein interactions can unveil the molecular complexes that form in the cell to carry out various functions. Expression analysis under multiple conditions, supplemented with protein-DNA binding data can highlight when genes are active and how they are regulated. Representing these data in networks and finding strongly connected sub-graphs has proven to be a powerful tool to predict the function of unknown genes. As such networks are gradually becoming available for various plant species, it becomes possible to study how networks evolve. This review summarizes currently available network data and related tools for plants. Furthermore we aim to provide an outlook of future analyses that can be done in plants based on work done in other fields.

CoExpNetViz: Comparative Co-Expression Networks Construction and Visualization Tool

MOTIVATION

Comparative transcriptomics is a common approach in functional gene discovery efforts. It allows for finding conserved co-expression patterns between orthologous genes in closely related plant species, suggesting that these genes potentially share similar function and regulation. Several efficient co-expression-based tools have been commonly used in plant research but most of these pipelines are limited to data from model systems, which greatly limit their utility. Moreover, in addition, none of the existing pipelines allow plant researchers to make use of their own unpublished gene expression data for performing a comparative co-expression analysis and generate multi-species co-expression networks.

RESULTS

We introduce CoExpNetViz, a computational tool that uses a set of query or "bait" genes as an input (chosen by the user) and a minimum of one pre-processed gene expression dataset. The CoExpNetViz algorithm proceeds in three main steps; (i) for every bait gene submitted, co-expression values are calculated using mutual information and Pearson correlation coefficients, (ii) non-bait (or target) genes are grouped based on cross-species orthology, and (iii) output files are generated and results can be visualized as network graphs in Cytoscape.

AVAILABILITY

The CoExpNetViz tool is freely available both as a PHP web server (link: http://bioinformatics.psb.ugent.be/webtools/coexpr/) (implemented in C++) and as a Cytoscape plugin (implemented in Java). Both versions of the CoExpNetViz tool support LINUX and Windows platforms.

OryzaGenome: Genome Diversity Database of Wild Oryza Species

The species in the genus Oryza, encompassing nine genome types and 23 species, are a rich genetic resource and may have applications in deeper genomic analyses aiming to understand the evolution of plant genomes. With the advancement of next-generation sequencing (NGS) technology, a flood of Oryza species reference genomes and genomic variation information has become available in recent years. This genomic information, combined with the comprehensive phenotypic information that we are accumulating in our Oryzabase, can serve as an excellent genotype-phenotype association resource for analyzing rice functional and structural evolution, and the associated diversity of the Oryza genus. Here we integrate our previous and future phenotypic/habitat information and newly determined genotype information into a united repository, named OryzaGenome, providing the variant information with hyperlinks to Oryzabase. The current version of OryzaGenome includes genotype information of 446 O. rufipogon accessions derived by imputation and of 17 accessions derived by imputation-free deep sequencing. Two variant viewers are implemented: SNP Viewer as a conventional genome browser interface and Variant Table as a text-based browser for precise inspection of each variant one by one. Portable VCF (variant call format) file or tab-delimited file download is also available. Following these SNP (single nucleotide polymorphism) data, reference pseudomolecules/scaffolds/contigs and genome-wide variation information for almost all of the closely and distantly related wild Oryza species from the NIG Wild Rice Collection will be available in future releases. All of the resources can be accessed through http://viewer.shigen.info/oryzagenome/.

ALCOdb: Gene Coexpression Database for Microalgae

In the era of energy and food shortage, microalgae have gained much attention as promising sources of biofuels and food ingredients. However, only a small fraction of microalgal genes have been functionally characterized. Here, we have developed the Algae Gene Coexpression database (ALCOdb; http://alcodb.jp), which provides gene coexpression information to survey gene modules for a function of interest. ALCOdb currently supports two model algae: the green alga Chlamydomonas reinhardtii and the red alga Cyanidioschyzon merolae. Users can retrieve coexpression information for genes of interest through three unique data pages: (i) Coexpressed Gene List; (ii) Gene Information; and (iii) Coexpressed Gene Network. In addition to the basal coexpression information, ALCOdb also provides several advanced functionalities such as an expression profile viewer and a differentially expressed gene search tool. Using these user interfaces, we demonstrated that our gene coexpression data have the potential to detect functionally related genes and are useful in extrapolating the biological roles of uncharacterized genes. ALCOdb will facilitate molecular and biochemical studies of microalgal biological phenomena, such as lipid metabolism and organelle development, and promote the evolutionary understanding of plant cellular systems.

Pleurochrysome: A Web Database of Pleurochrysis Transcripts and Orthologs Among Heterogeneous Algae

Pleurochrysis is a coccolithophorid genus, which belongs to the Coccolithales in the Haptophyta. The genus has been used extensively for biological research, together with Emiliania in the Isochrysidales, to understand distinctive features between the two coccolithophorid-including orders. However, molecular biological research on Pleurochrysis such as elucidation of the molecular mechanism behind coccolith formation has not made great progress at least in part because of lack of comprehensive gene information. To provide such information to the research community, we built an open web database, the Pleurochrysome (http://bioinf.mind.meiji.ac.jp/phapt/), which currently stores 9,023 unique gene sequences (designated as UNIGENEs) assembled from expressed sequence tag sequences of P. haptonemofera as core information. The UNIGENEs were annotated with gene sequences sharing significant homology, conserved domains, Gene Ontology, KEGG Orthology, predicted subcellular localization, open reading frames and orthologous relationship with genes of 10 other algal species, a cyanobacterium and the yeast Saccharomyces cerevisiae. This sequence and annotation information can be easily accessed via several search functions. Besides fundamental functions such as BLAST and keyword searches, this database also offers search functions to explore orthologous genes in the 12 organisms and to seek novel genes. The Pleurochrysome will promote molecular biological and phylogenetic research on coccolithophorids and other haptophytes by helping scientists mine data from the primary transcriptome of P. haptonemofera.