Leukemia Gene Atlas – A Public Platform for Integrative Exploration of Genome-Wide Molecular Data

Leukemias are exceptionally well studied at the molecular level and a wealth of high-throughput data has been published. But further utilization of these data by researchers is severely hampered by the lack of accessible integrative tools for viewing and analysis. We developed the Leukemia Gene Atlas (LGA) as a public platform designed to support research and analysis of diverse genomic data published in the field of leukemia. With respect to leukemia research, the LGA is a unique resource with comprehensive search and browse functions. It provides extensive analysis and visualization tools for various types of molecular data. Currently, its database contains data from more than 5,800 leukemia and hematopoiesis samples generated by microarray gene expression, DNA methylation, SNP and next generation sequencing analyses. The LGA allows easy retrieval of large published data sets and thus helps to avoid redundant investigations. It is accessible at www.leukemia-gene-atlas.org

Feedback within the Inter-Cellular Communication and Tumorigenesis in Carcinomas

The classical somatic mutation theory (SMT) of carcinogenesis and metastasis postulates that malignant transformation occurs in cells that accumulate a sufficient amount of mutations in the appropriate oncogenes and/or tumor suppressor genes. These mutations result in cell-autonomous activation of the mutated cell and a growth advantage relative to neighboring cells. However, the SMT cannot completely explain many characteristics of carcinomas. Contrary to the cell-centered view of the SMT with respect to carcinogenesis, recent research has revealed evidence that the tumor microenvironment plays a role in carcinogenesis as well. In this review, we present a new model that accommodates the role of the tumor microenvironment in carcinogenesis and complements the classical SMT. Our “feedback” model emphasizes the role of an altered spatiotemporal communication between epithelial and stromal cells during carcinogenesis: a dysfunctional intracellular signaling in tumorigenic epithelial cells leads to inappropriate cellular responses to stimuli from associated stromal or inflammatory cells. Thus, a positive feedback loop of the information flow between parenchymal and stromal cells results. This constant communication between the stromal cells and the tumor cells causes a perpetually activated state of tumor cells analogous to resonance disaster

Comparative study of unsupervised dimension reduction techniques for the visualization of microarray gene expression data

<p>Abstract</p> <p>Background</p> <p>Visualization of DNA microarray data in two or three dimensional spaces is an important exploratory analysis step in order to detect quality issues or to generate new hypotheses. Principal Component Analysis (PCA) is a widely used linear method to define the mapping between the high-dimensional data and its low-dimensional representation. During the last decade, many new nonlinear methods for dimension reduction have been proposed, but it is still unclear how well these methods capture the underlying structure of microarray gene expression data. In this study, we assessed the performance of the PCA approach and of six nonlinear dimension reduction methods, namely Kernel PCA, Locally Linear Embedding, Isomap, Diffusion Maps, Laplacian Eigenmaps and Maximum Variance Unfolding, in terms of visualization of microarray data.</p> <p>Results</p> <p>A systematic benchmark, consisting of Support Vector Machine classification, cluster validation and noise evaluations was applied to ten microarray and several simulated datasets. Significant differences between PCA and most of the nonlinear methods were observed in two and three dimensional target spaces. With an increasing number of dimensions and an increasing number of differentially expressed genes, all methods showed similar performance. PCA and Diffusion Maps responded less sensitive to noise than the other nonlinear methods.</p> <p>Conclusions</p> <p>Locally Linear Embedding and Isomap showed a superior performance on all datasets. In very low-dimensional representations and with few differentially expressed genes, these two methods preserve more of the underlying structure of the data than PCA, and thus are favorable alternatives for the visualization of microarray data.</p

Genome sequence of B. amyloliquefaciens type strain DSM7T reveals differences to plant-associated B. amyloliquefaciens FZB42

The complete genome sequence of Bacillus amyloliquefaciens type strain DSM7T is presented. A comparative analysis between the genome sequences of the plant associated strain FZB42 (Chen et al., 2007) with the genome of B. amyloliquefaciens DSM7T revealed obvious differences in the variable part of the genomes, whilst the core genomes were found to be very similar. The strains FZB42 and DSM7T have in common 3345 genes (CDS) in their core genomes; whilst 547 and 344 CDS were found to be unique in DSM7T and FZB42, respectively. The core genome shared by both strains exhibited 97.89% identity on amino acid level. The number of genes representing the core genome of the strains FZB42, DSM7T, and Bacillus subtilis DSM10T was calculated as being 3098 and their identity was 92.25%. The 3,980,199 bp genome of DSM7T contains numerous genomic islands (GI) detected by different methods. Many of them were located in vicinity of tRNA, glnA, and glmS gene copies. In contrast to FZB42, but similar to B. subtilis DSM10T, the GI were enriched in prophage sequences and often harbored transposases, integrases and recombinases. Compared to FZB42, B. amyloliquefaciens DSM7T possessed a reduced potential to non-ribosomally synthesize secondary metabolites with antibacterial and/or antifungal action. B. amyloliquefaciens DSM7T did not produce the polyketides difficidin and macrolactin and was impaired in its ability to produce lipopeptides other than surfactin. Differences established within the variable part of the genomes, justify our proposal to discriminate the plant-associated ecotype represented by FZB42 from the group of type strain related B. amyloliquefaciens soil bacteria.Financial support in frame of the competence network Genome Research on Bacteria (GenoMikPlus) and the Chinese–German collaboration program by the German Ministry for Education and Research. Oleg Reva acknowledges funding from the National Research Foundation of South Africa for computer program development.http://www.elsevier.com/locate/jbiotecnf201

Development of energy models for design space exploration of embedded many-core systems

This paper introduces a methodology to develop energy models for the design space exploration of embedded many-core systems. The design process of such systems can benefit from sophisticated models. Software and hardware can be specifically optimized based on comprehensive knowledge about application scenario and hardware behavior. The contribution of our work is an automated framework to estimate the energy consumption at an arbitrary abstraction level without the need to provide further information about the system. We validated our framework with the configurable many-core system CoreVA-MPSoC. Compared to a simulation of the CoreVA-MPSoC on gate level in a 28nm FD-SOI standard cell technology, our framework shows an average estimation error of about 4%