Large scale statistical inference of signaling pathways from RNAi and microarray data

<p>Abstract</p> <p>Background</p> <p>The advent of RNA interference techniques enables the selective silencing of biologically interesting genes in an efficient way. In combination with DNA microarray technology this enables researchers to gain insights into signaling pathways by observing downstream effects of individual knock-downs on gene expression. These secondary effects can be used to computationally reverse engineer features of the upstream signaling pathway.</p> <p>Results</p> <p>In this paper we address this challenging problem by extending previous work by Markowetz <it>et al</it>., who proposed a statistical framework to score networks hypotheses in a Bayesian manner. Our extensions go in three directions: First, we introduce a way to omit the data discretization step needed in the original framework via a calculation based on <it>p</it>-values instead. Second, we show how prior assumptions on the network structure can be incorporated into the scoring scheme using regularization techniques. Third and most important, we propose methods to scale up the original approach, which is limited to around 5 genes, to large scale networks.</p> <p>Conclusion</p> <p>Comparisons of these methods on artificial data are conducted. Our proposed module network is employed to infer the signaling network between 13 genes in the ER-<it>α </it>pathway in human MCF-7 breast cancer cells. Using a bootstrapping approach this reconstruction can be found with good statistical stability.</p> <p>The code for the module network inference method is available in the latest version of the <it>R</it>-package <it>nem</it>, which can be obtained from the Bioconductor homepage.</p

Beam test performance of a prototype module with Short Strip ASICs for the CMS HL-LHC tracker upgrade

The Short Strip ASIC (SSA) is one of the four front-end chips designed for the upgrade of the CMS Outer Tracker for the High Luminosity LHC. Together with the Macro-Pixel ASIC (MPA) it will instrument modules containing a strip and a macro-pixel sensor stacked on top of each other. The SSA provides both full readout of the strip hit information when triggered, and, together with the MPA, correlated clusters called stubs from the two sensors for use by the CMS Level-1 (L1) trigger system. Results from the first prototype module consisting of a sensor and two SSA chips are presented. The prototype module has been characterized at the Fermilab Test Beam Facility using a 120 GeV proton beam

Unsupervised automated high throughput phenotyping of RNAi time-lapse movies

Background: Gene perturbation experiments in combination with fluorescence time-lapse cell imaging are a powerful tool in reverse genetics. High content applications require tools for the automated processing of the large amounts of data. These tools include in general several image processing steps, the extraction of morphological descriptors, and the grouping of cells into phenotype classes according to their descriptors. This phenotyping can be applied in a supervised or an unsupervised manner. Unsupervised methods are suitable for the discovery of formerly unknown phenotypes, which are expected to occur in high-throughput RNAi time-lapse screens. Results: We developed an unsupervised phenotyping approach based on Hidden Markov Models (HMMs) with multivariate Gaussian emissions for the detection of knockdown-specific phenotypes in RNAi time-lapse movies. The automated detection of abnormal cell morphologies allows us to assign a phenotypic fingerprint to each gene knockdown. By applying our method to the Mitocheck database, we show that a phenotypic fingerprint is indicative of a gene's function. Conclusion: Our fully unsupervised HMM-based phenotyping is able to automatically identify cell morphologies that are specific for a certain knockdown. Beyond the identification of genes whose knockdown affects cell morphology, phenotypic fingerprints can be used to find modules of functionally related genes

Computation times (s) for the module network (white) and the simulated annealing (gray) approach

<p><b>Copyright information:</b></p><p>Taken from "Large scale statistical inference of signaling pathways from RNAi and microarray data"</p><p>http://www.biomedcentral.com/1471-2105/8/386</p><p>BMC Bioinformatics 2007;8():386-386.</p><p>Published online 15 Oct 2007</p><p>PMCID:PMC2241646.</p><p></p

Patients' perception of Parkinson's disease-associated pain following initiation of rotigotine: a multicenter non-interventional study

Objectives: To evaluate Parkinson's disease (PD)-associated pain as perceived by the patients (subjective characterization), and how this may change following initiation of rotigotine transdermal patch. Methods: SP1058 was a non-interventional study conducted in routine clinical practice in Germany and Austria in patients experiencing PD-associated pain (per the physician's assessment). Data were collected at baseline (ie, before rotigotine initiation) and at a routine visit after >= 25 days (-3 days allowed) of treatment on a maintenance dose of rotigotine (end of study [EoS]). Pain perception was assessed using the 12-item Pain Description List of the validated German Pain Questionnaire (each item ranked 0 = 'not true' to 3 = 'very true'). Primary effectiveness variable: change from baseline to EoS in the sum score of the 4 'affective dimension' items of the Pain Description List. Secondary effectiveness variables: change from baseline to EoS in Unified Parkinson's Disease Rating Scale (UPDRS) II, III, and II+III scores, and Parkinson's Disease Questionnaire (PDQ-8) total score (PD-related quality-of-life). Other variables included scores of the eight 'sensory dimension' items of the Pain Description List. Results: Of 93 enrolled patients (mean [SD] age: 71.1 [9.0] years; male: 48 [52%]), 77 (83%) completed the study, and 70 comprised the full analysis set. The mean (SD) change from baseline in the sum score of the four 'affective dimension' items was-1.3 (2.8) indicating a numerical improvement (baseline: 3.9 [3.4]). In the 'sensory dimension', pain was mostly perceived as 'pulling' at baseline (49/70 [70%]); ' largely true'/' very true'). Numerical improvements were observed in all UPDRS scores (mean [SD] change in UPDRS II+ III:-5.3 [10.5]; baseline: 36.0 [15.9]), and in PDQ-8 total score (-2.0 [4.8]; baseline: 10.7 [5.9]). Adverse drug reactions were consistent with dopaminergic stimulation and transdermal administration. Conclusion: The perception of the 'affective dimension' of PD-associated pain numerically improved in patients treated with rotigotine