Plant bioinformatics: from genome to phenome

Epistasis and pleiotropy-induced variation for plant breeding

Epistasis refers to nonallelic interaction between genes that cause bias in estimates of genetic parameters for a phenotype with interactions of two or more genes affecting the same trait. Partitioning of epistatic effects allows true estimation of the genetic parameters affecting phenotypes. Multigenic variation plays a central role in the evolution of complex characteristics, among which pleiotropy, where a single gene affects several phenotypic characters, has a large influence. While pleiotropic interactions provide functional specificity, they increase the challenge of gene discovery and functional analysis. Overcoming pleiotropy-based phenotypic trade-offs offers potential for assisting breeding for complex traits. Modelling higher order nonallelic epistatic interaction, pleiotropy and non-pleiotropy-induced variation, and genotype × environment interaction in genomic selection may provide new paths to increase the productivity and stress tolerance for next generation of crop cultivars. Advances in statistical models, software and algorithm developments, and genomic research have facilitated dissecting the nature and extent of pleiotropy and epistasis. We overview emerging approaches to exploit positive (and avoid negative) epistatic and pleiotropic interactions in a plant breeding context, including developing avenues of artificial intelligence, novel exploitation of large-scale genomics and phenomics data, and involvement of genes with minor effects to analyse epistatic interactions and pleiotropic quantitative trait loci, including missing heritability.

LettuceDB: an integrated multi-omics database for cultivated lettuce

Crop genomics has advanced rapidly during the past decade, which generated a great abundance of omics data from multi-omics studies. How to utilize the accumulating data becomes a critical and urgent demand in crop science. As an attempt to integrate multi-omics data, we developed a database, LettuceDB (https://db.cngb.org/lettuce/), aiming to assemble multidimensional data for cultivated and wild lettuce germplasm. The database includes genome, variome, phenome, microbiome and spatial transcriptome. By integrating user-friendly bioinformatics tools, LettuceDB will serve as a one-stop platform for lettuce research and breeding in the future. Database URL: https://db.cngb.org/lettuce/.

Toxic effects of lead on plants: integrating multi-omics with bioinformatics to develop Pb-tolerant crops

MAIN CONCLUSION

Lead disrupts plant metabolic homeostasis and key structural elements. Utilizing modern biotechnology tools, it's feasible to develop Pb-tolerant varieties by discovering biological players regulating plant metabolic pathways under stress. Lead (Pb) has been used for a variety of purposes since antiquity despite its toxic nature. After arsenic, lead is the most hazardous heavy metal without any known beneficial role in the biological system. It is a crucial inorganic pollutant that affects plant biochemical and morpho-physiological attributes. Lead toxicity harms plants throughout their life cycle and the extent of damage depends on the concentration and duration of exposure. Higher levels of lead exposure disrupt numerous key metabolic activities of plants including oxygen-evolving complex, organelles integrity, photosystem II connectivity, and electron transport chain. This review summarizes the detrimental effects of lead toxicity on seed germination, crop growth, and yield, oxidative and ultra-structural alterations, as well as nutrient absorption, transport, and assimilation. Further, it discusses the Pb-induced toxic modulation of stomatal conductance, photosynthesis, respiration, metabolic-enzymatic activity, osmolytes accumulation, and antioxidant activity. It is a comprehensive review that reports on omics-based studies along with morpho-physiological and biochemical modifications caused by lead stress. With advances in DNA sequencing technologies, genomics and transcriptomics are gradually becoming popular for studying Pb stress effects in plants. Proteomics and metabolomics are still underrated and there is a scarcity of published data, and this review highlights both their technical and research gaps. Besides, there is also a discussion on how the integration of omics with bioinformatics and the use of the latest biotechnological tools can aid in developing Pb-tolerant crops. The review concludes with core challenges and research directions that need to be addressed soon.

BnIR: A multi-omics database with various tools for Brassica napus research and breeding

In the post-genome-wide association study era, multi-omics techniques have shown great power and potential for candidate gene mining and functional genomics research. However, due to the lack of effective data integration and multi-omics analysis platforms, such techniques have not still been applied widely in rapeseed, an important oil crop worldwide. Here, we report a rapeseed multi-omics database (BnIR; http://yanglab.hzau.edu.cn/BnIR), which provides datasets of six omics including genomics, transcriptomics, variomics, epigenetics, phenomics, and metabolomics, as well as numerous "variation-gene expression-phenotype" associations by using multiple statistical methods. In addition, a series of multi-omics search and analysis tools are integrated to facilitate the browsing and application of these datasets. BnIR is the most comprehensive multi-omics database for rapeseed so far, and two case studies demonstrated its power to mine candidate genes associated with specific traits and analyze their potential regulatory mechanisms.

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.

Weighted deep factorizing heterogeneous molecular network for genome-phenome association prediction

Genome-phenome association (GPA) prediction can promote the understanding of biological mechanisms about complex pathology of phenotypes (i.e., traits and diseases). Traditional heterogeneous network-based GPA approaches overwhelmingly need to project heterogeneous data toward homogeneous network for data fusion and prediction, such projections result in the loss of heterogeneous network structure information. Matrix factorization based data fusion can avoid such projection by integrating multi-type data in a coherent way, but they typically perform linear factorization and cannot mine the nonlinear relationships between molecules, which compromise the accuracy of GPA analysis. Furthermore, most of them can not selectively synergy network topology and node attribution information in a principle way. In this paper, we propose a weighted deep matrix factorization based solution (WDGPA) to predict GPAs by selectively and differentially fusing heterogeneous molecular network and diverse attributes of nodes. WDGPA firstly assigns weights to inter/intra-relational data matrices and attribute data matrices, and performs deep matrix factorization on these matrices of heterogeneous network in a cooperative manner to obtain the nonlinear representations of different nodes. In addition, it performs low-rank representation learning on the attribute data with the shared nonlinear representations. In this way, both the network topology and node attributes are jointly mined to explore the representations of molecules and complex interplays between molecules and phenotypes. WDGPA then uses the representational vectors of gene and phenotype nodes to predict GPAs. Experimental results on maize and human datasets confirm that WDGPA outperforms competitive methods by a large margin under different evaluation protocols.

Phenotyping in the era of genomics: MaTrics—a digital character matrix to document mammalian phenotypic traits

A new and uniquely structured matrix of mammalian phenotypes, MaTrics (Mammalian Traits for Comparative Genomics) in a digital form is presented. By focussing on mammalian species for which genome assemblies are available, MaTrics provides an interface between mammalogy and comparative genomics.

MaTrics was developed within a project aimed to find genetic causes of phenotypic traits of mammals using Forward Genomics. This approach requires genomes and comprehensive and recorded information on homologous phenotypes that are coded as discrete categories in a matrix. MaTrics is an evolving online resource providing information on phenotypic traits in numeric code; traits are coded either as absent/present or with several states as multistate. The state record for each species is linked to at least one reference (e.g., literature, photographs, histological sections, CT scans, or museum specimens) and so MaTrics contributes to digitalization of museum collections. Currently, MaTrics covers 147 mammalian species and includes 231 characters related to structure, morphology, physiology, ecology, and ethology and available in a machine actionable NEXUS-format*. Filling MaTrics revealed substantial knowledge gaps, highlighting the need for phenotyping efforts. Studies based on selected data from MaTrics and using Forward Genomics identified associations between genes and certain phenotypes ranging from lifestyles (e.g., aquatic) to dietary specializations (e.g., herbivory, carnivory). These findings motivate the expansion of phenotyping in MaTrics by filling research gaps and by adding taxa and traits. Only databases like MaTrics will provide machine actionable information on phenotypic traits, an important limitation to genomics. MaTrics is available within the data repository Morph·D·Base (www.morphdbase.de).

A review of biotechnological approaches towards crop improvement in African yam bean (Sphenostylis stenocarpa Hochst. Ex A. Rich.)

Globally, climate change is a major factor that contributes significantly to food and nutrition insecurity, limiting crop yield and availability. Although efforts are being made to curb food insecurity, millions of people still suffer from malnutrition. For the United Nations (UN) Sustainable Development Goal of Food Security to be achieved, diverse cropping systems must be developed instead of relying mainly on a few staple crops. Many orphan legumes have untapped potential that can be of significance for developing improved cultivars with enhanced tolerance to changing climatic conditions. One typical example of such an orphan crop is Sphenostylis stenocarpa Hochst. Ex A. Rich. Harms, popularly known as African yam bean (AYB). The crop is an underutilised tropical legume that is climate-resilient and has excellent potential for smallholder agriculture in sub-Saharan Africa (SSA). Studies on AYB have featured morphological characterisation, assessment of genetic diversity using various molecular markers, and the development of tissue culture protocols for rapidly multiplying propagules. However, these have not translated into varietal development, and low yields remain a challenge. The application of suitable biotechnologies to improve AYB is imperative for increased yield, sustainable utilisation and conservation. This review discusses biotechnological strategies with prospective applications for AYB improvement. The potential risks of these strategies are also highlighted.

WinRoots: A High-Throughput Cultivation and Phenotyping System for Plant Phenomics Studies Under Soil Stress

Soil stress, such as salinity, is a primary cause of global crop yield reduction. Existing crop phenotyping platforms cannot fully meet the specific needs of phenomics studies of plant response to soil stress in terms of throughput, environmental controllability, or root phenotypic acquisition. Here, we report the WinRoots, a low-cost and high-throughput plant soil cultivation and phenotyping system that can provide uniform, controlled soil stress conditions and accurately quantify the whole-plant phenome, including roots. Using soybean seedlings exposed to salt stress as an example, we demonstrate the uniformity and controllability of the soil environment in this system. A high-throughput multiple-phenotypic assay among 178 soybean cultivars reveals that the cotyledon character can serve as a non-destructive indicator of the whole-seedling salt tolerance. Our results demonstrate that WinRoots is an effective tool for high-throughput plant cultivation and soil stress phenomics studies.

Proteomic similarity of the Littorinid snails in the evolutionary context

BACKGROUND

The introduction of DNA-based molecular markers made a revolution in biological systematics. However, in cases of very recent divergence events, the neutral divergence may be too slow, and the analysis of adaptive part of the genome is more informative to reconstruct the recent evolutionary history of young species. The advantage of proteomics is its ability to reflect the biochemical machinery of life. It may help both to identify rapidly evolving genes and to interpret their functions.

METHODS

Here we applied a comparative gel-based proteomic analysis to several species from the gastropod family Littorinidae. Proteomes were clustered to assess differences related to species, geographic location, sex and body part, using data on presence/absence of proteins in samples and data on protein occurrence frequency in samples of different species. Cluster support was assessed using multiscale bootstrap resampling and the stability of clustering-using cluster-wise index of cluster stability. Taxon-specific protein markers were derived using IndVal method. Proteomic trees were compared to consensus phylogenetic tree (based on neutral genetic markers) using estimates of the Robinson-Foulds distance, the Fowlkes-Mallows index and cophenetic correlation.

RESULTS

Overall, the DNA-based phylogenetic tree and the proteomic similarity tree had consistent topologies. Further, we observed some interesting deviations of the proteomic littorinid tree from the neutral expectations. (1) There were signs of molecular parallelism in two Littoraria species that phylogenetically are quite distant, but live in similar habitats. (2) Proteome divergence was unexpectedly high between very closely related Littorina fabalis and L. obtusata, possibly reflecting their ecology-driven divergence. (3) Conservative house-keeping proteins were usually identified as markers for cryptic species groups ("saxatilis" and "obtusata" groups in the Littorina genus) and for genera (Littoraria and Echinolittorina species pairs), while metabolic enzymes and stress-related proteins (both potentially adaptively important) were often identified as markers supporting species branches. (4) In all five Littorina species British populations were separated from the European mainland populations, possibly reflecting their recent phylogeographic history. Altogether our study shows that proteomic data, when interpreted in the context of DNA-based phylogeny, can bring additional information on the evolutionary history of species.

Longitudinal Transcriptomic, Proteomic, and Metabolomic Analyses of Citrus sinensis (L.) Osbeck Graft-Inoculated with "Candidatus Liberibacter asiaticus"

"Candidatus Liberibacter asiaticus" (CLas) is the bacterium associated with the citrus disease Huanglongbing (HLB). Current CLas detection methods are unreliable during presymptomatic infection, and understanding CLas pathogenicity to help develop new detection techniques is challenging because CLas has yet to be isolated in pure culture. To understand how CLas affects citrus metabolism and whether infected plants produce systemic signals that can be used to develop improved detection techniques, leaves from Washington Navel orange (Citrus sinensis (L.) Osbeck) plants were graft-inoculated with CLas and longitudinally studied using transcriptomics (RNA sequencing), proteomics (liquid chromatography-tandem mass spectrometry), and metabolomics (proton nuclear magnetic resonance). Photosynthesis gene expression and protein levels were lower in infected plants compared to controls during late infection, and lower levels of photosynthesis proteins were identified as early as 8 weeks post-grafting. These changes coordinated with higher sugar concentrations, which have been shown to accumulate during HLB. Cell wall modification and degradation gene expression and proteins were higher in infected plants during late infection. Changes in gene expression and proteins related to plant defense were observed in infected plants as early as 8 weeks post-grafting. These results reveal coordinated changes in greenhouse navel leaves during CLas infection at the transcript, protein, and metabolite levels, which can inform of biomarkers of early infection.

Functional phenomics: an emerging field integrating high-throughput phenotyping, physiology, and bioinformatics

The emergence of functional phenomics signifies the rebirth of physiology as a 21st century science through the use of advanced sensing technologies and big data analytics. Functional phenomics seeks to fill the significant knowledge gaps that still exist in the relationship of plant phenotype to function. Here, a general approach for the theory and practice of functional phenomics is outlined. The functional phenomics pipeline is proposed as a general method for conceptualizing, measuring, and validating utility of plant phenes, or elemental units of phenotype. The functional phenomics pipeline begins with ideotype development. Second, a phenotyping platform is developed to maximize the throughput of phene measurements. Target phenes and indicators of plant function, or performance, are measured in a mapping population. Forward genetics allows genetic mapping, while functional phenomics links phenes to plant performance. Based on these data, genotypes with contrasting phenotypes can be selected for smaller yet more intensive experiments to understand phene-environment interactions in depth. Simulation modeling is used to further understand the phenotypes, and all stages of the pipeline feed back to ideotype and phenotyping platform development. In total, functional phenomics represents an evolution of pre-existing disciplines, but the goals and unique methodologies constitute a novel research program.

Transfer learning across ontologies for phenome-genome association prediction

Motivation

To better predict and analyze gene associations with the collection of phenotypes organized in a phenotype ontology, it is crucial to effectively model the hierarchical structure among the phenotypes in the ontology and leverage the sparse known associations with additional training information. In this paper, we first introduce Dual Label Propagation (DLP) to impose consistent associations with the entire phenotype paths in predicting phenotype-gene associations in Human Phenotype Ontology (HPO). DLP is then used as the base model in a transfer learning framework (tlDLP) to incorporate functional annotations in Gene Ontology (GO). By simultaneously reconstructing GO term-gene associations and HPO phenotype-gene associations for all the genes in a protein-protein interaction network, tlDLP benefits from the enriched training associations indirectly through relation with GO terms.

Results

In the experiments to predict the associations between human genes and phenotypes in HPO based on human protein-protein interaction network, both DLP and tlDLP improved the prediction of gene associations with phenotype paths in HPO in cross-validation and the prediction of the most recent associations added after the snapshot of the training data. Moreover, the transfer learning through GO term-gene associations significantly improved association predictions for the phenotypes with no more specific known associations by a large margin. Examples are also shown to demonstrate how phenotype paths in phenotype ontology and transfer learning with gene ontology can improve the predictions.

Availability and Implementation

Source code is available at http://compbio.cs.umn.edu/onto phenome .

Contact

kuang@cs.umn.com.

Supplementary information

Supplementary data are available at Bioinformatics online.

Hierarchical Map of Orthologous Genomic Regions Reconstructed from Two Closely Related Genomes: Cucumber Case Study

Accurate identification of orthologous genomic regions (OGRs) between two closely related genomes is crucial for the reliable detection of genomic changes, which range from small-scale changes (e.g., single nucleotide or small nucleotides) to large-scale structural changes. Although diverse OGRs inferred at different levels have been successfully applied to address various biological questions, a limited number of studies have simultaneously integrated OGRs from different levels. Here, we report on a new approach to construct a hierarchical map of OGRs. Using different types of genomic markers, this approach was applied to two very closely related cucumber genomes [ L. var. and L. var. (Royle) Alef.]. We identified two different levels of OGRs using Mugsy (denoted as dnaOGRs) and i-ADHoRe (denoted as proOGRs). Using information regarding the anchored chromosomes of the two genomes, a third level of OGRs (denoted chrOGRs) could be built at the chromosomal level. Together, these OGRs could be organized into a hierarchical map that represented the parent-child relationships (chrOGR:proOGRs:dnaOGRs) between the two genomes. For this case study, the map consisted of seven chrOGRs, 540 proOGRs, and 22,321 dnaOGRs. Based on this map, we designed different methods to detect both small-scale and large-scale genomic changes. Surprisingly, many genomic changes were detected at each OGR level despite the very short divergence time between the two subspecies. Together, our results show that a hierarchical map of OGRs and their related genomic changes are useful resources for elucidating the diversity and evolution of cucumber genomes and phenotypes.

Non-destructive Phenotypic Analysis of Early Stage Tree Seedling Growth Using an Automated Stereovision Imaging Method

A plant phenotyping approach was applied to evaluate growth rate of containerized tree seedlings during the precultivation phase following seed germination. A simple and affordable stereo optical system was used to collect stereoscopic red-green-blue (RGB) images of seedlings at regular intervals of time. Comparative analysis of these images by means of a newly developed software enabled us to calculate (a) the increments of seedlings height and (b) the percentage greenness of seedling leaves. Comparison of these parameters with destructive biomass measurements showed that the height traits can be used to estimate seedling growth for needle-leaved plant species whereas the greenness trait can be used for broad-leaved plant species. Despite the need to adjust for plant type, growth stage and light conditions this new, cheap, rapid, and sustainable phenotyping approach can be used to study large-scale phenome variations due to genome variability and interaction with environmental factors.

Bioinformatics Approach in Plant Genomic Research

The advance in genomics technology leads to the dramatic change in plant biology research. Plant biologists now easily access to enormous genomic data to deeply study plant high-density genetic variation at molecular level. Therefore, fully understanding and well manipulating bioinformatics tools to manage and analyze these data are essential in current plant genome research. Many plant genome databases have been established and continued expanding recently. Meanwhile, analytical methods based on bioinformatics are also well developed in many aspects of plant genomic research including comparative genomic analysis, phylogenomics and evolutionary analysis, and genome-wide association study. However, constantly upgrading in computational infrastructures, such as high capacity data storage and high performing analysis software, is the real challenge for plant genome research. This review paper focuses on challenges and opportunities which knowledge and skills in bioinformatics can bring to plant scientists in present plant genomics era as well as future aspects in critical need for effective tools to facilitate the translation of knowledge from new sequencing data to enhancement of plant productivity.

Emerging tools, concepts and ideas to track the modulator genes underlying plant drought adaptive traits: An overview

Crop vulnerability to multiple abiotic stresses is increasing at an alarming rate in the current global climate change scenario, especially drought. Crop improvement for adaptive adjustments to accomplish stress tolerance requires a comprehensive understanding of the key contributory processes. This requires the identification and careful analysis of the critical morpho-physiological plant attributes and their genetic control. In this review we try to discuss the crucial traits underlying drought tolerance and the various modes followed to understand their molecular level regulation. Plant stress biology is progressing into new dimensions and a conscious attempt has been made to traverse through the various approaches and checkpoints that would be relevant to tackle drought stress limitations for sustainable crop production.

Understanding salinity responses and adopting 'omics-based' approaches to generate salinity tolerant cultivars of rice

Soil salinity is one of the main constraints affecting production of rice worldwide, by reducing growth, pollen viability as well as yield of the plant. Therefore, detailed understanding of the response of rice towards soil salinity at the physiological and molecular level is a prerequisite for its effective management. Various approaches have been adopted by molecular biologists or breeders to understand the mechanism for salinity tolerance in plants and to develop salt tolerant rice cultivars. Genome wide analysis using 'omics-based' tools followed by identification and functional validation of individual genes is becoming one of the popular approaches to tackle this task. On the other hand, mutation breeding and insertional mutagenesis has also been exploited to obtain salinity tolerant crop plants. This review looks into various responses at cellular and whole plant level generated in rice plants toward salinity stress thus, evaluating the suitability of intervention of functional genomics to raise stress tolerant plants. We have tried to highlight the usefulness of the contemporary 'omics-based' approaches such as genomics, proteomics, transcriptomics and phenomics towards dissecting out the salinity tolerance trait in rice. In addition, we have highlighted the importance of integration of various 'omics' approaches to develop an understanding of the machinery involved in salinity response in rice and to move forward to develop salt tolerant cultivars of rice.

Network-based Phenome-Genome Association Prediction by Bi-Random Walk

Motivation

The availability of ontologies and systematic documentations of phenotypes and their genetic associations has enabled large-scale network-based global analyses of the association between the complete collection of phenotypes (phenome) and genes. To provide a fundamental understanding of how the network information is relevant to phenotype-gene associations, we analyze the circular bigraphs (CBGs) in OMIM human disease phenotype-gene association network and MGI mouse phentoype-gene association network, and introduce a bi-random walk (BiRW) algorithm to capture the CBG patterns in the networks for unveiling human and mouse phenome-genome association. BiRW performs separate random walk simultaneously on gene interaction network and phenotype similarity network to explore gene paths and phenotype paths in CBGs of different sizes to summarize their associations as predictions.

Results

The analysis of both OMIM and MGI associations revealed that majority of the phenotype-gene associations are covered by CBG patterns of small path lengths, and there is a clear correlation between the CBG coverage and the predictability of the phenotype-gene associations. In the experiments on recovering known associations in cross-validations on human disease phenotypes and mouse phenotypes, BiRW effectively improved prediction performance over the compared methods. The constructed global human disease phenome-genome association map also revealed interesting new predictions and phenotype-gene modules by disease classes.

Analysis of environmental stress factors using an artificial growth system and plant fitness optimization

The environment promotes evolution. Evolutionary processes represent environmental adaptations over long time scales; evolution of crop genomes is not inducible within the relatively short time span of a human generation. Extreme environmental conditions can accelerate evolution, but such conditions are often stress inducing and disruptive. Artificial growth systems can be used to induce and select genomic variation by changing external environmental conditions, thus, accelerating evolution. By using cloud computing and big-data analysis, we analyzed environmental stress factors for Pleurotus ostreatus by assessing, evaluating, and predicting information of the growth environment. Through the indexing of environmental stress, the growth environment can be precisely controlled and developed into a technology for improving crop quality and production.

Evaluation of comprehensive two-dimensional gas chromatography with accurate mass time-of-flight mass spectrometry for the metabolic profiling of plant-fungus interaction in Aquilaria malaccensis

To explore the possible obligate interactions between the phytopathogenic fungus and Aquilaria malaccensis which result in generation of a complex array of secondary metabolites, we describe a comprehensive two-dimensional gas chromatography (GC × GC) method, coupled to accurate mass time-of-flight mass spectrometry (TOFMS) for the untargeted and comprehensive metabolic profiling of essential oils from naturally infected A. malaccensis trees. A polar/non-polar column configuration was employed, offering an improved separation pattern of components when compared to other column sets. Four different grades of the oils displayed quite different metabolic patterns, suggesting the evolution of a signalling relationship between the host tree (emergence of various phytoalexins) and fungi (activation of biotransformation). In total, ca. 550 peaks/metabolites were detected, of which tentative identification of 155 of these compounds was reported, representing between 20.1% and 53.0% of the total ion count. These are distributed over the chemical families of monoterpenic and sesquiterpenic hydrocarbons, oxygenated monoterpenes and sesquiterpenes (comprised of ketone, aldehyde, oxide, alcohol, lactone, keto-alcohol and diol), norterpenoids, diterpenoids, short chain glycols, carboxylic acids and others. The large number of metabolites detected, combined with the ease with which they are located in the 2D separation space, emphasises the importance of a comprehensive analytical approach for the phytochemical analysis of plant metabolomes. Furthermore, the potential of this methodology in grading agarwood oils by comparing the obtained metabolic profiles (pattern recognition for unique metabolite chemical families) is discussed. The phytocomplexity of the agarwood oils signified the production of a multitude of plant-fungus mediated secondary metabolites as chemical signals for natural ecological communication. To the best of our knowledge, this is the most complete information available so far about essential oils of A. malaccensis, which represents a valuable extension to available data for advanced studies on microbial-mediated biotransformation of terpenes, and offers promise for potential discovery of unanticipated phytochemicals, and biotechnological exploitation.

Toward Coalescing Gene Expression and Function with QTLs of Water-Deficit Stress in Cotton

Cotton exhibits moderately high vegetative tolerance to water-deficit stress but lint production is restricted by the available rainfed and irrigation capacity. We have described the impact of water-deficit stress on the genetic and metabolic control of fiber quality and production. Here we examine the association of tentative consensus sequences (TCs) derived from various cotton tissues under irrigated and water-limited conditions with stress-responsive QTLs. Three thousand sixteen mapped sequence-tagged-sites were used as anchored targets to examine sequence homology with 15,784 TCs to test the hypothesis that putative stress-responsive genes will map within QTLs associated with stress-related phenotypic variation more frequently than with other genomic regions not associated with these QTLs. Approximately 1,906 of 15,784 TCs were mapped to the consensus map. About 35% of the annotated TCs that mapped within QTL regions were genes involved in an abiotic stress response. By comparison, only 14.5% of the annotated TCs mapped outside these QTLs were classified as abiotic stress genes. A simple binomial probability calculation of this degree of bias being observed if QTL and non-QTL regions are equally likely to contain stress genes was P (x ≥ 85) = 7.99  × 10(-15). These results suggest that the QTL regions have a higher propensity to contain stress genes.

Bioinformatics: identification of markers from next-generation sequence data

With the advent of sequencing technology, next-generation sequencing (NGS) technology has dramatically revolutionized plant genomics. NGS technology combined with new software tools enables the discovery, validation, and assessment of genetic markers on a large scale. Among different markers systems, simple sequence repeats (SSRs) and Single nucleotide polymorphisms (SNPs) are the markers of choice for genetics and plant breeding. SSR markers have been a choice for large-scale characterization of germplasm collections, construction of genetic maps, and QTL identification. Similarly, SNPs are the most abundant genetic variations with higher frequencies throughout the genome of plant species. This chapter discusses various tools available for genome assembly and widely focuses on SSR and SNP marker discovery.

Molecular marker databases

The detection and analysis of genetic variation plays an important role in plant breeding and this role is increasing with the continued development of genome sequencing technologies. Molecular genetic markers are important tools to characterize genetic variation and assist with genomic breeding. Processing and storing the growing abundance of molecular marker data being produced requires the development of specific bioinformatics tools and advanced databases. Molecular marker databases range from species specific through to organism wide and often host a variety of additional related genetic, genomic, or phenotypic information. In this chapter, we will present some of the features of plant molecular genetic marker databases, highlight the various types of marker resources, and predict the potential future direction of crop marker databases.

POEAS: Automated Plant Phenomic Analysis Using Plant Ontology

Biological enrichment analysis using gene ontology (GO) provides a global overview of the functional role of genes or proteins identified from large-scale genomic or proteomic experiments. Phenomic enrichment analysis of gene lists can provide an important layer of information as well as cellular components, molecular functions, and biological processes associated with gene lists. Plant phenomic enrichment analysis will be useful for performing new experiments to better understand plant systems and for the interpretation of gene or proteins identified from high-throughput experiments. Plant ontology (PO) is a compendium of terms to define the diverse phenotypic characteristics of plant species, including plant anatomy, morphology, and development stages. Adoption of this highly useful ontology is limited, when compared to GO, because of the lack of user-friendly tools that enable the use of PO for statistical enrichment analysis. To address this challenge, we introduce Plant Ontology Enrichment Analysis Server (POEAS) in the public domain. POEAS uses a simple list of genes as input data and performs enrichment analysis using Ontologizer 2.0 to provide results in two levels, enrichment results and visualization utilities, to generate ontological graphs that are of publication quality. POEAS also offers interactive options to identify user-defined background population sets, various multiple-testing correction methods, different enrichment calculation methods, and resampling tests to improve statistical significance. The availability of such a tool to perform phenomic enrichment analyses using plant genes as a complementary resource will permit the adoption of PO-based phenomic analysis as part of analytical workflows. POEAS can be accessed using the URL http://caps.ncbs.res.in/poeas.

Polymorphism identification and improved genome annotation of Brassica rapa through Deep RNA sequencing

The mapping and functional analysis of quantitative traits in Brassica rapa can be greatly improved with the availability of physically positioned, gene-based genetic markers and accurate genome annotation. In this study, deep transcriptome RNA sequencing (RNA-Seq) of Brassica rapa was undertaken with two objectives: SNP detection and improved transcriptome annotation. We performed SNP detection on two varieties that are parents of a mapping population to aid in development of a marker system for this population and subsequent development of high-resolution genetic map. An improved Brassica rapa transcriptome was constructed to detect novel transcripts and to improve the current genome annotation. This is useful for accurate mRNA abundance and detection of expression QTL (eQTLs) in mapping populations. Deep RNA-Seq of two Brassica rapa genotypes—R500 (var. trilocularis, Yellow Sarson) and IMB211 (a rapid cycling variety)—using eight different tissues (root, internode, leaf, petiole, apical meristem, floral meristem, silique, and seedling) grown across three different environments (growth chamber, greenhouse and field) and under two different treatments (simulated sun and simulated shade) generated 2.3 billion high-quality Illumina reads. A total of 330,995 SNPs were identified in transcribed regions between the two genotypes with an average frequency of one SNP in every 200 bases. The deep RNA-Seq reassembled Brassica rapa transcriptome identified 44,239 protein-coding genes. Compared with current gene models of B. rapa, we detected 3537 novel transcripts, 23,754 gene models had structural modifications, and 3655 annotated proteins changed. Gaps in the current genome assembly of B. rapa are highlighted by our identification of 780 unmapped transcripts. All the SNPs, annotations, and predicted transcripts can be viewed at http://phytonetworks.ucdavis.edu/.

Plant systems biology: insights, advances and challenges

Plants dwelling at the base of biological food chain are of fundamental significance in providing solutions to some of the most daunting ecological and environmental problems faced by our planet. The reductionist views of molecular biology provide only a partial understanding to the phenotypic knowledge of plants. Systems biology offers a comprehensive view of plant systems, by employing a holistic approach integrating the molecular data at various hierarchical levels. In this review, we discuss the basics of systems biology including the various 'omics' approaches and their integration, the modeling aspects and the tools needed for the plant systems research. A particular emphasis is given to the recent analytical advances, updated published examples of plant systems biology studies and the future trends.

Structural variations in plant genomes

Differences between plant genomes range from single nucleotide polymorphisms to large-scale duplications, deletions and rearrangements. The large polymorphisms are termed structural variants (SVs). SVs have received significant attention in human genetics and were found to be responsible for various chronic diseases. However, little effort has been directed towards understanding the role of SVs in plants. Many recent advances in plant genetics have resulted from improvements in high-resolution technologies for measuring SVs, including microarray-based techniques, and more recently, high-throughput DNA sequencing. In this review we describe recent reports of SV in plants and describe the genomic technologies currently used to measure these SVs.

Integrated Analysis Platform: An Open-Source Information System for High-Throughput Plant Phenotyping

High-throughput phenotyping is emerging as an important technology to dissect phenotypic components in plants. Efficient image processing and feature extraction are prerequisites to quantify plant growth and performance based on phenotypic traits. Issues include data management, image analysis, and result visualization of large-scale phenotypic data sets. Here, we present Integrated Analysis Platform (IAP), an open-source framework for high-throughput plant phenotyping. IAP provides user-friendly interfaces, and its core functions are highly adaptable. Our system supports image data transfer from different acquisition environments and large-scale image analysis for different plant species based on real-time imaging data obtained from different spectra. Due to the huge amount of data to manage, we utilized a common data structure for efficient storage and organization of data for both input data and result data. We implemented a block-based method for automated image processing to extract a representative list of plant phenotypic traits. We also provide tools for build-in data plotting and result export. For validation of IAP, we performed an example experiment that contains 33 maize (Zea mays 'Fernandez') plants, which were grown for 9 weeks in an automated greenhouse with nondestructive imaging. Subsequently, the image data were subjected to automated analysis with the maize pipeline implemented in our system. We found that the computed digital volume and number of leaves correlate with our manually measured data in high accuracy up to 0.98 and 0.95, respectively. In summary, IAP provides a multiple set of functionalities for import/export, management, and automated analysis of high-throughput plant phenotyping data, and its analysis results are highly reliable.

Predicting transcriptional circuitry underlying seed coat development

Filling, protection, and dispersal of angiosperm seeds are largely dependent on the development of the maternally derived seed coat. The development of the seed coat in plants such as Arabidopsis thaliana and Glycine max (soybean) is regulated by a complex network of genes and gene products responsible for the establishment and identity of this multicellular structure. Recent studies support the hypothesis that the structure, development, and function of the seed coat are under the control of transcriptional regulators that are specified in space and time. Furthermore, these transcriptional regulators can act in combination to orchestrate the expression of large gene sets. We discuss the underlying transcriptional circuits of the seed coat sub-regions through the interrogation of large-scale datasets, and also provide some ideas on how the identification and analysis of these datasets can be further improved in these two model oilseed systems.

Next generation characterisation of cereal genomes for marker discovery

Cereal crops form the bulk of the world’s food sources, and thus their importance cannot be understated. Crop breeding programs increasingly rely on high-resolution molecular genetic markers to accelerate the breeding process. The development of these markers is hampered by the complexity of some of the major cereal crop genomes, as well as the time and cost required. In this review, we address current and future methods available for the characterisation of cereal genomes, with an emphasis on faster and more cost effective approaches for genome sequencing and the development of markers for trait association and marker assisted selection (MAS) in crop breeding programs.

A review of the "Omics" approach to biomarkers of oxidative stress in Oryza sativa

Physiological and ecological constraints that cause the slow growth and depleted production of crops have raised a major concern in the agriculture industry as they represent a possible threat of short food supply in the future. The key feature that regulates the stress signaling pathway is always related to the reactive oxygen species (ROS). The accumulation of ROS in plant cells would leave traces of biomarkers at the genome, proteome, and metabolome levels, which could be identified with the recent technological breakthrough coupled with improved performance of bioinformatics. This review highlights the recent breakthrough in molecular strategies (comprising transcriptomics, proteomics, and metabolomics) in identifying oxidative stress biomarkers and the arising opportunities and obstacles observed in research on biomarkers in rice. The major issue in incorporating bioinformatics to validate the biomarkers from different omic platforms for the use of rice-breeding programs is also discussed. The development of powerful techniques for identification of oxidative stress-related biomarkers and the integration of data from different disciplines shed light on the oxidative response pathways in plants.

Discovery of Single Nucleotide Polymorphisms in Complex Genomes Using SGSautoSNP

Single nucleotide polymorphisms (SNPs) are becoming the dominant form of molecular marker for genetic and genomic analysis. The advances in second generation DNA sequencing provide opportunities to identify very large numbers of SNPs in a range of species. However, SNP identification remains a challenge for large and polyploid genomes due to their size and complexity. We have developed a pipeline for the robust identification of SNPs in large and complex genomes using Illumina second generation DNA sequence data and demonstrated this by the discovery of SNPs in the hexaploid wheat genome. We have developed a SNP discovery pipeline called SGSautoSNP (Second-Generation Sequencing AutoSNP) and applied this to discover more than 800,000 SNPs between four hexaploid wheat cultivars across chromosomes 7A, 7B and 7D. All SNPs are presented for download and viewing within a public GBrowse database. Validation suggests an accuracy of greater than 93% of SNPs represent polymorphisms between wheat cultivars and hence are valuable for detailed diversity analysis, marker assisted selection and genotyping by sequencing. The pipeline produces output in GFF3, VCF, Flapjack or Illumina Infinium design format for further genotyping diverse populations. As well as providing an unprecedented resource for wheat diversity analysis, the method establishes a foundation for high resolution SNP discovery in other large and complex genomes.

Single nucleotide polymorphism discovery from wheat next-generation sequence data

Single nucleotide polymorphisms (SNPs) are the most abundant type of molecular genetic marker and can be used for producing high-resolution genetic maps, marker-trait association studies and marker-assisted breeding. Large polyploid genomes such as wheat present a challenge for SNP discovery because of the potential presence of multiple homoeologs for each gene. AutoSNPdb has been successfully applied to identify SNPs from Sanger sequence data for several species, including barley, rice and Brassica, but the volume of data required to accurately call SNPs in the complex genome of wheat has prevented its application to this important crop. DNA sequencing technology has been revolutionized by the introduction of next-generation sequencing, and it is now possible to generate several million sequence reads in a timely and cost-effective manner. We have produced wheat transcriptome sequence data using 454 sequencing technology and applied this for SNP discovery using a modified autoSNPdb method, which integrates SNP and gene annotation information with a graphical viewer. A total of 4,694,141 sequence reads from three bread wheat varieties were assembled to identify a total of 38 928 candidate SNPs. Each SNP is within an assembly complete with annotation, enabling the selection of polymorphism within genes of interest.

Co-clustering phenome-genome for phenotype classification and disease gene discovery

Understanding the categorization of human diseases is critical for reliably identifying disease causal genes. Recently, genome-wide studies of abnormal chromosomal locations related to diseases have mapped >2000 phenotype–gene relations, which provide valuable information for classifying diseases and identifying candidate genes as drug targets. In this article, a regularized non-negative matrix tri-factorization (R-NMTF) algorithm is introduced to co-cluster phenotypes and genes, and simultaneously detect associations between the detected phenotype clusters and gene clusters. The R-NMTF algorithm factorizes the phenotype–gene association matrix under the prior knowledge from phenotype similarity network and protein–protein interaction network, supervised by the label information from known disease classes and biological pathways. In the experiments on disease phenotype–gene associations in OMIM and KEGG disease pathways, R-NMTF significantly improved the classification of disease phenotypes and disease pathway genes compared with support vector machines and Label Propagation in cross-validation on the annotated phenotypes and genes. The newly predicted phenotypes in each disease class are highly consistent with human phenotype ontology annotations. The roles of the new member genes in the disease pathways are examined and validated in the protein–protein interaction subnetworks. Extensive literature review also confirmed many new members of the disease classes and pathways as well as the predicted associations between disease phenotype classes and pathways.

Hyperspectral imaging techniques for rapid identification of Arabidopsis mutants with altered leaf pigment status

The spectral reflectance signature of living organisms provides information that closely reflects their physiological status. Because of its high potential for the estimation of geomorphic biological parameters, particularly of gross photosynthesis of plants, two-dimensional spectroscopy, via the use of hyperspectral instruments, has been widely used in remote sensing applications. In genetics research, in contrast, the reflectance phenotype has rarely been the subject of quantitative analysis; its potential for illuminating the pathway leading from the gene to phenotype remains largely unexplored. In this study, we employed hyperspectral imaging techniques to identify Arabidopsis mutants with altered leaf pigment status. The techniques are comprised of two modes; the first is referred to as the 'targeted mode' and the second as the 'non-targeted mode'. The 'targeted' mode is aimed at visualizing individual concentrations and compositional parameters of leaf pigments based on reflectance indices (RIs) developed for Chls a and b, carotenoids and anthocyanins. The 'non-targeted' mode highlights differences in reflectance spectra of leaf samples relative to reference spectra from the wild-type leaves. Through the latter approach, three mutant lines with weak irregular reflectance phenotypes, that are hardly identifiable by simple observation, were isolated. Analysis of these and other mutants revealed that the RI-based targeted pigment estimation was robust at least against changes in trichome density, but was confounded by genetic defects in chloroplast photorelocation movement. Notwithstanding such a limitation, the techniques presented here provide rapid and high-sensitive means to identify genetic mechanisms that coordinate leaf pigment status with developmental stages and/or environmental stress conditions.

Proteomics in evolutionary ecology: linking the genotype with the phenotype

The study of the proteome (proteomics), which includes the dynamics of protein expression, regulation, interactions and its function, has played a less prominent role in evolutionary and ecological investigations in comparison with the study of the genome and transcriptome. There are, however, a number of arguments suggesting that this situation should change. First, the proteome is closer to the phenotype than the genome or the transcriptome, and as such may be more directly responsive to natural selection, and thus closely linked to adaptation. Second, there is evidence of a low correlation between protein and transcript expression levels across genes in many different organisms. Finally, there have been some recent important technological improvements in proteomics methods that make them feasible, practical and useful to address a wide range of evolutionary questions even in nonmodel organisms. The different proteomic methods, their limitations and problems when interpreting empirical data are described and discussed. In addition, the proteomic literature pertaining to evolutionary ecology is reviewed with examples, and potential applications of proteomics in a variety of evolutionary contexts are outlined. New proteomic research trends such as the study of posttranslational modifications and protein-protein interactions, as well as the combined use of the different -omics approaches, are discussed in relation to the development of a more functional and integrated perspective, needed for achieving a more comprehensive knowledge of evolutionary change.

Application of HR-MAS NMR spectroscopy for studying chemotype variations of Withania somnifera (L.) Dunal

Withania somnifera (L.) Dunal (Solanaceae), commonly known as Ashwagandha, is one of the most valued Indian medicinal plants with a number of pharmaceutical and nutraceutical applications. Metabolic profiling has been performed by HR-MAS NMR spectroscopy on fresh leaf and root tissue specimens from four chemotypes of W. somnifera. The HR-MAS NMR spectroscopy of lyophilized defatted leaf tissue specimens clearly distinguishes resonances of medicinally important secondary metabolites (withaferin A and withanone) and its distinctive quantitative variability among the chemotypes. A total of 41 metabolites were identified from both the leaf and root tissues of the chemotypes. The presence of methanol in leaf and root tissues of W. somnifera was detected by HR-MAS NMR spectroscopy. Multivariate principal component analysis (PCA) on HR-MAS (1) H NMR spectra of leaves revealed clear variations in primary metabolites among the chemotypes. The results of the present study demonstrated an efficient method, which can be utilized for metabolite profiling of primary and secondary metabolites in medicinally important plants.

Future tools for association mapping in crop plants

Association mapping currently relies on the identification of genetic markers. Several technologies have been adopted for genetic marker analysis, with single nucleotide polymorphisms (SNPs) being the most popular where a reasonable quantity of genome sequence data are available. We describe several tools we have developed for the discovery, annotation, and visualization of molecular markers for association mapping. These include autoSNPdb for SNP discovery from assembled sequence data; TAGdb for the identification of gene specific paired read Illumina GAII data; CMap3D for the comparison of mapped genetic and physical markers; and BAC and Gene Annotator for the online annotation of genes and genomic sequences.

The FAST technique: a simplified Agrobacterium-based transformation method for transient gene expression analysis in seedlings of Arabidopsis and other plant species

BACKGROUND

Plant genome sequencing has resulted in the identification of a large number of uncharacterized genes. To investigate these unknown gene functions, several transient transformation systems have been developed as quick and convenient alternatives to the lengthy transgenic assay. These transient assays include biolistic bombardment, protoplast transfection and Agrobacterium-mediated transient transformation, each having advantages and disadvantages depending on the research purposes.

RESULTS

We present a novel transient assay based on cocultivation of young Arabidopsis (Arabidopsis thaliana) seedlings with Agrobacterium tumefaciens in the presence of a surfactant which does not require any dedicated equipment and can be carried out within one week from sowing seeds to protein analysis. This Fast Agro-mediated Seedling Transformation (FAST) was used successfully to express a wide variety of constructs driven by different promoters in Arabidopsis seedling cotyledons (but not roots) in diverse genetic backgrounds. Localizations of three previously uncharacterized proteins were identified by cotransformation with fluorescent organelle markers. The FAST procedure requires minimal handling of seedlings and was also adaptable for use in 96-well plates. The high transformation efficiency of the FAST procedure enabled protein detection from eight transformed seedlings by immunoblotting. Protein-protein interaction, in this case HY5 homodimerization, was readily detected in FAST-treated seedlings with Förster resonance energy transfer and bimolecular fluorescence complementation techniques. Initial tests demonstrated that the FAST procedure can also be applied to other dicot and monocot species, including tobacco, tomato, rice and switchgrass.

CONCLUSION

The FAST system provides a rapid, efficient and economical assay of gene function in intact plants with minimal manual handling and without dedicated device. This method is potentially ideal for future automated high-throughput analysis.