Björn Usadel

Bio: Björn Usadel is an academic researcher from Forschungszentrum Jülich. The author has contributed to research in topics: Cell wall & Arabidopsis. The author has an hindex of 58, co-authored 167 publications receiving 14373 citations. Previous affiliations of Björn Usadel include University of Düsseldorf & RWTH Aachen University.

GMD@CSB.DB: the Golm Metabolome Database

Abstract: Summary: Metabolomics, in particular gas chromatography--mass spectrometry (GC--MS) based metabolite profiling of biological extracts, is rapidly becoming one of the cornerstones of functional genomics and systems biology. Metabolite profiling has profound applications in discovering the mode of action of drugs or herbicides, and in unravelling the effect of altered gene expression on metabolism and organism performance in biotechnological applications. As such the technology needs to be available to many laboratories. For this, an open exchange of information is required, like that already achieved for transcript and protein data. One of the key-steps in metabolite profiling is the unambiguous identification of metabolites in highly complex metabolite preparations from biological samples. Collections of mass spectra, which comprise frequently observed metabolites of either known or unknown exact chemical structure, represent the most effective means to pool the identification efforts currently performed in many laboratories around the world. Here we present GMD, The Golm Metabolome Database, an open access metabolome database, which should enable these processes. GMD provides public access to custom mass spectral libraries, metabolite profiling experiments as well as additional information and tools, e.g. with regard to methods, spectral information or compounds. The main goal will be the representation of an exchange platform for experimental research activities and bioinformatics to develop and improve metabolomics by multidisciplinary cooperation. Availability: http://csbdb.mpimp-golm.mpg.de/gmd.html Contact: Steinhauser@mpimp-golm.mpg.de Supplementary information: http://csbdb.mpimp-golm.mpg.de/

RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics

Abstract: 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.

Sugars and Circadian Regulation Make Major Contributions to the Global Regulation of Diurnal Gene Expression in Arabidopsis

Abstract: The diurnal cycle strongly influences many plant metabolic and physiological processes. Arabidopsis thaliana rosettes were harvested six times during 12-h-light/12-h-dark treatments to investigate changes in gene expression using ATH1 arrays. Diagnostic gene sets were identified from published or in-house expression profiles of the response to light, sugar, nitrogen, and water deficit in seedlings and 4 h of darkness or illumination at ambient or compensation point [CO2]. Many sugar-responsive genes showed large diurnal expression changes, whose timing matched that of the diurnal changes of sugars. A set of circadian-regulated genes also showed large diurnal changes in expression. Comparison of published results from a free-running cycle with the diurnal changes in Columbia-0 (Col-0) and the starchless phosphoglucomutase (pgm) mutant indicated that sugars modify the expression of up to half of the clock-regulated genes. Principle component analysis identified genes that make large contributions to diurnal changes and confirmed that sugar and circadian regulation are the major inputs in Col-0 but that sugars dominate the response in pgm. Most of the changes in pgm are triggered by low sugar levels during the night rather than high levels in the light, highlighting the importance of responses to low sugar in diurnal gene regulation. We identified a set of candidate regulatory genes that show robust responses to alterations in sugar levels and change markedly during the diurnal cycle.

Extension of the Visualization Tool MapMan to Allow Statistical Analysis of Arrays, Display of Coresponding Genes, and Comparison with Known Responses

Abstract: MapMan is a user-driven tool that displays large genomics datasets onto diagrams of metabolic pathways or other processes. Here, we present new developments, including improvements of the gene assignments and the user interface, a strategy to visualize multilayered datasets, the incorporation of statistics packages, and extensions of the software to incorporate more biological information including visualization of coresponding genes and horizontal searches for similar global responses across large numbers of arrays.

Genome-wide reprogramming of metabolism and regulatory networks of Arabidopsis in response to phosphorus

Abstract: Affymetrix ATH1 arrays, large-scale real-time reverse transcription PCR of approximately 2200 transcription factor genes and other gene families, and analyses of metabolites and enzyme activities were used to investigate the response of Arabidopsis to phosphate (Pi) deprivation and re-supply. Transcript data were analysed with MapMan software to identify coordinated, system-wide changes in metabolism and other cellular processes. Phosphorus (P) deprivation led to induction or repression of > 1000 genes involved in many processes. A subset, including the induction of genes involved in P uptake, the mobilization of organic Pi, the conversion of phosphorylated glycolytic intermediates to carbohydrates and organic acids, the replacement of P-containing phospholipids with galactolipids and the repression of genes involved in nucleotide/nucleic acid synthesis, was reversed within 3 h after Pi re-supply. Analyses of 22 enzyme activities revealed that changes in transcript levels often, but not always, led to changes in the activities of the encoded enzymes in P-deprived plants. Analyses of metabolites confirmed that P deprivation leads to a shift towards the accumulation of carbohydrates, organic acids and amino acids, and that Pi re-supply leads to use of the latter. P-deprived plants also showed large changes in the expression of many genes involved in, for example, secondary metabolism and photosynthesis. These changes were not reversed rapidly upon Pi re-supply and were probably secondary in origin. Differentially expressed and highly P-specific putative regulator genes were identified that presumably play central roles in coordinating the complex responses of plants to changes in P nutrition. The specific responses to Pi differ markedly from those found for nitrate, whereas the long-term responses during P and N deprivation share common and non-specific features.

Trimmomatic: a flexible trimmer for Illumina sequence data

Abstract: 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: ed.nehcaa-htwr.1oib@ledasu Supplementary information: Supplementary data are available at Bioinformatics online.

HTSeq—a Python framework to work with high-throughput sequencing data

Abstract: Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an opensource software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de

Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

Abstract: Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Abstract: The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.