Determinants influencing the choice of a cooperation partner

This paper provides empirical tests of hypotheses of cooperative behavior provided by evolutionary approaches in the resource-based view of the firm. The influences of "technological proximity", individual incentives to cooperate and managerial tools to the choice of research partner are analyzed. Using German patent data we can show the positive influence of those three determinants. The results of this paper confirm theories dealing with the path-dependency of research activities.innovation, resource-based view of the firm, cooperation, technological proximity, organizational know-how

The Bright and Dark Side of Cooperation for Regional Innovation Performance

Studies analyzing the importance of intra- and inter-regional cooperation for regional innovation performance are mainly of qualitative nature and focus strongly on the positive effects that high levels of cooperation can yield. For the case of the German labor market regions and the Electrics & Electronics industry the paper provides a quantitative-empirical analysis taking into account the possibility of negative effects related to regional lock-in, lock-out, and cooperation overload situations. Using conditional nonparametric frontier techniques and cooperation behavior measures we find positive as well as substantial negative effects of cooperation with the latter being induced by excessive and unbalanced cooperation behavior.regional innovation performance, cooperation, lock-out, lock-in, cooperation overload

Die Wirkung von Forschungskooperationen auf den Unternehmenserfolg - eine Fallstudie zum Landkreis Saalfeld Rudolstadt

Dieses Paper untersucht auf Basis des ressourcen-basierten Ansatzes der Theorie des Unternehmens die Wirkungen von Forschungskooperationen auf den Unternehmenserfolg in einer explorativen Fallstudie. Dazu wird der Einfluss kooperativen Verhaltens im Bereich der Forschung und Entwicklung auf verschiedene Ebenen der Performance hin getest. Für die vorliegende Datenbasis kann gezeigt werden, dass die Wahrscheinlichkeit einer erfolgreichen Innovation nicht durch kooperatives Verhalten erhöht werden kann. Positive Wirkungen zeigten sich jedoch auf den langfristigen ökonomischen Erfolg.

Regional and technological patterns of cooperative innovation activities

Die Dissertation „Regional and sectoral patterns of cooperative innovation activities“ befasst sich mit den Determinanten systemischer Innovationen allgemein und mit den Einflussfaktoren auf Kooperationen im Bereich der Forschung und Entwicklung im Speziellen. In der Arbeit wird auf Basis des Konzeptes der Innovationssysteme der Zusammenhang zwischen den Auswirkungen technologischer und räumlicher Nähe auf das Kooperationsverhalten von Akteuren analysiert. Das Kernproblem, welches in dieser Arbeit diskutiert und empirisch analysiert wird, ist dabei die Schlussfolgerung aus der Bestandsaufnahme der existierenden Literatur, dass regionale und sektorale Innovationssysteme bisher unabhängig voneinander untersucht wurden, aber jeweils für sich in Anspruch nehmen, die für interaktives Lernen essentielle Dimension der Nähe zu betrachten. Zudem beruht die bisherige empirische Literatur zum Themenkomplex der Innovationssysteme ausschließlich auf Fallstudiendesigns. In der vorliegenden Arbeit wird unter Berücksichtigung der Existenz von sektoralen Innovationssystemen eine Methodik zur Bestimmung des relativen regionalen Effektes auf das Kooperationsverhalten entwickelt und auf einen Datensatz von Deutschland angewandt. Dabei galt es zu klären, ob das beobachtbare Kooperationsverhalten und somit auch die regionalen Unterschiede des Selbigen ausschließlich auf Unterschieden in der sektoralen Ausstattung von Regionen beruht oder ob eine regionale Komponente existiert, welche einen eigenen, unabhängigen Erklärungsbeitrag zum beobachtbaren Verhalten regionaler Akteure leistet. In der Arbeit kann gezeigt werden, dass sowohl regionale als auch sektorale Effekte das Kooperationsverhalten in Deutschland bestimmen. Zudem werden die regionalen Effekte von der Verbundenheit der regionalen Wissensbasis determiniert und es kann gezeigt werden, dass die Effekte sich positiv auf die Innovationsperformance von Regionen auswirkt

miRTargetLink—miRNAs, Genes and Interaction Networks

Information on miRNA targeting genes is growing rapidly. For high-throughput experiments, but also for targeted analyses of few genes or miRNAs, easy analysis with concise representation of results facilitates the work of life scientists. We developed miRTargetLink, a tool for automating respective analysis procedures that are frequently applied. Input of the web-based solution is either a single gene or single miRNA, but also sets of genes or miRNAs, can be entered. Validated and predicted targets are extracted from databases and an interaction network is presented. Users can select whether predicted targets, experimentally validated targets with strong or weak evidence, or combinations of those are considered. Central genes or miRNAs are highlighted and users can navigate through the network interactively. To discover the most relevant biochemical processes influenced by the target network, gene set analysis and miRNA set analysis are integrated. As a showcase for miRTargetLink, we analyze targets of five cardiac miRNAs. miRTargetLink is freely available without restrictions at www.ccb.uni-saarland.de/mirtargetlink

BALL-SNP: combining genetic and structural information to identify candidate non-synonymous single nucleotide polymorphisms

Background: High-throughput genetic testing is increasingly applied in clinics. Next-Generation Sequencing (NGS) data analysis however still remains a great challenge. The interpretation of pathogenicity of single variants or combinations of variants is crucial to provide accurate diagnostic information or guide therapies. Methods: To facilitate the interpretation of variants and the selection of candidate non-synonymous polymorphisms (nsSNPs) for further clinical studies, we developed BALL-SNP. Starting from genetic variants in variant call format (VCF) files or tabular input, our tool, first, visualizes the three-dimensional (3D) structure of the respective proteins from the Protein Data Bank (PDB) and highlights mutated residues, automatically. Second, a hierarchical bottom up clustering on the nsSNPs within the 3D structure is performed to identify nsSNPs, which are close to each other. The modular and flexible implementation allows for straightforward integration of different databases for pathogenic and benign variants, but also enables the integration of pathogenicity prediction tools. The collected background information of all variants is presented below the 3D structure in an easily interpretable table format. Results: First, we integrated different data resources into BALL-SNP, including databases containing information on genetic variants such as ClinVar or HUMSAVAR; third party tools that predict stability or pathogenicity in silico such as I-Mutant2.0; and additional information derived from the 3D structure such as a prediction of binding pockets. We then explored the applicability of BALL-SNP on the example of patients suffering from cardiomyopathies. Here, the analysis highlighted accumulation of variations in the genes JUP, VCL, and SMYD2. Conclusion: Software solutions for analyzing high-throughput genomics data are important to support diagnosis and therapy selection. Our tool BALL-SNP, which is freely available at http://www.ccb.uni-saarland.de/BALL-SNP , combines genetic information with an easily interpretable and interactive, graphical representation of amino acid changes in proteins. Thereby relevant information from databases and computational tools is presented. Beyond this, proximity to functional sites or accumulations of mutations with a potential collective effect can be discovered

miFRame: analysis and visualization of miRNA sequencing data in neurological disorders

Background: While in the past decades nucleic acid analysis has been predominantly carried out using quantitative low- and high-throughput approaches such as qRT-PCR and microarray technology, next-generation sequencing (NGS) with its single base resolution is now frequently applied in DNA and RNA testing. Especially for small non-coding RNAs such as microRNAs there is a need for analysis and visualization tools that facilitate interpretation of the results also for clinicians. Methods: We developed miFRame, which supports the analysis of human small RNA NGS data. Our tool carries out different data analyses for known as well as predicted novel mature microRNAs from known precursors and presents the results in a well interpretable manner. Analyses include among others expression analysis of precursors and mature miRNAs, detection of novel precursors and detection of potential iso-microRNAs. Aggregation of results from different users moreover allows for evaluation whether remarkable results, such as novel mature miRNAs, are indeed specific for the respective experimental set-up or are frequently detected across a broad range of experiments. Results: We demonstrate the capabilities of miFRame, which is freely available at http://www.ccb.uni-saarland.de/miframe on two studies, circulating biomarker screening for Multiple Sclerosis (cohort includes clinically isolated syndrome, relapse remitting MS, matched controls) as well as Alzheimer Disease (cohort includes Alzheimer Disease, Mild Cognitive Impairment, matched controls). Here, our tool allowed for an improved biomarker discovery by identifying likely false positive marker candidates

Multi-omics assessment of dilated cardiomyopathy using non-negative matrix factorization

Dilated cardiomyopathy (DCM), a myocardial disease, is heterogeneous and often results in heart failure and sudden cardiac death. Unavailability of cardiac tissue has hindered the comprehensive exploration of gene regulatory networks and nodal players in DCM. In this study, we carried out integrated analysis of transcriptome and methylome data using nonnegative matrix factorization from a cohort of DCM patients to uncover underlying latent factors and covarying features between whole-transcriptome and epigenome omics datasets from tissue biopsies of living patients. DNA methylation data from Infinium HM450 and mRNA Illumina sequencing of n = 33 DCM and n = 24 control probands were filtered, analyzed and used as input for matrix factorization using R NMF package. Mann-Whitney U test showed 4 out of 5 latent factors are significantly different between DCM and control probands (P<0.05). Characterization of top 10% features driving each latent factor showed a significant enrichment of biological processes known to be involved in DCM pathogenesis, including immune response (P = 3.97E-21), nucleic acid binding (P = 1.42E-18), extracellular matrix (P = 9.23E-14) and myofibrillar structure (P = 8.46E-12). Correlation network analysis revealed interaction of important sarcomeric genes like Nebulin, Tropomyosin alpha-3 and ERC-protein 2 with CpG methylation of ATPase Phospholipid Transporting 11A0, Solute Carrier Family 12 Member 7 and Leucine Rich Repeat Containing 14B, all with significant P values associated with correlation coefficients >0.7. Using matrix factorization, multiomics data derived from human tissue samples can be integrated and novel interactions can be identified. Hypothesis generating nature of such analysis could help to better understand the pathophysiology of complex traits such as DCM

miRTrail - a comprehensive webserver for analyzing gene and miRNA patterns to enhance the understanding of regulatory mechanisms in diseases

<p>Abstract</p> <p>Background</p> <p>Expression profiling provides new insights into regulatory and metabolic processes and in particular into pathogenic mechanisms associated with diseases. Besides genes, non-coding transcripts as microRNAs (miRNAs) gained increasing relevance in the last decade. To understand the regulatory processes of miRNAs on genes, integrative computer-aided approaches are essential, especially in the light of complex human diseases as cancer.</p> <p>Results</p> <p>Here, we present miRTrail, an integrative tool that allows for performing comprehensive analyses of interactions of genes and miRNAs based on expression profiles. The integrated analysis of mRNA and miRNA data should generate more robust and reliable results on deregulated pathogenic processes and may also offer novel insights into the regulatory interactions between miRNAs and genes. Our web-server excels in carrying out gene sets analysis, analysis of miRNA sets as well as the combination of both in a systems biology approach. To this end, miRTrail integrates information on 20.000 genes, almost 1.000 miRNAs, and roughly 280.000 putative interactions, for Homo sapiens and accordingly for Mus musculus and Danio rerio. The well-established, classical Chi-squared test is one of the central techniques of our tool for the joint consideration of miRNAs and their targets. For interactively visualizing obtained results, it relies on the network analyzers and viewers BiNA or Cytoscape-web, also enabling direct access to relevant literature. We demonstrated the potential of miRTrail by applying our tool to mRNA and miRNA data of malignant melanoma. MiRTrail identified several deregulated miRNAs that target deregulated mRNAs including miRNAs hsa-miR-23b and hsa-miR-223, which target the highest numbers of deregulated mRNAs and regulate the pathway "basal cell carcinoma". In addition, both miRNAs target genes like PTCH1 and RASA1 that are involved in many oncogenic processes.</p> <p>Conclusions</p> <p>The application on melanoma samples demonstrates that the miRTrail platform may open avenues for investigating the regulatory interactions between genes and miRNAs for a wide range of human diseases. Moreover, miRTrail cannot only be applied to microarray based expression profiles, but also to NGS-based transcriptomic data. The program is freely available as web-server at mirtrail.bioinf.uni-sb.de.</p

Epigenetic Regulation of Alternative mRNA Splicing in Dilated Cardiomyopathy

In recent years, the genetic architecture of dilated cardiomyopathy (DCM) has been more thoroughly elucidated. However, there is still insufficient knowledge on the modifiers and regulatory principles that lead to the failure of myocardial function. The current study investigates the association of epigenome-wide DNA methylation and alternative splicing, both of which are important regulatory principles in DCM. We analyzed screening and replication cohorts of cases and controls and identified distinct transcriptomic patterns in the myocardium that differ significantly, and we identified a strong association of intronic DNA methylation and flanking exons usage (p < 2 × 10−16). By combining differential exon usage (DEU) and differential methylation regions (DMR), we found a significant change of regulation in important sarcomeric and other DCM-associated pathways. Interestingly, inverse regulation of Titin antisense non-coding RNA transcript splicing and DNA methylation of a locus reciprocal to TTN substantiate these findings and indicate an additional role for non-protein-coding transcripts. In summary, this study highlights for the first time the close interrelationship between genetic imprinting by DNA methylation and the transport of this epigenetic information towards the dynamic mRNA splicing landscape. This expands our knowledge of the genome–environment interaction in DCM besides simple gene expression regulation