"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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Gene expression patterns reveal tissue-specific signaling networks controlling programmed cell death and ABA- regulated maturation in developing barley seeds.

Gene expression patterns covering over 10,000 seed-expressed sequences were analyzed by macroarray technology in maternal tissue (mainly pericarp) and filial endosperm and embryo during barley seed development from anthesis until late maturation. Defined sets of genes showing distinct expression patterns characterized both tissue type and major developmental phases. The analysis focused on regulatory networks involved in programmed cell death (PCD) and abscisic acid (ABA)-mediated maturation.

Recursive self-organizing network models.

Self-organizing models constitute valuable tools for data visualization, clustering, and data mining. Here, we focus on extensions of basic vector-based models by recursive computation in such a way that sequential and tree-structured data can be processed directly. The aim of this article is to give a unified review of important models recently proposed in literature, to investigate fundamental mathematical properties of these models, and to compare the approaches by experiments.