Topological defects in spinor condensates

We investigate the structure of topological defects in the ground states of spinor Bose-Einstein condensates with spin F=1 or F=2. The type and number of defects are determined by calculating the first and second homotopy groups of the order-parameter space. The order-parameter space is identified with a set of degenerate ground state spinors. Because the structure of the ground state depends on whether or not there is an external magnetic field applied to the system, defects are sensitive to the magnetic field. We study both cases and find that the defects in zero and non-zero field are different.Comment: 10 pages, 1 figure. Published versio

Leading-effect vs. Risk-taking in Dynamic Tournaments: Evidence from a Real-life Randomized Experiment

Two 'order effects' may emerge in dynamic tournaments with information feedback. First, participants adjust effort across stages, which could advantage the leading participant who faces a larger 'effective prize' after an initial victory (leading-effect). Second, participants lagging behind may increase risk at the final stage as they have 'nothing to lose' (risk-taking). We use a randomized natural experiment in professional two-game soccer tournaments where the treatment (order of a stage-specific advantage) and team characteristics, e.g. ability, are independent. We develop an identification strategy to test for leading-effects controlling for risk-taking. We find no evidence of leading-effects and negligible risk-taking effects

A Bayesian Search for Transcriptional Motifs

Identifying transcription factor (TF) binding sites (TFBSs) is an important step towards understanding transcriptional regulation. A common approach is to use gaplessly aligned, experimentally supported TFBSs for a particular TF, and algorithmically search for more occurrences of the same TFBSs. The largest publicly available databases of TF binding specificities contain models which are represented as position weight matrices (PWM). There are other methods using more sophisticated representations, but these have more limited databases, or aren't publicly available. Therefore, this paper focuses on methods that search using one PWM per TF. An algorithm, MATCHTM, for identifying TFBSs corresponding to a particular PWM is available, but is not based on a rigorous statistical model of TF binding, making it difficult to interpret or adjust the parameters and output of the algorithm. Furthermore, there is no public description of the algorithm sufficient to exactly reproduce it. Another algorithm, MAST, computes a p-value for the presence of a TFBS using true probabilities of finding each base at each offset from that position. We developed a statistical model, BaSeTraM, for the binding of TFs to TFBSs, taking into account random variation in the base present at each position within a TFBS. Treating the counts in the matrices and the sequences of sites as random variables, we combine this TFBS composition model with a background model to obtain a Bayesian classifier. We implemented our classifier in a package (SBaSeTraM). We tested SBaSeTraM against a MATCHTM implementation by searching all probes used in an experimental Saccharomyces cerevisiae TF binding dataset, and comparing our predictions to the data. We found no statistically significant differences in sensitivity between the algorithms (at fixed selectivity), indicating that SBaSeTraM's performance is at least comparable to the leading currently available algorithm. Our software is freely available at: http://wiki.github.com/A1kmm/sbasetram/building-the-tools

Integration of gene expression data with prior knowledge for network analysis and validation

<p>Abstract</p> <p>Background</p> <p>Reconstruction of protein-protein interaction or metabolic networks based on expression data often involves in silico predictions, while on the other hand, there are unspecific networks of in vivo interactions derived from knowledge bases.</p> <p>We analyze networks designed to come as close as possible to data measured in vivo, both with respect to the set of nodes which were taken to be expressed in experiment as well as with respect to the interactions between them which were taken from manually curated databases</p> <p>Results</p> <p>A signaling network derived from the TRANSPATH database and a metabolic network derived from KEGG LIGAND are each filtered onto expression data from breast cancer (SAGE) considering different levels of restrictiveness in edge and vertex selection.</p> <p>We perform several validation steps, in particular we define pathway over-representation tests based on refined null models to recover functional modules. The prominent role of the spindle checkpoint-related pathways in breast cancer is exhibited. High-ranking key nodes cluster in functional groups retrieved from literature. Results are consistent between several functional and topological analyses and between signaling and metabolic aspects.</p> <p>Conclusions</p> <p>This construction involved as a crucial step the passage to a mammalian protein identifier format as well as to a reaction-based semantics of metabolism. This yielded good connectivity but also led to the need to perform benchmark tests to exclude loss of essential information. Such validation, albeit tedious due to limitations of existing methods, turned out to be informative, and in particular provided biological insights as well as information on the degrees of coherence of the networks despite fragmentation of experimental data.</p> <p>Key node analysis exploited the networks for potentially interesting proteins in view of drug target prediction.</p

Left ventricular remodeling in swine after myocardial infarction: a transcriptional genomics approach

Despite the apparent appropriateness of left ventricular (LV) remodeling following myocardial infarction (MI), it poses an independent risk factor for development of heart failure. There is a paucity of studies into the molecular mechanisms of LV remodeling in large animal species. We took an unbiased molecular approach to identify candidate transcription factors (TFs) mediating the genetic reprogramming involved in post-MI LV remodeling in swine. Left ventricular tissue was collected from remote, non-infarcted myocardium, 3 weeks after MI-induction or sham-surgery. Microarray analysis identified 285 upregulated and 278 downregulated genes (FDR < 0.05). Of these differentially expressed genes, the promoter regions of the human homologs were searched for common TF binding sites (TFBS). Eighteen TFBS were overrepresented >two-fold (p < 0.01) in upregulated and 13 in downregulated genes. Left ventricular nuclear protein extracts were assayed for DNA-binding activity by protein/DNA array. Out of 345 DNA probes, 30 showed signal intensity changes >two-fold. Five TFs were identified in both TFBS and protein/DNA array analyses, which showed matching changes for COUP-TFII and glucocorticoid receptor (GR) only. Treatment of swine with the GR antagonist mifepristone after MI reduced the post-MI increase in LV mass, but LV dilation remained unaffected. Thus, using an unbiased approach to study post-MI LV remodeling in a physiologically relevant large animal model, we identified COUP-TFII and GR as potential key mediators of post-MI remodeling

Unifying generative and discriminative learning principles

<p>Abstract</p> <p>Background</p> <p>The recognition of functional binding sites in genomic DNA remains one of the fundamental challenges of genome research. During the last decades, a plethora of different and well-adapted models has been developed, but only little attention has been payed to the development of different and similarly well-adapted learning principles. Only recently it was noticed that discriminative learning principles can be superior over generative ones in diverse bioinformatics applications, too.</p> <p>Results</p> <p>Here, we propose a generalization of generative and discriminative learning principles containing the maximum likelihood, maximum a posteriori, maximum conditional likelihood, maximum supervised posterior, generative-discriminative trade-off, and penalized generative-discriminative trade-off learning principles as special cases, and we illustrate its efficacy for the recognition of vertebrate transcription factor binding sites.</p> <p>Conclusions</p> <p>We find that the proposed learning principle helps to improve the recognition of transcription factor binding sites, enabling better computational approaches for extracting as much information as possible from valuable wet-lab data. We make all implementations available in the open-source library Jstacs so that this learning principle can be easily applied to other classification problems in the field of genome and epigenome analysis.</p

dPORE-miRNA: Polymorphic Regulation of MicroRNA Genes

Background: MicroRNAs (miRNAs) are short non-coding RNA molecules that act as post-transcriptional regulators and affect the regulation of protein-coding genes. Mostly transcribed by PolII, miRNA genes are regulated at the transcriptional level similarly to protein-coding genes. In this study we focus on human miRNAs. These miRNAs are involved in a variety of pathways and can affect many diseases. Our interest is on possible deregulation of the transcription initiation of the miRNA encoding genes, which is facilitated by variations in the genomic sequence of transcriptional control regions (promoters). Methodology: Our aim is to provide an online resource to facilitate the investigation of the potential effects of single nucleotide polymorphisms (SNPs) on miRNA gene regulation. We analyzed SNPs overlapped with predicted transcription factor binding sites (TFBSs) in promoters of miRNA genes. We also accounted for the creation of novel TFBSs due to polymorphisms not present in the reference genome. The resulting changes in the original TFBSs and potential creation of new TFBSs were incorporated into the Dragon Database of Polymorphic Regulation of miRNA genes (dPORE-miRNA). Conclusions: The dPORE-miRNA database enables researchers to explore potential effects of SNPs on the regulation of miRNAs. dPORE-miRNA can be interrogated with regards to: a/miRNAs (their targets, or involvement in diseases, or biological pathways), b/SNPs, or c/transcription factors. dPORE-miRNA can be accessed a

Genome-wide assessment of differential roles for p300 and CBP in transcription regulation

Despite high levels of homology, transcription coactivators p300 and CREB binding protein (CBP) are both indispensable during embryogenesis. They are largely known to regulate the same genes. To identify genes preferentially regulated by p300 or CBP, we performed an extensive genome-wide survey using the ChIP-seq on cell-cycle synchronized cells. We found that 57% of the tags were within genes or proximal promoters, with an overall preference for binding to transcription start and end sites. The heterogeneous binding patterns possibly reflect the divergent roles of CBP and p300 in transcriptional regulation. Most of the 16 103 genes were bound by both CBP and p300. However, after stimulation 89 and 1944 genes were preferentially bound by CBP or p300, respectively. Target genes were found to be primarily involved in the regulation of metabolic and developmental processes, and transcription, with CBP showing a stronger preference than p300 for genes active in negative regulation of transcription. Analysis of transcription factor binding sites suggest that CBP and p300 have many partners in common, but AP-1 and Serum Response Factor (SRF) appear to be more prominent in CBP-specific sequences, whereas AP-2 and SP1 are enriched in p300-specific targets. Taken together, our findings further elucidate the distinct roles of coactivators p300 and CBP in transcriptional regulation

Statistical significance of cis-regulatory modules

BACKGROUND: It is becoming increasingly important for researchers to be able to scan through large genomic regions for transcription factor binding sites or clusters of binding sites forming cis-regulatory modules. Correspondingly, there has been a push to develop algorithms for the rapid detection and assessment of cis-regulatory modules. While various algorithms for this purpose have been introduced, most are not well suited for rapid, genome scale scanning. RESULTS: We introduce methods designed for the detection and statistical evaluation of cis-regulatory modules, modeled as either clusters of individual binding sites or as combinations of sites with constrained organization. In order to determine the statistical significance of module sites, we first need a method to determine the statistical significance of single transcription factor binding site matches. We introduce a straightforward method of estimating the statistical significance of single site matches using a database of known promoters to produce data structures that can be used to estimate p-values for binding site matches. We next introduce a technique to calculate the statistical significance of the arrangement of binding sites within a module using a max-gap model. If the module scanned for has defined organizational parameters, the probability of the module is corrected to account for organizational constraints. The statistical significance of single site matches and the architecture of sites within the module can be combined to provide an overall estimation of statistical significance of cis-regulatory module sites. CONCLUSION: The methods introduced in this paper allow for the detection and statistical evaluation of single transcription factor binding sites and cis-regulatory modules. The features described are implemented in the Search Tool for Occurrences of Regulatory Motifs (STORM) and MODSTORM software