"Macrobot": An Automated Segmentation-Based System for Powdery Mildew Disease Quantification.

Managing plant diseases is increasingly difficult due to reasons such as intensifying the field production, climatic change-driven expansion of pests, redraw and loss of effectiveness of pesticides, rapid breakdown of the disease resistance in the field, and other factors. The substantial progress in genomics of both plants and pathogens, achieved in the last decades, has the potential to counteract this negative trend, however, only when the genomic data is supported by relevant phenotypic data that allows linking the genomic information to specific traits. We have developed a set of methods and equipment and combined them into a "Macrophenomics facility." The pipeline has been optimized for the quantification of powdery mildew infection symptoms on wheat and barley, but it can be adapted to other diseases and host plants. The Macrophenomics pipeline scores the visible powdery mildew disease symptoms, typically 5-7 days after inoculation (dai), in a highly automated manner. The system can precisely and reproducibly quantify the percentage of the infected leaf area with a theoretical throughput of up to 10000 individual samples per day, making it appropriate for phenotyping of large germplasm collections and crossing populations.

siRNA-Finder (si-Fi) Software for RNAi-Target Design and Off-Target Prediction.

RNA interference (RNAi) is a technique used for transgene-mediated gene silencing based on the mechanism of posttranscriptional gene silencing (PTGS). PTGS is an ubiquitous basic biological phenomenon involved in the regulation of transcript abundance and plants' immune response to viruses. PTGS also mediates genomic stability by silencing of retroelements. RNAi has become an important research tool for studying gene function by strong and selective suppression of target genes. Here, we present si-Fi, a software tool for design optimization of RNAi constructs necessary for specific target gene knock-down. It offers efficiency prediction of RNAi sequences and off-target search, required for the practical application of RNAi. si-Fi is an open-source (CC BY-SA license) desktop software that works in Microsoft Windows environment and can use custom sequence databases in standard FASTA format.

Down-regulation of the sucrose transporters HvSUT1 and HvSUT2 affects sucrose homeostasis along its delivery path in barley grains.

Sucrose transport and partitioning are crucial for seed filling. While many plasma-membrane-localised sucrose transporters (SUT1 family members) have been analysed in seeds, the functions of vacuolar SUT2 members are still obscure. In barley grains, expression of HvSUT1 and HvSUT2 overlap temporally and spatially, suggesting concerted functions to regulate sucrose homeostasis. Using HvSUT2-RNAi plants, we found that grains were also deficient in HvSUT1 expression and seemingly sucrose-limited during mid-to-late grain filling. Transgenic endosperms accumulated less starch and dry weight, although overall sucrose and hexose contents were higher. Comprehensive transcript and metabolite profiling revealed that genes related to glycolysis, the tricarboxylic acid cycle, starch and amino acid synthesis, grain maturation, and abscisic acid signalling were down-regulated together with most glycolytic intermediates and amino acids. Sucrose was increased along the sucrose delivery route in the nucellar projection, the endosperm transfer cells, and the starchy endosperm, indicating that suppressed transporter activity diminished sucrose efflux from vacuoles, which generated sugar deficiency in the cytoplasm. Thus, endosperm vacuoles may buffer sucrose concentrations to regulate homeostasis at grain filling. Transcriptional changes revealed that limited endosperm sucrose initiated sugar starvation responses, such as sugar recycling from starch, hemicelluloses and celluloses together with vacuolar protein degradation, thereby supporting formation of nucleotide sugars. Barley endosperm cells can thus suppress certain pathways to retrieve resources to maintain essential cell functions.

DistAMo: A Web-Based Tool to Characterize DNA-Motif Distribution on Bacterial Chromosomes.

Short DNA motifs are involved in a multitude of functions such as for example chromosome segregation, DNA replication or mismatch repair. Distribution of such motifs is often not random and the specific chromosomal pattern relates to the respective motif function. Computational approaches which quantitatively assess such chromosomal motif patterns are necessary. Here we present a new computer tool DistAMo (Distribution Analysis of DNA Motifs). The algorithm uses codon redundancy to calculate the relative abundance of short DNA motifs from single genes to entire chromosomes. Comparative genomics analyses of the GATC-motif distribution in γ-proteobacterial genomes using DistAMo revealed that (i) genes beside the replication origin are enriched in GATCs, (ii) genome-wide GATC distribution follows a distinct pattern, and (iii) genes involved in DNA replication and repair are enriched in GATCs. These features are specific for bacterial chromosomes encoding a Dam methyltransferase. The new software is available as a stand-alone or as an easy-to-use web-based server version at http://www.computational.bio.uni-giessen.de/distamo.

Comprehensive transcriptome analysis unravels the existence of crucial genes regulating primary metabolism during adventitious root formation in Petunia hybrida.

To identify specific genes determining the initiation and formation of adventitious roots (AR), a microarray-based transcriptome analysis in the stem base of the cuttings of Petunia hybrida (line W115) was conducted. A microarray carrying 24,816 unique, non-redundant annotated sequences was hybridized to probes derived from different stages of AR formation. After exclusion of wound-responsive and root-regulated genes, 1,354 of them were identified which were significantly and specifically induced during various phases of AR formation. Based on a recent physiological model distinguishing three metabolic phases in AR formation, the present paper focuses on the response of genes related to particular metabolic pathways. Key genes involved in primary carbohydrate metabolism such as those mediating apoplastic sucrose unloading were induced at the early sink establishment phase of AR formation. Transcriptome changes also pointed to a possible role of trehalose metabolism and SnRK1 (sucrose non-fermenting 1- related protein kinase) in sugar sensing during this early step of AR formation. Symplastic sucrose unloading and nucleotide biosynthesis were the major processes induced during the later recovery and maintenance phases. Moreover, transcripts involved in peroxisomal beta-oxidation were up-regulated during different phases of AR formation. In addition to metabolic pathways, the analysis revealed the activation of cell division at the two later phases and in particular the induction of G1-specific genes in the maintenance phase. Furthermore, results point towards a specific demand for certain mineral nutrients starting in the recovery phase.

Salinity stress in roots of contrasting barley genotypes reveals time-distinct and genotype-specific patterns for defined proteins.

Soil salinity is one of the most severe abiotic stress factors threatening agriculture worldwide. Hence, particular interest exists in unraveling mechanisms leading to salt tolerance and improved crop plant performance on saline soils. Barley is considered to be one of the most salinity-tolerant crops, but varying levels of tolerance are well characterized. A proteomic analysis of the roots of two contrasting cultivars (cv. Steptoe and cv. Morex) is presented. Young plants were exposed to a period of 1, 4, 7, or 10 d at 0, 100, or 150 mM NaCl. The root proteome was analyzed based on two-dimensional gel electrophoresis. A number of cultivar-specific and salinity stress-responsive proteins were identified. Mass spectrometry-based identification was successful for 74 proteins, and a hierarchical clustering analysis grouped these into five clusters based on similarity of expression profile. The rank product method was applied to statistically access the early and late responses, and this delivered a number of new candidate proteins underlying salinity tolerance in barley. Among these were some germin-like proteins, some pathogenesis-related proteins, and numerous as-yet uncharacterized proteins. Notably, proteins involved in detoxification pathways and terpenoid biosynthesis were detected as early responsive to salinity and may function as a means of modulating growth-regulating mechanisms and membrane stability via fine tuning of phytohormone and secondary metabolism in the root.

Dispom: a discriminative de-novo motif discovery tool based on the jstacs library.

DNA-binding proteins are a main component of gene regulation as they activate or repress gene expression by binding to specific binding sites in target regions of genomic DNA. However, de-novo discovery of these binding sites in target regions obtained by wet-lab experiments is a challenging problem in computational biology, which has not yet been solved satisfactorily. Here, we present a detailed description and analysis of the de-novo motif discovery tool Dispom, which has been developed for finding binding sites of DNA-binding proteins that are differentially abundant in a set of target regions compared to a set of control regions. Two additional features of Dispom are its capability of modeling positional preferences of binding sites and adjusting the length of the motif in the learning process. Dispom yields an increased prediction accuracy compared to existing tools for de-novo motif discovery, suggesting that the combination of searching for differentially abundant motifs, inferring their positional distributions, and adjusting the motif lengths is beneficial for de-novo motif discovery. When applying Dispom to promoters of auxin-responsive genes and those of ABI3 target genes from Arabidopsis thaliana, we identify relevant binding motifs with pronounced positional distributions. These results suggest that learning motifs, their positional distributions, and their lengths by a discriminative learning principle may aid motif discovery from ChIP-chip and gene expression data. We make Dispom freely available as part of Jstacs, an open-source Java library that is tailored to statistical sequence analysis. To facilitate extensions of Dispom, we describe its implementation using Jstacs in this manuscript. In addition, we provide a stand-alone application of Dispom at http://www.jstacs.de/index.php/Dispom for instant use.

Parsimonious higher-order hidden Markov models for improved array-CGH analysis with applications to Arabidopsis thaliana.

Array-based comparative genomic hybridization (Array-CGH) is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs) are popular tools for the analysis of Array-CGH data, but current methods are only based on first-order HMMs having constrained abilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. Here, we develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. We apply parsimonious higher-order HMMs to the analysis of Array-CGH data of the accessions C24 and Col-0 of the model plant Arabidopsis thaliana. We compare these models against first-order HMMs and other existing methods using a reference of known deletions and sequence deviations. We find that parsimonious higher-order HMMs clearly improve the identification of these polymorphisms. Moreover, we perform a functional analysis of identified polymorphisms revealing novel details of genomic differences between C24 and Col-0. Additional model evaluations are done on widely considered Array-CGH data of human cell lines indicating that parsimonious HMMs are also well-suited for the analysis of non-plant specific data. All these results indicate that parsimonious higher-order HMMs are useful for Array-CGH analyses. An implementation of parsimonious higher-order HMMs is available as part of the open source Java library Jstacs (www.jstacs.de/index.php/PHHMM).

Exploiting prior knowledge and gene distances in the analysis of tumor expression profiles with extended Hidden Markov Models.

MOTIVATION: Changes in gene expression levels play a central role in tumors. Additional information about the distribution of gene expression levels and distances between adjacent genes on chromosomes should be integrated into the analysis of tumor expression profiles. RESULTS: We use a Hidden Markov Model with distance-scaled transition matrices (DSHMM) to incorporate chromosomal distances of adjacent genes on chromosomes into the identification of differentially expressed genes in breast cancer. We train the DSHMM by integrating prior knowledge about potential distributions of expression levels of differentially expressed and unchanged genes in tumor. We find that especially the combination of these data and to a lesser extent the modeling of distances between adjacent genes contribute to a substantial improvement of the identification of differentially expressed genes in comparison to other existing methods. This performance benefit is also supported by the identification of genes well known to be associated with breast cancer. That suggests applications of DSHMMs for screening of other tumor expression profiles. AVAILABILITY: The DSHMM is available as part of the open-source Java library Jstacs (www.jstacs.de/index.php/DSHMM).

ABA biosynthesis and degradation contributing to ABA homeostasis during barley seed development under control and terminal drought-stress conditions.

Drought is one of the most severe environmental stress factors limiting crop yield especially when occurring during anthesis and seed filling. This terminal drought is characterized by an excess production of the phytohormone abscisic acid (ABA) which plays an important role during seed development and dormancy. All the genes putatively involved in ABA biosynthesis and inactivation in barley were identified and their expression studied during plant ontogeny under standard and drought-stress conditions to learn more about ABA homeostasis and the possible mode of cross-talk between source and sink tissues. Out of 41 genes related to ABA biosynthesis and inactivation 19 were found to be differentially regulated under drought stress in both flag leaves and developing seed during seed filling. Transcripts of plastid-located enzymes are regulated similarly in flag leaf and seed under terminal drought whereas transcripts of cytosolic enzymes are differentially regulated in the two tissues. Detailed information on the expression of defined gene family members is supplemented by measurements of ABA and its degradation and conjugation products, respectively. Under drought stress, flag leaves in particular contain high concentrations of both ABA and the ABA degradation products phaseic acid (PA) and diphaseic acid (DPA); whereas, in seeds, besides ABA, DPA was mainly found. The measurements also revealed a positive correlation between ABA level and starch content in developing seeds for the following reasons: (i) genes of the ABA controlled SnRK2.6 and RCAR/PP2C-mediated signal transduction pathway to the ABF transcription factor HvABI5 are activated in the developing grain under drought, (ii) novel ABA- and dehydration-responsive cis-elements have been found in the promoters of key genes of starch biosynthesis (HvSUS1, HvAGP-L1) and degradation (HvBAM1) and these transcripts/activity are prominently induced in developing seeds during 12 and 16 DAF, (iii) spraying of fluridone (an ABA biosynthesis inhibitor) to drought-stressed plants results in severely impaired starch content and thousand grain weight of mature seeds.

De-novo discovery of differentially abundant transcription factor binding sites including their positional preference.

Transcription factors are a main component of gene regulation as they activate or repress gene expression by binding to specific binding sites in promoters. The de-novo discovery of transcription factor binding sites in target regions obtained by wet-lab experiments is a challenging problem in computational biology, which has not been fully solved yet. Here, we present a de-novo motif discovery tool called Dispom for finding differentially abundant transcription factor binding sites that models existing positional preferences of binding sites and adjusts the length of the motif in the learning process. Evaluating Dispom, we find that its prediction performance is superior to existing tools for de-novo motif discovery for 18 benchmark data sets with planted binding sites, and for a metazoan compendium based on experimental data from micro-array, ChIP-chip, ChIP-DSL, and DamID as well as Gene Ontology data. Finally, we apply Dispom to find binding sites differentially abundant in promoters of auxin-responsive genes extracted from Arabidopsis thaliana microarray data, and we find a motif that can be interpreted as a refined auxin responsive element predominately positioned in the 250-bp region upstream of the transcription start site. Using an independent data set of auxin-responsive genes, we find in genome-wide predictions that the refined motif is more specific for auxin-responsive genes than the canonical auxin-responsive element. In general, Dispom can be used to find differentially abundant motifs in sequences of any origin. However, the positional distribution learned by Dispom is especially beneficial if all sequences are aligned to some anchor point like the transcription start site in case of promoter sequences. We demonstrate that the combination of searching for differentially abundant motifs and inferring a position distribution from the data is beneficial for de-novo motif discovery. Hence, we make the tool freely available as a component of the open-source Java framework Jstacs and as a stand-alone application at http://www.jstacs.de/index.php/Dispom.

Target-Driven Subspace Mapping Methods and Their Applicability Domain Estimation.

This work describes a methodology for assisting virtual screening of drugs during the early stages of the drug development process. This methodology is proposed to improve the reliability of in silico property prediction and it is structured in two steps. Firstly, a transformation is sought for mapping a high-dimensional space defined by potentially redundant or irrelevant molecular descriptors into a low-dimensional application-related space. For this task we evaluate three different target-driven subspace mapping methods, out of which we highlight the recent Correlative Matrix Mapping (CMM) as the most stable. Secondly, we apply an applicability domain model on the low-dimensional space for assessing confidentiality of compound classification. By a probabilistic framework the applicability domain approach identifies poorly represented compounds in the training set (extrapolation problems) and regions in the space where the uncertainty about the correct class is higher than normal (interpolation problems). This two-step approach represents an important contribution to the development of confident prediction tools in the chemoinformatics area, where the field is in need of both interpretable models and methods that estimate the confidence of predictions.

De-regulation of abscisic acid contents causes abnormal endosperm development in the barley mutant seg8.

Grain development of the maternal effect shrunken endosperm mutant seg8 was analysed by comprehensive molecular, biochemical and histological methods. The most obvious finding was de-regulation of ABA levels, which were lower compared to wild-type during the pre-storage phase but higher during the transition from cell division/differentiation to accumulation of storage products. Ploidy levels and ABA amounts were inversely correlated in the developing endosperms of both mutant and wild-type, suggesting an influence of ABA on cell-cycle regulation. The low ABA levels found in seg8 grains between anthesis and beginning endosperm cellularization may result from a gene dosage effect in the syncytial endosperm that causes impaired transfer of ABA synthesized in vegetative tissues into filial grain parts. Increased ABA levels during the transition phase are accompanied by higher chlorophyll and carotenoid/xanthophyll contents. The data suggest a disturbed ABA-releasing biosynthetic pathway. This is indicated by up-regulation of expression of the geranylgeranyl reductase (GGR) gene, which may be induced by ABA deficiency during the pre-storage phase. Abnormal cellularization/differentiation of the developing seg8 endosperm and reduced accumulation of starch are phenotypic characteristics that reflect these disturbances. The present study did not reveal the primary gene defect causing the seg8 phenotype, but presents new insights into the maternal/filial relationships regulating barley endosperm development.

Array platforms and bioinformatics tools for the analysis of plant transcriptome in response to abiotic stress.

Current microarray technologies allow high-density in situ synthesis of oligonucleotides or ex situ spotting of target molecules (cDNA) for conducting genome-wide comparative gene expression profiling studies. The avalanche of available microarray gene expression data from model plant species covering cell-related, tissue-specific, and developmental events, as well as perturbations to a variety of environmental stimuli has triggered many activities regarding the development of adequate bioinformatics tools for the analysis of these complex data sets. In this chapter we summarize the technical issues of different microarray technologies, discuss the availability of bioinformatics tools, and present approaches to extract biologically meaningful knowledge. For case studies of abiotic stress transcriptome analysis we highlight the unprecedented opportunities provided by these high-throughput technologies to understand networks of regulatory and metabolic pathway responses of plant cells to the application of abiotic stress stimuli.

Unifying generative and discriminative learning principles.

BACKGROUND: The recognition of functional binding sites in genomic DNA remains one of the fundamental challenges of genome research. During the last decades, a plethora of different and well-adapted models has been developed, but only little attention has been payed to the development of different and similarly well-adapted learning principles. Only recently it was noticed that discriminative learning principles can be superior over generative ones in diverse bioinformatics applications, too. RESULTS: Here, we propose a generalization of generative and discriminative learning principles containing the maximum likelihood, maximum a posteriori, maximum conditional likelihood, maximum supervised posterior, generative-discriminative trade-off, and penalized generative-discriminative trade-off learning principles as special cases, and we illustrate its efficacy for the recognition of vertebrate transcription factor binding sites. CONCLUSIONS: We find that the proposed learning principle helps to improve the recognition of transcription factor binding sites, enabling better computational approaches for extracting as much information as possible from valuable wet-lab data. We make all implementations available in the open-source library Jstacs so that this learning principle can be easily applied to other classification problems in the field of genome and epigenome analysis.

Increasing sucrose uptake capacity of wheat grains stimulates storage protein synthesis.

Increasing grain sink strength by improving assimilate uptake capacity could be a promising approach toward getting higher yield. The barley (Hordeum vulgare) sucrose transporter HvSUT1 (SUT) was expressed under control of the endosperm-specific Hordein B1 promoter (HO). Compared with the wild type, transgenic HOSUT grains take up more sucrose (Suc) in vitro, showing that the transgene is functional. Grain Suc levels are not altered, indicating that Suc fluxes are influenced rather than steady-state levels. HOSUT grains have increased percentages of total nitrogen and prolamins, which is reflected in increased levels of phenylalanine, tyrosine, tryptophan, isoleucine, and leucine at late grain development. Transcript profiling indicates specific stimulation of prolamin gene expression at the onset of storage phase. Changes in gene expression and metabolite levels related to carbon metabolism and amino acid biosynthesis suggest deregulated carbon-nitrogen balance, which together indicate carbon sufficiency and relative depletion of nitrogen. Genes, deregulated together with prolamin genes, might represent candidates, which respond positively to assimilate supply and are related to sugar-starch metabolism, cytokinin and brassinosteroid functions, cell proliferation, and sugar/abscisic acid signaling. Genes showing inverse expression patterns represent potential negative regulators. It is concluded that HvSUT1 overexpression increases grain protein content but also deregulates the metabolic status of wheat (Triticum aestivum) grains, accompanied by up-regulated gene expression of positive and negative regulators related to sugar signaling and assimilate supply. In HOSUT grains, alternating stimulation of positive and negative regulators causes oscillatory patterns of gene expression and highlights the capacity and great flexibility to adjust wheat grain storage metabolism in response to metabolic alterations.

Utilizing gene pair orientations for HMM-based analysis of promoter array ChIP-chip data.

MOTIVATION: Array-based analysis of chromatin immunoprecipitation (ChIP-chip) data is a powerful technique for identifying DNA target regions of individual transcription factors. The identification of these target regions from comprehensive promoter array ChIP-chip data is challenging. Here, three approaches for the identification of transcription factor target genes from promoter array ChIP-chip data are presented. We compare (i) a standard log-fold-change analysis (LFC); (ii) a basic method based on a Hidden Markov Model (HMM); and (iii) a new extension of the HMM approach to an HMM with scaled transition matrices (SHMM) that incorporates information about the relative orientation of adjacent gene pairs on DNA. RESULTS: All three methods are applied to different promoter array ChIP-chip datasets of the yeast Saccharomyces cerevisiae and the important model plant Arabidopsis thaliana to compare the prediction of transcription factor target genes. In the context of the yeast cell cycle, common target genes bound by the transcription factors ACE2 and SWI5, and ACE2 and FKH2 are identified and evaluated using the Saccharomyces Genome Database. Regarding A.thaliana, target genes of the seed-specific transcription factor ABI3 are predicted and evaluate based on publicly available gene expression profiles and transient assays performed in the wet laboratory experiments. The application of the novel SHMM to these two different promoter array ChIP-chip datasets leads to an improved identification of transcription factor target genes in comparison to the two standard approaches LFC and HMM. AVAILABILITY: The software of LFC, HMM and SHMM, the ABI3 ChIP-chip dataset, and Supplementary Material can be downloaded from http://dig.ipk-gatersleben.de/SHMMs/ChIPchip/ChIPchip.html.

Different hormonal regulation of cellular differentiation and function in nucellar projection and endosperm transfer cells: a microdissection-based transcriptome study of young barley grains.

Nucellar projection (NP) and endosperm transfer cells (ETC) are essential tissues in growing barley (Hordeum vulgare) grains, responsible for nutrient transfer from maternal to filial tissues, endosperm/embryo nutrition, and grain development. A laser microdissection pressure catapulting-based transcriptome analysis was established to study NP and ETC separately using a barley 12K macroarray. A major challenge was to isolate high-quality mRNA from preembedded, fixed tissue while maintaining tissue integrity. We show that probes generated from fixed and embedded tissue sections represent largely the transcriptome (>70%) of nonchemically treated and nonamplified references. In NP, the top-down gradient of cellular differentiation is reflected by the expression of C3HC4-type ubiquitin ligases and different histone genes, cell wall biosynthesis and expansin/extensin genes, as well as genes involved in programmed cell death-related proteolysis coupled to nitrogen remobilization, indicating distinct areas simultaneously undergoing mitosis, cell elongation, and disintegration. Activated gene expression related to gibberellin synthesis and function suggests a regulatory role for gibberellins in establishment of the differentiation gradient. Up-regulation of plasmalemma-intrinsic protein and tonoplast-intrinsic protein genes indicates involvement in nutrient transfer and/or unloading. In ETC, AP2/EREBP-like transcription factors and ethylene functions are transcriptionally activated, a response possibly coupled to activated defense mechanisms. Transcriptional activation of nucleotide sugar metabolism may be attributed to ascorbate synthesis and/or cell wall biosynthesis. These processes are potentially controlled by trehalose-6-P synthase/phosphatase, as suggested by expression of their respective genes. Up-regulation of amino acid permeases in ETC indicates important roles in active nutrient uptake from the apoplastic space into the endosperm.

Barley grain maturation and germination: metabolic pathway and regulatory network commonalities and differences highlighted by new MapMan/PageMan profiling tools.

Plant seeds prepare for germination already during seed maturation. We performed a detailed transcriptome analysis of barley (Hordeum vulgare) grain maturation, desiccation, and germination in two tissue fractions (starchy endosperm/aleurone and embryo/scutellum) using the Affymetrix Barley1 GeneChip. To aid data evaluation, Arabidopsis thaliana MapMan and PageMan tools were adapted to barley. The analyses allow a number of conclusions: (1) Cluster analysis revealed a smooth transition in transcription programs between late seed maturation and germination within embryo tissues, but not in the endosperm/aleurone. (2) More than 12,000 transcripts are stored in the embryo of dry barley grains, many of which are presumably activated during germination. (3) Transcriptional activation of storage reserve mobilization events occurs at an early stage of germination, well before radicle protrusion. (4) Key genes of gibberellin (GA) biosynthesis are already active during grain maturation at a time when abscisic acid peaks suggesting the formation of an endogenous store of GA in the aleurone. This GA probably acts later during germination in addition to newly synthesized GA. (5) Beside the well-known role of GA in gene activation during germination spatiotemporal expression data and cis-element searches in homologous rice promoters confirm an equally important gene-activating role of abscisic acid during this developmental period. The respective regulatory webs are linked to auxin and ethylene controlled networks. In summary, new bioinformatics PageMan and MapMan tools developed in barley have been successfully used to investigate in detail the transcriptome relationships between seed maturation and germination in an important crop plant.

Correlation-maximizing surrogate gene space for visual mining of gene expression patterns in developing barley endosperm tissue.

BACKGROUND: Micro- and macroarray technologies help acquire thousands of gene expression patterns covering important biological processes during plant ontogeny. Particularly, faithful visualization methods are beneficial for revealing interesting gene expression patterns and functional relationships of coexpressed genes. Such screening helps to gain deeper insights into regulatory behavior and cellular responses, as will be discussed for expression data of developing barley endosperm tissue. For that purpose, high-throughput multidimensional scaling (HiT-MDS), a recent method for similarity-preserving data embedding, is substantially refined and used for (a) assessing the quality and reliability of centroid gene expression patterns, and for (b) derivation of functional relationships of coexpressed genes of endosperm tissue during barley grain development (0-26 days after flowering). RESULTS: Temporal expression profiles of 4824 genes at 14 time points are faithfully embedded into two-dimensional displays. Thereby, similar shapes of coexpressed genes get closely grouped by a correlation-based similarity measure. As a main result, by using power transformation of correlation terms, a characteristic cloud of points with bipolar sandglass shape is obtained that is inherently connected to expression patterns of pre-storage, intermediate and storage phase of endosperm development. CONCLUSION: The new HiT-MDS-2 method helps to create global views of expression patterns and to validate centroids obtained from clustering programs. Furthermore, functional gene annotation for developing endosperm barley tissue is successfully mapped to the visualization, making easy localization of major centroids of enriched functional categories possible.