Biomedical subjects

Rainer Schrader

Publications and source records attributed to Rainer Schrader.

4 recordsLinked to original sources

PubMed2006

Temperature-sensitive eIF5A mutant accumulates transcripts targeted to the nonsense-mediated decay pathway.

The highly conserved protein eIF5A found in Archaea and all eukaryotes uniquely contains the posttranslationally formed amino acid hypusine. Despite being essential the functions of this protein and its modification remain unclear. To gain more insight into these functions temperature-sensitive mutants of the human EIF5A1 were characterized in the yeast Saccharomyces cerevisiae. Expression of the point mutated form V81G in a DeltaeIF5A strain of yeast led to a strongly temperature-sensitive phenotype and to a significantly reduced protein level at restrictive temperature. The mutant showed accumulation of a subset of mRNAs that was also observed in nonsense-mediated decay (NMD)-deficient yeast strains. After short incubation at restrictive temperature the mutant exhibited increased half-lives of the intron containing CYH2 pre-mRNA and mature transcripts of NMD-dependent genes. Reduced telomere silencing and shortening was detected in the V81G mutant further supporting similarities to NMD-deficient strains. Our data suggest that eIF5A mediates important cellular processes like cell viability and senescence through its effects on the stability of certain mRNAs.

Humans↗

PubMed2006

CASPAR: a hierarchical bayesian approach to predict survival times in cancer from gene expression data.

MOTIVATION: DNA microarrays allow the simultaneous measurement of thousands of gene expression levels in any given patient sample. Gene expression data have been shown to correlate with survival in several cancers, however, analysis of the data is difficult, since typically at most a few hundred patients are available, resulting in severely underdetermined regression or classification models. Several approaches exist to classify patients in different risk classes, however, relatively little has been done with respect to the prediction of actual survival times. We introduce CASPAR, a novel method to predict true survival times for the individual patient based on microarray measurements. CASPAR is based on a multivariate Cox regression model that is embedded in a Bayesian framework. A hierarchical prior distribution on the regression parameters is specifically designed to deal with high dimensionality (large number of genes) and low sample size settings, that are typical for microarray measurements. This enables CASPAR to automatically select small, most informative subsets of genes for prediction. RESULTS: Validity of the method is demonstrated on two publicly available datasets on diffuse large B-cell lymphoma (DLBCL) and on adenocarcinoma of the lung. The method successfully identifies long and short survivors, with high sensitivity and specificity. We compare our method with two alternative methods from the literature, demonstrating superior results of our approach. In addition, we show that CASPAR can further refine predictions made using clinical scoring systems such as the International Prognostic Index (IPI) for DLBCL and clinical staging for lung cancer, thus providing an additional tool for the clinician. An analysis of the genes identified confirms previously published results, and furthermore, new candidate genes correlated with survival are identified.

Bayes Theorem↗

PubMed2005

A fractional programming approach to efficient DNA melting temperature calculation.

MOTIVATION: In a wide range of experimental techniques in biology, there is a need for an efficient method to calculate the melting temperature of pairings of two single DNA strands. Avoiding cross-hybridization when choosing primers for the polymerase chain reaction or selecting probes for large-scale DNA assays are examples where the exact determination of melting temperatures is important. Beyond being exact, the method has to be efficient, as these techniques often require the simultaneous calculation of melting temperatures of up to millions of possible pairings. The problem is to simultaneously determine the most stable alignment of two sequences, including potential loops and bulges, and calculate the corresponding melting temperature. RESULTS: As the melting temperature can be expressed as a fraction in terms of enthalpy and entropy differences of the corresponding annealing reaction, we propose to use a fractional programming algorithm, the Dinkelbach algorithm, to solve the problem. To calculate the required differences of enthalpy and entropy, the Nearest Neighbor model is applied. Using this model, the substeps of the Dinkelbach algorithm in our problem setting turn out to be calculations of alignments which optimize an additive score function. Thus, the usual dynamic programming techniques can be applied. The result is an efficient algorithm to determine melting temperatures of two DNA strands, suitable for large-scale applications such as primer or probe design. AVAILABILITY: The software is available for academic purposes from the authors. A web interface is provided at http://www.zaik.uni-koeln.de/bioinformatik/fptm.html

Algorithms↗

PubMed2004

Fast and robust registration of PET and MR images of human brain.

In recent years, mutual information has proved to be an excellent criterion for registration of intra-individual images from different modalities. Multi-resolution coarse-to-fine optimization was proposed for speeding-up of the registration process. The aim of our work was to further improve registration speed without compromising robustness or accuracy. We present and evaluate two procedures for co-registration of positron emission tomography (PET) and magnetic resonance (MR) images of human brain that combine a multi-resolution approach with an automatic segmentation of input image volumes into areas of interest and background. We show that an acceleration factor of 10 can be achieved for clinical data and that a suitable preprocessing can improve robustness of registration. Emphasis was laid on creation of an automatic registration system that could be used routinely in a clinical environment. For this purpose, an easy-to-use graphical user interface has been developed. It allows physicians with no special knowledge of the registration algorithm to perform a fast and reliable alignment of images. Registration progress is presented on the fly on a fusion of images and enables visual checking during a registration.

Algorithms↗