Christian Klukas

VANTED: A system for advanced data analysis and visualization in the context of biological networks

BackgroundRecent advances with high-throughput methods in life-science research have increased the need for automatized data analysis and visual exploration techniques. Sophisticated bioinformatics tools are essential to deduct biologically meaningful interpretations from the large amount of experimental data, and help to understand biological processes.ResultsWe present VANTED, a tool for the v isualization and a nalysis of n etworks with related e xperimental d ata. Data from large-scale biochemical experiments is uploaded into the software via a Microsoft Excel-based form. Then it can be mapped on a network that is either drawn with the tool itself, downloaded from the KEGG Pathway database, or imported using standard network exchange formats. Transcript, enzyme, and metabolite data can be presented in the context of their underlying networks, e. g. metabolic pathways or classification hierarchies. Visualization and navigation methods support the visual exploration of the data-enriched networks. Statistical methods allow analysis and comparison of multiple data sets such as different developmental stages or genetically different lines. Correlation networks can be automatically generated from the data and substances can be clustered according to similar behavior over time. As examples, metabolite profiling and enzyme activity data sets have been visualized in different metabolic maps, correlation networks have been generated and similar time patterns detected. Some relationships between different metabolites were discovered which are in close accordance with the literature.ConclusionVANTED greatly helps researchers in the analysis and interpretation of biochemical data, and thus is a useful tool for modern biological research. VANTED as a Java Web Start Application including a user guide and example data sets is available free of charge at http://vanted.ipk-gatersleben.de.

Dissecting the Phenotypic Components of Crop Plant Growth and Drought Responses Based on High-Throughput Image Analysis

A framework for high-throughput analysis of phenotypic traits from nondestructive plant imaging is used to dissect phenotypic components of barley growth and crop performance. Significantly improved crop varieties are urgently needed to feed the rapidly growing human population under changing climates. While genome sequence information and excellent genomic tools are in place for major crop species, the systematic quantification of phenotypic traits or components thereof in a high-throughput fashion remains an enormous challenge. In order to help bridge the genotype to phenotype gap, we developed a comprehensive framework for high-throughput phenotype data analysis in plants, which enables the extraction of an extensive list of phenotypic traits from nondestructive plant imaging over time. As a proof of concept, we investigated the phenotypic components of the drought responses of 18 different barley (Hordeum vulgare) cultivars during vegetative growth. We analyzed dynamic properties of trait expression over growth time based on 54 representative phenotypic features. The data are highly valuable to understand plant development and to further quantify growth and crop performance features. We tested various growth models to predict plant biomass accumulation and identified several relevant parameters that support biological interpretation of plant growth and stress tolerance. These image-based traits and model-derived parameters are promising for subsequent genetic mapping to uncover the genetic basis of complex agronomic traits. Taken together, we anticipate that the analytical framework and analysis results presented here will be useful to advance our views of phenotypic trait components underlying plant development and their responses to environmental cues.

VANTED v2: a framework for systems biology applications

BackgroundExperimental datasets are becoming larger and increasingly complex, spanning different data domains, thereby expanding the requirements for respective tool support for their analysis. Networks provide a basis for the integration, analysis and visualization of multi-omics experimental datasets.ResultsHere we present Vanted (version 2), a framework for systems biology applications, which comprises a comprehensive set of seven main tasks. These range from network reconstruction, data visualization, integration of various data types, network simulation to data exploration combined with a manifold support of systems biology standards for visualization and data exchange. The offered set of functionalities is instantiated by combining several tasks in order to enable users to view and explore a comprehensive dataset from different perspectives. We describe the system as well as an exemplary workflow.ConclusionsVanted is a stand-alone framework which supports scientists during the data analysis and interpretation phase. It is available as a Java open source tool from http://www.vanted.org

Apomictic and Sexual Ovules of Boechera Display Heterochronic Global Gene Expression Patterns

It is difficult for a purely mutational model to explain the evolution of asexuality in plants and animals. This work finds that expression patterns of many reproduction genes, including an overabundance of regulatory factors, differ during sexual and asexual ovule development, thus providing a possible mechanism for inducing the complex reproductive changes required to generate clonal offspring. We have compared the transcriptomic profiles of microdissected live ovules at four developmental stages between a diploid sexual and diploid apomictic Boechera. We sequenced >2 million SuperSAGE tags and identified (1) heterochronic tags (n = 595) that demonstrated significantly different patterns of expression between sexual and apomictic ovules across all developmental stages, (2) stage-specific tags (n = 577) that were found in a single developmental stage and differentially expressed between the sexual and apomictic ovules, and (3) sex-specific (n = 237) and apomixis-specific (n = 1106) tags that were found in all four developmental stages but in only one reproductive mode. Most heterochronic and stage-specific tags were significantly downregulated during early apomictic ovule development, and 110 were associated with reproduction. By contrast, most late stage-specific tags were upregulated in the apomictic ovules, likely the result of increased gene copy number in apomictic (hexaploid) versus sexual (triploid) endosperm or of parthenogenesis. Finally, we show that apomixis-specific gene expression is characterized by a significant overrepresentation of transcription factor activity. We hypothesize that apomeiosis is associated with global downregulation at the megaspore mother cell stage. As the diploid apomict analyzed here is an ancient hybrid, these data are consistent with the postulated link between hybridization and asexuality and provide a hypothesis for multiple evolutionary origins of apomixis in the genus Boechera.

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

An open-source information system for high-throughput plant phenotyping enables large-scale image analysis for different species based on real-time imaging data obtained from different spectra. 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.

Dynamic exploration and editing of KEGG pathway diagrams

MOTIVATION The Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway database is a very valuable information resource for researchers in the fields of life sciences. It contains metabolic and regulatory processes in the form of wiring diagrams, which can be used for browsing and information retrieval as well as a base for modeling and simulation. Thus it helps in understanding biological processes and higher-order functions of biological systems. Currently the KEGG website uses semi-static visualizations for the presentation and navigation of its pathway information. While this visualization style offers a good pathway presentation and navigation, it does not provide some of the possibilities related to dynamic visualizations, most importantly, the creation and visualization of user-specific pathways. RESULTS This paper presents methods for the dynamic visualization, interactive navigation and editing of KEGG pathway diagrams. These diagrams, given as KEGG Markup Language (KGML) files, can be visually explored using novel approaches combining semi-static and dynamic visualization, but also edited or even newly created and then exported into KGML files. AVAILABILITY KGML-ED, a program implementing the presented methods, is available free of charge to the scientific community at http://kgml-ed.ipk-gatersleben.de.

Advanced phenotyping and phenotype data analysis for the study of plant growth and development

Due to an increase in the consumption of food, feed, fuel and to meet global food security needs for the rapidly growing human population, there is a necessity to breed high yielding crops that can adapt to the future climate changes, particularly in developing countries. To solve these global challenges, novel approaches are required to identify quantitative phenotypes and to explain the genetic basis of agriculturally important traits. These advances will facilitate the screening of germplasm with high performance characteristics in resource-limited environments. Recently, plant phenomics has offered and integrated a suite of new technologies, and we are on a path to improve the description of complex plant phenotypes. High-throughput phenotyping platforms have also been developed that capture phenotype data from plants in a non-destructive manner. In this review, we discuss recent developments of high-throughput plant phenotyping infrastructure including imaging techniques and corresponding principles for phenotype data analysis.

The Differential Transcription Network between Embryo and Endosperm in the Early Developing Maize Seed

Transcriptomic analysis of maize seed soon after pollination aids understanding of how maize embryo and endosperm are differentially regulated in the early development stage. Transcriptome analysis of early-developing maize (Zea mays) seed was conducted using Illumina sequencing. We mapped 11,074,508 and 11,495,788 paired-end reads from endosperm and embryo, respectively, at 9 d after pollination to define gene structure and alternative splicing events as well as transcriptional regulators of gene expression to quantify transcript abundance in both embryo and endosperm. We identified a large number of novel transcribed regions that did not fall within maize annotated regions, and many of the novel transcribed regions were tissue-specifically expressed. We found that 50.7% (8,556 of 16,878) of multiexonic genes were alternatively spliced, and some transcript isoforms were specifically expressed either in endosperm or in embryo. In addition, a total of 46 trans-splicing events, with nine intrachromosomal events and 37 interchromosomal events, were found in our data set. Many metabolic activities were specifically assigned to endosperm and embryo, such as starch biosynthesis in endosperm and lipid biosynthesis in embryo. Finally, a number of transcription factors and imprinting genes were found to be specifically expressed in embryo or endosperm. This data set will aid in understanding how embryo/endosperm development in maize is differentially regulated.

Integration of -omics data and networks for biomedical research with VANTED

More and more often research focus in the fields of biology and medicine moves from the investigation of single phenomena to the analysis of complex cause and effect relations. The clarification of complicated relations requires the consideration of different domains, for instance, gene expression, protein, and metabolite data. Furthermore, it is often sensible not to analyze measured data in isolation, but to consider the context of relevant biological networks. In this paper newly developed functionalities of the VANTED system are presented. They allow users from medicine and biology to interactively structure extensive experiment data, to filter, to evaluate, and to visualize the data and the analysis results in context of biological networks and classification hierarchies.

Editing, validating and translating of SBGN maps

Motivation: The recently proposed Systems Biology Graphical Notation (SBGN) provides a standard for the visual representation of biochemical and cellular processes. It aims to support more efficient and accurate communication of biological knowledge between different research communities in the life sciences. However, to increase the use of SBGN, tools for editing, validating and translating SBGN maps are desirable. Results: We present SBGN-ED, a tool which allows the creation of all three types of SBGN maps from scratch or the editing of existing maps, the validation of these maps for syntactical and semantical correctness, the translation of networks from the KEGG and MetaCrop databases into SBGN and the export of SBGN maps into several file and image formats. Availability: SBGN-ED is freely available from http://vanted.ipk-gatersleben.de/addons/sbgn-ed. The web site contains also tutorials and example files. Contact: schreibe@ipk-gatersleben.de