Выбор способа хранения результатов научных экспериментов в базе данных на примере MS SQL Server

This article describes the use of MS SQL server as file storage to store files of scientific researches. The article describes the advantages and disadvantages of different methods of storing files to MS SQL Server

Computational identification of signalling pathways in Plasmodium falciparum

Malaria is one of the world’s most common and serious diseases causing death of about 3 million people each year. Its most severe occurrence is caused by the protozoan Plasmodium falciparum. Reports have shown that the resistance of the parasite to existing drugs is increasing. Therefore, there is a huge and urgent need to discover and validate new drug or vaccine targets to enable the development of new treatments for malaria. The ability to discover these drug or vaccine targets can only be enhanced from our deep understanding of the detailed biology of the parasite, for example how cells function and how proteins organize into modules such as metabolic, regulatory and signal transduction pathways. It has been noted that the knowledge of signalling transduction pathways in Plasmodium is fundamental to aid the design of new strategies against malaria. This work uses a linear-time algorithm for finding paths in a network under modified biologically motivated constraints. We predicted several important signalling transduction pathways in Plasmodium falciparum. We have predicted a viable signalling pathway characterized in terms of the genes responsible that may be the PfPKB pathway recently elucidated in Plasmodium falciparum. We obtained from the FIKK family, a signal transduction pathway that ends up on a chloroquine resistance marker protein, which indicates that interference with FIKK proteins might reverse Plasmodium falciparum from resistant to sensitive phenotype. We also proposed a hypothesis that showed the FIKK proteins in this pathway as enabling the resistance parasite to have a mechanism for releasing chloroquine (via an efflux process). Furthermore, we also predicted a signalling pathway that may have been responsible for signalling the start of the invasion process of Red Blood Cell (RBC) by the merozoites. It has been noted that the understanding of this pathway will give insight into the parasite virulence and will facilitate rational vaccine design against merozoites invasion. And we have a host of other predicted pathways, some of which have been used in this work to predict the functionality of some proteins

In Silico Gene Regulatory Network of the Maurer’s Cleft Pathway in Plasmodium falciparum

The Maurer’s clefts (MCs) are very important for the survival of Plasmodium falciparum within an infected cell as they are induced by the parasite itself in the erythrocyte for protein trafficking. The MCs form an interesting part of the parasite’s biology as they shed more light on how the parasite remodels the erythrocyte leading to host pathogenesis and death. Here, we predicted and analyzed the genetic regulatory network of genes identified to belong to the MCs using regularized graphical Gaussian model. Our network shows four major activators, their corresponding target genes, and predicted binding sites. One of these master activators is the serine repeat antigen 5 (SERA5), predominantly expressed among the SERA multigene family of P. falciparum, which is one of the blood-stage malaria vaccine candidates. Our results provide more details about functional interactions and the regulation of the genes in the MCs’ pathway of P. falciparum

Decision-Tree Based Model Analysis for Efficient Identification of Parameter Relations Leading to Different Signaling States

In systems biology, a mathematical description of signal transduction processes is used to gain a more detailed mechanistic understanding of cellular signaling networks. Such models typically depend on a number of parameters that have different influence on the model behavior. Local sensitivity analysis is able to identify parameters that have the largest effect on signaling strength. Bifurcation analysis shows on which parameters a qualitative model response depends. Most methods for model analysis are intrinsically univariate. They typically cannot consider combinations of parameters since the search space for such analysis would be too large. This limitation is important since activation of a signaling pathway often relies on multiple rather than on single factors. Here, we present a novel method for model analysis that overcomes this limitation. As input to a model defined by a system of ordinary differential equations, we consider parameters for initial chemical species concentrations. The model is used to simulate the system response, which is then classified into pre-defined classes (e.g., active or not active). This is combined with a scan of the parameter space. Parameter sets leading to a certain system response are subjected to a decision tree algorithm, which learns conditions that lead to this response. We compare our method to two alternative multivariate approaches to model analysis: analytical solution for steady states combined with a parameter scan, and direct Lyapunov exponent (DLE) analysis. We use three previously published models including a model for EGF receptor internalization and two apoptosis models to demonstrate the power of our approach. Our method reproduces critical parameter relations previously obtained by both steady-state and DLE analysis while being more generally applicable and substantially less computationally expensive. The method can be used as a general tool to predict multivariate control strategies for pathway activation and to suggest strategies for drug intervention

In Silico Gene Regulatory Network of the Maurer’s Cleft Pathway in Plasmodium falciparum

The Maurer’s clefts (MCs) are very important for the survival of Plasmodium falciparum within an infected cell as they are induced by the parasite itself in the erythrocyte for protein trafficking. The MCs form an interesting part of the parasite’s biology as they shed more light on how the parasite remodels the erythrocyte leading to host pathogenesis and death. Here, we predicted and analyzed the genetic regulatory network of genes identified to belong to the MCs using regularized graphical Gaussian model. Our network shows four major activators, their corresponding target genes, and predicted binding sites. One of these master activators is the serine repeat antigen 5 (SERA5), predominantly expressed among the SERA multigene family of P. falciparum, which is one of the blood-stage malaria vaccine candidates. Our results provide more details about functional interactions and the regulation of the genes in the MCs’ pathway of P. falciparum

Between-species differences in gene copy number are enriched among functions critical for adaptive evolution in Arabidopsis halleri

Complete dataset expanded from Additional file 3. (XLS 16282 kb

Modelling p-value distributions to improve theme-driven survival analysis of cancer transcriptome datasets

<p>Abstract</p> <p>Background</p> <p>Theme-driven cancer survival studies address whether the expression signature of genes related to a biological process can predict patient survival time. Although this should ideally be achieved by testing two separate null hypotheses, current methods treat both hypotheses as one. The first test should assess whether a geneset, independent of its composition, is associated with prognosis (frequently done with a survival test). The second test then verifies whether the theme of the geneset is relevant (usually done with an empirical test that compares the geneset of interest with random genesets). Current methods do not test this second null hypothesis because it has been assumed that the distribution of p-values for random genesets (when tested against the first null hypothesis) is uniform. Here we demonstrate that such an assumption is generally incorrect and consequently, such methods may erroneously associate the biology of a particular geneset with cancer prognosis.</p> <p>Results</p> <p>To assess the impact of non-uniform distributions for random genesets in such studies, an automated theme-driven method was developed. This method empirically approximates the p-value distribution of sets of unrelated genes based on a permutation approach, and tests whether predefined sets of biologically-related genes are associated with survival. The results from a comparison with a published theme-driven approach revealed non-uniform distributions, suggesting a significant problem exists with false positive rates in the original study. When applied to two public cancer datasets our technique revealed novel ontological categories with prognostic power, including significant correlations between "fatty acid metabolism" with overall survival in breast cancer, as well as "receptor mediated endocytosis", "brain development", "apical plasma membrane" and "MAPK signaling pathway" with overall survival in lung cancer.</p> <p>Conclusions</p> <p>Current methods of theme-driven survival studies assume uniformity of p-values for random genesets, which can lead to false conclusions. Our approach provides a method to correct for this pitfall, and provides a novel route to identifying higher-level biological themes and pathways with prognostic power in clinical microarray datasets.</p

Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes

BACKGROUND: The extensive use of DNA microarray technology in the characterization of the cell transcriptome is leading to an ever increasing amount of microarray data from cancer studies. Although similar questions for the same type of cancer are addressed in these different studies, a comparative analysis of their results is hampered by the use of heterogeneous microarray platforms and analysis methods. RESULTS: In contrast to a meta-analysis approach where results of different studies are combined on an interpretative level, we investigate here how to directly integrate raw microarray data from different studies for the purpose of supervised classification analysis. We use median rank scores and quantile discretization to derive numerically comparable measures of gene expression from different platforms. These transformed data are then used for training of classifiers based on support vector machines. We apply this approach to six publicly available cancer microarray gene expression data sets, which consist of three pairs of studies, each examining the same type of cancer, i.e. breast cancer, prostate cancer or acute myeloid leukemia. For each pair, one study was performed by means of cDNA microarrays and the other by means of oligonucleotide microarrays. In each pair, high classification accuracies (> 85%) were achieved with training and testing on data instances randomly chosen from both data sets in a cross-validation analysis. To exemplify the potential of this cross-platform classification analysis, we use two leukemia microarray data sets to show that important genes with regard to the biology of leukemia are selected in an integrated analysis, which are missed in either single-set analysis. CONCLUSION: Cross-platform classification of multiple cancer microarray data sets yields discriminative gene expression signatures that are found and validated on a large number of microarray samples, generated by different laboratories and microarray technologies. Predictive models generated by this approach are better validated than those generated on a single data set, while showing high predictive power and improved generalization performance

Epigenetic dynamics of monocyte-to-macrophage differentiation

Background Monocyte-to-macrophage differentiation involves major biochemical and structural changes. In order to elucidate the role of gene regulatory changes during this process, we used high-throughput sequencing to analyze the complete transcriptome and epigenome of human monocytes that were differentiated in vitro by addition of colony-stimulating factor 1 in serum-free medium. Results Numerous mRNAs and miRNAs were significantly up- or down-regulated. More than 100 discrete DNA regions, most often far away from transcription start sites, were rapidly demethylated by the ten eleven translocation enzymes, became nucleosome-free and gained histone marks indicative of active enhancers. These regions were unique for macrophages and associated with genes involved in the regulation of the actin cytoskeleton, phagocytosis and innate immune response. Conclusions In summary, we have discovered a phagocytic gene network that is repressed by DNA methylation in monocytes and rapidly de-repressed after the onset of macrophage differentiation

New Insights into the Genetic Regulation of Plasmodium Falciparum Obtained by Bayesian Modeling

The most fatal and prevalent form of malaria is caused by the bloodborne pathogen Plasmodium falciparum (henceforth P.f). Annually, approximately three million people died of malaria. Despite P.f devastivating effect globally, the vast majority of its proteins have not been characterized experimentally. In this work, we provide computational insight that explore the modalities of the regulation for some important group of genes of P.f, namely components of the glycolytic pathway, and those involved in apicoplast metabolism. Glycolysis is a crucial pathway in the maintenance of the parasite while the recently discovered apicoplast contains a range of metabolic pathways and housekeeping processes that differ radically to those of the host, which makes it ideal for drug therapy