Publishes on Photosynthetic Processes and Mechanisms, Genomics and Phylogenetic Studies, Plant Molecular Biology Research. 35 papers and 77.7k citations.
MOTIVATION: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. RESULTS: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. AVAILABILITY AND IMPLEMENTATION: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic CONTACT: usadel@bio1.rwth-aachen.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Mitochondria and plastids (chloroplasts) are cell organelles of endosymbiotic origin that possess their own genetic information. Most organellar DNAs map as circular double-stranded genomes. Across the eukaryotic kingdom, organellar genomes display great size variation, ranging from ∼15 to 20 kb (the size of the mitochondrial genome in most animals) to >10 Mb (the size of the mitochondrial genome in some lineages of flowering plants). We have developed OrganellarGenomeDraw (OGDRAW), a suite of software tools that enable users to create high-quality visual representations of both circular and linear annotated genome sequences provided as GenBank files or accession numbers. Although all types of DNA sequences are accepted as input, the software has been specifically optimized to properly depict features of organellar genomes. A recent extension facilitates the plotting of quantitative gene expression data, such as transcript or protein abundance data, directly onto the genome map. OGDRAW has already become widely used and is available as a free web tool (http://ogdraw.mpimp-golm.mpg.de/). The core processing components can be downloaded as a Perl module, thus also allowing for convenient integration into custom processing pipelines.
Recent rapid advances in next generation RNA sequencing (RNA-Seq)-based provide researchers with unprecedentedly large data sets and open new perspectives in transcriptomics. Furthermore, RNA-Seq-based transcript profiling can be applied to non-model and newly discovered organisms because it does not require a predefined measuring platform (like e.g. microarrays). However, these novel technologies pose new challenges: the raw data need to be rigorously quality checked and filtered prior to analysis, and proper statistical methods have to be applied to extract biologically relevant information. Given the sheer volume of data, this is no trivial task and requires a combination of considerable technical resources along with bioinformatics expertise. To aid the individual researcher, we have developed RobiNA as an integrated solution that consolidates all steps of RNA-Seq-based differential gene-expression analysis in one user-friendly cross-platform application featuring a rich graphical user interface. RobiNA accepts raw FastQ files, SAM/BAM alignment files and counts tables as input. It supports quality checking, flexible filtering and statistical analysis of differential gene expression based on state-of-the art biostatistical methods developed in the R/Bioconductor projects. In-line help and a step-by-step manual guide users through the analysis. Installer packages for Mac OS X, Windows and Linux are available under the LGPL licence from http://mapman.gabipd.org/web/guest/robin.
The specificity of intracellular signaling and developmental patterning in biological systems relies on selective interactions between different proteins in specific cellular compartments. The identification of such protein-protein interactions is essential for unraveling complex signaling and regulatory networks. Recently, bimolecular fluorescence complementation (BiFC) has emerged as a powerful technique for the efficient detection of protein interactions in their native subcellular localization. Here we report significant technical advances in the methodology of plant BiFC. We describe a series of versatile BiFC vector sets that are fully compatible with previously generated vectors. The new vectors enable the generation of both C-terminal and N-terminal fusion proteins and carry optimized fluorescent protein genes that considerably improve the sensitivity of BiFC. Using these vectors, we describe a multicolor BiFC (mcBiFC) approach for the simultaneous visualization of multiple protein interactions in the same cell. Application to a protein interaction network acting in calcium-mediated signal transduction revealed the concurrent interaction of the protein kinase CIPK24 with the calcium sensors CBL1 and CBL10 at the plasma membrane and tonoplast, respectively. We have also visualized by mcBiFC the simultaneous formation of CBL1/CIPK1 and CBL9/CIPK1 protein complexes at the plasma membrane. Thus, mcBiFC provides a useful new tool for exploring complex regulatory networks in plants.