Sebastian Kloska

Estimating mutual information using B-spline functions – an improved similarity measure for analysing gene expression data

BackgroundThe information theoretic concept of mutual information provides a general framework to evaluate dependencies between variables. In the context of the clustering of genes with similar patterns of expression it has been suggested as a general quantity of similarity to extend commonly used linear measures. Since mutual information is defined in terms of discrete variables, its application to continuous data requires the use of binning procedures, which can lead to significant numerical errors for datasets of small or moderate size.ResultsIn this work, we propose a method for the numerical estimation of mutual information from continuous data. We investigate the characteristic properties arising from the application of our algorithm and show that our approach outperforms commonly used algorithms: The significance, as a measure of the power of distinction from random correlation, is significantly increased. This concept is subsequently illustrated on two large-scale gene expression datasets and the results are compared to those obtained using other similarity measures.A C++ source code of our algorithm is available for non-commercial use from kloska@scienion.de upon request.ConclusionThe utilisation of mutual information as similarity measure enables the detection of non-linear correlations in gene expression datasets. Frequently applied linear correlation measures, which are often used on an ad-hoc basis without further justification, are thereby extended.

A complete BAC-based physical map of the Arabidopsis thaliana genome.

Arabidopsis thaliana is a small flowering plant that serves as the major model system in plant molecular genetics. The efforts of many scientists have produced genetic maps that provide extensive coverage of the genome (http://genome-www.stanford.edu/Arabidopsis/maps.html). Recently, detailed YAC, BAC, P1 and cosmid-based physical maps (that is, representations of genomic regions as sets of overlapping clones of corresponding libraries) have been established that extend over wide genomic areas ranging from several hundreds of kilobases to entire chromosomes. These maps provide an entry to gain deeper insight into the A. thaliana genome structure. A. thaliana has been chosen as the subject of the first large-scale project intended to determine the full genome sequence of a plant. This sequencing project, together with the increasing interest in map-based gene cloning, has highlighted the requirement for a complete and accurate physical map of this plant species. To supply the scientific community with a high-quality resource, we present here a complete physical map of A. thaliana using essentially the IGF BAC library. The map consists of 27 contigs that cover the entire genome, except for the presumptive centromeric regions, nucleolar organization regions (NOR) and telomeric areas. This is the first reported map of a complex organism based entirely on BAC clones and it represents the most homogeneous and complete physical map established to date for any plant genome. Furthermore, the analysis performed here serves as a model for an efficient physical mapping procedure using BAC clones that can be applied to other complex genomes.

MetaGeneAlyse: analysis of integrated transcriptional and metabolite data.

UNLABELLED New techniques in sample preparation allow high throughput analysis of samples on the transcriptional as well as on the metabolic level. We present a service accessible via the web that allows the analysis of integrated data sets that combine gene-expression data and metabolic data. After uploading, data sets can be normalized, clustered by various methods and results can be graphically visualized. All calculations are carried out on a server, so even time- and memory-consuming analyses can be done independently of the performance of the client. AVAILABILITY The service is accessible via web-interface at http://metagenealyse.mpimp-golm.mpg.de/

Lotus japonicus expression database

Haruspex is a data management and analysis system developed at the Max Planck Institute of Plant Molecular Physiology that enables researchers to screen large sets of profiling data. Haruspex provides a common platform for the web-based storage, retrieval, and statistical analysis of expression profiles, while links to external databases provide fast access to comprehensive and up to date information.