Objective method of comparing DNA microarray image analysis systems

Many image analysis systems are available for processing the images produced by laser scanning of DNA microarrays. The image processing system takes pixel-level intensity data and converts it to a set of gene-level expression or copy number summaries that will be used in further analyses. Image analysis systems currently in use differ with regard to the specific algorithms they implement, ease of use, and cost. Thus, it would be desirable to have an objective means of comparing systems. Here we describe a systematic method of comparing image processing results produced by different image analysis systems using a series of replicate microarray experiments. We demonstrate the method with a comparison of cDNA microarray data generated by the UCSF Spot and the GenePix® image processing systems

Nitrogen metabolism and nitrogen control in corynebacteria: Variations of a common theme

Walter B, Haenssler E, Kalinowski J, Burkovski A. Nitrogen metabolism and nitrogen control in corynebacteria: Variations of a common theme. JOURNAL OF MOLECULAR MICROBIOLOGY AND BIOTECHNOLOGY. 2007;12(1-2):131-138.The published genome sequences of Corynebacterium diphtheriae, Corynebacterium efficiens, Corynebacterium glutamicum and Corynebacterium jeikeium were screened for genes encoding central components of nitrogen source uptake, nitrogen assimilation and nitrogen control systems. Interestingly, the soil-living species C. efficiens and C. glutamicum exhibit a broader spectrum of genes for nitrogen transport and metabolism than the pathogenic species C. diphtheriae and C. jeikeium. The latter are characterized by gene decay and loss of functions like urea metabolism and nitrogen-dependent transcription control. The global regulator of nitrogen regulation AmtR and its DNA-binding motif are conserved in C. diphtheriae, C. efficiens and C. glutamicum, while in C. jeikeium, an AmtR-encoding gene as well as putative AmtR-binding motifs are missing. Copyright (c) 2007 S. Karger AG, Basel

Novel biomarkers for prostate cancer revealed by (α,β)-k-feature sets

In this chapter we present a method based on the (α,β)-k-feature set problem for identifying relevant attributes in high-dimensional datasets for classification purposes. We present a case-study of biomedical interest. Using the gene expression of thousands of genes, we show that the method can give a reduced set that can identify samples as belonging to prostate cancer tumors or not. We thus address the need of finding novel methods that can deal with classification problems that involve feature selection from several thousand features, while we only have on the order of one hundred samples. The methodology appears to be very robust in this prostate cancer case study. It has lead to the identification of a set of differentially expressed genes that are highly predictive of the cells transition to a more malignant type, thus departing from the profile which is characteristic of its originating tissue. Although the method is presented with a particular bioinformatics application in mind, it can clearly be used in other domains. A biological analysis illustrates on the relevance of the genes found, and links to the most current developments in prostate cancer biomarker studies

Characteristics, management, and outcomes of patients with left‐sided infective endocarditis complicated by heart failure: a substudy of the ESC‐EORP EURO‐ENDO (European infective endocarditis) registry

International audienc