Christian Klukas

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Recent 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. We present(More)
Experimental 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. Here we present Vanted (version 2), a framework for systems(More)
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(More)
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(More)
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(More)
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.(More)
Image-based plant phenotyping is a growing application area of computer vision in agriculture. A key task is the segmentation of all individual leaves in images. Here we focus on the most common rosette model plants, Arabidopsis and young tobacco. Although leaves do share appearance and shape characteristics, the presence of occlusions and variability in(More)
Recognition and segmentation of plant organs like leaves is one of the challenges in digital plant phenotyping. Here we present a 3D histogram-based segmentation and recognition approach for top view images of rosette plants such as Arabidopsis thaliana and tobacco. Furthermore a Euclidean-distance-map-based method for the detection of leaves and the(More)
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(More)
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.(More)