Comprehensive post-genomic data analysis approaches integrating biochemical pathway maps

Site-Directed Mutagenesis of the Carotenoid Isomerase Gene BnaCRTISO Alters the Color of Petals and Leaves in Brassica napus L

The diversity of petal and leaf color can improve the ornamental value of rapeseed and promote the development of agriculture and tourism. The two copies of carotenoid isomerase gene (BnaCRTISO) in Brassica napus (BnaA09.CRTISO and BnaC08.CRTISO) was edited using the CRISPR/Cas9 system in the present study. The mutation phenotype of creamy white petals and yellowish leaves could be recovered only in targeted mutants of both BnaCRTISO functional copies, indicating that the redundant roles of BnaA09.CRTISO and BnaC08.CRTISO are vital for the regulation of petal and leaf color. The carotenoid content in the petals and leaves of the BnaCRTISO double mutant was significantly reduced. The chalcone content, a vital substance that makes up the yellow color, also decreased significantly in petals. Whereas, the contents of some carotenes (lycopene, α-carotene, γ-carotene) were increased significantly in petals. Further, transcriptome analysis showed that the targeted mutation of BnaCRTISO resulted in the significant down-regulation of important genes BnaPSY and BnaC4H in the carotenoid and flavonoid synthesis pathways, respectively; however, the expression of other genes related to carotenes and xanthophylls synthesis, such as BnaPDS3, BnaZEP, BnaBCH1 and BCH2, was up-regulated. This indicates that the molecular mechanism regulating petal color variation in B. napus is more complicated than those reported in Arabidopsis and other Brassica species. These results provide insight into the molecular mechanisms underlying flower color variation in rapeseed and provides valuable resources for rapeseed breeding.

SolCyc: a database hub at the Sol Genomics Network (SGN) for the manual curation of metabolic networks in Solanum and Nicotiana specific databases

Database URL

https://solgenomics.net/tools/solcyc/.

Integrated analysis of transcriptomics and metabolomics profiles

BACKGROUND

Integrated analysis of transcriptomics and metabolomics data has the potential greatly to increase our understanding of metabolic networks and biological systems leading to various potential clinical applications.

OBJECTIVE

The aim is to present different applications as well as analysis tools utilized for the parallel study of gene and metabolite expressions.

METHODS

Publications dealing with integrated analysis of gene and metabolite expression data as well as publications describing tools that can be used for integrated analysis are reviewed.

RESULTS/CONCLUSION

The full benefit of integrated analysis can be achieved only if data from all utilized methods are treated equally by multidisciplinary teams. This approach can lead to advances in functional genomics with possible clinical developments in diagnostics and improved drug target selection.

Carotenoid biosynthesis in Arabidopsis: a colorful pathway

Plant carotenoids are a family of pigments that participate in light harvesting and are essential for photoprotection against excess light. Furthermore, they act as precursors for the production of apocarotenoid hormones such as abscisic acid and strigolactones. In this review, we summarize the current knowledge on the genes and enzymes of the carotenoid biosynthetic pathway (which is now almost completely elucidated) and on the regulation of carotenoid biosynthesis at both transcriptional and post-transcriptional levels. We also discuss the relevance of Arabidopsis as a model system for the study of carotenogenesis and how metabolic engineering approaches in this plant have taught important lessons for carotenoid biotechnology.

Chloroplast proteomics and the compartmentation of plastidial isoprenoid biosynthetic pathways

Recent advances in the proteomic field have allowed high-throughput experiments to be conducted on chloroplast samples. Many proteomic investigations have focused on either whole chloroplast or sub-plastidial fractions. To date, the Plant Protein Database (PPDB, Sun et al., 2009) presents the most exhaustive chloroplast proteome available online. However, the accurate localization of many proteins that were identified in different sub-plastidial compartments remains hypothetical. Ferro et al. (2009) went a step further into the knowledge of Arabidopsis thaliana chloroplast proteins with regards to their accurate localization within the chloroplast by using a semi-quantitative proteomic approach known as spectral counting. Their proteomic strategy was based on the accurate mass and time tags (AMT) database approach and they built up AT_CHLORO, a comprehensive chloroplast proteome database with sub-plastidial localization and curated information on envelope proteins. Comparing these two extensive databases, we focus here on about 100 enzymes involved in the synthesis of chloroplast-specific isoprenoids. Well known pathways (i.e. compartmentation of the methyl erythritol phosphate biosynthetic pathway, of tetrapyrroles and chlorophyll biosynthesis and breakdown within chloroplasts) validate the spectral counting-based strategy. The same strategy was then used to identify the precise localization of the biosynthesis of carotenoids and prenylquinones within chloroplasts (i.e. in envelope membranes, stroma, and/or thylakoids) that remains unclear until now.

An extensive (co-)expression analysis tool for the cytochrome P450 superfamily in Arabidopsis thaliana

BACKGROUND

Sequencing of the first plant genomes has revealed that cytochromes P450 have evolved to become the largest family of enzymes in secondary metabolism. The proportion of P450 enzymes with characterized biochemical function(s) is however very small. If P450 diversification mirrors evolution of chemical diversity, this points to an unexpectedly poor understanding of plant metabolism. We assumed that extensive analysis of gene expression might guide towards the function of P450 enzymes, and highlight overlooked aspects of plant metabolism.

RESULTS

We have created a comprehensive database, 'CYPedia', describing P450 gene expression in four data sets: organs and tissues, stress response, hormone response, and mutants of Arabidopsis thaliana, based on public Affymetrix ATH1 microarray expression data. P450 expression was then combined with the expression of 4,130 re-annotated genes, predicted to act in plant metabolism, for co-expression analyses. Based on the annotation of co-expressed genes from diverse pathway annotation databases, co-expressed pathways were identified. Predictions were validated for most P450s with known functions. As examples, co-expression results for P450s related to plastidial functions/photosynthesis, and to phenylpropanoid, triterpenoid and jasmonate metabolism are highlighted here.

CONCLUSION

The large scale hypothesis generation tools presented here provide leads to new pathways, unexpected functions, and regulatory networks for many P450s in plant metabolism. These can now be exploited by the community to validate the proposed functions experimentally using reverse genetics, biochemistry, and metabolic profiling.

Metabolomics for plant stress response

Stress in plants could be defined as any change in growth condition(s) that disrupts metabolic homeostasis and requires an adjustment of metabolic pathways in a process that is usually referred to as acclimation. Metabolomics could contribute significantly to the study of stress biology in plants and other organisms by identifying different compounds, such as by-products of stress metabolism, stress signal transduction molecules or molecules that are part of the acclimation response of plants. These could be further tested by direct measurements, correlated with changes in transcriptome and proteome expression and confirmed by mutant analysis. In this review, we will discuss recent application of metabolomics and system biology to the area of plant stress response. We will describe approaches such as metabolic profiling and metabolic fingerprinting as well as combination of different 'omics' platforms to achieve a holistic view of the plant response stress and conduct detailed pathway analysis.

Isotopomer-based metabolomic analysis by NMR and mass spectrometry

Nuclear magnetic resonance (NMR) and mass spectrometry (MS) together are synergistic in their ability to profile comprehensively the metabolome of cells and tissues. In addition to identification and quantification of metabolites, changes in metabolic pathways and fluxes in response to external perturbations can be reliably determined by using stable isotope tracer methodologies. NMR and MS together are able to define both positional isotopomer distribution in product metabolites that derive from a given stable isotope-labeled precursor molecule and the degree of enrichment at each site with good precision. Together with modeling tools, this information provides a rich functional biochemical readout of cellular activity and how it responds to external influences. In this chapter, we describe NMR- and MS-based methodologies for isotopomer analysis in metabolomics and its applications for different biological systems.

A molecular timetable for apical bud formation and dormancy induction in poplar

The growth of perennial plants in the temperate zone alternates with periods of dormancy that are typically initiated during bud development in autumn. In a systems biology approach to unravel the underlying molecular program of apical bud development in poplar (Populus tremula x Populus alba), combined transcript and metabolite profiling were applied to a high-resolution time course from short-day induction to complete dormancy. Metabolite and gene expression dynamics were used to reconstruct the temporal sequence of events during bud development. Importantly, bud development could be dissected into bud formation, acclimation to dehydration and cold, and dormancy. To each of these processes, specific sets of regulatory and marker genes and metabolites are associated and provide a reference frame for future functional studies. Light, ethylene, and abscisic acid signal transduction pathways consecutively control bud development by setting, modifying, or terminating these processes. Ethylene signal transduction is positioned temporally between light and abscisic acid signals and is putatively activated by transiently low hexose pools. The timing and place of cell proliferation arrest (related to dormancy) and of the accumulation of storage compounds (related to acclimation processes) were established within the bud by electron microscopy. Finally, the identification of a large set of genes commonly expressed during the growth-to-dormancy transitions in poplar apical buds, cambium, or Arabidopsis thaliana seeds suggests parallels in the underlying molecular mechanisms in different plant organs.

Experimental and mathematical approaches to modeling plant metabolic networks

To support their sessile and autotrophic lifestyle higher plants have evolved elaborate networks of metabolic pathways. Dynamic changes in these metabolic networks are among the developmental forces underlying the functional differentiation of organs, tissues and specialized cell types. They are also important in the various interactions of a plant with its environment. Further complexity is added by the extensive compartmentation of the various interconnected metabolic pathways in plants. Thus, although being used widely for assessing the control of metabolic flux in microbes, mathematical modeling approaches that require steady-state approximations are of limited utility for understanding complex plant metabolic networks. However, considerable progress has been made when manageable metabolic subsystems were studied. In this article, we will explain in general terms and using simple examples the concepts underlying stoichiometric modeling (metabolic flux analysis and metabolic pathway analysis) and kinetic approaches to modeling (including metabolic control analysis as a special case). Selected studies demonstrating the prospects of these approaches, or combinations of them, for understanding the control of flux through particular plant pathways are discussed. We argue that iterative cycles of (dry) mathematical modeling and (wet) laboratory testing will become increasingly important for simulating the distribution of flux in plant metabolic networks and deriving rational experimental designs for metabolic engineering efforts.

Compartment-specific labeling information in 13C metabolic flux analysis of plants

Metabolic engineering of plants has great potential for the low cost production of chemical feedstocks and novel compounds, but to take full advantage of this potential a better understanding of plant central carbon metabolism is needed. Flux studies define the cellular phenotype of living systems and can facilitate rational metabolic engineering. However the measurements usually made in these analyses are often not sufficient to reliably determine many fluxes that are distributed between different subcellular compartments of eukaryotic cells. We have begun to address this shortcoming by increasing the number and quality of measurements that provide (13)C labeling information from specific compartments within the plant cell. The analysis of fatty acid groups, cell wall components, protein glycans, and starch, using both gas chromatography/mass spectrometry and nuclear magnetic resonance spectroscopy are presented here. Fatty acid labeling determinations are sometimes highly convoluted. Derivatization to butyl amides reduces the errors in isotopomer resolution and quantification, resulting in better determination of fluxes into seed lipid reserves, including both plastidic and cytosolic reactions. While cell walls can account for a third or more of biomass in many seeds, no quantitative cell wall labeling measurements have been reported for plant flux analysis. Hydrolyzing cell wall and derivatizing sugars to the alditol acetates, provides novel labeling information and thereby can improve identification of flux through upper glycolytic intermediates of the cytosol. These strategies improve the quantification of key carbon fluxes in the compartmentalized flux network of plant cells.

MedicCyc: a biochemical pathway database for Medicago truncatula

MOTIVATION

There is an imperative need to integrate functional genomics data to obtain a more comprehensive systems-biology view of the results. We believe that this is best achieved through the visualization of data within the biological context of metabolic pathways. Accordingly, metabolic pathway reconstruction was used to predict the metabolic composition for Medicago truncatula and these pathways were engineered to enable the correlated visualization of integrated functional genomics data.

RESULTS

Metabolic pathway reconstruction was used to generate a pathway database for M. truncatula (MedicCyc), which currently features more than 250 pathways with related genes, enzymes and metabolites. MedicCyc was assembled from more than 225,000 M. truncatula ESTs (MtGI Release 8.0) and available genomic sequences using the Pathway Tools software and the MetaCyc database. The predicted pathways in MedicCyc were verified through comparison with other plant databases such as AraCyc and RiceCyc. The comparison with other plant databases provided crucial information concerning enzymes still missing from the ongoing, but currently incomplete M. truncatula genome sequencing project. MedicCyc was further manually curated to remove non-plant pathways, and Medicago-specific pathways including isoflavonoid, lignin and triterpene saponin biosynthesis were modified or added based upon available literature and in-house expertise. Additional metabolites identified in metabolic profiling experiments were also used for pathway predictions. Once the metabolic reconstruction was completed, MedicCyc was engineered to visualize M. truncatula functional genomics datasets within the biological context of metabolic pathways.

AVAILABILITY

freely accessible at http://www.noble.org/MedicCyc/

Functional annotation of the Arabidopsis P450 superfamily based on large-scale co-expression analysis

Cytochrome P450 mono-oxygenases play prominent roles in a diverse set of metabolic pathways, but the function of most of these enzymes remains obscure. A bottleneck in the functional genomics of this superfamily constitutes hypothesis generation to identify potential substrates (or substrate classes) individual P450s may act on. We used publicly available large-scale expression data to perform co-expression analysis comparing the expression matrix of each P450 with those from more than 4000 selected genes across thousands of microarrays. Based on functional annotations of co-expressed genes from a diverse set of databases, co-expressed pathways were thus identified for each P450. Using this approach, most P450s with known functions were placed into their respective pathways, thereby proofing the concept. As examples, pathway mapping results identifying novel P450s potentially acting on flower-specific monoterpenes and root-specific triterpenes are described. Co-expression results for all Arabidopsis P450s will be presented as a web resource on the 'CYPedia' web pages (http://ibmp.u-strasbg.fr/CYPedia/).

Molecular interactions between the specialist herbivore Manduca sexta (Lepidoptera, Sphingidae) and its natural host Nicotiana attenuata. VII. Changes in the plant's proteome

When Manduca sexta attacks Nicotiana attenuata, fatty acid-amino acid conjugates (FACs) in the larvae's oral secretions (OS) are introduced into feeding wounds. These FACs trigger a transcriptional response that is similar to the response induced by insect damage. Using two-dimensional gel electrophoresis, matrix-assisted laser desorption ionization-time of flight, and liquid chromatography-tandem mass spectrometry, we characterized the proteins in phenolic extracts and in a nuclear fraction of leaves elicited by larval attack, and/or in leaves wounded and treated with OS, FAC-free OS, and synthetic FACs. Phenolic extracts yielded approximately 600 protein spots, many of which were altered by elicitation, whereas nuclear protein fractions yielded approximately 100 spots, most of which were unchanged by elicitation. Reproducible elicitor-induced changes in 90 spots were characterized. In general, proteins that increased were involved in primary metabolism, defense, and transcriptional and translational regulation; those that decreased were involved in photosynthesis. Like the transcriptional defense responses, proteomic changes were strongly elicited by the FACs in OS. A semiquantitative reverse transcription-PCR approach based on peptide sequences was used to compare transcript and protein accumulation patterns for 17 candidate proteins. In six cases the patterns of elicited transcript accumulation were consistent with those of elicited protein accumulation. Functional analysis of one of the identified proteins involved in photosynthesis, RuBPCase activase, was accomplished by virus-induced gene silencing. Plants with decreased levels of RuBPCase activase protein had reduced photosynthetic rates and RuBPCase activity, and less biomass, responses consistent with those of herbivore-attacked plants. We conclude that the response of the plant's proteome to herbivore elicitation is complex, and integrated transcriptome-proteome-metabolome analysis is required to fully understand this ubiquitous ecological interaction.

Advances in plant proteomics

With the avalanche of genomic information and improvements in analytical technology, proteomics is becoming increasingly important for the study of many different aspects of plant functions. Since proteins serve as important components of major signaling and biochemical pathways, studies at protein levels are essential to reveal molecular mechanisms underlying plant growth, development, and interactions with the environment. The plant proteome is highly complex and dynamic. Although great strides need to be taken towards the ultimate goal of characterizing all the proteins in a proteome, current technologies have provided immense opportunities for high-throughput proteomic studies that have gone beyond simple protein identification to analyzing various functional aspects, such as quantification, PTM, subcellular localization, and protein-protein interactions. In this review of plant proteomics, advances in protein fractionation, separation, and MS will be outlined. Focus will be on recent development in functional analysis of plant proteins, which paves the way towards the comprehensive integration with transcriptomics, metabolomics, and other large scale "-omics" into systems biology.

Transcriptional regulators of stamen development in Arabidopsis identified by transcriptional profiling

In Arabidopsis, jasmonate is required for stamen and pollen maturation. Mutants deficient in jasmonate synthesis, such as opr3, are male-sterile but become fertile when jasmonate is applied to developing flower buds. We have used ATH1 oligonucleotide arrays to follow gene expression in opr3 stamens for 22 h following jasmonate treatment. In these experiments, a total of 821 genes were specifically induced by jasmonate and 480 genes were repressed. Comparisons with data from previous studies indicate that these genes constitute a stamen-specific jasmonate transcriptome, with a large proportion (70%) of the genes expressed in the sporophytic tissue but not in the pollen. Bioinformatics tools allowed us to associate many of the induced genes with metabolic pathways that are probably upregulated during jasmonate-induced maturation. Our pathway analysis led to the identification of specific genes within larger families of homologues that apparently encode stamen-specific isozymes. Extensive additional analysis of our dataset identified 13 transcription factors that may be key regulators of the stamen maturation processes triggered by jasmonate. Two of these transcription factors, MYB21 and MYB24, are the only members of subgroup 19 of the R2R3 family of MYB proteins. A myb21 mutant obtained by reverse genetics exhibited shorter anther filaments, delayed anther dehiscence and greatly reduced male fertility. A myb24 mutant was phenotypically wild-type, but production of a myb21myb24 double mutant indicated that introduction of the myb24 mutation exacerbated all three aspects of the myb21 phenotype. Exogenous jasmonate could not restore fertility to myb21 or myb21myb24 mutant plants. Together with the data from transcriptional profiling, these results indicate that MYB21 and MYB24 are induced by jasmonate and mediate important aspects of the jasmonate response during stamen development.

Integrative analysis of metabolic networks: from peaks to flux models?

Recent developments in genomic and post-genomic technologies have led to the amassment of data describing genome sequences, transcript, protein and metabolite abundances, protein modifications, and protein-protein and protein-DNA interactions. Such technologies have vastly expanded the inventory of detectable molecular species and can be used to describe their interdependence, but they have yet to fulfill their promise in enhancing our knowledge of how flux through metabolic pathways is regulated. A convergence of traditional reductionistic and novel holistic experimental approaches could aid in elucidating flux control.

The Cinderella story of metabolic profiling: does metabolomics get to go to the functional genomics ball?

To date most global approaches to functional genomics have centred on genomics, transcriptomics and proteomics. However, since a number of high-profile publications, interest in metabolomics, the global profiling of metabolites in a cell, tissue or organism, has been rapidly increasing. A range of analytical techniques, including 1H NMR spectroscopy, gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), Fourier Transform mass spectrometry (FT-MS), high performance liquid chromatography (HPLC) and electrochemical array (EC-array), are required in order to maximize the number of metabolites that can be identified in a matrix. Applications have included phenotyping of yeast, mice and plants, understanding drug toxicity in pharmaceutical drug safety assessment, monitoring tumour treatment regimes and disease diagnosis in human populations. These successes are likely to be built on as other analytical and bioinformatic approaches are developed to fully exploit the information obtained in metabolic profiles. To assist in this process, databases of metabolomic data will be necessary to allow the passage of information between laboratories. In this prospective review, the capabilities of metabolomics in the field of medicine will be assessed in an attempt to predict the impact this 'Cinderella approach' will have at the 'functional genomic ball'.

A systems approach to understanding root development

Systems theory has been applied to process analysis in a variety of scientific disciplines from engineering to evolutionary biology. In the recent postgenomic era, the accumulation of an enormous amount of data gained from a variety of technologies has led to a revisiting of systems theory concepts. This systems biology approach has been integral in understanding a variety of processes in a number of model organisms. This review gives an overview of systems biology approaches, from component identification to modeling of networks. Various features of the root, including its development and the availability of high resolution gene expression data sets that describe root development, make the root amenable to a systems approach. The current status of systems approaches to understanding root development is reviewed.

Integrative analysis of transcript and metabolite profiling data sets to evaluate the regulation of biochemical pathways during photomorphogenesis

One of the key developmental processes during photomorphogenesis is the differentiation of prolamellar bodies of proplastids into thylakoid membranes containing the photosynthetic pigment-protein complexes of chloroplasts. To study the regulatory events controlling pigment-protein complex assembly, including the biosynthesis of metabolic precursors and pigment end products, etiolated Arabidopsis thaliana seedlings were irradiated with continuous red light (Rc), which led to rapid greening, or continuous far-red light (FRc), which did not result in visible greening, and subjected to analysis by oligonucleotide microarrays and targeted metabolite profiling. An analysis using BioPathAt, a bioinformatic tool that allows the visualization of post-genomic data sets directly on biochemical pathway maps, indicated that in Rc-treated seedlings mRNA expression and metabolite patterns were tightly correlated (e.g., Calvin cycle, biosynthesis of chlorophylls, carotenoids, isoprenoid quinones, thylakoid lipids, sterols, and amino acids). K-means clustering revealed that gene expression patterns across various biochemical pathways were very similar in Rc- and FRc-treated seedlings (despite the visible phenotypic differences), whereas a principal component analysis of metabolite pools allowed a clear distinction between both treatments (in accordance with the visible phenotype). Our results illustrate the general importance of integrative approaches to correlate post-genomic data sets with phenotypic outcomes.

Plant metabolomics: from holistic hope, to hype, to hot topic

In a short time, plant metabolomics has gone from being just an ambitious concept to being a rapidly growing, valuable technology applied in the stride to gain a more global picture of the molecular organization of multicellular organisms. The combination of improved analytical capabilities with newly designed, dedicated statistical, bioinformatics and data mining strategies, is beginning to broaden the horizons of our understanding of how plants are organized and how metabolism is both controlled but highly flexible. Metabolomics is predicted to play a significant, if not indispensable role in bridging the phenotype-genotype gap and thus in assisting us in our desire for full genome sequence annotation as part of the quest to link gene to function. Plants are a fabulously rich source of diverse functional biochemicals and metabolomics is also already proving valuable in an applied context. By creating unique opportunities for us to interrogate plant systems and characterize their biochemical composition, metabolomics will greatly assist in identifying and defining much of the still unexploited biodiversity available today.

Metabolomics reviewed: a new "omics" platform technology for systems biology and implications for natural products research

Metabolomics is the study of global metabolite profiles in a system (cell, tissue, or organism) under a given set of conditions. The analysis of the metabolome is particularly challenging due to the diverse chemical nature of metabolites. Metabolites are the result of the interaction of the system's genome with its environment and are not merely the end product of gene expression but also form part of the regulatory system in an integrated manner. Metabolomics has its roots in early metabolite profiling studies but is now a rapidly expanding area of scientific research in its own right. Metabolomics (or metabonomics) has been labeled one of the new "omics", joining genomics, transcriptomics, and proteomics as a science employed toward the understanding of global systems biology. Metabolomics is fast becoming one of the platform sciences of the "omics", with the majority of the papers in this field having been published only in the last two years. In this review metabolomic methodologies are discussed briefly followed by a more detailed review of the use of metabolomics in integrated applications where metabolomics information has been combined with other "omic" data sets (proteomics, transcriptomics) to enable greater understanding of a biological system. The potential of metabolomics for natural product drug discovery and functional food analysis, primarily as incorporated into broader "omic" data sets, is discussed.

KaPPA-view: a web-based analysis tool for integration of transcript and metabolite data on plant metabolic pathway maps

The application of DNA array technology and chromatographic separation techniques coupled with mass spectrometry to transcriptomic and metabolomic analyses in plants has resulted in the generation of considerable quantitative data related to transcription and metabolism. The integration of "omic" data is one of the major concerns associated with research into identifying gene function. Thus, we developed a Web-based tool, KaPPA-View, for representing quantitative data for individual transcripts and/or metabolites on plant metabolic pathway maps. We prepared a set of comprehensive metabolic pathway maps for Arabidopsis (Arabidopsis thaliana) and depicted these graphically in Scalable Vector Graphics format. Individual transcripts assigned to a reaction are represented symbolically together with the symbols of the reaction and metabolites on metabolic pathway maps. Using quantitative values for transcripts and/or metabolites submitted by the user as Comma Separated Value-formatted text through the Internet, the KaPPA-View server inserts colored symbols corresponding to a defined metabolic process at that site on the maps and returns them to the user's browser. The server also provides information on transcripts and metabolites in pop-up windows. To demonstrate the process, we describe the dataset obtained for transgenic plants that overexpress the PAP1 gene encoding a MYB transcription factor on metabolic pathway maps. The presentation of data in this manner is useful for viewing metabolic data in a way that facilitates the discussion of gene function.

Metabolomics data analysis, visualization, and integration

Metabolomics is the large-scale analysis of metabolites and as such requires bioinformatics tools for data analysis, visualization, and integration. This chapter describes the basic composition of chromatographically coupled mass spectrometry (MS) data sets used in metabolomics and describes in detail the steps necessary for extracting large-scale qualitative and quantitative information. This process involves noise filtering, peak picking and deconvolution, peak identification, peak alignment, and the creation of a final data matrix for statistical processing. Multivariate tools for comparative analysis are presented and illustrated using data for Medicago truncatula. Additional tools for visualizing and integrating metabolomics data within a biological context are discussed. Two tables are provided listing current metabolomics data processing and visualization software. Because metabolomics is rapidly maturing, a final section is presented concerning the need for data standardization and current efforts.