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

BACKGROUND: The 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. RESULTS: In 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. CONCLUSION: The 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.

Novel aspects of symbiotic nitrogen fixation uncovered by transcript profiling with cDNA arrays.

An array of 2,304 cDNA clones derived from nitrogen-fixing nodules of Lotus japonicus was produced and used to detect differences in relative gene transcript abundance between nodules and uninfected roots. Transcripts of 83 different genes were found to be more abundant in nodules than in roots. More than 50 of these have never before been identified as nodule-induced in any species. Expression of 36 genes was detected in nodules but not in roots. Several known nodulin genes were included among the nodule-induced genes. Also included were genes involved in sucrose breakdown and glycolysis, CO2 recycling, and amino acid synthesis, processes that are known to be accelerated in nodules compared with roots. Genes involved in membrane transport, hormone metabolism, cell wall and protein synthesis, and signal transduction and regulation of transcription were also induced in nodules. Genes that may subvert normal plant defense responses, including two encoding enzymes involved in detoxification of active oxygen species and one that may prohibit phytoalexin synthesis, were also identified. The data represent a rich source of information for hypothesis building and future exploration of symbiotic nitrogen fixation.

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/

Using array hybridization to monitor gene expression at the single cell level.

Advances in high-throughput genome sequencing demand the development of more efficient ways of examining gene expression at a cellular level. During recent years, polymerase chain reaction (PCR)-based methods have been developed that allow the amplification of mRNA from small amounts of material, even from single animal cells. In parallel, several analytical tools permit a global monitoring of gene expression. To date, high throughput analysis methods have not been accessible for single plant cell samples. In the protocol described here, cDNA array hybridization (expression profiling) and an amplification strategy using reverse transcriptase PCR are merged with high spatial resolution sampling from undamaged plant tissue. This protocol gives us a new tool to examine tissue-specific gene expression patterns on a comprehensive scale. To demonstrate the usefulness of this tool, gene expression patterns in samples from Arabidopsis thaliana L. cv. C24 leaf epidermal and mesophyll cells were measured; several differentially expressed genes were identified when single cell samples were compared. The protocol described has the potential of increasing the efficiency of tissue-specific expression analysis by combining high-throughput profiling with straightforward sampling and amplification procedures.

Global changes in transcription orchestrate metabolic differentiation during symbiotic nitrogen fixation in Lotus japonicus.

Research on legume nodule metabolism has contributed greatly to our knowledge of primary carbon and nitrogen metabolism in plants in general, and in symbiotic nitrogen fixation in particular. However, most previous studies focused on one or a few genes/enzymes involved in selected metabolic pathways in many different legume species. We utilized the tools of transcriptomics and metabolomics to obtain an unprecedented overview of the metabolic differentiation that results from nodule development in the model legume, Lotus japonicus. Using an array of more than 5000 nodule cDNA clones, representing 2500 different genes, we identified approximately 860 genes that were more highly expressed in nodules than in roots. One-third of these are involved in metabolism and transport, and over 100 encode proteins that are likely to be involved in signalling, or regulation of gene expression at the transcriptional or post-transcriptional level. Several metabolic pathways appeared to be co-ordinately upregulated in nodules, including glycolysis, CO(2) fixation, amino acid biosynthesis, and purine, haem, and redox metabolism. Insight into the physiological conditions that prevail within nodules was obtained from specific sets of induced genes. In addition to the expected signs of hypoxia, numerous indications were obtained that nodule cells also experience P-limitation and osmotic stress. Several potential regulators of these stress responses were identified. Metabolite profiling by gas chromatography coupled to mass spectrometry revealed a distinct metabolic phenotype for nodules that reflected the global changes in metabolism inferred from transcriptome analysis.