• Our aim is to advance our understanding of biological systems,

    ranging from single species to multi-species systems and ecosystems,

    based on data from large-scale bioanalytical methods.

  • We develop, improve and apply

    computational methods

    for the interpretation of molecular information in biology.

  • We establish and analyse

    quantitative mathematical models.

CUBE News

Latest publications

NVT: a fast and simple tool for the assessment of RNA-Seq normalization strategies.

Measuring differential gene expression is a common task in the analysis of RNA-Seq data. To identify differentially expressed genes between two samples, it is crucial to normalize the datasets. While multiple normalization methods are available, all of them are based on certain assumptions that may or may not be suitable for the type of data they are applied on. Researchers therefore need to select an adequate normalization strategy for each RNA-Seq experiment. This selection includes exploration of different normalization methods as well as their comparison. Methods that agree with each other most likely represent realistic assumptions under the particular experimental conditions.
We developed the NVT package, which provides a fast and simple way to analyze and evaluate multiple normalization methods via visualization and representation of correlation values, based on a user-defined set of uniformly expressed genes.
The R package is freely available under https://github.com/Edert/NVT CONTACT: thomas.rattei@univie.ac.at SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Eder T, Grebien F, Rattei T
2016 - Bioinformatics, in press

Comprehensive Identification of Meningococcal Genes and Small Noncoding RNAs Required for Host Cell Colonization.

Neisseria meningitidis is a leading cause of bacterial meningitis and septicemia, affecting infants and adults worldwide. N. meningitidis is also a common inhabitant of the human nasopharynx and, as such, is highly adapted to its niche. During bacteremia, N. meningitidis gains access to the blood compartment, where it adheres to endothelial cells of blood vessels and causes dramatic vascular damage. Colonization of the nasopharyngeal niche and communication with the different human cell types is a major issue of the N. meningitidis life cycle that is poorly understood. Here, highly saturated random transposon insertion libraries of N. meningitidis were engineered, and the fitness of mutations during routine growth and that of colonization of endothelial and epithelial cells in a flow device were assessed in a transposon insertion site sequencing (Tn-seq) analysis. This allowed the identification of genes essential for bacterial growth and genes specifically required for host cell colonization. In addition, after having identified the small noncoding RNAs (sRNAs) located in intergenic regions, the phenotypes associated with mutations in those sRNAs were defined. A total of 383 genes and 8 intergenic regions containing sRNA candidates were identified to be essential for growth, while 288 genes and 33 intergenic regions containing sRNA candidates were found to be specifically required for host cell colonization.
Meningococcal meningitis is a common cause of meningitis in infants and adults. Neisseria meningitidis (meningococcus) is also a commensal bacterium of the nasopharynx and is carried by 3 to 30% of healthy humans. Under some unknown circumstances, N. meningitidis is able to invade the bloodstream and cause either meningitis or a fatal septicemia known as purpura fulminans. The onset of symptoms is sudden, and death can follow within hours. Although many meningococcal virulence factors have been identified, the mechanisms that allow the bacterium to switch from the commensal to pathogen state remain unknown. Therefore, we used a Tn-seq strategy coupled to high-throughput DNA sequencing technologies to find genes for proteins used by N. meningitidis to specifically colonize epithelial cells and primary brain endothelial cells. We identified 383 genes and 8 intergenic regions containing sRNAs essential for growth and 288 genes and 33 intergenic regions containing sRNAs required specifically for host cell colonization.

Capel E, Zomer AL, Nussbaumer T, Bole C, Izac B, Frapy E, Meyer J, Bouzinba-Ségard H, Bille E, Jamet A, Cavau A, Letourneur F, Bourdoulous S, Rattei T, Nassif X, Coureuil M
2016 - mBio, 4: in press

ConsPred - a rule-based (re-)annotation framework for prokaryotic genomes.

The rapidly growing number of available prokaryotic genome sequences requires fully automated and high-quality software solutions for their initial and re-annotation. Here we present ConsPred, a prokaryotic genome annotation framework that performs intrinsic gene predictions, homology searches, predictions of non-coding genes as well as CRISPR repeats and integrates all evidence into a consensus annotation. ConsPred achieves comprehensive, high-quality annotations based on rules and priorities, similar to decision-making in manual curation and avoids conflicting predictions. Parameters controlling the annotation process are configurable by the user. ConsPred has been used in the institutions of the authors for longer than 5 years and can easily be extended and adapted to specific needs.
The ConsPred algorithm for producing a consensus from the varying scores of multiple gene prediction programs approaches manual curation in accuracy. Its rule-based approach for choosing final predictions avoids overriding previous manual curations.Implementation and availability: ConsPred is implemented in Java, Perl, and Shell and is freely available under the Creative Commons license as a stand-alone in-house pipeline or as an Amazon Machine Image for cloud computing, see https://sourceforge.net/projects/conspred/.

Weinmaier T, Platzer A, Frank J, Hellinger HJ, Tischler P, Rattei T
2016 - Bioinformatics, in press