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Sample records for gene set analysis

  1. Gene set analysis for GWAS

    DEFF Research Database (Denmark)

    Debrabant, Birgit; Soerensen, Mette

    2014-01-01

    Abstract We discuss the use of modified Kolmogorov-Smirnov (KS) statistics in the context of gene set analysis and review corresponding null and alternative hypotheses. Especially, we show that, when enhancing the impact of highly significant genes in the calculation of the test statistic, the co...

  2. Gene set analysis using variance component tests.

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    Huang, Yen-Tsung; Lin, Xihong

    2013-06-28

    Gene set analyses have become increasingly important in genomic research, as many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional repertoire, e.g., a biological pathway/network and are highly correlated. However, most of the existing gene set analysis methods do not fully account for the correlation among the genes. Here we propose to tackle this important feature of a gene set to improve statistical power in gene set analyses. We propose to model the effects of an independent variable, e.g., exposure/biological status (yes/no), on multiple gene expression values in a gene set using a multivariate linear regression model, where the correlation among the genes is explicitly modeled using a working covariance matrix. We develop TEGS (Test for the Effect of a Gene Set), a variance component test for the gene set effects by assuming a common distribution for regression coefficients in multivariate linear regression models, and calculate the p-values using permutation and a scaled chi-square approximation. We show using simulations that type I error is protected under different choices of working covariance matrices and power is improved as the working covariance approaches the true covariance. The global test is a special case of TEGS when correlation among genes in a gene set is ignored. Using both simulation data and a published diabetes dataset, we show that our test outperforms the commonly used approaches, the global test and gene set enrichment analysis (GSEA). We develop a gene set analyses method (TEGS) under the multivariate regression framework, which directly models the interdependence of the expression values in a gene set using a working covariance. TEGS outperforms two widely used methods, GSEA and global test in both simulation and a diabetes microarray data.

  3. Gene set analysis of the EADGENE chicken data-set

    DEFF Research Database (Denmark)

    Skarman, Axel; Jiang, Li; Hornshøj, Henrik

    2009-01-01

     Abstract Background: Gene set analysis is considered to be a way of improving our biological interpretation of the observed expression patterns. This paper describes different methods applied to analyse expression data from a chicken DNA microarray dataset. Results: Applying different gene set...... analyses to the chicken expression data led to different ranking of the Gene Ontology terms tested. A method for prediction of possible annotations was applied. Conclusion: Biological interpretation based on gene set analyses dependent on the statistical method used. Methods for predicting the possible...

  4. Gene set analysis for interpreting genetic studies

    DEFF Research Database (Denmark)

    Pers, Tune H

    2016-01-01

    Interpretation of genome-wide association study (GWAS) results is lacking behind the discovery of new genetic associations. Consequently, there is an urgent need for data-driven methods for interpreting genetic association studies. Gene set analysis (GSA) can identify aetiologic pathways...

  5. MAGMA: generalized gene-set analysis of GWAS data.

    NARCIS (Netherlands)

    de Leeuw, C.A.; Mooij, J.M.; Heskes, T.; Posthuma, D.

    2015-01-01

    By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Statistical

  6. MAGMA: Generalized Gene-Set Analysis of GWAS Data

    NARCIS (Netherlands)

    de Leeuw, C.A.; Mooij, J.M.; Heskes, T.; Posthuma, D.

    2015-01-01

    By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Statistical

  7. MAGMA: generalized gene-set analysis of GWAS data.

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    de Leeuw, Christiaan A; Mooij, Joris M; Heskes, Tom; Posthuma, Danielle

    2015-04-01

    By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Statistical power for most methods is strongly affected by linkage disequilibrium between markers, multi-marker associations are often hard to detect, and the reliance on permutation to compute p-values tends to make the analysis computationally very expensive. To address these issues we have developed MAGMA, a novel tool for gene and gene-set analysis. The gene analysis is based on a multiple regression model, to provide better statistical performance. The gene-set analysis is built as a separate layer around the gene analysis for additional flexibility. This gene-set analysis also uses a regression structure to allow generalization to analysis of continuous properties of genes and simultaneous analysis of multiple gene sets and other gene properties. Simulations and an analysis of Crohn's Disease data are used to evaluate the performance of MAGMA and to compare it to a number of other gene and gene-set analysis tools. The results show that MAGMA has significantly more power than other tools for both the gene and the gene-set analysis, identifying more genes and gene sets associated with Crohn's Disease while maintaining a correct type 1 error rate. Moreover, the MAGMA analysis of the Crohn's Disease data was found to be considerably faster as well.

  8. Gene set analysis of purine and pyrimidine antimetabolites cancer therapies.

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    Fridley, Brooke L; Batzler, Anthony; Li, Liang; Li, Fang; Matimba, Alice; Jenkins, Gregory D; Ji, Yuan; Wang, Liewei; Weinshilboum, Richard M

    2011-11-01

    Responses to therapies, either with regard to toxicities or efficacy, are expected to involve complex relationships of gene products within the same molecular pathway or functional gene set. Therefore, pathways or gene sets, as opposed to single genes, may better reflect the true underlying biology and may be more appropriate units for analysis of pharmacogenomic studies. Application of such methods to pharmacogenomic studies may enable the detection of more subtle effects of multiple genes in the same pathway that may be missed by assessing each gene individually. A gene set analysis of 3821 gene sets is presented assessing the association between basal messenger RNA expression and drug cytotoxicity using ethnically defined human lymphoblastoid cell lines for two classes of drugs: pyrimidines [gemcitabine (dFdC) and arabinoside] and purines [6-thioguanine and 6-mercaptopurine]. The gene set nucleoside-diphosphatase activity was found to be significantly associated with both dFdC and arabinoside, whereas gene set γ-aminobutyric acid catabolic process was associated with dFdC and 6-thioguanine. These gene sets were significantly associated with the phenotype even after adjusting for multiple testing. In addition, five associated gene sets were found in common between the pyrimidines and two gene sets for the purines (3',5'-cyclic-AMP phosphodiesterase activity and γ-aminobutyric acid catabolic process) with a P value of less than 0.0001. Functional validation was attempted with four genes each in gene sets for thiopurine and pyrimidine antimetabolites. All four genes selected from the pyrimidine gene sets (PSME3, CANT1, ENTPD6, ADRM1) were validated, but only one (PDE4D) was validated for the thiopurine gene sets. In summary, results from the gene set analysis of pyrimidine and purine therapies, used often in the treatment of various cancers, provide novel insight into the relationship between genomic variation and drug response.

  9. IGSA: Individual Gene Sets Analysis, including Enrichment and Clustering.

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    Wu, Lingxiang; Chen, Xiujie; Zhang, Denan; Zhang, Wubing; Liu, Lei; Ma, Hongzhe; Yang, Jingbo; Xie, Hongbo; Liu, Bo; Jin, Qing

    2016-01-01

    Analysis of gene sets has been widely applied in various high-throughput biological studies. One weakness in the traditional methods is that they neglect the heterogeneity of genes expressions in samples which may lead to the omission of some specific and important gene sets. It is also difficult for them to reflect the severities of disease and provide expression profiles of gene sets for individuals. We developed an application software called IGSA that leverages a powerful analytical capacity in gene sets enrichment and samples clustering. IGSA calculates gene sets expression scores for each sample and takes an accumulating clustering strategy to let the samples gather into the set according to the progress of disease from mild to severe. We focus on gastric, pancreatic and ovarian cancer data sets for the performance of IGSA. We also compared the results of IGSA in KEGG pathways enrichment with David, GSEA, SPIA, ssGSEA and analyzed the results of IGSA clustering and different similarity measurement methods. Notably, IGSA is proved to be more sensitive and specific in finding significant pathways, and can indicate related changes in pathways with the severity of disease. In addition, IGSA provides with significant gene sets profile for each sample.

  10. Model-based gene set analysis for Bioconductor.

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    Bauer, Sebastian; Robinson, Peter N; Gagneur, Julien

    2011-07-01

    Gene Ontology and other forms of gene-category analysis play a major role in the evaluation of high-throughput experiments in molecular biology. Single-category enrichment analysis procedures such as Fisher's exact test tend to flag large numbers of redundant categories as significant, which can complicate interpretation. We have recently developed an approach called model-based gene set analysis (MGSA), that substantially reduces the number of redundant categories returned by the gene-category analysis. In this work, we present the Bioconductor package mgsa, which makes the MGSA algorithm available to users of the R language. Our package provides a simple and flexible application programming interface for applying the approach. The mgsa package has been made available as part of Bioconductor 2.8. It is released under the conditions of the Artistic license 2.0. peter.robinson@charite.de; julien.gagneur@embl.de.

  11. Ranking metrics in gene set enrichment analysis: do they matter?

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    Zyla, Joanna; Marczyk, Michal; Weiner, January; Polanska, Joanna

    2017-05-12

    There exist many methods for describing the complex relation between changes of gene expression in molecular pathways or gene ontologies under different experimental conditions. Among them, Gene Set Enrichment Analysis seems to be one of the most commonly used (over 10,000 citations). An important parameter, which could affect the final result, is the choice of a metric for the ranking of genes. Applying a default ranking metric may lead to poor results. In this work 28 benchmark data sets were used to evaluate the sensitivity and false positive rate of gene set analysis for 16 different ranking metrics including new proposals. Furthermore, the robustness of the chosen methods to sample size was tested. Using k-means clustering algorithm a group of four metrics with the highest performance in terms of overall sensitivity, overall false positive rate and computational load was established i.e. absolute value of Moderated Welch Test statistic, Minimum Significant Difference, absolute value of Signal-To-Noise ratio and Baumgartner-Weiss-Schindler test statistic. In case of false positive rate estimation, all selected ranking metrics were robust with respect to sample size. In case of sensitivity, the absolute value of Moderated Welch Test statistic and absolute value of Signal-To-Noise ratio gave stable results, while Baumgartner-Weiss-Schindler and Minimum Significant Difference showed better results for larger sample size. Finally, the Gene Set Enrichment Analysis method with all tested ranking metrics was parallelised and implemented in MATLAB, and is available at https://github.com/ZAEDPolSl/MrGSEA . Choosing a ranking metric in Gene Set Enrichment Analysis has critical impact on results of pathway enrichment analysis. The absolute value of Moderated Welch Test has the best overall sensitivity and Minimum Significant Difference has the best overall specificity of gene set analysis. When the number of non-normally distributed genes is high, using Baumgartner

  12. Evaluating the consistency of gene sets used in the analysis of bacterial gene expression data

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    Tintle Nathan L

    2012-08-01

    Full Text Available Abstract Background Statistical analyses of whole genome expression data require functional information about genes in order to yield meaningful biological conclusions. The Gene Ontology (GO and Kyoto Encyclopedia of Genes and Genomes (KEGG are common sources of functionally grouped gene sets. For bacteria, the SEED and MicrobesOnline provide alternative, complementary sources of gene sets. To date, no comprehensive evaluation of the data obtained from these resources has been performed. Results We define a series of gene set consistency metrics directly related to the most common classes of statistical analyses for gene expression data, and then perform a comprehensive analysis of 3581 Affymetrix® gene expression arrays across 17 diverse bacteria. We find that gene sets obtained from GO and KEGG demonstrate lower consistency than those obtained from the SEED and MicrobesOnline, regardless of gene set size. Conclusions Despite the widespread use of GO and KEGG gene sets in bacterial gene expression data analysis, the SEED and MicrobesOnline provide more consistent sets for a wide variety of statistical analyses. Increased use of the SEED and MicrobesOnline gene sets in the analysis of bacterial gene expression data may improve statistical power and utility of expression data.

  13. Effect of the absolute statistic on gene-sampling gene-set analysis methods.

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    Nam, Dougu

    2017-06-01

    Gene-set enrichment analysis and its modified versions have commonly been used for identifying altered functions or pathways in disease from microarray data. In particular, the simple gene-sampling gene-set analysis methods have been heavily used for datasets with only a few sample replicates. The biggest problem with this approach is the highly inflated false-positive rate. In this paper, the effect of absolute gene statistic on gene-sampling gene-set analysis methods is systematically investigated. Thus far, the absolute gene statistic has merely been regarded as a supplementary method for capturing the bidirectional changes in each gene set. Here, it is shown that incorporating the absolute gene statistic in gene-sampling gene-set analysis substantially reduces the false-positive rate and improves the overall discriminatory ability. Its effect was investigated by power, false-positive rate, and receiver operating curve for a number of simulated and real datasets. The performances of gene-set analysis methods in one-tailed (genome-wide association study) and two-tailed (gene expression data) tests were also compared and discussed.

  14. Time-Course Gene Set Analysis for Longitudinal Gene Expression Data.

    Directory of Open Access Journals (Sweden)

    Boris P Hejblum

    2015-06-01

    Full Text Available Gene set analysis methods, which consider predefined groups of genes in the analysis of genomic data, have been successfully applied for analyzing gene expression data in cross-sectional studies. The time-course gene set analysis (TcGSA introduced here is an extension of gene set analysis to longitudinal data. The proposed method relies on random effects modeling with maximum likelihood estimates. It allows to use all available repeated measurements while dealing with unbalanced data due to missing at random (MAR measurements. TcGSA is a hypothesis driven method that identifies a priori defined gene sets with significant expression variations over time, taking into account the potential heterogeneity of expression within gene sets. When biological conditions are compared, the method indicates if the time patterns of gene sets significantly differ according to these conditions. The interest of the method is illustrated by its application to two real life datasets: an HIV therapeutic vaccine trial (DALIA-1 trial, and data from a recent study on influenza and pneumococcal vaccines. In the DALIA-1 trial TcGSA revealed a significant change in gene expression over time within 69 gene sets during vaccination, while a standard univariate individual gene analysis corrected for multiple testing as well as a standard a Gene Set Enrichment Analysis (GSEA for time series both failed to detect any significant pattern change over time. When applied to the second illustrative data set, TcGSA allowed the identification of 4 gene sets finally found to be linked with the influenza vaccine too although they were found to be associated to the pneumococcal vaccine only in previous analyses. In our simulation study TcGSA exhibits good statistical properties, and an increased power compared to other approaches for analyzing time-course expression patterns of gene sets. The method is made available for the community through an R package.

  15. Principal Angle Enrichment Analysis (PAEA): Dimensionally Reduced Multivariate Gene Set Enrichment Analysis Tool.

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    Clark, Neil R; Szymkiewicz, Maciej; Wang, Zichen; Monteiro, Caroline D; Jones, Matthew R; Ma'ayan, Avi

    2015-11-01

    Gene set analysis of differential expression, which identifies collectively differentially expressed gene sets, has become an important tool for biology. The power of this approach lies in its reduction of the dimensionality of the statistical problem and its incorporation of biological interpretation by construction. Many approaches to gene set analysis have been proposed, but benchmarking their performance in the setting of real biological data is difficult due to the lack of a gold standard. In a previously published work we proposed a geometrical approach to differential expression which performed highly in benchmarking tests and compared well to the most popular methods of differential gene expression. As reported, this approach has a natural extension to gene set analysis which we call Principal Angle Enrichment Analysis (PAEA). PAEA employs dimensionality reduction and a multivariate approach for gene set enrichment analysis. However, the performance of this method has not been assessed nor its implementation as a web-based tool. Here we describe new benchmarking protocols for gene set analysis methods and find that PAEA performs highly. The PAEA method is implemented as a user-friendly web-based tool, which contains 70 gene set libraries and is freely available to the community.

  16. GSMA: Gene Set Matrix Analysis, An Automated Method for Rapid Hypothesis Testing of Gene Expression Data

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    Chris Cheadle

    2007-01-01

    Full Text Available Background: Microarray technology has become highly valuable for identifying complex global changes in gene expression patterns. The assignment of functional information to these complex patterns remains a challenging task in effectively interpreting data and correlating results from across experiments, projects and laboratories. Methods which allow the rapid and robust evaluation of multiple functional hypotheses increase the power of individual researchers to data mine gene expression data more efficiently.Results: We have developed (gene set matrix analysis GSMA as a useful method for the rapid testing of group-wise up- or downregulation of gene expression simultaneously for multiple lists of genes (gene sets against entire distributions of gene expression changes (datasets for single or multiple experiments. The utility of GSMA lies in its flexibility to rapidly poll gene sets related by known biological function or as designated solely by the end-user against large numbers of datasets simultaneously.Conclusions: GSMA provides a simple and straightforward method for hypothesis testing in which genes are tested by groups across multiple datasets for patterns of expression enrichment.

  17. Integrative analysis of survival-associated gene sets in breast cancer.

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    Varn, Frederick S; Ung, Matthew H; Lou, Shao Ke; Cheng, Chao

    2015-03-12

    Patient gene expression information has recently become a clinical feature used to evaluate breast cancer prognosis. The emergence of prognostic gene sets that take advantage of these data has led to a rich library of information that can be used to characterize the molecular nature of a patient's cancer. Identifying robust gene sets that are consistently predictive of a patient's clinical outcome has become one of the main challenges in the field. We inputted our previously established BASE algorithm with patient gene expression data and gene sets from MSigDB to develop the gene set activity score (GSAS), a metric that quantitatively assesses a gene set's activity level in a given patient. We utilized this metric, along with patient time-to-event data, to perform survival analyses to identify the gene sets that were significantly correlated with patient survival. We then performed cross-dataset analyses to identify robust prognostic gene sets and to classify patients by metastasis status. Additionally, we created a gene set network based on component gene overlap to explore the relationship between gene sets derived from MSigDB. We developed a novel gene set based on this network's topology and applied the GSAS metric to characterize its role in patient survival. Using the GSAS metric, we identified 120 gene sets that were significantly associated with patient survival in all datasets tested. The gene overlap network analysis yielded a novel gene set enriched in genes shared by the robustly predictive gene sets. This gene set was highly correlated to patient survival when used alone. Most interestingly, removal of the genes in this gene set from the gene pool on MSigDB resulted in a large reduction in the number of predictive gene sets, suggesting a prominent role for these genes in breast cancer progression. The GSAS metric provided a useful medium by which we systematically investigated how gene sets from MSigDB relate to breast cancer patient survival. We used

  18. Tracking difference in gene expression in a time-course experiment using gene set enrichment analysis.

    Directory of Open Access Journals (Sweden)

    Pui Shan Wong

    Full Text Available Fistulifera sp. strain JPCC DA0580 is a newly sequenced pennate diatom that is capable of simultaneously growing and accumulating lipids. This is a unique trait, not found in other related microalgae so far. It is able to accumulate between 40 to 60% of its cell weight in lipids, making it a strong candidate for the production of biofuel. To investigate this characteristic, we used RNA-Seq data gathered at four different times while Fistulifera sp. strain JPCC DA0580 was grown in oil accumulating and non-oil accumulating conditions. We then adapted gene set enrichment analysis (GSEA to investigate the relationship between the difference in gene expression of 7,822 genes and metabolic functions in our data. We utilized information in the KEGG pathway database to create the gene sets and changed GSEA to use re-sampling so that data from the different time points could be included in the analysis. Our GSEA method identified photosynthesis, lipid synthesis and amino acid synthesis related pathways as processes that play a significant role in oil production and growth in Fistulifera sp. strain JPCC DA0580. In addition to GSEA, we visualized the results by creating a network of compounds and reactions, and plotted the expression data on top of the network. This made existing graph algorithms available to us which we then used to calculate a path that metabolizes glucose into triacylglycerol (TAG in the smallest number of steps. By visualizing the data this way, we observed a separate up-regulation of genes at different times instead of a concerted response. We also identified two metabolic paths that used less reactions than the one shown in KEGG and showed that the reactions were up-regulated during the experiment. The combination of analysis and visualization methods successfully analyzed time-course data, identified important metabolic pathways and provided new hypotheses for further research.

  19. Uniform approximation is more appropriate for Wilcoxon Rank-Sum Test in gene set analysis.

    Directory of Open Access Journals (Sweden)

    Zhide Fang

    Full Text Available Gene set analysis is widely used to facilitate biological interpretations in the analyses of differential expression from high throughput profiling data. Wilcoxon Rank-Sum (WRS test is one of the commonly used methods in gene set enrichment analysis. It compares the ranks of genes in a gene set against those of genes outside the gene set. This method is easy to implement and it eliminates the dichotomization of genes into significant and non-significant in a competitive hypothesis testing. Due to the large number of genes being examined, it is impractical to calculate the exact null distribution for the WRS test. Therefore, the normal distribution is commonly used as an approximation. However, as we demonstrate in this paper, the normal approximation is problematic when a gene set with relative small number of genes is tested against the large number of genes in the complementary set. In this situation, a uniform approximation is substantially more powerful, more accurate, and less intensive in computation. We demonstrate the advantage of the uniform approximations in Gene Ontology (GO term analysis using simulations and real data sets.

  20. LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights.

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    Dong, Xinran; Hao, Yun; Wang, Xiao; Tian, Weidong

    2016-01-11

    Pathway or gene set over-representation analysis (ORA) has become a routine task in functional genomics studies. However, currently widely used ORA tools employ statistical methods such as Fisher's exact test that reduce a pathway into a list of genes, ignoring the constitutive functional non-equivalent roles of genes and the complex gene-gene interactions. Here, we develop a novel method named LEGO (functional Link Enrichment of Gene Ontology or gene sets) that takes into consideration these two types of information by incorporating network-based gene weights in ORA analysis. In three benchmarks, LEGO achieves better performance than Fisher and three other network-based methods. To further evaluate LEGO's usefulness, we compare LEGO with five gene expression-based and three pathway topology-based methods using a benchmark of 34 disease gene expression datasets compiled by a recent publication, and show that LEGO is among the top-ranked methods in terms of both sensitivity and prioritization for detecting target KEGG pathways. In addition, we develop a cluster-and-filter approach to reduce the redundancy among the enriched gene sets, making the results more interpretable to biologists. Finally, we apply LEGO to two lists of autism genes, and identify relevant gene sets to autism that could not be found by Fisher.

  1. ADAGE signature analysis: differential expression analysis with data-defined gene sets.

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    Tan, Jie; Huyck, Matthew; Hu, Dongbo; Zelaya, René A; Hogan, Deborah A; Greene, Casey S

    2017-11-22

    Gene set enrichment analysis and overrepresentation analyses are commonly used methods to determine the biological processes affected by a differential expression experiment. This approach requires biologically relevant gene sets, which are currently curated manually, limiting their availability and accuracy in many organisms without extensively curated resources. New feature learning approaches can now be paired with existing data collections to directly extract functional gene sets from big data. Here we introduce a method to identify perturbed processes. In contrast with methods that use curated gene sets, this approach uses signatures extracted from public expression data. We first extract expression signatures from public data using ADAGE, a neural network-based feature extraction approach. We next identify signatures that are differentially active under a given treatment. Our results demonstrate that these signatures represent biological processes that are perturbed by the experiment. Because these signatures are directly learned from data without supervision, they can identify uncurated or novel biological processes. We implemented ADAGE signature analysis for the bacterial pathogen Pseudomonas aeruginosa. For the convenience of different user groups, we implemented both an R package (ADAGEpath) and a web server ( http://adage.greenelab.com ) to run these analyses. Both are open-source to allow easy expansion to other organisms or signature generation methods. We applied ADAGE signature analysis to an example dataset in which wild-type and ∆anr mutant cells were grown as biofilms on the Cystic Fibrosis genotype bronchial epithelial cells. We mapped active signatures in the dataset to KEGG pathways and compared with pathways identified using GSEA. The two approaches generally return consistent results; however, ADAGE signature analysis also identified a signature that revealed the molecularly supported link between the MexT regulon and Anr. We designed

  2. The null hypothesis of GSEA, and a novel statistical model for competitive gene set analysis

    DEFF Research Database (Denmark)

    Debrabant, Birgit

    2017-01-01

    MOTIVATION: Competitive gene set analysis intends to assess whether a specific set of genes is more associated with a trait than the remaining genes. However, the statistical models assumed to date to underly these methods do not enable a clear cut formulation of the competitive null hypothesis....... This is a major handicap to the interpretation of results obtained from a gene set analysis. RESULTS: This work presents a hierarchical statistical model based on the notion of dependence measures, which overcomes this problem. The two levels of the model naturally reflect the modular structure of many gene set...... analysis methods. We apply the model to show that the popular GSEA method, which recently has been claimed to test the self-contained null hypothesis, actually tests the competitive null if the weight parameter is zero. However, for this result to hold strictly, the choice of the dependence measures...

  3. Gene set analysis: limitations in popular existing methods and proposed improvements.

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    Mishra, Pashupati; Törönen, Petri; Leino, Yrjö; Holm, Liisa

    2014-10-01

    Gene set analysis is the analysis of a set of genes that collectively contribute to a biological process. Most popular gene set analysis methods are based on empirical P-value that requires large number of permutations. Despite numerous gene set analysis methods developed in the past decade, the most popular methods still suffer from serious limitations. We present a gene set analysis method (mGSZ) based on Gene Set Z-scoring function (GSZ) and asymptotic P-values. Asymptotic P-value calculation requires fewer permutations, and thus speeds up the gene set analysis process. We compare the GSZ-scoring function with seven popular gene set scoring functions and show that GSZ stands out as the best scoring function. In addition, we show improved performance of the GSA method when the max-mean statistics is replaced by the GSZ scoring function. We demonstrate the importance of both gene and sample permutations by showing the consequences in the absence of one or the other. A comparison of asymptotic and empirical methods of P-value estimation demonstrates a clear advantage of asymptotic P-value over empirical P-value. We show that mGSZ outperforms the state-of-the-art methods based on two different evaluations. We compared mGSZ results with permutation and rotation tests and show that rotation does not improve our asymptotic P-values. We also propose well-known asymptotic distribution models for three of the compared methods. mGSZ is available as R package from cran.r-project.org. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  4. Application of biclustering of gene expression data and gene set enrichment analysis methods to identify potentially disease causing nanomaterials

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    Andrew Williams

    2015-12-01

    Full Text Available Background: The presence of diverse types of nanomaterials (NMs in commerce is growing at an exponential pace. As a result, human exposure to these materials in the environment is inevitable, necessitating the need for rapid and reliable toxicity testing methods to accurately assess the potential hazards associated with NMs. In this study, we applied biclustering and gene set enrichment analysis methods to derive essential features of altered lung transcriptome following exposure to NMs that are associated with lung-specific diseases. Several datasets from public microarray repositories describing pulmonary diseases in mouse models following exposure to a variety of substances were examined and functionally related biclusters of genes showing similar expression profiles were identified. The identified biclusters were then used to conduct a gene set enrichment analysis on pulmonary gene expression profiles derived from mice exposed to nano-titanium dioxide (nano-TiO2, carbon black (CB or carbon nanotubes (CNTs to determine the disease significance of these data-driven gene sets.Results: Biclusters representing inflammation (chemokine activity, DNA binding, cell cycle, apoptosis, reactive oxygen species (ROS and fibrosis processes were identified. All of the NM studies were significant with respect to the bicluster related to chemokine activity (DAVID; FDR p-value = 0.032. The bicluster related to pulmonary fibrosis was enriched in studies where toxicity induced by CNT and CB studies was investigated, suggesting the potential for these materials to induce lung fibrosis. The pro-fibrogenic potential of CNTs is well established. Although CB has not been shown to induce fibrosis, it induces stronger inflammatory, oxidative stress and DNA damage responses than nano-TiO2 particles.Conclusion: The results of the analysis correctly identified all NMs to be inflammogenic and only CB and CNTs as potentially fibrogenic. In addition to identifying several

  5. GeneAnalytics: An Integrative Gene Set Analysis Tool for Next Generation Sequencing, RNAseq and Microarray Data.

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    Ben-Ari Fuchs, Shani; Lieder, Iris; Stelzer, Gil; Mazor, Yaron; Buzhor, Ella; Kaplan, Sergey; Bogoch, Yoel; Plaschkes, Inbar; Shitrit, Alina; Rappaport, Noa; Kohn, Asher; Edgar, Ron; Shenhav, Liraz; Safran, Marilyn; Lancet, Doron; Guan-Golan, Yaron; Warshawsky, David; Shtrichman, Ronit

    2016-03-01

    Postgenomics data are produced in large volumes by life sciences and clinical applications of novel omics diagnostics and therapeutics for precision medicine. To move from "data-to-knowledge-to-innovation," a crucial missing step in the current era is, however, our limited understanding of biological and clinical contexts associated with data. Prominent among the emerging remedies to this challenge are the gene set enrichment tools. This study reports on GeneAnalytics™ ( geneanalytics.genecards.org ), a comprehensive and easy-to-apply gene set analysis tool for rapid contextualization of expression patterns and functional signatures embedded in the postgenomics Big Data domains, such as Next Generation Sequencing (NGS), RNAseq, and microarray experiments. GeneAnalytics' differentiating features include in-depth evidence-based scoring algorithms, an intuitive user interface and proprietary unified data. GeneAnalytics employs the LifeMap Science's GeneCards suite, including the GeneCards®--the human gene database; the MalaCards-the human diseases database; and the PathCards--the biological pathways database. Expression-based analysis in GeneAnalytics relies on the LifeMap Discovery®--the embryonic development and stem cells database, which includes manually curated expression data for normal and diseased tissues, enabling advanced matching algorithm for gene-tissue association. This assists in evaluating differentiation protocols and discovering biomarkers for tissues and cells. Results are directly linked to gene, disease, or cell "cards" in the GeneCards suite. Future developments aim to enhance the GeneAnalytics algorithm as well as visualizations, employing varied graphical display items. Such attributes make GeneAnalytics a broadly applicable postgenomics data analyses and interpretation tool for translation of data to knowledge-based innovation in various Big Data fields such as precision medicine, ecogenomics, nutrigenomics, pharmacogenomics, vaccinomics

  6. Investigating the effect of paralogs on microarray gene-set analysis

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    Faure, Andre J

    2011-01-24

    Abstract Background In order to interpret the results obtained from a microarray experiment, researchers often shift focus from analysis of individual differentially expressed genes to analyses of sets of genes. These gene-set analysis (GSA) methods use previously accumulated biological knowledge to group genes into sets and then aim to rank these gene sets in a way that reflects their relative importance in the experimental situation in question. We suspect that the presence of paralogs affects the ability of GSA methods to accurately identify the most important sets of genes for subsequent research. Results We show that paralogs, which typically have high sequence identity and similar molecular functions, also exhibit high correlation in their expression patterns. We investigate this correlation as a potential confounding factor common to current GSA methods using Indygene http:\\/\\/www.cbio.uct.ac.za\\/indygene, a web tool that reduces a supplied list of genes so that it includes no pairwise paralogy relationships above a specified sequence similarity threshold. We use the tool to reanalyse previously published microarray datasets and determine the potential utility of accounting for the presence of paralogs. Conclusions The Indygene tool efficiently removes paralogy relationships from a given dataset and we found that such a reduction, performed prior to GSA, has the ability to generate significantly different results that often represent novel and plausible biological hypotheses. This was demonstrated for three different GSA approaches when applied to the reanalysis of previously published microarray datasets and suggests that the redundancy and non-independence of paralogs is an important consideration when dealing with GSA methodologies.

  7. Using OWL reasoning to support the generation of novel gene sets for enrichment analysis.

    Science.gov (United States)

    Osumi-Sutherland, David J; Ponta, Enrico; Courtot, Melanie; Parkinson, Helen; Badi, Laura

    2018-02-14

    The Gene Ontology (GO) consists of over 40,000 terms for biological processes, cell components and gene product activities linked into a graph structure by over 90,000 relationships. It has been used to annotate the functions and cellular locations of several million gene products. The graph structure is used by a variety of tools to group annotated genes into sets whose products share function or location. These gene sets are widely used to interpret the results of genomics experiments by assessing which sets are significantly over- or under-represented in results lists. F Hoffmann-La Roche Ltd. has developed a bespoke, manually maintained controlled vocabulary (RCV) for use in over-representation analysis. Many terms in this vocabulary group GO terms in novel ways that cannot easily be derived using the graph structure of the GO. For example, some RCV terms group GO terms by the cell, chemical or tissue type they refer to. Recent improvements in the content and formal structure of the GO make it possible to use logical queries in Web Ontology Language (OWL) to automatically map these cross-cutting classifications to sets of GO terms. We used this approach to automate mapping between RCV and GO, largely replacing the increasingly unsustainable manual mapping process. We then tested the utility of the resulting groupings for over-representation analysis. We successfully mapped 85% of RCV terms to logical OWL definitions and showed that these could be used to recapitulate and extend manual mappings between RCV terms and the sets of GO terms subsumed by them. We also show that gene sets derived from the resulting GO terms sets can be used to detect the signatures of cell and tissue types in whole genome expression data. The rich formal structure of the GO makes it possible to use reasoning to dynamically generate novel, biologically relevant groupings of GO terms. GO term groupings generated with this approach can be used in. over-representation analysis to detect

  8. Meta-analysis of differentiating mouse embryonic stem cell gene expression kinetics reveals early change of a small gene set.

    Directory of Open Access Journals (Sweden)

    Clive H Glover

    2006-11-01

    Full Text Available Stem cell differentiation involves critical changes in gene expression. Identification of these should provide endpoints useful for optimizing stem cell propagation as well as potential clues about mechanisms governing stem cell maintenance. Here we describe the results of a new meta-analysis methodology applied to multiple gene expression datasets from three mouse embryonic stem cell (ESC lines obtained at specific time points during the course of their differentiation into various lineages. We developed methods to identify genes with expression changes that correlated with the altered frequency of functionally defined, undifferentiated ESC in culture. In each dataset, we computed a novel statistical confidence measure for every gene which captured the certainty that a particular gene exhibited an expression pattern of interest within that dataset. This permitted a joint analysis of the datasets, despite the different experimental designs. Using a ranking scheme that favored genes exhibiting patterns of interest, we focused on the top 88 genes whose expression was consistently changed when ESC were induced to differentiate. Seven of these (103728_at, 8430410A17Rik, Klf2, Nr0b1, Sox2, Tcl1, and Zfp42 showed a rapid decrease in expression concurrent with a decrease in frequency of undifferentiated cells and remained predictive when evaluated in additional maintenance and differentiating protocols. Through a novel meta-analysis, this study identifies a small set of genes whose expression is useful for identifying changes in stem cell frequencies in cultures of mouse ESC. The methods and findings have broader applicability to understanding the regulation of self-renewal of other stem cell types.

  9. A cross-study gene set enrichment analysis identifies critical pathways in endometriosis

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    Bai Chunyan

    2009-09-01

    Full Text Available Abstract Background Endometriosis is an enigmatic disease. Gene expression profiling of endometriosis has been used in several studies, but few studies went further to classify subtypes of endometriosis based on expression patterns and to identify possible pathways involved in endometriosis. Some of the observed pathways are more inconsistent between the studies, and these candidate pathways presumably only represent a fraction of the pathways involved in endometriosis. Methods We applied a standardised microarray preprocessing and gene set enrichment analysis to six independent studies, and demonstrated increased concordance between these gene datasets. Results We find 16 up-regulated and 19 down-regulated pathways common in ovarian endometriosis data sets, 22 up-regulated and one down-regulated pathway common in peritoneal endometriosis data sets. Among them, 12 up-regulated and 1 down-regulated were found consistent between ovarian and peritoneal endometriosis. The main canonical pathways identified are related to immunological and inflammatory disease. Early secretory phase has the most over-represented pathways in the three uterine cycle phases. There are no overlapping significant pathways between the dataset from human endometrial endothelial cells and the datasets from ovarian endometriosis which used whole tissues. Conclusion The study of complex diseases through pathway analysis is able to highlight genes weakly connected to the phenotype which may be difficult to detect by using classical univariate statistics. By standardised microarray preprocessing and GSEA, we have increased the concordance in identifying many biological mechanisms involved in endometriosis. The identified gene pathways will shed light on the understanding of endometriosis and promote the development of novel therapies.

  10. Selection and validation of a set of reliable reference genes for quantitative sod gene expression analysis in C. elegans

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    Vandesompele Jo

    2008-01-01

    Full Text Available Abstract Background In the nematode Caenorhabditis elegans the conserved Ins/IGF-1 signaling pathway regulates many biological processes including life span, stress response, dauer diapause and metabolism. Detection of differentially expressed genes may contribute to a better understanding of the mechanism by which the Ins/IGF-1 signaling pathway regulates these processes. Appropriate normalization is an essential prerequisite for obtaining accurate and reproducible quantification of gene expression levels. The aim of this study was to establish a reliable set of reference genes for gene expression analysis in C. elegans. Results Real-time quantitative PCR was used to evaluate the expression stability of 12 candidate reference genes (act-1, ama-1, cdc-42, csq-1, eif-3.C, mdh-1, gpd-2, pmp-3, tba-1, Y45F10D.4, rgs-6 and unc-16 in wild-type, three Ins/IGF-1 pathway mutants, dauers and L3 stage larvae. After geNorm analysis, cdc-42, pmp-3 and Y45F10D.4 showed the most stable expression pattern and were used to normalize 5 sod expression levels. Significant differences in mRNA levels were observed for sod-1 and sod-3 in daf-2 relative to wild-type animals, whereas in dauers sod-1, sod-3, sod-4 and sod-5 are differentially expressed relative to third stage larvae. Conclusion Our findings emphasize the importance of accurate normalization using stably expressed reference genes. The methodology used in this study is generally applicable to reliably quantify gene expression levels in the nematode C. elegans using quantitative PCR.

  11. Microarray analysis identifies a common set of cellular genes modulated by different HCV replicon clones

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    Gerosolimo Germano

    2008-06-01

    Full Text Available Abstract Background Hepatitis C virus (HCV RNA synthesis and protein expression affect cell homeostasis by modulation of gene expression. The impact of HCV replication on global cell transcription has not been fully evaluated. Thus, we analysed the expression profiles of different clones of human hepatoma-derived Huh-7 cells carrying a self-replicating HCV RNA which express all viral proteins (HCV replicon system. Results First, we compared the expression profile of HCV replicon clone 21-5 with both the Huh-7 parental cells and the 21-5 cured (21-5c cells. In these latter, the HCV RNA has been eliminated by IFN-α treatment. To confirm data, we also analyzed microarray results from both the 21-5 and two other HCV replicon clones, 22-6 and 21-7, compared to the Huh-7 cells. The study was carried out by using the Applied Biosystems (AB Human Genome Survey Microarray v1.0 which provides 31,700 probes that correspond to 27,868 human genes. Microarray analysis revealed a specific transcriptional program induced by HCV in replicon cells respect to both IFN-α-cured and Huh-7 cells. From the original datasets of differentially expressed genes, we selected by Venn diagrams a final list of 38 genes modulated by HCV in all clones. Most of the 38 genes have never been described before and showed high fold-change associated with significant p-value, strongly supporting data reliability. Classification of the 38 genes by Panther System identified functional categories that were significantly enriched in this gene set, such as histones and ribosomal proteins as well as extracellular matrix and intracellular protein traffic. The dataset also included new genes involved in lipid metabolism, extracellular matrix and cytoskeletal network, which may be critical for HCV replication and pathogenesis. Conclusion Our data provide a comprehensive analysis of alterations in gene expression induced by HCV replication and reveal modulation of new genes potentially useful

  12. Genome-Wide Gene Set Analysis for Identification of Pathways Associated with Alcohol Dependence

    Science.gov (United States)

    Biernacka, Joanna M.; Geske, Jennifer; Jenkins, Gregory D.; Colby, Colin; Rider, David N.; Karpyak, Victor M.; Choi, Doo-Sup; Fridley, Brooke L.

    2013-01-01

    It is believed that multiple genetic variants with small individual effects contribute to the risk of alcohol dependence. Such polygenic effects are difficult to detect in genome-wide association studies that test for association of the phenotype with each single nucleotide polymorphism (SNP) individually. To overcome this challenge, gene set analysis (GSA) methods that jointly test for the effects of pre-defined groups of genes have been proposed. Rather than testing for association between the phenotype and individual SNPs, these analyses evaluate the global evidence of association with a set of related genes enabling the identification of cellular or molecular pathways or biological processes that play a role in development of the disease. It is hoped that by aggregating the evidence of association for all available SNPs in a group of related genes, these approaches will have enhanced power to detect genetic associations with complex traits. We performed GSA using data from a genome-wide study of 1165 alcohol dependent cases and 1379 controls from the Study of Addiction: Genetics and Environment (SAGE), for all 200 pathways listed in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Results demonstrated a potential role of the “Synthesis and Degradation of Ketone Bodies” pathway. Our results also support the potential involvement of the “Neuroactive Ligand Receptor Interaction” pathway, which has previously been implicated in addictive disorders. These findings demonstrate the utility of GSA in the study of complex disease, and suggest specific directions for further research into the genetic architecture of alcohol dependence. PMID:22717047

  13. Meta-analysis of Drosophila circadian microarray studies identifies a novel set of rhythmically expressed genes.

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    Kevin P Keegan

    2007-11-01

    Full Text Available Five independent groups have reported microarray studies that identify dozens of rhythmically expressed genes in the fruit fly Drosophila melanogaster. Limited overlap among the lists of discovered genes makes it difficult to determine which, if any, exhibit truly rhythmic patterns of expression. We reanalyzed data from all five reports and found two sources for the observed discrepancies, the use of different expression pattern detection algorithms and underlying variation among the datasets. To improve upon the methods originally employed, we developed a new analysis that involves compilation of all existing data, application of identical transformation and standardization procedures followed by ANOVA-based statistical prescreening, and three separate classes of post hoc analysis: cross-correlation to various cycling waveforms, autocorrelation, and a previously described fast Fourier transform-based technique. Permutation-based statistical tests were used to derive significance measures for all post hoc tests. We find application of our method, most significantly the ANOVA prescreening procedure, significantly reduces the false discovery rate relative to that observed among the results of the original five reports while maintaining desirable statistical power. We identify a set of 81 cycling transcripts previously found in one or more of the original reports as well as a novel set of 133 transcripts not found in any of the original studies. We introduce a novel analysis method that compensates for variability observed among the original five Drosophila circadian array reports. Based on the statistical fidelity of our meta-analysis results, and the results of our initial validation experiments (quantitative RT-PCR, we predict many of our newly found genes to be bona fide cyclers, and suggest that they may lead to new insights into the pathways through which clock mechanisms regulate behavioral rhythms.

  14. In silico analysis of stomach lineage specific gene set expression pattern in gastric cancer

    International Nuclear Information System (INIS)

    Pandi, Narayanan Sathiya; Suganya, Sivagurunathan; Rajendran, Suriliyandi

    2013-01-01

    Highlights: •Identified stomach lineage specific gene set (SLSGS) was found to be under expressed in gastric tumors. •Elevated expression of SLSGS in gastric tumor is a molecular predictor of metabolic type gastric cancer. •In silico pathway scanning identified estrogen-α signaling is a putative regulator of SLSGS in gastric cancer. •Elevated expression of SLSGS in GC is associated with an overall increase in the survival of GC patients. -- Abstract: Stomach lineage specific gene products act as a protective barrier in the normal stomach and their expression maintains the normal physiological processes, cellular integrity and morphology of the gastric wall. However, the regulation of stomach lineage specific genes in gastric cancer (GC) is far less clear. In the present study, we sought to investigate the role and regulation of stomach lineage specific gene set (SLSGS) in GC. SLSGS was identified by comparing the mRNA expression profiles of normal stomach tissue with other organ tissue. The obtained SLSGS was found to be under expressed in gastric tumors. Functional annotation analysis revealed that the SLSGS was enriched for digestive function and gastric epithelial maintenance. Employing a single sample prediction method across GC mRNA expression profiles identified the under expression of SLSGS in proliferative type and invasive type gastric tumors compared to the metabolic type gastric tumors. Integrative pathway activation prediction analysis revealed a close association between estrogen-α signaling and SLSGS expression pattern in GC. Elevated expression of SLSGS in GC is associated with an overall increase in the survival of GC patients. In conclusion, our results highlight that estrogen mediated regulation of SLSGS in gastric tumor is a molecular predictor of metabolic type GC and prognostic factor in GC

  15. In silico analysis of stomach lineage specific gene set expression pattern in gastric cancer

    Energy Technology Data Exchange (ETDEWEB)

    Pandi, Narayanan Sathiya, E-mail: sathiyapandi@gmail.com; Suganya, Sivagurunathan; Rajendran, Suriliyandi

    2013-10-04

    Highlights: •Identified stomach lineage specific gene set (SLSGS) was found to be under expressed in gastric tumors. •Elevated expression of SLSGS in gastric tumor is a molecular predictor of metabolic type gastric cancer. •In silico pathway scanning identified estrogen-α signaling is a putative regulator of SLSGS in gastric cancer. •Elevated expression of SLSGS in GC is associated with an overall increase in the survival of GC patients. -- Abstract: Stomach lineage specific gene products act as a protective barrier in the normal stomach and their expression maintains the normal physiological processes, cellular integrity and morphology of the gastric wall. However, the regulation of stomach lineage specific genes in gastric cancer (GC) is far less clear. In the present study, we sought to investigate the role and regulation of stomach lineage specific gene set (SLSGS) in GC. SLSGS was identified by comparing the mRNA expression profiles of normal stomach tissue with other organ tissue. The obtained SLSGS was found to be under expressed in gastric tumors. Functional annotation analysis revealed that the SLSGS was enriched for digestive function and gastric epithelial maintenance. Employing a single sample prediction method across GC mRNA expression profiles identified the under expression of SLSGS in proliferative type and invasive type gastric tumors compared to the metabolic type gastric tumors. Integrative pathway activation prediction analysis revealed a close association between estrogen-α signaling and SLSGS expression pattern in GC. Elevated expression of SLSGS in GC is associated with an overall increase in the survival of GC patients. In conclusion, our results highlight that estrogen mediated regulation of SLSGS in gastric tumor is a molecular predictor of metabolic type GC and prognostic factor in GC.

  16. In silico analysis of stomach lineage specific gene set expression pattern in gastric cancer.

    Science.gov (United States)

    Pandi, Narayanan Sathiya; Suganya, Sivagurunathan; Rajendran, Suriliyandi

    2013-10-04

    Stomach lineage specific gene products act as a protective barrier in the normal stomach and their expression maintains the normal physiological processes, cellular integrity and morphology of the gastric wall. However, the regulation of stomach lineage specific genes in gastric cancer (GC) is far less clear. In the present study, we sought to investigate the role and regulation of stomach lineage specific gene set (SLSGS) in GC. SLSGS was identified by comparing the mRNA expression profiles of normal stomach tissue with other organ tissue. The obtained SLSGS was found to be under expressed in gastric tumors. Functional annotation analysis revealed that the SLSGS was enriched for digestive function and gastric epithelial maintenance. Employing a single sample prediction method across GC mRNA expression profiles identified the under expression of SLSGS in proliferative type and invasive type gastric tumors compared to the metabolic type gastric tumors. Integrative pathway activation prediction analysis revealed a close association between estrogen-α signaling and SLSGS expression pattern in GC. Elevated expression of SLSGS in GC is associated with an overall increase in the survival of GC patients. In conclusion, our results highlight that estrogen mediated regulation of SLSGS in gastric tumor is a molecular predictor of metabolic type GC and prognostic factor in GC. Copyright © 2013 Elsevier Inc. All rights reserved.

  17. Comprehensive Analysis of MILE Gene Expression Data Set Advances Discovery of Leukaemia Type and Subtype Biomarkers.

    Science.gov (United States)

    Labaj, Wojciech; Papiez, Anna; Polanski, Andrzej; Polanska, Joanna

    2017-03-01

    Large collections of data in studies on cancer such as leukaemia provoke the necessity of applying tailored analysis algorithms to ensure supreme information extraction. In this work, a custom-fit pipeline is demonstrated for thorough investigation of the voluminous MILE gene expression data set. Three analyses are accomplished, each for gaining a deeper understanding of the processes underlying leukaemia types and subtypes. First, the main disease groups are tested for differential expression against the healthy control as in a standard case-control study. Here, the basic knowledge on molecular mechanisms is confirmed quantitatively and by literature references. Second, pairwise comparison testing is performed for juxtaposing the main leukaemia types among each other. In this case by means of the Dice coefficient similarity measure the general relations are pointed out. Moreover, lists of candidate main leukaemia group biomarkers are proposed. Finally, with this approach being successful, the third analysis provides insight into all of the studied subtypes, followed by the emergence of four leukaemia subtype biomarkers. In addition, the class enhanced DEG signature obtained on the basis of novel pipeline processing leads to significantly better classification power of multi-class data classifiers. The developed methodology consisting of batch effect adjustment, adaptive noise and feature filtration coupled with adequate statistical testing and biomarker definition proves to be an effective approach towards knowledge discovery in high-throughput molecular biology experiments.

  18. A Meta-Analysis of Multiple Matched Copy Number and Transcriptomics Data Sets for Inferring Gene Regulatory Relationships

    Science.gov (United States)

    Newton, Richard; Wernisch, Lorenz

    2014-01-01

    Inferring gene regulatory relationships from observational data is challenging. Manipulation and intervention is often required to unravel causal relationships unambiguously. However, gene copy number changes, as they frequently occur in cancer cells, might be considered natural manipulation experiments on gene expression. An increasing number of data sets on matched array comparative genomic hybridisation and transcriptomics experiments from a variety of cancer pathologies are becoming publicly available. Here we explore the potential of a meta-analysis of thirty such data sets. The aim of our analysis was to assess the potential of in silico inference of trans-acting gene regulatory relationships from this type of data. We found sufficient correlation signal in the data to infer gene regulatory relationships, with interesting similarities between data sets. A number of genes had highly correlated copy number and expression changes in many of the data sets and we present predicted potential trans-acted regulatory relationships for each of these genes. The study also investigates to what extent heterogeneity between cell types and between pathologies determines the number of statistically significant predictions available from a meta-analysis of experiments. PMID:25148247

  19. Classification of Non-Small Cell Lung Cancer Using Significance Analysis of Microarray-Gene Set Reduction Algorithm

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    Lei Zhang

    2016-01-01

    Full Text Available Among non-small cell lung cancer (NSCLC, adenocarcinoma (AC, and squamous cell carcinoma (SCC are two major histology subtypes, accounting for roughly 40% and 30% of all lung cancer cases, respectively. Since AC and SCC differ in their cell of origin, location within the lung, and growth pattern, they are considered as distinct diseases. Gene expression signatures have been demonstrated to be an effective tool for distinguishing AC and SCC. Gene set analysis is regarded as irrelevant to the identification of gene expression signatures. Nevertheless, we found that one specific gene set analysis method, significance analysis of microarray-gene set reduction (SAMGSR, can be adopted directly to select relevant features and to construct gene expression signatures. In this study, we applied SAMGSR to a NSCLC gene expression dataset. When compared with several novel feature selection algorithms, for example, LASSO, SAMGSR has equivalent or better performance in terms of predictive ability and model parsimony. Therefore, SAMGSR is a feature selection algorithm, indeed. Additionally, we applied SAMGSR to AC and SCC subtypes separately to discriminate their respective stages, that is, stage II versus stage I. Few overlaps between these two resulting gene signatures illustrate that AC and SCC are technically distinct diseases. Therefore, stratified analyses on subtypes are recommended when diagnostic or prognostic signatures of these two NSCLC subtypes are constructed.

  20. Gene-Based Analysis of Regionally Enriched Cortical Genes in GWAS Data Sets of Cognitive Traits and Psychiatric Disorders

    DEFF Research Database (Denmark)

    Ersland, Kari M; Christoforou, Andrea; Stansberg, Christine

    2012-01-01

    the regionally enriched cortical genes to mine a genome-wide association study (GWAS) of the Norwegian Cognitive NeuroGenetics (NCNG) sample of healthy adults for association to nine psychometric tests measures. In addition, we explored GWAS data sets for the serious psychiatric disorders schizophrenia (SCZ) (n...

  1. Gene-set analysis based on the pharmacological profiles of drugs to identify repurposing opportunities in schizophrenia.

    Science.gov (United States)

    de Jong, Simone; Vidler, Lewis R; Mokrab, Younes; Collier, David A; Breen, Gerome

    2016-08-01

    Genome-wide association studies (GWAS) have identified thousands of novel genetic associations for complex genetic disorders, leading to the identification of potential pharmacological targets for novel drug development. In schizophrenia, 108 conservatively defined loci that meet genome-wide significance have been identified and hundreds of additional sub-threshold associations harbour information on the genetic aetiology of the disorder. In the present study, we used gene-set analysis based on the known binding targets of chemical compounds to identify the 'drug pathways' most strongly associated with schizophrenia-associated genes, with the aim of identifying potential drug repositioning opportunities and clues for novel treatment paradigms, especially in multi-target drug development. We compiled 9389 gene sets (2496 with unique gene content) and interrogated gene-based p-values from the PGC2-SCZ analysis. Although no single drug exceeded experiment wide significance (corrected pneratinib. This is a proof of principle analysis showing the potential utility of GWAS data of schizophrenia for the direct identification of candidate drugs and molecules that show polypharmacy. © The Author(s) 2016.

  2. Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods

    Science.gov (United States)

    Väremo, Leif; Nielsen, Jens; Nookaew, Intawat

    2013-01-01

    Gene set analysis (GSA) is used to elucidate genome-wide data, in particular transcriptome data. A multitude of methods have been proposed for this step of the analysis, and many of them have been compared and evaluated. Unfortunately, there is no consolidated opinion regarding what methods should be preferred, and the variety of available GSA software and implementations pose a difficulty for the end-user who wants to try out different methods. To address this, we have developed the R package Piano that collects a range of GSA methods into the same system, for the benefit of the end-user. Further on we refine the GSA workflow by using modifications of the gene-level statistics. This enables us to divide the resulting gene set P-values into three classes, describing different aspects of gene expression directionality at gene set level. We use our fully implemented workflow to investigate the impact of the individual components of GSA by using microarray and RNA-seq data. The results show that the evaluated methods are globally similar and the major separation correlates well with our defined directionality classes. As a consequence of this, we suggest to use a consensus scoring approach, based on multiple GSA runs. In combination with the directionality classes, this constitutes a more thorough basis for an enriched biological interpretation. PMID:23444143

  3. An 80-gene set to predict response to preoperative chemoradiotherapy for rectal cancer by principle component analysis.

    Science.gov (United States)

    Empuku, Shinichiro; Nakajima, Kentaro; Akagi, Tomonori; Kaneko, Kunihiko; Hijiya, Naoki; Etoh, Tsuyoshi; Shiraishi, Norio; Moriyama, Masatsugu; Inomata, Masafumi

    2016-05-01

    Preoperative chemoradiotherapy (CRT) for locally advanced rectal cancer not only improves the postoperative local control rate, but also induces downstaging. However, it has not been established how to individually select patients who receive effective preoperative CRT. The aim of this study was to identify a predictor of response to preoperative CRT for locally advanced rectal cancer. This study is additional to our multicenter phase II study evaluating the safety and efficacy of preoperative CRT using oral fluorouracil (UMIN ID: 03396). From April, 2009 to August, 2011, 26 biopsy specimens obtained prior to CRT were analyzed by cyclopedic microarray analysis. Response to CRT was evaluated according to a histological grading system using surgically resected specimens. To decide on the number of genes for dividing into responder and non-responder groups, we statistically analyzed the data using a dimension reduction method, a principle component analysis. Of the 26 cases, 11 were responders and 15 non-responders. No significant difference was found in clinical background data between the two groups. We determined that the optimal number of genes for the prediction of response was 80 of 40,000 and the functions of these genes were analyzed. When comparing non-responders with responders, genes expressed at a high level functioned in alternative splicing, whereas those expressed at a low level functioned in the septin complex. Thus, an 80-gene expression set that predicts response to preoperative CRT for locally advanced rectal cancer was identified using a novel statistical method.

  4. Comparative genomic analysis of SET domain family reveals the origin, expansion, and putative function of the arthropod-specific SmydA genes as histone modifiers in insects.

    Science.gov (United States)

    Jiang, Feng; Liu, Qing; Wang, Yanli; Zhang, Jie; Wang, Huimin; Song, Tianqi; Yang, Meiling; Wang, Xianhui; Kang, Le

    2017-06-01

    The SET domain is an evolutionarily conserved motif present in histone lysine methyltransferases, which are important in the regulation of chromatin and gene expression in animals. In this study, we searched for SET domain-containing genes (SET genes) in all of the 147 arthropod genomes sequenced at the time of carrying out this experiment to understand the evolutionary history by which SET domains have evolved in insects. Phylogenetic and ancestral state reconstruction analysis revealed an arthropod-specific SET gene family, named SmydA, that is ancestral to arthropod animals and specifically diversified during insect evolution. Considering that pseudogenization is the most probable fate of the new emerging gene copies, we provided experimental and evolutionary evidence to demonstrate their essential functions. Fluorescence in situ hybridization analysis and in vitro methyltransferase activity assays showed that the SmydA-2 gene was transcriptionally active and retained the original histone methylation activity. Expression knockdown by RNA interference significantly increased mortality, implying that the SmydA genes may be essential for insect survival. We further showed predominantly strong purifying selection on the SmydA gene family and a potential association between the regulation of gene expression and insect phenotypic plasticity by transcriptome analysis. Overall, these data suggest that the SmydA gene family retains essential functions that may possibly define novel regulatory pathways in insects. This work provides insights into the roles of lineage-specific domain duplication in insect evolution. © The Authors 2017. Published by Oxford University Press.

  5. NetGen: a novel network-based probabilistic generative model for gene set functional enrichment analysis.

    Science.gov (United States)

    Sun, Duanchen; Liu, Yinliang; Zhang, Xiang-Sun; Wu, Ling-Yun

    2017-09-21

    High-throughput experimental techniques have been dramatically improved and widely applied in the past decades. However, biological interpretation of the high-throughput experimental results, such as differential expression gene sets derived from microarray or RNA-seq experiments, is still a challenging task. Gene Ontology (GO) is commonly used in the functional enrichment studies. The GO terms identified via current functional enrichment analysis tools often contain direct parent or descendant terms in the GO hierarchical structure. Highly redundant terms make users difficult to analyze the underlying biological processes. In this paper, a novel network-based probabilistic generative model, NetGen, was proposed to perform the functional enrichment analysis. An additional protein-protein interaction (PPI) network was explicitly used to assist the identification of significantly enriched GO terms. NetGen achieved a superior performance than the existing methods in the simulation studies. The effectiveness of NetGen was explored further on four real datasets. Notably, several GO terms which were not directly linked with the active gene list for each disease were identified. These terms were closely related to the corresponding diseases when accessed to the curated literatures. NetGen has been implemented in the R package CopTea publicly available at GitHub ( http://github.com/wulingyun/CopTea/ ). Our procedure leads to a more reasonable and interpretable result of the functional enrichment analysis. As a novel term combination-based functional enrichment analysis method, NetGen is complementary to current individual term-based methods, and can help to explore the underlying pathogenesis of complex diseases.

  6. Optimization to the Culture Conditions for Phellinus Production with Regression Analysis and Gene-Set Based Genetic Algorithm

    Science.gov (United States)

    Li, Zhongwei; Xin, Yuezhen; Wang, Xun; Sun, Beibei; Xia, Shengyu; Li, Hui

    2016-01-01

    Phellinus is a kind of fungus and is known as one of the elemental components in drugs to avoid cancers. With the purpose of finding optimized culture conditions for Phellinus production in the laboratory, plenty of experiments focusing on single factor were operated and large scale of experimental data were generated. In this work, we use the data collected from experiments for regression analysis, and then a mathematical model of predicting Phellinus production is achieved. Subsequently, a gene-set based genetic algorithm is developed to optimize the values of parameters involved in culture conditions, including inoculum size, PH value, initial liquid volume, temperature, seed age, fermentation time, and rotation speed. These optimized values of the parameters have accordance with biological experimental results, which indicate that our method has a good predictability for culture conditions optimization. PMID:27610365

  7. Data set for diet specific differential gene expression analysis in three Spodoptera moths

    Directory of Open Access Journals (Sweden)

    A. Roy

    2016-09-01

    Full Text Available Examination of closely related species pairs is suggested for evolutionary comparisons of different degrees of polyphagy, which we did here with three taxa of lepidopteran herbivores, Spodoptera spp (S. littoralis, S. frugiperda maize (C and rice (R strains for a RNAseq analysis of the midguts from the 3rd instar insect larvae for differential metabolic responses after feeding on pinto bean based artificial diet vs maize leaves. Paired-end (2×100 bp Illumina HiSeq2500 sequencing resulted in a total of 24, 23, 24, and 21 million reads for the SF-C-Maize, SF-C-Pinto, SF-R-Maize, SF-R Pinto, and a total of 35 and 36 million reads for the SL-Maize and SL-Pinto samples, respectively. After quality control measures, a total of 62.2 million reads from SL and 71.7 million reads from SF were used for transcriptome assembly (TA. The resulting final de novo reference TA (backbone for the SF taxa contained 37,985 contigs with a N50 contig size of 1030 bp and a maximum contig length of 17,093 bp, while for SL, 28,329 contigs were generated with a N50 contig size of 1980 bp and a maximum contig length of 18,267 bp. The data presented herein contains supporting information related to our research article Roy et al. (2016 http://dx.doi.org/10.1016/j.ibmb.2016.02.006 [1]. Keywords: Differential expression analysis (DGE, Transcriptomics, Spodoptera, Adaptation, Generalist, Specialist, RPKM (reads per kilo base of transcript per million mapped reads, RNA seq

  8. Comparative genomic analysis of Brucella abortus vaccine strain 104M reveals a set of candidate genes associated with its virulence attenuation.

    Science.gov (United States)

    Yu, Dong; Hui, Yiming; Zai, Xiaodong; Xu, Junjie; Liang, Long; Wang, Bingxiang; Yue, Junjie; Li, Shanhu

    2015-01-01

    The Brucella abortus strain 104M, a spontaneously attenuated strain, has been used as a vaccine strain in humans against brucellosis for 6 decades in China. Despite many studies, the molecular mechanisms that cause the attenuation are still unclear. Here, we determined the whole-genome sequence of 104M and conducted a comprehensive comparative analysis against the whole genome sequences of the virulent strain, A13334, and other reference strains. This analysis revealed a highly similar genome structure between 104M and A13334. The further comparative genomic analysis between 104M and A13334 revealed a set of genes missing in 104M. Some of these genes were identified to be directly or indirectly associated with virulence. Similarly, a set of mutations in the virulence-related genes was also identified, which may be related to virulence alteration. This study provides a set of candidate genes associated with virulence attenuation in B.abortus vaccine strain 104M.

  9. Social Set Analysis

    DEFF Research Database (Denmark)

    Vatrapu, Ravi; Mukkamala, Raghava Rao; Hussain, Abid

    2016-01-01

    , conceptual and formal models of social data, and an analytical framework for combining big social data sets with organizational and societal data sets. Three empirical studies of big social data are presented to illustrate and demonstrate social set analysis in terms of fuzzy set-theoretical sentiment...... automata and agent-based modeling). However, when it comes to organizational and societal units of analysis, there exists no approach to conceptualize, model, analyze, explain, and predict social media interactions as individuals' associations with ideas, values, identities, and so on. To address...... analysis, crisp set-theoretical interaction analysis, and event-studies-oriented set-theoretical visualizations. Implications for big data analytics, current limitations of the set-theoretical approach, and future directions are outlined....

  10. Gene set-based analysis of polymorphisms: finding pathways or biological processes associated to traits in genome-wide association studies

    Science.gov (United States)

    Medina, Ignacio; Montaner, David; Bonifaci, Nuria; Pujana, Miguel Angel; Carbonell, José; Tarraga, Joaquin; Al-Shahrour, Fatima; Dopazo, Joaquin

    2009-01-01

    Genome-wide association studies have become a popular strategy to find associations of genes to traits of interest. Despite the high-resolution available today to carry out genotyping studies, the success of its application in real studies has been limited by the testing strategy used. As an alternative to brute force solutions involving the use of very large cohorts, we propose the use of the Gene Set Analysis (GSA), a different analysis strategy based on testing the association of modules of functionally related genes. We show here how the Gene Set-based Analysis of Polymorphisms (GeSBAP), which is a simple implementation of the GSA strategy for the analysis of genome-wide association studies, provides a significant increase in the power testing for this type of studies. GeSBAP is freely available at http://bioinfo.cipf.es/gesbap/ PMID:19502494

  11. A MultiSite GatewayTM vector set for the functional analysis of genes in the model Saccharomyces cerevisiae

    Directory of Open Access Journals (Sweden)

    Nagels Durand Astrid

    2012-09-01

    Full Text Available Abstract Background Recombinatorial cloning using the GatewayTM technology has been the method of choice for high-throughput omics projects, resulting in the availability of entire ORFeomes in GatewayTM compatible vectors. The MultiSite GatewayTM system allows combining multiple genetic fragments such as promoter, ORF and epitope tag in one single reaction. To date, this technology has not been accessible in the yeast Saccharomyces cerevisiae, one of the most widely used experimental systems in molecular biology, due to the lack of appropriate destination vectors. Results Here, we present a set of three-fragment MultiSite GatewayTM destination vectors that have been developed for gene expression in S. cerevisiae and that allow the assembly of any promoter, open reading frame, epitope tag arrangement in combination with any of four auxotrophic markers and three distinct replication mechanisms. As an example of its applicability, we used yeast three-hybrid to provide evidence for the assembly of a ternary complex of plant proteins involved in jasmonate signalling and consisting of the JAZ, NINJA and TOPLESS proteins. Conclusion Our vectors make MultiSite GatewayTM cloning accessible in S. cerevisiae and implement a fast and versatile cloning method for the high-throughput functional analysis of (heterologous proteins in one of the most widely used model organisms for molecular biology research.

  12. Literature mining, gene-set enrichment and pathway analysis for target identification in Behçet's disease.

    Science.gov (United States)

    Wilson, Paul; Larminie, Christopher; Smith, Rona

    2016-01-01

    To use literature mining to catalogue Behçet's associated genes, and advanced computational methods to improve the understanding of the pathways and signalling mechanisms that lead to the typical clinical characteristics of Behçet's patients. To extend this technique to identify potential treatment targets for further experimental validation. Text mining methods combined with gene enrichment tools, pathway analysis and causal analysis algorithms. This approach identified 247 human genes associated with Behçet's disease and the resulting disease map, comprising 644 nodes and 19220 edges, captured important details of the relationships between these genes and their associated pathways, as described in diverse data repositories. Pathway analysis has identified how Behçet's associated genes are likely to participate in innate and adaptive immune responses. Causal analysis algorithms have identified a number of potential therapeutic strategies for further investigation. Computational methods have captured pertinent features of the prominent disease characteristics presented in Behçet's disease and have highlighted NOD2, ICOS and IL18 signalling as potential therapeutic strategies.

  13. Association of Protein Translation and Extracellular Matrix Gene Sets with Breast Cancer Metastasis: Findings Uncovered on Analysis of Multiple Publicly Available Datasets Using Individual Patient Data Approach.

    Directory of Open Access Journals (Sweden)

    Nilotpal Chowdhury

    Full Text Available Microarray analysis has revolutionized the role of genomic prognostication in breast cancer. However, most studies are single series studies, and suffer from methodological problems. We sought to use a meta-analytic approach in combining multiple publicly available datasets, while correcting for batch effects, to reach a more robust oncogenomic analysis.The aim of the present study was to find gene sets associated with distant metastasis free survival (DMFS in systemically untreated, node-negative breast cancer patients, from publicly available genomic microarray datasets.Four microarray series (having 742 patients were selected after a systematic search and combined. Cox regression for each gene was done for the combined dataset (univariate, as well as multivariate - adjusted for expression of Cell cycle related genes and for the 4 major molecular subtypes. The centre and microarray batch effects were adjusted by including them as random effects variables. The Cox regression coefficients for each analysis were then ranked and subjected to a Gene Set Enrichment Analysis (GSEA.Gene sets representing protein translation were independently negatively associated with metastasis in the Luminal A and Luminal B subtypes, but positively associated with metastasis in Basal tumors. Proteinaceous extracellular matrix (ECM gene set expression was positively associated with metastasis, after adjustment for expression of cell cycle related genes on the combined dataset. Finally, the positive association of the proliferation-related genes with metastases was confirmed.To the best of our knowledge, the results depicting mixed prognostic significance of protein translation in breast cancer subtypes are being reported for the first time. We attribute this to our study combining multiple series and performing a more robust meta-analytic Cox regression modeling on the combined dataset, thus discovering 'hidden' associations. This methodology seems to yield new and

  14. Association of Protein Translation and Extracellular Matrix Gene Sets with Breast Cancer Metastasis: Findings Uncovered on Analysis of Multiple Publicly Available Datasets Using Individual Patient Data Approach.

    Science.gov (United States)

    Chowdhury, Nilotpal; Sapru, Shantanu

    2015-01-01

    Microarray analysis has revolutionized the role of genomic prognostication in breast cancer. However, most studies are single series studies, and suffer from methodological problems. We sought to use a meta-analytic approach in combining multiple publicly available datasets, while correcting for batch effects, to reach a more robust oncogenomic analysis. The aim of the present study was to find gene sets associated with distant metastasis free survival (DMFS) in systemically untreated, node-negative breast cancer patients, from publicly available genomic microarray datasets. Four microarray series (having 742 patients) were selected after a systematic search and combined. Cox regression for each gene was done for the combined dataset (univariate, as well as multivariate - adjusted for expression of Cell cycle related genes) and for the 4 major molecular subtypes. The centre and microarray batch effects were adjusted by including them as random effects variables. The Cox regression coefficients for each analysis were then ranked and subjected to a Gene Set Enrichment Analysis (GSEA). Gene sets representing protein translation were independently negatively associated with metastasis in the Luminal A and Luminal B subtypes, but positively associated with metastasis in Basal tumors. Proteinaceous extracellular matrix (ECM) gene set expression was positively associated with metastasis, after adjustment for expression of cell cycle related genes on the combined dataset. Finally, the positive association of the proliferation-related genes with metastases was confirmed. To the best of our knowledge, the results depicting mixed prognostic significance of protein translation in breast cancer subtypes are being reported for the first time. We attribute this to our study combining multiple series and performing a more robust meta-analytic Cox regression modeling on the combined dataset, thus discovering 'hidden' associations. This methodology seems to yield new and interesting

  15. AnovArray: a set of SAS macros for the analysis of variance of gene expression data

    Directory of Open Access Journals (Sweden)

    Renard Jean-Paul

    2005-06-01

    Full Text Available Abstract Background Analysis of variance is a powerful approach to identify differentially expressed genes in a complex experimental design for microarray and macroarray data. The advantage of the anova model is the possibility to evaluate multiple sources of variation in an experiment. Results AnovArray is a package implementing ANOVA for gene expression data using SAS® statistical software. The originality of the package is 1 to quantify the different sources of variation on all genes together, 2 to provide a quality control of the model, 3 to propose two models for a gene's variance estimation and to perform a correction for multiple comparisons. Conclusion AnovArray is freely available at http://www-mig.jouy.inra.fr/stat/AnovArray and requires only SAS® statistical software.

  16. Bi-directional gene set enrichment and canonical correlation analysis identify key diet-sensitive pathways and biomarkers of metabolic syndrome

    Directory of Open Access Journals (Sweden)

    Gaora Peadar Ó

    2010-10-01

    Full Text Available Abstract Background Currently, a number of bioinformatics methods are available to generate appropriate lists of genes from a microarray experiment. While these lists represent an accurate primary analysis of the data, fewer options exist to contextualise those lists. The development and validation of such methods is crucial to the wider application of microarray technology in the clinical setting. Two key challenges in clinical bioinformatics involve appropriate statistical modelling of dynamic transcriptomic changes, and extraction of clinically relevant meaning from very large datasets. Results Here, we apply an approach to gene set enrichment analysis that allows for detection of bi-directional enrichment within a gene set. Furthermore, we apply canonical correlation analysis and Fisher's exact test, using plasma marker data with known clinical relevance to aid identification of the most important gene and pathway changes in our transcriptomic dataset. After a 28-day dietary intervention with high-CLA beef, a range of plasma markers indicated a marked improvement in the metabolic health of genetically obese mice. Tissue transcriptomic profiles indicated that the effects were most dramatic in liver (1270 genes significantly changed; p Conclusion Bi-directional gene set enrichment analysis more accurately reflects dynamic regulatory behaviour in biochemical pathways, and as such highlighted biologically relevant changes that were not detected using a traditional approach. In such cases where transcriptomic response to treatment is exceptionally large, canonical correlation analysis in conjunction with Fisher's exact test highlights the subset of pathways showing strongest correlation with the clinical markers of interest. In this case, we have identified selenoamino acid metabolism and steroid biosynthesis as key pathways mediating the observed relationship between metabolic health and high-CLA beef. These results indicate that this type of

  17. Delimiting Coalescence Genes (C-Genes) in Phylogenomic Data Sets.

    Science.gov (United States)

    Springer, Mark S; Gatesy, John

    2018-02-26

    coalescence methods have emerged as a popular alternative for inferring species trees with large genomic datasets, because these methods explicitly account for incomplete lineage sorting. However, statistical consistency of summary coalescence methods is not guaranteed unless several model assumptions are true, including the critical assumption that recombination occurs freely among but not within coalescence genes (c-genes), which are the fundamental units of analysis for these methods. Each c-gene has a single branching history, and large sets of these independent gene histories should be the input for genome-scale coalescence estimates of phylogeny. By contrast, numerous studies have reported the results of coalescence analyses in which complete protein-coding sequences are treated as c-genes even though exons for these loci can span more than a megabase of DNA. Empirical estimates of recombination breakpoints suggest that c-genes may be much shorter, especially when large clades with many species are the focus of analysis. Although this idea has been challenged recently in the literature, the inverse relationship between c-gene size and increased taxon sampling in a dataset-the 'recombination ratchet'-is a fundamental property of c-genes. For taxonomic groups characterized by genes with long intron sequences, complete protein-coding sequences are likely not valid c-genes and are inappropriate units of analysis for summary coalescence methods unless they occur in recombination deserts that are devoid of incomplete lineage sorting (ILS). Finally, it has been argued that coalescence methods are robust when the no-recombination within loci assumption is violated, but recombination must matter at some scale because ILS, a by-product of recombination, is the raison d'etre for coalescence methods. That is, extensive recombination is required to yield the large number of independently segregating c-genes used to infer a species tree. If coalescent methods are powerful

  18. Novel gene sets improve set-level classification of prokaryotic gene expression data.

    Science.gov (United States)

    Holec, Matěj; Kuželka, Ondřej; Železný, Filip

    2015-10-28

    Set-level classification of gene expression data has received significant attention recently. In this setting, high-dimensional vectors of features corresponding to genes are converted into lower-dimensional vectors of features corresponding to biologically interpretable gene sets. The dimensionality reduction brings the promise of a decreased risk of overfitting, potentially resulting in improved accuracy of the learned classifiers. However, recent empirical research has not confirmed this expectation. Here we hypothesize that the reported unfavorable classification results in the set-level framework were due to the adoption of unsuitable gene sets defined typically on the basis of the Gene ontology and the KEGG database of metabolic networks. We explore an alternative approach to defining gene sets, based on regulatory interactions, which we expect to collect genes with more correlated expression. We hypothesize that such more correlated gene sets will enable to learn more accurate classifiers. We define two families of gene sets using information on regulatory interactions, and evaluate them on phenotype-classification tasks using public prokaryotic gene expression data sets. From each of the two gene-set families, we first select the best-performing subtype. The two selected subtypes are then evaluated on independent (testing) data sets against state-of-the-art gene sets and against the conventional gene-level approach. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. Novel gene sets defined on the basis of regulatory interactions improve set-level classification of gene expression data. The experimental scripts and other material needed to reproduce the experiments are available at http://ida.felk.cvut.cz/novelgenesets.tar.gz.

  19. Differential gene expression in granulosa cells from polycystic ovary syndrome patients with and without insulin resistance: identification of susceptibility gene sets through network analysis.

    Science.gov (United States)

    Kaur, Surleen; Archer, Kellie J; Devi, M Gouri; Kriplani, Alka; Strauss, Jerome F; Singh, Rita

    2012-10-01

    Polycystic ovary syndrome (PCOS) is a heterogeneous, genetically complex, endocrine disorder of uncertain etiology in women. Our aim was to compare the gene expression profiles in stimulated granulosa cells of PCOS women with and without insulin resistance vs. matched controls. This study included 12 normal ovulatory women (controls), 12 women with PCOS without evidence for insulin resistance (PCOS non-IR), and 16 women with insulin resistance (PCOS-IR) undergoing in vitro fertilization. Granulosa cell gene expression profiling was accomplished using Affymetrix Human Genome-U133 arrays. Differentially expressed genes were classified according to gene ontology using ingenuity pathway analysis tools. Microarray results for selected genes were confirmed by real-time quantitative PCR. A total of 211 genes were differentially expressed in PCOS non-IR and PCOS-IR granulosa cells (fold change≥1.5; P≤0.001) vs. matched controls. Diabetes mellitus and inflammation genes were significantly increased in PCOS-IR patients. Real-time quantitative PCR confirmed higher expression of NCF2 (2.13-fold), TCF7L2 (1.92-fold), and SERPINA1 (5.35-fold). Increased expression of inflammation genes ITGAX (3.68-fold) and TAB2 (1.86-fold) was confirmed in PCOS non-IR. Different cardiometabolic disease genes were differentially expressed in the two groups. Decreased expression of CAV1 (-3.58-fold) in PCOS non-IR and SPARC (-1.88-fold) in PCOS-IR was confirmed. Differential expression of genes involved in TGF-β signaling (IGF2R, increased; and HAS2, decreased), and oxidative stress (TXNIP, increased) was confirmed in both groups. Microarray analysis demonstrated differential expression of genes linked to diabetes mellitus, inflammation, cardiovascular diseases, and infertility in the granulosa cells of PCOS women with and without insulin resistance. Because these dysregulated genes are also involved in oxidative stress, lipid metabolism, and insulin signaling, we hypothesize that these

  20. An integrative data analysis platform for gene set analysis and knowledge discovery in a data warehouse framework.

    Science.gov (United States)

    Chen, Yi-An; Tripathi, Lokesh P; Mizuguchi, Kenji

    2016-01-01

    Data analysis is one of the most critical and challenging steps in drug discovery and disease biology. A user-friendly resource to visualize and analyse high-throughput data provides a powerful medium for both experimental and computational biologists to understand vastly different biological data types and obtain a concise, simplified and meaningful output for better knowledge discovery. We have previously developed TargetMine, an integrated data warehouse optimized for target prioritization. Here we describe how upgraded and newly modelled data types in TargetMine can now survey the wider biological and chemical data space, relevant to drug discovery and development. To enhance the scope of TargetMine from target prioritization to broad-based knowledge discovery, we have also developed a new auxiliary toolkit to assist with data analysis and visualization in TargetMine. This toolkit features interactive data analysis tools to query and analyse the biological data compiled within the TargetMine data warehouse. The enhanced system enables users to discover new hypotheses interactively by performing complicated searches with no programming and obtaining the results in an easy to comprehend output format. Database URL: http://targetmine.mizuguchilab.org. © The Author(s) 2016. Published by Oxford University Press.

  1. Cogena, a novel tool for co-expressed gene-set enrichment analysis, applied to drug repositioning and drug mode of action discovery.

    Science.gov (United States)

    Jia, Zhilong; Liu, Ying; Guan, Naiyang; Bo, Xiaochen; Luo, Zhigang; Barnes, Michael R

    2016-05-27

    Drug repositioning, finding new indications for existing drugs, has gained much recent attention as a potentially efficient and economical strategy for accelerating new therapies into the clinic. Although improvement in the sensitivity of computational drug repositioning methods has identified numerous credible repositioning opportunities, few have been progressed. Arguably the "black box" nature of drug action in a new indication is one of the main blocks to progression, highlighting the need for methods that inform on the broader target mechanism in the disease context. We demonstrate that the analysis of co-expressed genes may be a critical first step towards illumination of both disease pathology and mode of drug action. We achieve this using a novel framework, co-expressed gene-set enrichment analysis (cogena) for co-expression analysis of gene expression signatures and gene set enrichment analysis of co-expressed genes. The cogena framework enables simultaneous, pathway driven, disease and drug repositioning analysis. Cogena can be used to illuminate coordinated changes within disease transcriptomes and identify drugs acting mechanistically within this framework. We illustrate this using a psoriatic skin transcriptome, as an exemplar, and recover two widely used Psoriasis drugs (Methotrexate and Ciclosporin) with distinct modes of action. Cogena out-performs the results of Connectivity Map and NFFinder webservers in similar disease transcriptome analyses. Furthermore, we investigated the literature support for the other top-ranked compounds to treat psoriasis and showed how the outputs of cogena analysis can contribute new insight to support the progression of drugs into the clinic. We have made cogena freely available within Bioconductor or https://github.com/zhilongjia/cogena . In conclusion, by targeting co-expressed genes within disease transcriptomes, cogena offers novel biological insight, which can be effectively harnessed for drug discovery and

  2. Genome-wide methylation analysis identifies a core set of hypermethylated genes in CIMP-H colorectal cancer.

    Science.gov (United States)

    McInnes, Tyler; Zou, Donghui; Rao, Dasari S; Munro, Francesca M; Phillips, Vicky L; McCall, John L; Black, Michael A; Reeve, Anthony E; Guilford, Parry J

    2017-03-28

    Aberrant DNA methylation profiles are a characteristic of all known cancer types, epitomized by the CpG island methylator phenotype (CIMP) in colorectal cancer (CRC). Hypermethylation has been observed at CpG islands throughout the genome, but it is unclear which factors determine whether an individual island becomes methylated in cancer. DNA methylation in CRC was analysed using the Illumina HumanMethylation450K array. Differentially methylated loci were identified using Significance Analysis of Microarrays (SAM) and the Wilcoxon Signed Rank (WSR) test. Unsupervised hierarchical clustering was used to identify methylation subtypes in CRC. In this study we characterized the DNA methylation profiles of 94 CRC tissues and their matched normal counterparts. Consistent with previous studies, unsupervized hierarchical clustering of genome-wide methylation data identified three subtypes within the tumour samples, designated CIMP-H, CIMP-L and CIMP-N, that showed high, low and very low methylation levels, respectively. Differential methylation between normal and tumour samples was analysed at the individual CpG level, and at the gene level. The distribution of hypermethylation in CIMP-N tumours showed high inter-tumour variability and appeared to be highly stochastic in nature, whereas CIMP-H tumours exhibited consistent hypermethylation at a subset of genes, in addition to a highly variable background of hypermethylated genes. EYA4, TFPI2 and TLX1 were hypermethylated in more than 90% of all tumours examined. One-hundred thirty-two genes were hypermethylated in 100% of CIMP-H tumours studied and these were highly enriched for functions relating to skeletal system development (Bonferroni adjusted p value =2.88E-15), segment specification (adjusted p value =9.62E-11), embryonic development (adjusted p value =1.52E-04), mesoderm development (adjusted p value =1.14E-20), and ectoderm development (adjusted p value =7.94E-16). Our genome-wide characterization of DNA

  3. Social Set Analysis

    DEFF Research Database (Denmark)

    Vatrapu, Ravi; Hussain, Abid; Buus Lassen, Niels

    2015-01-01

    of Facebook or Twitter data. However, there exist no other holistic computational social science approach beyond the relational sociology and graph theory of SNA. To address this limitation, this paper presents an alternative holistic approach to Big Social Data analytics called Social Set Analysis (SSA......This paper argues that the basic premise of Social Network Analysis (SNA) -- namely that social reality is constituted by dyadic relations and that social interactions are determined by structural properties of networks-- is neither necessary nor sufficient, for Big Social Data analytics...

  4. Redundancy control in pathway databases (ReCiPa): an application for improving gene-set enrichment analysis in Omics studies and "Big data" biology.

    Science.gov (United States)

    Vivar, Juan C; Pemu, Priscilla; McPherson, Ruth; Ghosh, Sujoy

    2013-08-01

    Abstract Unparalleled technological advances have fueled an explosive growth in the scope and scale of biological data and have propelled life sciences into the realm of "Big Data" that cannot be managed or analyzed by conventional approaches. Big Data in the life sciences are driven primarily via a diverse collection of 'omics'-based technologies, including genomics, proteomics, metabolomics, transcriptomics, metagenomics, and lipidomics. Gene-set enrichment analysis is a powerful approach for interrogating large 'omics' datasets, leading to the identification of biological mechanisms associated with observed outcomes. While several factors influence the results from such analysis, the impact from the contents of pathway databases is often under-appreciated. Pathway databases often contain variously named pathways that overlap with one another to varying degrees. Ignoring such redundancies during pathway analysis can lead to the designation of several pathways as being significant due to high content-similarity, rather than truly independent biological mechanisms. Statistically, such dependencies also result in correlated p values and overdispersion, leading to biased results. We investigated the level of redundancies in multiple pathway databases and observed large discrepancies in the nature and extent of pathway overlap. This prompted us to develop the application, ReCiPa (Redundancy Control in Pathway Databases), to control redundancies in pathway databases based on user-defined thresholds. Analysis of genomic and genetic datasets, using ReCiPa-generated overlap-controlled versions of KEGG and Reactome pathways, led to a reduction in redundancy among the top-scoring gene-sets and allowed for the inclusion of additional gene-sets representing possibly novel biological mechanisms. Using obesity as an example, bioinformatic analysis further demonstrated that gene-sets identified from overlap-controlled pathway databases show stronger evidence of prior association

  5. Principles for the organization of gene-sets.

    Science.gov (United States)

    Li, Wentian; Freudenberg, Jan; Oswald, Michaela

    2015-12-01

    A gene-set, an important concept in microarray expression analysis and systems biology, is a collection of genes and/or their products (i.e. proteins) that have some features in common. There are many different ways to construct gene-sets, but a systematic organization of these ways is lacking. Gene-sets are mainly organized ad hoc in current public-domain databases, with group header names often determined by practical reasons (such as the types of technology in obtaining the gene-sets or a balanced number of gene-sets under a header). Here we aim at providing a gene-set organization principle according to the level at which genes are connected: homology, physical map proximity, chemical interaction, biological, and phenotypic-medical levels. We also distinguish two types of connections between genes: actual connection versus sharing of a label. Actual connections denote direct biological interactions, whereas shared label connection denotes shared membership in a group. Some extensions of the framework are also addressed such as overlapping of gene-sets, modules, and the incorporation of other non-protein-coding entities such as microRNAs. Copyright © 2015 Elsevier Ltd. All rights reserved.

  6. gsSKAT: Rapid gene set analysis and multiple testing correction for rare-variant association studies using weighted linear kernels.

    Science.gov (United States)

    Larson, Nicholas B; McDonnell, Shannon; Cannon Albright, Lisa; Teerlink, Craig; Stanford, Janet; Ostrander, Elaine A; Isaacs, William B; Xu, Jianfeng; Cooney, Kathleen A; Lange, Ethan; Schleutker, Johanna; Carpten, John D; Powell, Isaac; Bailey-Wilson, Joan E; Cussenot, Olivier; Cancel-Tassin, Geraldine; Giles, Graham G; MacInnis, Robert J; Maier, Christiane; Whittemore, Alice S; Hsieh, Chih-Lin; Wiklund, Fredrik; Catalona, William J; Foulkes, William; Mandal, Diptasri; Eeles, Rosalind; Kote-Jarai, Zsofia; Ackerman, Michael J; Olson, Timothy M; Klein, Christopher J; Thibodeau, Stephen N; Schaid, Daniel J

    2017-05-01

    Next-generation sequencing technologies have afforded unprecedented characterization of low-frequency and rare genetic variation. Due to low power for single-variant testing, aggregative methods are commonly used to combine observed rare variation within a single gene. Causal variation may also aggregate across multiple genes within relevant biomolecular pathways. Kernel-machine regression and adaptive testing methods for aggregative rare-variant association testing have been demonstrated to be powerful approaches for pathway-level analysis, although these methods tend to be computationally intensive at high-variant dimensionality and require access to complete data. An additional analytical issue in scans of large pathway definition sets is multiple testing correction. Gene set definitions may exhibit substantial genic overlap, and the impact of the resultant correlation in test statistics on Type I error rate control for large agnostic gene set scans has not been fully explored. Herein, we first outline a statistical strategy for aggregative rare-variant analysis using component gene-level linear kernel score test summary statistics as well as derive simple estimators of the effective number of tests for family-wise error rate control. We then conduct extensive simulation studies to characterize the behavior of our approach relative to direct application of kernel and adaptive methods under a variety of conditions. We also apply our method to two case-control studies, respectively, evaluating rare variation in hereditary prostate cancer and schizophrenia. Finally, we provide open-source R code for public use to facilitate easy application of our methods to existing rare-variant analysis results. © 2017 WILEY PERIODICALS, INC.

  7. Genetic analysis and fine mapping of LH1 and LH2, a set of complementary genes controlling late heading in rice (Oryza sativa L.).

    Science.gov (United States)

    Liu, Shuang; Wang, Feng; Gao, Li Jun; Li, Jin Hua; Li, Rong Bai; Gao, Han Liang; Deng, Guo Fu; Yang, Jin Shui; Luo, Xiao Jin

    2012-12-01

    Heading date in rice (Oryza sativa L.) is a critical agronomic trait with a complex inheritance. To investigate the genetic basis and mechanism of gene interaction in heading date, we conducted genetic analysis on segregation populations derived from crosses among the indica cultivars Bo B, Yuefeng B and Baoxuan 2. A set of dominant complementary genes controlling late heading, designated LH1 and LH2, were detected by molecular marker mapping. Genetic analysis revealed that Baoxuan 2 contains both dominant genes, while Bo B and Yuefeng B each possess either LH1 or LH2. Using larger populations with segregant ratios of 3 : 1, we fine-mapped LH1 to a 63-kb region near the centromere of chromosome 7 flanked by markers RM5436 and RM8034, and LH2 to a 177-kb region on the short arm of chromosome 8 between flanking markers Indel22468-3 and RM25. Some candidate genes were identified through sequencing of Bo B and Yuefeng B in these target regions. Our work provides a solid foundation for further study on gene interaction in heading date and has application in marker-assisted breeding of photosensitive hybrid rice in China.

  8. Zebrafish Expression Ontology of Gene Sets (ZEOGS): a tool to analyze enrichment of zebrafish anatomical terms in large gene sets.

    Science.gov (United States)

    Prykhozhij, Sergey V; Marsico, Annalisa; Meijsing, Sebastiaan H

    2013-09-01

    The zebrafish (Danio rerio) is an established model organism for developmental and biomedical research. It is frequently used for high-throughput functional genomics experiments, such as genome-wide gene expression measurements, to systematically analyze molecular mechanisms. However, the use of whole embryos or larvae in such experiments leads to a loss of the spatial information. To address this problem, we have developed a tool called Zebrafish Expression Ontology of Gene Sets (ZEOGS) to assess the enrichment of anatomical terms in large gene sets. ZEOGS uses gene expression pattern data from several sources: first, in situ hybridization experiments from the Zebrafish Model Organism Database (ZFIN); second, it uses the Zebrafish Anatomical Ontology, a controlled vocabulary that describes connected anatomical structures; and third, the available connections between expression patterns and anatomical terms contained in ZFIN. Upon input of a gene set, ZEOGS determines which anatomical structures are overrepresented in the input gene set. ZEOGS allows one for the first time to look at groups of genes and to describe them in terms of shared anatomical structures. To establish ZEOGS, we first tested it on random gene selections and on two public microarray datasets with known tissue-specific gene expression changes. These tests showed that ZEOGS could reliably identify the tissues affected, whereas only very few enriched terms to none were found in the random gene sets. Next we applied ZEOGS to microarray datasets of 24 and 72 h postfertilization zebrafish embryos treated with beclomethasone, a potent glucocorticoid. This analysis resulted in the identification of several anatomical terms related to glucocorticoid-responsive tissues, some of which were stage-specific. Our studies highlight the ability of ZEOGS to extract spatial information from datasets derived from whole embryos, indicating that ZEOGS could be a useful tool to automatically analyze gene expression

  9. Zebrafish Expression Ontology of Gene Sets (ZEOGS): A Tool to Analyze Enrichment of Zebrafish Anatomical Terms in Large Gene Sets

    Science.gov (United States)

    Marsico, Annalisa

    2013-01-01

    Abstract The zebrafish (Danio rerio) is an established model organism for developmental and biomedical research. It is frequently used for high-throughput functional genomics experiments, such as genome-wide gene expression measurements, to systematically analyze molecular mechanisms. However, the use of whole embryos or larvae in such experiments leads to a loss of the spatial information. To address this problem, we have developed a tool called Zebrafish Expression Ontology of Gene Sets (ZEOGS) to assess the enrichment of anatomical terms in large gene sets. ZEOGS uses gene expression pattern data from several sources: first, in situ hybridization experiments from the Zebrafish Model Organism Database (ZFIN); second, it uses the Zebrafish Anatomical Ontology, a controlled vocabulary that describes connected anatomical structures; and third, the available connections between expression patterns and anatomical terms contained in ZFIN. Upon input of a gene set, ZEOGS determines which anatomical structures are overrepresented in the input gene set. ZEOGS allows one for the first time to look at groups of genes and to describe them in terms of shared anatomical structures. To establish ZEOGS, we first tested it on random gene selections and on two public microarray datasets with known tissue-specific gene expression changes. These tests showed that ZEOGS could reliably identify the tissues affected, whereas only very few enriched terms to none were found in the random gene sets. Next we applied ZEOGS to microarray datasets of 24 and 72 h postfertilization zebrafish embryos treated with beclomethasone, a potent glucocorticoid. This analysis resulted in the identification of several anatomical terms related to glucocorticoid-responsive tissues, some of which were stage-specific. Our studies highlight the ability of ZEOGS to extract spatial information from datasets derived from whole embryos, indicating that ZEOGS could be a useful tool to automatically analyze gene

  10. Analysis of successive data sets

    NARCIS (Netherlands)

    Spreeuwers, Lieuwe Jan; Breeuwer, Marcel; Haselhoff, Eltjo Hans

    2008-01-01

    The invention relates to the analysis of successive data sets. A local intensity variation is formed from such successive data sets, that is, from data values in successive data sets at corresponding positions in each of the data sets. A region of interest is localized in the individual data sets on

  11. Analysis of successive data sets

    NARCIS (Netherlands)

    Spreeuwers, Lieuwe Jan; Breeuwer, Marcel; Haselhoff, Eltjo Hans

    2002-01-01

    The invention relates to the analysis of successive data sets. A local intensity variation is formed from such successive data sets, that is, from data values in successive data sets at corresponding positions in each of the data sets. A region of interest is localized in the individual data sets on

  12. Pathway profiles based on gene-set enrichment analysis in the honey bee Apis mellifera under brood rearing-suppressed conditions.

    Science.gov (United States)

    Kim, Kyungmun; Kim, Ju Hyeon; Kim, Young Ho; Hong, Seong-Eui; Lee, Si Hyeock

    2018-01-01

    Perturbation of normal behaviors in honey bee colonies by any external factor can immediately reduce the colony's capacity for brood rearing, which can eventually lead to colony collapse. To investigate the effects of brood-rearing suppression on the biology of honey bee workers, gene-set enrichment analysis of the transcriptomes of worker bees with or without suppressed brood rearing was performed. When brood rearing was suppressed, pathways associated with both protein degradation and synthesis were simultaneously over-represented in both nurses and foragers, and their overall pathway representation profiles resembled those of normal foragers and nurses, respectively. Thus, obstruction of normal labor induced over-representation in pathways related with reshaping of worker bee physiology, suggesting that transition of labor is physiologically reversible. In addition, some genes associated with the regulation of neuronal excitability, cellular and nutritional stress and aggressiveness were over-expressed under brood rearing suppression perhaps to manage in-hive stress under unfavorable conditions. Copyright © 2017 Elsevier Inc. All rights reserved.

  13. Interaction between Social/Psychosocial Factors and Genetic Variants on Body Mass Index: A Gene-Environment Interaction Analysis in a Longitudinal Setting.

    Science.gov (United States)

    Zhao, Wei; Ware, Erin B; He, Zihuai; Kardia, Sharon L R; Faul, Jessica D; Smith, Jennifer A

    2017-09-29

    Obesity, which develops over time, is one of the leading causes of chronic diseases such as cardiovascular disease. However, hundreds of BMI (body mass index)-associated genetic loci identified through large-scale genome-wide association studies (GWAS) only explain about 2.7% of BMI variation. Most common human traits are believed to be influenced by both genetic and environmental factors. Past studies suggest a variety of environmental features that are associated with obesity, including socioeconomic status and psychosocial factors. This study combines both gene/regions and environmental factors to explore whether social/psychosocial factors (childhood and adult socioeconomic status, social support, anger, chronic burden, stressful life events, and depressive symptoms) modify the effect of sets of genetic variants on BMI in European American and African American participants in the Health and Retirement Study (HRS). In order to incorporate longitudinal phenotype data collected in the HRS and investigate entire sets of single nucleotide polymorphisms (SNPs) within gene/region simultaneously, we applied a novel set-based test for gene-environment interaction in longitudinal studies (LGEWIS). Childhood socioeconomic status (parental education) was found to modify the genetic effect in the gene/region around SNP rs9540493 on BMI in European Americans in the HRS. The most significant SNP (rs9540488) by childhood socioeconomic status interaction within the rs9540493 gene/region was suggestively replicated in the Multi-Ethnic Study of Atherosclerosis (MESA) ( p = 0.07).

  14. Interaction between Social/Psychosocial Factors and Genetic Variants on Body Mass Index: A Gene-Environment Interaction Analysis in a Longitudinal Setting

    Directory of Open Access Journals (Sweden)

    Wei Zhao

    2017-09-01

    Full Text Available Obesity, which develops over time, is one of the leading causes of chronic diseases such as cardiovascular disease. However, hundreds of BMI (body mass index-associated genetic loci identified through large-scale genome-wide association studies (GWAS only explain about 2.7% of BMI variation. Most common human traits are believed to be influenced by both genetic and environmental factors. Past studies suggest a variety of environmental features that are associated with obesity, including socioeconomic status and psychosocial factors. This study combines both gene/regions and environmental factors to explore whether social/psychosocial factors (childhood and adult socioeconomic status, social support, anger, chronic burden, stressful life events, and depressive symptoms modify the effect of sets of genetic variants on BMI in European American and African American participants in the Health and Retirement Study (HRS. In order to incorporate longitudinal phenotype data collected in the HRS and investigate entire sets of single nucleotide polymorphisms (SNPs within gene/region simultaneously, we applied a novel set-based test for gene-environment interaction in longitudinal studies (LGEWIS. Childhood socioeconomic status (parental education was found to modify the genetic effect in the gene/region around SNP rs9540493 on BMI in European Americans in the HRS. The most significant SNP (rs9540488 by childhood socioeconomic status interaction within the rs9540493 gene/region was suggestively replicated in the Multi-Ethnic Study of Atherosclerosis (MESA (p = 0.07.

  15. RBiomirGS: an all-in-one miRNA gene set analysis solution featuring target mRNA mapping and expression profile integration

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    Jing Zhang

    2018-01-01

    Full Text Available Background With the continuous discovery of microRNA’s (miRNA association with a wide range of biological and cellular processes, expression profile-based functional characterization of such post-transcriptional regulation is crucial for revealing its significance behind particular phenotypes. Profound advancement in bioinformatics has been made to enable in depth investigation of miRNA’s role in regulating cellular and molecular events, resulting in a huge quantity of software packages covering different aspects of miRNA functional analysis. Therefore, an all-in-one software solution is in demand for a comprehensive yet highly efficient workflow. Here we present RBiomirGS, an R package for a miRNA gene set (GS analysis. Methods The package utilizes multiple databases for target mRNA mapping, estimates miRNA effect on the target mRNAs through miRNA expression profile and conducts a logistic regression-based GS enrichment. Additionally, human ortholog Entrez ID conversion functionality is included for target mRNAs. Results By incorporating all the core steps into one package, RBiomirGS eliminates the need for switching between different software packages. The modular structure of RBiomirGS enables various access points to the analysis, with which users can choose the most relevant functionalities for their workflow. Conclusions With RBiomirGS, users are able to assess the functional significance of the miRNA expression profile under the corresponding experimental condition by minimal input and intervention. Accordingly, RBiomirGS encompasses an all-in-one solution for miRNA GS analysis. RBiomirGS is available on GitHub (http://github.com/jzhangc/RBiomirGS. More information including instruction and examples can be found on website (http://kenstoreylab.com/?page_id=2865.

  16. Studying the Complex Expression Dependences between Sets of Coexpressed Genes

    Directory of Open Access Journals (Sweden)

    Mario Huerta

    2014-01-01

    Full Text Available Organisms simplify the orchestration of gene expression by coregulating genes whose products function together in the cell. The use of clustering methods to obtain sets of coexpressed genes from expression arrays is very common; nevertheless there are no appropriate tools to study the expression networks among these sets of coexpressed genes. The aim of the developed tools is to allow studying the complex expression dependences that exist between sets of coexpressed genes. For this purpose, we start detecting the nonlinear expression relationships between pairs of genes, plus the coexpressed genes. Next, we form networks among sets of coexpressed genes that maintain nonlinear expression dependences between all of them. The expression relationship between the sets of coexpressed genes is defined by the expression relationship between the skeletons of these sets, where this skeleton represents the coexpressed genes with a well-defined nonlinear expression relationship with the skeleton of the other sets. As a result, we can study the nonlinear expression relationships between a target gene and other sets of coexpressed genes, or start the study from the skeleton of the sets, to study the complex relationships of activation and deactivation between the sets of coexpressed genes that carry out the different cellular processes present in the expression experiments.

  17. Analysis of the real EADGENE data set:

    DEFF Research Database (Denmark)

    Jaffrézic, Florence; de Koning, Dirk-Jan; Boettcher, Paul J

    2007-01-01

    A large variety of methods has been proposed in the literature for microarray data analysis. The aim of this paper was to present techniques used by the EADGENE (European Animal Disease Genomics Network of Excellence) WP1.4 participants for data quality control, normalisation and statistical...... methods for the detection of differentially expressed genes in order to provide some more general data analysis guidelines. All the workshop participants were given a real data set obtained in an EADGENE funded microarray study looking at the gene expression changes following artificial infection with two...... quarters. Very little transcriptional variation was observed for the bacteria S. aureus. Lists of differentially expressed genes found by the different research teams were, however, quite dependent on the method used, especially concerning the data quality control step. These analyses also emphasised...

  18. Using the gene ontology to scan multilevel gene sets for associations in genome wide association studies.

    Science.gov (United States)

    Schaid, Daniel J; Sinnwell, Jason P; Jenkins, Gregory D; McDonnell, Shannon K; Ingle, James N; Kubo, Michiaki; Goss, Paul E; Costantino, Joseph P; Wickerham, D Lawrence; Weinshilboum, Richard M

    2012-01-01

    Gene-set analyses have been widely used in gene expression studies, and some of the developed methods have been extended to genome wide association studies (GWAS). Yet, complications due to linkage disequilibrium (LD) among single nucleotide polymorphisms (SNPs), and variable numbers of SNPs per gene and genes per gene-set, have plagued current approaches, often leading to ad hoc "fixes." To overcome some of the current limitations, we developed a general approach to scan GWAS SNP data for both gene-level and gene-set analyses, building on score statistics for generalized linear models, and taking advantage of the directed acyclic graph structure of the gene ontology when creating gene-sets. However, other types of gene-set structures can be used, such as the popular Kyoto Encyclopedia of Genes and Genomes (KEGG). Our approach combines SNPs into genes, and genes into gene-sets, but assures that positive and negative effects of genes on a trait do not cancel. To control for multiple testing of many gene-sets, we use an efficient computational strategy that accounts for LD and provides accurate step-down adjusted P-values for each gene-set. Application of our methods to two different GWAS provide guidance on the potential strengths and weaknesses of our proposed gene-set analyses. © 2011 Wiley Periodicals, Inc.

  19. A Bayesian variable selection procedure for ranking overlapping gene sets

    DEFF Research Database (Denmark)

    Skarman, Axel; Mahdi Shariati, Mohammad; Janss, Luc

    2012-01-01

    Background Genome-wide expression profiling using microarrays or sequence-based technologies allows us to identify genes and genetic pathways whose expression patterns influence complex traits. Different methods to prioritize gene sets, such as the genes in a given molecular pathway, have been de...

  20. Constellation Map: Downstream visualization and interpretation of gene set enrichment results [version 1; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Yan Tan

    2015-06-01

    Full Text Available Summary: Gene set enrichment analysis (GSEA approaches are widely used to identify coordinately regulated genes associated with phenotypes of interest. Here, we present Constellation Map, a tool to visualize and interpret the results when enrichment analyses yield a long list of significantly enriched gene sets. Constellation Map identifies commonalities that explain the enrichment of multiple top-scoring gene sets and maps the relationships between them. Constellation Map can help investigators take full advantage of GSEA and facilitates the biological interpretation of enrichment results. Availability: Constellation Map is freely available as a GenePattern module at http://www.genepattern.org.

  1. Comparative study on gene set and pathway topology-based enrichment methods.

    Science.gov (United States)

    Bayerlová, Michaela; Jung, Klaus; Kramer, Frank; Klemm, Florian; Bleckmann, Annalen; Beißbarth, Tim

    2015-10-22

    Enrichment analysis is a popular approach to identify pathways or sets of genes which are significantly enriched in the context of differentially expressed genes. The traditional gene set enrichment approach considers a pathway as a simple gene list disregarding any knowledge of gene or protein interactions. In contrast, the new group of so called pathway topology-based methods integrates the topological structure of a pathway into the analysis. We comparatively investigated gene set and pathway topology-based enrichment approaches, considering three gene set and four topological methods. These methods were compared in two extensive simulation studies and on a benchmark of 36 real datasets, providing the same pathway input data for all methods. In the benchmark data analysis both types of methods showed a comparable ability to detect enriched pathways. The first simulation study was conducted with KEGG pathways, which showed considerable gene overlaps between each other. In this study with original KEGG pathways, none of the topology-based methods outperformed the gene set approach. Therefore, a second simulation study was performed on non-overlapping pathways created by unique gene IDs. Here, methods accounting for pathway topology reached higher accuracy than the gene set methods, however their sensitivity was lower. We conducted one of the first comprehensive comparative works on evaluating gene set against pathway topology-based enrichment methods. The topological methods showed better performance in the simulation scenarios with non-overlapping pathways, however, they were not conclusively better in the other scenarios. This suggests that simple gene set approach might be sufficient to detect an enriched pathway under realistic circumstances. Nevertheless, more extensive studies and further benchmark data are needed to systematically evaluate these methods and to assess what gain and cost pathway topology information introduces into enrichment analysis. Both

  2. An Independent Filter for Gene Set Testing Based on Spectral Enrichment

    NARCIS (Netherlands)

    Frost, H Robert; Li, Zhigang; Asselbergs, Folkert W; Moore, Jason H

    2015-01-01

    Gene set testing has become an indispensable tool for the analysis of high-dimensional genomic data. An important motivation for testing gene sets, rather than individual genomic variables, is to improve statistical power by reducing the number of tested hypotheses. Given the dramatic growth in

  3. Gene set of nuclear-encoded mitochondrial regulators is enriched for common inherited variation in obesity.

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    Nadja Knoll

    Full Text Available There are hints of an altered mitochondrial function in obesity. Nuclear-encoded genes are relevant for mitochondrial function (3 gene sets of known relevant pathways: (1 16 nuclear regulators of mitochondrial genes, (2 91 genes for oxidative phosphorylation and (3 966 nuclear-encoded mitochondrial genes. Gene set enrichment analysis (GSEA showed no association with type 2 diabetes mellitus in these gene sets. Here we performed a GSEA for the same gene sets for obesity. Genome wide association study (GWAS data from a case-control approach on 453 extremely obese children and adolescents and 435 lean adult controls were used for GSEA. For independent confirmation, we analyzed 705 obesity GWAS trios (extremely obese child and both biological parents and a population-based GWAS sample (KORA F4, n = 1,743. A meta-analysis was performed on all three samples. In each sample, the distribution of significance levels between the respective gene set and those of all genes was compared using the leading-edge-fraction-comparison test (cut-offs between the 50(th and 95(th percentile of the set of all gene-wise corrected p-values as implemented in the MAGENTA software. In the case-control sample, significant enrichment of associations with obesity was observed above the 50(th percentile for the set of the 16 nuclear regulators of mitochondrial genes (p(GSEA,50 = 0.0103. This finding was not confirmed in the trios (p(GSEA,50 = 0.5991, but in KORA (p(GSEA,50 = 0.0398. The meta-analysis again indicated a trend for enrichment (p(MAGENTA,50 = 0.1052, p(MAGENTA,75 = 0.0251. The GSEA revealed that weak association signals for obesity might be enriched in the gene set of 16 nuclear regulators of mitochondrial genes.

  4. A hybrid approach of gene sets and single genes for the prediction of survival risks with gene expression data.

    Science.gov (United States)

    Seok, Junhee; Davis, Ronald W; Xiao, Wenzhong

    2015-01-01

    Accumulated biological knowledge is often encoded as gene sets, collections of genes associated with similar biological functions or pathways. The use of gene sets in the analyses of high-throughput gene expression data has been intensively studied and applied in clinical research. However, the main interest remains in finding modules of biological knowledge, or corresponding gene sets, significantly associated with disease conditions. Risk prediction from censored survival times using gene sets hasn't been well studied. In this work, we propose a hybrid method that uses both single gene and gene set information together to predict patient survival risks from gene expression profiles. In the proposed method, gene sets provide context-level information that is poorly reflected by single genes. Complementarily, single genes help to supplement incomplete information of gene sets due to our imperfect biomedical knowledge. Through the tests over multiple data sets of cancer and trauma injury, the proposed method showed robust and improved performance compared with the conventional approaches with only single genes or gene sets solely. Additionally, we examined the prediction result in the trauma injury data, and showed that the modules of biological knowledge used in the prediction by the proposed method were highly interpretable in biology. A wide range of survival prediction problems in clinical genomics is expected to benefit from the use of biological knowledge.

  5. Discovery of cancer common and specific driver gene sets

    Science.gov (United States)

    2017-01-01

    Abstract Cancer is known as a disease mainly caused by gene alterations. Discovery of mutated driver pathways or gene sets is becoming an important step to understand molecular mechanisms of carcinogenesis. However, systematically investigating commonalities and specificities of driver gene sets among multiple cancer types is still a great challenge, but this investigation will undoubtedly benefit deciphering cancers and will be helpful for personalized therapy and precision medicine in cancer treatment. In this study, we propose two optimization models to de novo discover common driver gene sets among multiple cancer types (ComMDP) and specific driver gene sets of one certain or multiple cancer types to other cancers (SpeMDP), respectively. We first apply ComMDP and SpeMDP to simulated data to validate their efficiency. Then, we further apply these methods to 12 cancer types from The Cancer Genome Atlas (TCGA) and obtain several biologically meaningful driver pathways. As examples, we construct a common cancer pathway model for BRCA and OV, infer a complex driver pathway model for BRCA carcinogenesis based on common driver gene sets of BRCA with eight cancer types, and investigate specific driver pathways of the liquid cancer lymphoblastic acute myeloid leukemia (LAML) versus other solid cancer types. In these processes more candidate cancer genes are also found. PMID:28168295

  6. Phylogenetics and evolution of Trx SET genes in fully sequenced land plants.

    Science.gov (United States)

    Zhu, Xinyu; Chen, Caoyi; Wang, Baohua

    2012-04-01

    Plant Trx SET proteins are involved in H3K4 methylation and play a key role in plant floral development. Genes encoding Trx SET proteins constitute a multigene family in which the copy number varies among plant species and functional divergence appears to have occurred repeatedly. To investigate the evolutionary history of the Trx SET gene family, we made a comprehensive evolutionary analysis on this gene family from 13 major representatives of green plants. A novel clustering (here named as cpTrx clade), which included the III-1, III-2, and III-4 orthologous groups, previously resolved was identified. Our analysis showed that plant Trx proteins possessed a variety of domain organizations and gene structures among paralogs. Additional domains such as PHD, PWWP, and FYR were early integrated into primordial SET-PostSET domain organization of cpTrx clade. We suggested that the PostSET domain was lost in some members of III-4 orthologous group during the evolution of land plants. At least four classes of gene structures had been formed at the early evolutionary stage of land plants. Three intronless orphan Trx SET genes from the Physcomitrella patens (moss) were identified, and supposedly, their parental genes have been eliminated from the genome. The structural differences among evolutionary groups of plant Trx SET genes with different functions were described, contributing to the design of further experimental studies.

  7. Functional Multiple-Set Canonical Correlation Analysis

    Science.gov (United States)

    Hwang, Heungsun; Jung, Kwanghee; Takane, Yoshio; Woodward, Todd S.

    2012-01-01

    We propose functional multiple-set canonical correlation analysis for exploring associations among multiple sets of functions. The proposed method includes functional canonical correlation analysis as a special case when only two sets of functions are considered. As in classical multiple-set canonical correlation analysis, computationally, the…

  8. The Molecular Signatures Database (MSigDB) hallmark gene set collection.

    Science.gov (United States)

    Liberzon, Arthur; Birger, Chet; Thorvaldsdóttir, Helga; Ghandi, Mahmoud; Mesirov, Jill P; Tamayo, Pablo

    2015-12-23

    The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of "hallmark" gene sets as part of MSigDB. Each hallmark in this collection consists of a "refined" gene set, derived from multiple "founder" sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder sets and, by reducing both variation and redundancy, provide more refined and concise inputs for gene set enrichment analysis.

  9. Annotating gene sets by mining large literature collections with protein networks.

    Science.gov (United States)

    Wang, Sheng; Ma, Jianzhu; Yu, Michael Ku; Zheng, Fan; Huang, Edward W; Han, Jiawei; Peng, Jian; Ideker, Trey

    2018-01-01

    Analysis of patient genomes and transcriptomes routinely recognizes new gene sets associated with human disease. Here we present an integrative natural language processing system which infers common functions for a gene set through automatic mining of the scientific literature with biological networks. This system links genes with associated literature phrases and combines these links with protein interactions in a single heterogeneous network. Multiscale functional annotations are inferred based on network distances between phrases and genes and then visualized as an ontology of biological concepts. To evaluate this system, we predict functions for gene sets representing known pathways and find that our approach achieves substantial improvement over the conventional text-mining baseline method. Moreover, our system discovers novel annotations for gene sets or pathways without previously known functions. Two case studies demonstrate how the system is used in discovery of new cancer-related pathways with ontological annotations.

  10. Genome-wide association analysis for heat tolerance at flowering detected a large set of genes involved in adaptation to thermal and other stresses.

    Directory of Open Access Journals (Sweden)

    Tanguy Lafarge

    Full Text Available Fertilization sensitivity to heat in rice is a major issue within climate change scenarios in the tropics. A panel of 167 indica landraces and improved varieties was phenotyped for spikelet sterility (SPKST under 38°C during anthesis and for several secondary traits potentially affecting panicle micro-climate and thus the fertilization process. The panel was genotyped with an average density of one marker per 29 kb using genotyping by sequencing. Genome-wide association analyses (GWAS were conducted using three methods based on single marker regression, haplotype regression and simultaneous fitting of all markers, respectively. Fourteen loci significantly associated with SPKST under at least two GWAS methods were detected. A large number of associations was also detected for the secondary traits. Analysis of co-localization of SPKST associated loci with QTLs detected in progenies of bi-parental crosses reported in the literature allowed to narrow -down the position of eight of those QTLs, including the most documented one, qHTSF4.1. Gene families underlying loci associated with SPKST corresponded to functions ranging from sensing abiotic stresses and regulating plant response, such as wall-associated kinases and heat shock proteins, to cell division and gametophyte development. Analysis of diversity at the vicinity of loci associated with SPKST within the rice three thousand genomes, revealed widespread distribution of the favourable alleles across O. sativa genetic groups. However, few accessions assembled the favourable alleles at all loci. Effective donors included the heat tolerant variety N22 and some Indian and Taiwanese varieties. These results provide a basis for breeding for heat tolerance during anthesis and for functional validation of major loci governing this trait.

  11. Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data

    Science.gov (United States)

    2013-01-01

    Background Availability of chemical response-specific lists of genes (gene sets) for pharmacological and/or toxic effect prediction for compounds is limited. We hypothesize that more gene sets can be created by next-generation text mining (next-gen TM), and that these can be used with gene set analysis (GSA) methods for chemical treatment identification, for pharmacological mechanism elucidation, and for comparing compound toxicity profiles. Methods We created 30,211 chemical response-specific gene sets for human and mouse by next-gen TM, and derived 1,189 (human) and 588 (mouse) gene sets from the Comparative Toxicogenomics Database (CTD). We tested for significant differential expression (SDE) (false discovery rate -corrected p-values sets and the CTD-derived gene sets in gene expression (GE) data sets of five chemicals (from experimental models). We tested for SDE of gene sets for six fibrates in a peroxisome proliferator-activated receptor alpha (PPARA) knock-out GE dataset and compared to results from the Connectivity Map. We tested for SDE of 319 next-gen TM-derived gene sets for environmental toxicants in three GE data sets of triazoles, and tested for SDE of 442 gene sets associated with embryonic structures. We compared the gene sets to triazole effects seen in the Whole Embryo Culture (WEC), and used principal component analysis (PCA) to discriminate triazoles from other chemicals. Results Next-gen TM-derived gene sets matching the chemical treatment were significantly altered in three GE data sets, and the corresponding CTD-derived gene sets were significantly altered in five GE data sets. Six next-gen TM-derived and four CTD-derived fibrate gene sets were significantly altered in the PPARA knock-out GE dataset. None of the fibrate signatures in cMap scored significant against the PPARA GE signature. 33 environmental toxicant gene sets were significantly altered in the triazole GE data sets. 21 of these toxicants had a similar toxicity pattern as the

  12. APPRIS 2017: principal isoforms for multiple gene sets

    Science.gov (United States)

    Rodriguez-Rivas, Juan; Di Domenico, Tomás; Vázquez, Jesús; Valencia, Alfonso

    2018-01-01

    Abstract The APPRIS database (http://appris-tools.org) uses protein structural and functional features and information from cross-species conservation to annotate splice isoforms in protein-coding genes. APPRIS selects a single protein isoform, the ‘principal’ isoform, as the reference for each gene based on these annotations. A single main splice isoform reflects the biological reality for most protein coding genes and APPRIS principal isoforms are the best predictors of these main proteins isoforms. Here, we present the updates to the database, new developments that include the addition of three new species (chimpanzee, Drosophila melangaster and Caenorhabditis elegans), the expansion of APPRIS to cover the RefSeq gene set and the UniProtKB proteome for six species and refinements in the core methods that make up the annotation pipeline. In addition APPRIS now provides a measure of reliability for individual principal isoforms and updates with each release of the GENCODE/Ensembl and RefSeq reference sets. The individual GENCODE/Ensembl, RefSeq and UniProtKB reference gene sets for six organisms have been merged to produce common sets of splice variants. PMID:29069475

  13. CAsubtype: An R Package to Identify Gene Sets Predictive of Cancer Subtypes and Clinical Outcomes.

    Science.gov (United States)

    Kong, Hualei; Tong, Pan; Zhao, Xiaodong; Sun, Jielin; Li, Hua

    2018-03-01

    In the past decade, molecular classification of cancer has gained high popularity owing to its high predictive power on clinical outcomes as compared with traditional methods commonly used in clinical practice. In particular, using gene expression profiles, recent studies have successfully identified a number of gene sets for the delineation of cancer subtypes that are associated with distinct prognosis. However, identification of such gene sets remains a laborious task due to the lack of tools with flexibility, integration and ease of use. To reduce the burden, we have developed an R package, CAsubtype, to efficiently identify gene sets predictive of cancer subtypes and clinical outcomes. By integrating more than 13,000 annotated gene sets, CAsubtype provides a comprehensive repertoire of candidates for new cancer subtype identification. For easy data access, CAsubtype further includes the gene expression and clinical data of more than 2000 cancer patients from TCGA. CAsubtype first employs principal component analysis to identify gene sets (from user-provided or package-integrated ones) with robust principal components representing significantly large variation between cancer samples. Based on these principal components, CAsubtype visualizes the sample distribution in low-dimensional space for better understanding of the distinction between samples and classifies samples into subgroups with prevalent clustering algorithms. Finally, CAsubtype performs survival analysis to compare the clinical outcomes between the identified subgroups, assessing their clinical value as potentially novel cancer subtypes. In conclusion, CAsubtype is a flexible and well-integrated tool in the R environment to identify gene sets for cancer subtype identification and clinical outcome prediction. Its simple R commands and comprehensive data sets enable efficient examination of the clinical value of any given gene set, thus facilitating hypothesis generating and testing in biological and

  14. FunGeneNet: a web tool to estimate enrichment of functional interactions in experimental gene sets.

    Science.gov (United States)

    Tiys, Evgeny S; Ivanisenko, Timofey V; Demenkov, Pavel S; Ivanisenko, Vladimir A

    2018-02-09

    Estimation of functional connectivity in gene sets derived from genome-wide or other biological experiments is one of the essential tasks of bioinformatics. A promising approach for solving this problem is to compare gene networks built using experimental gene sets with random networks. One of the resources that make such an analysis possible is CrossTalkZ, which uses the FunCoup database. However, existing methods, including CrossTalkZ, do not take into account individual types of interactions, such as protein/protein interactions, expression regulation, transport regulation, catalytic reactions, etc., but rather work with generalized types characterizing the existence of any connection between network members. We developed the online tool FunGeneNet, which utilizes the ANDSystem and STRING to reconstruct gene networks using experimental gene sets and to estimate their difference from random networks. To compare the reconstructed networks with random ones, the node permutation algorithm implemented in CrossTalkZ was taken as a basis. To study the FunGeneNet applicability, the functional connectivity analysis of networks constructed for gene sets involved in the Gene Ontology biological processes was conducted. We showed that the method sensitivity exceeds 0.8 at a specificity of 0.95. We found that the significance level of the difference between gene networks of biological processes and random networks is determined by the type of connections considered between objects. At the same time, the highest reliability is achieved for the generalized form of connections that takes into account all the individual types of connections. By taking examples of the thyroid cancer networks and the apoptosis network, it is demonstrated that key participants in these processes are involved in the interactions of those types by which these networks differ from random ones. FunGeneNet is a web tool aimed at proving the functionality of networks in a wide range of sizes of

  15. GeneTopics - interpretation of gene sets via literature-driven topic models

    Science.gov (United States)

    2013-01-01

    Background Annotation of a set of genes is often accomplished through comparison to a library of labelled gene sets such as biological processes or canonical pathways. However, this approach might fail if the employed libraries are not up to date with the latest research, don't capture relevant biological themes or are curated at a different level of granularity than is required to appropriately analyze the input gene set. At the same time, the vast biomedical literature offers an unstructured repository of the latest research findings that can be tapped to provide thematic sub-groupings for any input gene set. Methods Our proposed method relies on a gene-specific text corpus and extracts commonalities between documents in an unsupervised manner using a topic model approach. We automatically determine the number of topics summarizing the corpus and calculate a gene relevancy score for each topic allowing us to eliminate non-specific topics. As a result we obtain a set of literature topics in which each topic is associated with a subset of the input genes providing directly interpretable keywords and corresponding documents for literature research. Results We validate our method based on labelled gene sets from the KEGG metabolic pathway collection and the genetic association database (GAD) and show that the approach is able to detect topics consistent with the labelled annotation. Furthermore, we discuss the results on three different types of experimentally derived gene sets, (1) differentially expressed genes from a cardiac hypertrophy experiment in mice, (2) altered transcript abundance in human pancreatic beta cells, and (3) genes implicated by GWA studies to be associated with metabolite levels in a healthy population. In all three cases, we are able to replicate findings from the original papers in a quick and semi-automated manner. Conclusions Our approach provides a novel way of automatically generating meaningful annotations for gene sets that are directly

  16. Gene expression analysis by cDNA-AFLP highlights a set of new signaling networks and translational control during seed dormancy breaking in Nicotiana plumbaginifolia.

    Science.gov (United States)

    Bove, Jérôme; Lucas, Philippe; Godin, Béatrice; Ogé, Laurent; Jullien, Marc; Grappin, Philippe

    2005-03-01

    Seed dormancy in Nicotiana plumbaginifolia is characterized by an abscisic acid accumulation linked to a pronounced germination delay. Dormancy can be released by 1 year after-ripening treatment. Using a cDNA-amplified fragment length polymorphism (cDNA-AFLP) approach we compared the gene expression patterns of dormant and after-ripened seeds, air-dry or during one day imbibition and analyzed 15,000 cDNA fragments. Among them 1020 were found to be differentially regulated by dormancy. Of 412 sequenced cDNA fragments, 83 were assigned to a known function by search similarities to public databases. The functional categories of the identified dormancy maintenance and breaking responsive genes, give evidence that after-ripening turns in the air-dry seed to a new developmental program that modulates, at the RNA level, components of translational control, signaling networks, transcriptional control and regulated proteolysis.

  17. Genome-wide survey and developmental expression mapping of zebrafish SET domain-containing genes.

    Directory of Open Access Journals (Sweden)

    Xiao-Jian Sun

    Full Text Available SET domain-containing proteins represent an evolutionarily conserved family of epigenetic regulators, which are responsible for most histone lysine methylation. Since some of these genes have been revealed to be essential for embryonic development, we propose that the zebrafish, a vertebrate model organism possessing many advantages for developmental studies, can be utilized to study the biological functions of these genes and the related epigenetic mechanisms during early development. To this end, we have performed a genome-wide survey of zebrafish SET domain genes. 58 genes total have been identified. Although gene duplication events give rise to several lineage-specific paralogs, clear reciprocal orthologous relationship reveals high conservation between zebrafish and human SET domain genes. These data were further subject to an evolutionary analysis ranging from yeast to human, leading to the identification of putative clusters of orthologous groups (COGs of this gene family. By means of whole-mount mRNA in situ hybridization strategy, we have also carried out a developmental expression mapping of these genes. A group of maternal SET domain genes, which are implicated in the programming of histone modification states in early development, have been identified and predicted to be responsible for all known sites of SET domain-mediated histone methylation. Furthermore, some genes show specific expression patterns in certain tissues at certain stages, suggesting the involvement of epigenetic mechanisms in the development of these systems. These results provide a global view of zebrafish SET domain histone methyltransferases in evolutionary and developmental dimensions and pave the way for using zebrafish to systematically study the roles of these genes during development.

  18. Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data

    Directory of Open Access Journals (Sweden)

    Hettne Kristina M

    2013-01-01

    Full Text Available Abstract Background Availability of chemical response-specific lists of genes (gene sets for pharmacological and/or toxic effect prediction for compounds is limited. We hypothesize that more gene sets can be created by next-generation text mining (next-gen TM, and that these can be used with gene set analysis (GSA methods for chemical treatment identification, for pharmacological mechanism elucidation, and for comparing compound toxicity profiles. Methods We created 30,211 chemical response-specific gene sets for human and mouse by next-gen TM, and derived 1,189 (human and 588 (mouse gene sets from the Comparative Toxicogenomics Database (CTD. We tested for significant differential expression (SDE (false discovery rate -corrected p-values Results Next-gen TM-derived gene sets matching the chemical treatment were significantly altered in three GE data sets, and the corresponding CTD-derived gene sets were significantly altered in five GE data sets. Six next-gen TM-derived and four CTD-derived fibrate gene sets were significantly altered in the PPARA knock-out GE dataset. None of the fibrate signatures in cMap scored significant against the PPARA GE signature. 33 environmental toxicant gene sets were significantly altered in the triazole GE data sets. 21 of these toxicants had a similar toxicity pattern as the triazoles. We confirmed embryotoxic effects, and discriminated triazoles from other chemicals. Conclusions Gene set analysis with next-gen TM-derived chemical response-specific gene sets is a scalable method for identifying similarities in gene responses to other chemicals, from which one may infer potential mode of action and/or toxic effect.

  19. Integrating genome-wide association study and expression quantitative trait loci data identifies multiple genes and gene set associated with neuroticism.

    Science.gov (United States)

    Fan, Qianrui; Wang, Wenyu; Hao, Jingcan; He, Awen; Wen, Yan; Guo, Xiong; Wu, Cuiyan; Ning, Yujie; Wang, Xi; Wang, Sen; Zhang, Feng

    2017-08-01

    Neuroticism is a fundamental personality trait with significant genetic determinant. To identify novel susceptibility genes for neuroticism, we conducted an integrative analysis of genomic and transcriptomic data of genome wide association study (GWAS) and expression quantitative trait locus (eQTL) study. GWAS summary data was driven from published studies of neuroticism, totally involving 170,906 subjects. eQTL dataset containing 927,753 eQTLs were obtained from an eQTL meta-analysis of 5311 samples. Integrative analysis of GWAS and eQTL data was conducted by summary data-based Mendelian randomization (SMR) analysis software. To identify neuroticism associated gene sets, the SMR analysis results were further subjected to gene set enrichment analysis (GSEA). The gene set annotation dataset (containing 13,311 annotated gene sets) of GSEA Molecular Signatures Database was used. SMR single gene analysis identified 6 significant genes for neuroticism, including MSRA (p value=2.27×10 -10 ), MGC57346 (p value=6.92×10 -7 ), BLK (p value=1.01×10 -6 ), XKR6 (p value=1.11×10 -6 ), C17ORF69 (p value=1.12×10 -6 ) and KIAA1267 (p value=4.00×10 -6 ). Gene set enrichment analysis observed significant association for Chr8p23 gene set (false discovery rate=0.033). Our results provide novel clues for the genetic mechanism studies of neuroticism. Copyright © 2017. Published by Elsevier Inc.

  20. Identification of a conserved set of upregulated genes in mouse skeletal muscle hypertrophy and regrowth.

    Science.gov (United States)

    Chaillou, Thomas; Jackson, Janna R; England, Jonathan H; Kirby, Tyler J; Richards-White, Jena; Esser, Karyn A; Dupont-Versteegden, Esther E; McCarthy, John J

    2015-01-01

    The purpose of this study was to compare the gene expression profile of mouse skeletal muscle undergoing two forms of growth (hypertrophy and regrowth) with the goal of identifying a conserved set of differentially expressed genes. Expression profiling by microarray was performed on the plantaris muscle subjected to 1, 3, 5, 7, 10, and 14 days of hypertrophy or regrowth following 2 wk of hind-limb suspension. We identified 97 differentially expressed genes (≥2-fold increase or ≥50% decrease compared with control muscle) that were conserved during the two forms of muscle growth. The vast majority (∼90%) of the differentially expressed genes was upregulated and occurred at a single time point (64 out of 86 genes), which most often was on the first day of the time course. Microarray analysis from the conserved upregulated genes showed a set of genes related to contractile apparatus and stress response at day 1, including three genes involved in mechanotransduction and four genes encoding heat shock proteins. Our analysis further identified three cell cycle-related genes at day and several genes associated with extracellular matrix (ECM) at both days 3 and 10. In conclusion, we have identified a core set of genes commonly upregulated in two forms of muscle growth that could play a role in the maintenance of sarcomere stability, ECM remodeling, cell proliferation, fast-to-slow fiber type transition, and the regulation of skeletal muscle growth. These findings suggest conserved regulatory mechanisms involved in the adaptation of skeletal muscle to increased mechanical loading. Copyright © 2015 the American Physiological Society.

  1. Gene set analysis of GWAS data for human longevity highlights the relevance of the insulin/IGF-1 signaling and telomere maintenance pathways

    NARCIS (Netherlands)

    J. Deelen (Joris); H.-W. Uh; R. Monajemi (Ramin); D. van Heemst (Diana); P.E. Thijssen (Peter); S. Böhringer (Stefan); E.B. van den Akker (Erik); A.J.M. de Craen (Anton J.M.); F. Rivadeneira Ramirez (Fernando); R.G.J. Westendorp (Rudi); A.G. Uitterlinden (André); J.J. Goeman (Jelle); P.E. Slagboom (Eline); J.J. Houwing-Duistermaat (Jeanine); M. Beekman (Marian)

    2013-01-01

    textabstractIn genome-wide association studies (GWAS) of complex traits, single SNP analysis is still the most applied approach. However, the identified SNPs have small effects and provide limited biological insight. A more appropriate approach to interpret GWAS data of complex traits is to analyze

  2. Set theoretical aspects of real analysis

    CERN Document Server

    Kharazishvili, Alexander B

    2014-01-01

    This book addresses a number of questions in real analysis and classical measure theory that are of a set-theoretic flavor. Accessible to graduate students, the beginning of the book presents introductory topics on real analysis and Lebesque measure theory. These topics highlight the boundary between fundamental concepts of measurability and non-measurability for point sets and functions. The remainder of the book deals with more specialized material on set-theoretical real analysis. Problems are included at the end of each chapter.

  3. Function analysis of unknown genes

    DEFF Research Database (Denmark)

    Rogowska-Wrzesinska, A.

    2002-01-01

      This thesis entitled "Function analysis of unknown genes" presents the use of proteome analysis for the characterisation of yeast (Saccharomyces cerevisiae) genes and their products (proteins especially those of unknown function). This study illustrates that proteome analysis can be used...... to describe different aspects of molecular biology of the cell, to study changes that occur in the cell due to overexpression or deletion of a gene and to identify various protein modifications. The biological questions and the results of the described studies show the diversity of the information that can...... genes and proteins. It reports the first global proteome database collecting 36 yeast single gene deletion mutants and selecting over 650 differences between analysed mutants and the wild type strain. The obtained results show that two-dimensional gel electrophoresis and mass spectrometry based proteome...

  4. Common-cause analysis using sets

    International Nuclear Information System (INIS)

    Worrell, R.B.; Stack, D.W.

    1977-12-01

    Common-cause analysis was developed at the Aerojet Nuclear Company for studying the behavior of a system that is affected by special conditions and secondary causes. Common-cause analysis is related to fault tree analysis. Common-cause candidates are minimal cut sets whose primary events are closely linked by a special condition or are susceptible to the same secondary cause. It is shown that common-cause candidates can be identified using the Set Equation Transformation System (SETS). A Boolean equation is used to establish the special conditions and secondary cause susceptibilities for each primary event in the fault tree. A transformation of variables (substituting equals for equals), executed on a minimal cut set equation, results in replacing each primary event by the right side of its special condition/secondary cause equation and leads to the identification of the common-cause candidates

  5. Glutamatergic and GABAergic gene sets in attention-deficit/hyperactivity disorder

    DEFF Research Database (Denmark)

    Naaijen, Jill; Bralten, Janita; Poelmans, Geert

    2017-01-01

    Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorders (ASD) often co-occur. Both are highly heritable; however, it has been difficult to discover genetic risk variants. Glutamate and GABA are main excitatory and inhibitory neurotransmitters in the brain; their balance...... within glutamatergic and GABAergic genes were investigated using the MAGMA software in an ADHD case-only sample (n=931), in which we assessed ASD symptoms and response inhibition on a Stop task. Gene set analysis for ADHD symptom severity, divided into inattention and hyperactivity/impulsivity symptoms...... is essential for proper brain development and functioning. In this study we investigated the role of glutamate and GABA genetics in ADHD severity, autism symptom severity and inhibitory performance, based on gene set analysis, an approach to investigate multiple genetic variants simultaneously. Common variants...

  6. Intervene: a tool for intersection and visualization of multiple gene or genomic region sets.

    Science.gov (United States)

    Khan, Aziz; Mathelier, Anthony

    2017-05-31

    A common task for scientists relies on comparing lists of genes or genomic regions derived from high-throughput sequencing experiments. While several tools exist to intersect and visualize sets of genes, similar tools dedicated to the visualization of genomic region sets are currently limited. To address this gap, we have developed the Intervene tool, which provides an easy and automated interface for the effective intersection and visualization of genomic region or list sets, thus facilitating their analysis and interpretation. Intervene contains three modules: venn to generate Venn diagrams of up to six sets, upset to generate UpSet plots of multiple sets, and pairwise to compute and visualize intersections of multiple sets as clustered heat maps. Intervene, and its interactive web ShinyApp companion, generate publication-quality figures for the interpretation of genomic region and list sets. Intervene and its web application companion provide an easy command line and an interactive web interface to compute intersections of multiple genomic and list sets. They have the capacity to plot intersections using easy-to-interpret visual approaches. Intervene is developed and designed to meet the needs of both computer scientists and biologists. The source code is freely available at https://bitbucket.org/CBGR/intervene , with the web application available at https://asntech.shinyapps.io/intervene .

  7. Gene coexpression network analysis as a source of functional annotation for rice genes.

    Directory of Open Access Journals (Sweden)

    Kevin L Childs

    Full Text Available With the existence of large publicly available plant gene expression data sets, many groups have undertaken data analyses to construct gene coexpression networks and functionally annotate genes. Often, a large compendium of unrelated or condition-independent expression data is used to construct gene networks. Condition-dependent expression experiments consisting of well-defined conditions/treatments have also been used to create coexpression networks to help examine particular biological processes. Gene networks derived from either condition-dependent or condition-independent data can be difficult to interpret if a large number of genes and connections are present. However, algorithms exist to identify modules of highly connected and biologically relevant genes within coexpression networks. In this study, we have used publicly available rice (Oryza sativa gene expression data to create gene coexpression networks using both condition-dependent and condition-independent data and have identified gene modules within these networks using the Weighted Gene Coexpression Network Analysis method. We compared the number of genes assigned to modules and the biological interpretability of gene coexpression modules to assess the utility of condition-dependent and condition-independent gene coexpression networks. For the purpose of providing functional annotation to rice genes, we found that gene modules identified by coexpression analysis of condition-dependent gene expression experiments to be more useful than gene modules identified by analysis of a condition-independent data set. We have incorporated our results into the MSU Rice Genome Annotation Project database as additional expression-based annotation for 13,537 genes, 2,980 of which lack a functional annotation description. These results provide two new types of functional annotation for our database. Genes in modules are now associated with groups of genes that constitute a collective functional

  8. Human Error Analysis by Fuzzy-Set

    International Nuclear Information System (INIS)

    Situmorang, Johnny

    1996-01-01

    In conventional HRA the probability of Error is treated as a single and exact value through constructing even tree, but in this moment the Fuzzy-Set Theory is used. Fuzzy set theory treat the probability of error as a plausibility which illustrate a linguistic variable. Most parameter or variable in human engineering been defined verbal good, fairly good, worst etc. Which describe a range of any value of probability. For example this analysis is quantified the human error in calibration task, and the probability of miscalibration is very low

  9. Identification of a set of genes showing regionally enriched expression in the mouse brain

    Directory of Open Access Journals (Sweden)

    Marra Marco A

    2008-07-01

    Full Text Available Abstract Background The Pleiades Promoter Project aims to improve gene therapy by designing human mini-promoters ( Results We have utilized LongSAGE to identify regionally enriched transcripts in the adult mouse brain. As supplemental strategies, we also performed a meta-analysis of published literature and inspected the Allen Brain Atlas in situ hybridization data. From a set of approximately 30,000 mouse genes, 237 were identified as showing specific or enriched expression in 30 target regions of the mouse brain. GO term over-representation among these genes revealed co-involvement in various aspects of central nervous system development and physiology. Conclusion Using a multi-faceted expression validation approach, we have identified mouse genes whose human orthologs are good candidates for design of mini-promoters. These mouse genes represent molecular markers in several discrete brain regions/cell-types, which could potentially provide a mechanistic explanation of unique functions performed by each region. This set of markers may also serve as a resource for further studies of gene regulatory elements influencing brain expression.

  10. ANALYSIS OF FORMING TREAD WHEEL SETS

    Directory of Open Access Journals (Sweden)

    Igor IVANOV

    2017-09-01

    Full Text Available This paper shows the results of a theoretical study of profile high-speed grinding (PHSG for forming tread wheel sets during repair instead of turning and mold-milling. Significant disadvantages of these methods are low capacity to adapt to the tool and inhomogeneous structure of the wheel material. This leads to understated treatment regimens and difficulties in recovering wheel sets with thermal and mechanical defects. This study carried out modeling and analysis of emerging cutting forces. Proposed algorithms describe the random occurrence of the components of the cutting forces in the restoration profile of wheel sets with an inhomogeneous structure of the material. To identify the statistical features of randomly generated structures fractal dimension and the method of random additions were used. The multifractal spectrum formed is decomposed into monofractals by wavelet transform. The proposed method allows you to create the preconditions for controlling the parameters of the treatment process.

  11. Optimal structural inference of signaling pathways from unordered and overlapping gene sets.

    Science.gov (United States)

    Acharya, Lipi R; Judeh, Thair; Wang, Guangdi; Zhu, Dongxiao

    2012-02-15

    A plethora of bioinformatics analysis has led to the discovery of numerous gene sets, which can be interpreted as discrete measurements emitted from latent signaling pathways. Their potential to infer signaling pathway structures, however, has not been sufficiently exploited. Existing methods accommodating discrete data do not explicitly consider signal cascading mechanisms that characterize a signaling pathway. Novel computational methods are thus needed to fully utilize gene sets and broaden the scope from focusing only on pairwise interactions to the more general cascading events in the inference of signaling pathway structures. We propose a gene set based simulated annealing (SA) algorithm for the reconstruction of signaling pathway structures. A signaling pathway structure is a directed graph containing up to a few hundred nodes and many overlapping signal cascades, where each cascade represents a chain of molecular interactions from the cell surface to the nucleus. Gene sets in our context refer to discrete sets of genes participating in signal cascades, the basic building blocks of a signaling pathway, with no prior information about gene orderings in the cascades. From a compendium of gene sets related to a pathway, SA aims to search for signal cascades that characterize the optimal signaling pathway structure. In the search process, the extent of overlap among signal cascades is used to measure the optimality of a structure. Throughout, we treat gene sets as random samples from a first-order Markov chain model. We evaluated the performance of SA in three case studies. In the first study conducted on 83 KEGG pathways, SA demonstrated a significantly better performance than Bayesian network methods. Since both SA and Bayesian network methods accommodate discrete data, use a 'search and score' network learning strategy and output a directed network, they can be compared in terms of performance and computational time. In the second study, we compared SA and

  12. Identification of a robust gene signature that predicts breast cancer outcome in independent data sets

    International Nuclear Information System (INIS)

    Korkola, James E; Waldman, Frederic M; Blaveri, Ekaterina; DeVries, Sandy; Moore, Dan H II; Hwang, E Shelley; Chen, Yunn-Yi; Estep, Anne LH; Chew, Karen L; Jensen, Ronald H

    2007-01-01

    Breast cancer is a heterogeneous disease, presenting with a wide range of histologic, clinical, and genetic features. Microarray technology has shown promise in predicting outcome in these patients. We profiled 162 breast tumors using expression microarrays to stratify tumors based on gene expression. A subset of 55 tumors with extensive follow-up was used to identify gene sets that predicted outcome. The predictive gene set was further tested in previously published data sets. We used different statistical methods to identify three gene sets associated with disease free survival. A fourth gene set, consisting of 21 genes in common to all three sets, also had the ability to predict patient outcome. To validate the predictive utility of this derived gene set, it was tested in two published data sets from other groups. This gene set resulted in significant separation of patients on the basis of survival in these data sets, correctly predicting outcome in 62–65% of patients. By comparing outcome prediction within subgroups based on ER status, grade, and nodal status, we found that our gene set was most effective in predicting outcome in ER positive and node negative tumors. This robust gene selection with extensive validation has identified a predictive gene set that may have clinical utility for outcome prediction in breast cancer patients

  13. SETS, Boolean Manipulation for Network Analysis and Fault Tree Analysis

    International Nuclear Information System (INIS)

    Worrell, R.B.

    1985-01-01

    Description of problem or function - SETS is used for symbolic manipulation of set (or Boolean) equations, particularly the reduction of set equations by the application of set identities. It is a flexible and efficient tool for performing probabilistic risk analysis (PRA), vital area analysis, and common cause analysis. The equation manipulation capabilities of SETS can also be used to analyze non-coherent fault trees and determine prime implicants of Boolean functions, to verify circuit design implementation, to determine minimum cost fire protection requirements for nuclear reactor plants, to obtain solutions to combinatorial optimization problems with Boolean constraints, and to determine the susceptibility of a facility to unauthorized access through nullification of sensors in its protection system. 4. Method of solution - The SETS program is used to read, interpret, and execute the statements of a SETS user program which is an algorithm that specifies the particular manipulations to be performed and the order in which they are to occur. 5. Restrictions on the complexity of the problem - Any properly formed set equation involving the set operations of union, intersection, and complement is acceptable for processing by the SETS program. Restrictions on the size of a set equation that can be processed are not absolute but rather are related to the number of terms in the disjunctive normal form of the equation, the number of literals in the equation, etc. Nevertheless, set equations involving thousands and even hundreds of thousands of terms can be processed successfully

  14. Three gene expression vector sets for concurrently expressing multiple genes in Saccharomyces cerevisiae.

    Science.gov (United States)

    Ishii, Jun; Kondo, Takashi; Makino, Harumi; Ogura, Akira; Matsuda, Fumio; Kondo, Akihiko

    2014-05-01

    Yeast has the potential to be used in bulk-scale fermentative production of fuels and chemicals due to its tolerance for low pH and robustness for autolysis. However, expression of multiple external genes in one host yeast strain is considerably labor-intensive due to the lack of polycistronic transcription. To promote the metabolic engineering of yeast, we generated systematic and convenient genetic engineering tools to express multiple genes in Saccharomyces cerevisiae. We constructed a series of multi-copy and integration vector sets for concurrently expressing two or three genes in S. cerevisiae by embedding three classical promoters. The comparative expression capabilities of the constructed vectors were monitored with green fluorescent protein, and the concurrent expression of genes was monitored with three different fluorescent proteins. Our multiple gene expression tool will be helpful to the advanced construction of genetically engineered yeast strains in a variety of research fields other than metabolic engineering. © 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved.

  15. Evidence for intron length conservation in a set of mammalian genes associated with embryonic development

    LENUS (Irish Health Repository)

    2011-10-05

    Abstract Background We carried out an analysis of intron length conservation across a diverse group of nineteen mammalian species. Motivated by recent research suggesting a role for time delays associated with intron transcription in gene expression oscillations required for early embryonic patterning, we searched for examples of genes that showed the most extreme conservation of total intron content in mammals. Results Gene sets annotated as being involved in pattern specification in the early embryo or containing the homeobox DNA-binding domain, were significantly enriched among genes with highly conserved intron content. We used ancestral sequences reconstructed with probabilistic models that account for insertion and deletion mutations to distinguish insertion and deletion events on lineages leading to human and mouse from their last common ancestor. Using a randomization procedure, we show that genes containing the homeobox domain show less change in intron content than expected, given the number of insertion and deletion events within their introns. Conclusions Our results suggest selection for gene expression precision or the existence of additional development-associated genes for which transcriptional delay is functionally significant.

  16. Glutamatergic and GABAergic gene sets in attention-deficit/hyperactivity disorder: association to overlapping traits in ADHD and autism.

    Science.gov (United States)

    Naaijen, J; Bralten, J; Poelmans, G; Glennon, J C; Franke, B; Buitelaar, J K

    2017-01-10

    Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorders (ASD) often co-occur. Both are highly heritable; however, it has been difficult to discover genetic risk variants. Glutamate and GABA are main excitatory and inhibitory neurotransmitters in the brain; their balance is essential for proper brain development and functioning. In this study we investigated the role of glutamate and GABA genetics in ADHD severity, autism symptom severity and inhibitory performance, based on gene set analysis, an approach to investigate multiple genetic variants simultaneously. Common variants within glutamatergic and GABAergic genes were investigated using the MAGMA software in an ADHD case-only sample (n=931), in which we assessed ASD symptoms and response inhibition on a Stop task. Gene set analysis for ADHD symptom severity, divided into inattention and hyperactivity/impulsivity symptoms, autism symptom severity and inhibition were performed using principal component regression analyses. Subsequently, gene-wide association analyses were performed. The glutamate gene set showed an association with severity of hyperactivity/impulsivity (P=0.009), which was robust to correcting for genome-wide association levels. The GABA gene set showed nominally significant association with inhibition (P=0.04), but this did not survive correction for multiple comparisons. None of single gene or single variant associations was significant on their own. By analyzing multiple genetic variants within candidate gene sets together, we were able to find genetic associations supporting the involvement of excitatory and inhibitory neurotransmitter systems in ADHD and ASD symptom severity in ADHD.

  17. Shrinkage covariance matrix approach based on robust trimmed mean in gene sets detection

    Science.gov (United States)

    Karjanto, Suryaefiza; Ramli, Norazan Mohamed; Ghani, Nor Azura Md; Aripin, Rasimah; Yusop, Noorezatty Mohd

    2015-02-01

    Microarray involves of placing an orderly arrangement of thousands of gene sequences in a grid on a suitable surface. The technology has made a novelty discovery since its development and obtained an increasing attention among researchers. The widespread of microarray technology is largely due to its ability to perform simultaneous analysis of thousands of genes in a massively parallel manner in one experiment. Hence, it provides valuable knowledge on gene interaction and function. The microarray data set typically consists of tens of thousands of genes (variables) from just dozens of samples due to various constraints. Therefore, the sample covariance matrix in Hotelling's T2 statistic is not positive definite and become singular, thus it cannot be inverted. In this research, the Hotelling's T2 statistic is combined with a shrinkage approach as an alternative estimation to estimate the covariance matrix to detect significant gene sets. The use of shrinkage covariance matrix overcomes the singularity problem by converting an unbiased to an improved biased estimator of covariance matrix. Robust trimmed mean is integrated into the shrinkage matrix to reduce the influence of outliers and consequently increases its efficiency. The performance of the proposed method is measured using several simulation designs. The results are expected to outperform existing techniques in many tested conditions.

  18. Gene set-based module discovery in the breast cancer transcriptome

    Directory of Open Access Journals (Sweden)

    Zhang Michael Q

    2009-02-01

    Full Text Available Abstract Background Although microarray-based studies have revealed global view of gene expression in cancer cells, we still have little knowledge about regulatory mechanisms underlying the transcriptome. Several computational methods applied to yeast data have recently succeeded in identifying expression modules, which is defined as co-expressed gene sets under common regulatory mechanisms. However, such module discovery methods are not applied cancer transcriptome data. Results In order to decode oncogenic regulatory programs in cancer cells, we developed a novel module discovery method termed EEM by extending a previously reported module discovery method, and applied it to breast cancer expression data. Starting from seed gene sets prepared based on cis-regulatory elements, ChIP-chip data, and gene locus information, EEM identified 10 principal expression modules in breast cancer based on their expression coherence. Moreover, EEM depicted their activity profiles, which predict regulatory programs in each subtypes of breast tumors. For example, our analysis revealed that the expression module regulated by the Polycomb repressive complex 2 (PRC2 is downregulated in triple negative breast cancers, suggesting similarity of transcriptional programs between stem cells and aggressive breast cancer cells. We also found that the activity of the PRC2 expression module is negatively correlated to the expression of EZH2, a component of PRC2 which belongs to the E2F expression module. E2F-driven EZH2 overexpression may be responsible for the repression of the PRC2 expression modules in triple negative tumors. Furthermore, our network analysis predicts regulatory circuits in breast cancer cells. Conclusion These results demonstrate that the gene set-based module discovery approach is a powerful tool to decode regulatory programs in cancer cells.

  19. Music analysis and point-set compression

    DEFF Research Database (Denmark)

    Meredith, David

    A musical analysis represents a particular way of understanding certain aspects of the structure of a piece of music. The quality of an analysis can be evaluated to some extent by the degree to which knowledge of it improves performance on tasks such as mistake spotting, memorising a piece...... as the minimum description length principle and relates closely to certain ideas in the theory of Kolmogorov complexity. Inspired by this general principle, the hypothesis explored in this paper is that the best ways of understanding (or explanations for) a piece of music are those that are represented...... by the shortest possible descriptions of the piece. With this in mind, two compression algorithms are presented, COSIATEC and SIATECCompress. Each of these algorithms takes as input an in extenso description of a piece of music as a set of points in pitch-time space representing notes. Each algorithm...

  20. Evaluation of endogenous control genes for gene expression studies across multiple tissues and in the specific sets of fat- and muscle-type samples of the pig.

    Science.gov (United States)

    Gu, Y R; Li, M Z; Zhang, K; Chen, L; Jiang, A A; Wang, J Y; Li, X W

    2011-08-01

    To normalize a set of quantitative real-time PCR (q-PCR) data, it is essential to determine an optimal number/set of housekeeping genes, as the abundance of housekeeping genes can vary across tissues or cells during different developmental stages, or even under certain environmental conditions. In this study, of the 20 commonly used endogenous control genes, 13, 18 and 17 genes exhibited credible stability in 56 different tissues, 10 types of adipose tissue and five types of muscle tissue, respectively. Our analysis clearly showed that three optimal housekeeping genes are adequate for an accurate normalization, which correlated well with the theoretical optimal number (r ≥ 0.94). In terms of economical and experimental feasibility, we recommend the use of the three most stable housekeeping genes for calculating the normalization factor. Based on our results, the three most stable housekeeping genes in all analysed samples (TOP2B, HSPCB and YWHAZ) are recommended for accurate normalization of q-PCR data. We also suggest that two different sets of housekeeping genes are appropriate for 10 types of adipose tissue (the HSPCB, ALDOA and GAPDH genes) and five types of muscle tissue (the TOP2B, HSPCB and YWHAZ genes), respectively. Our report will serve as a valuable reference for other studies aimed at measuring tissue-specific mRNA abundance in porcine samples. © 2011 Blackwell Verlag GmbH.

  1. Beyond main effects of gene-sets: harsh parenting moderates the association between a dopamine gene-set and child externalizing behavior.

    Science.gov (United States)

    Windhorst, Dafna A; Mileva-Seitz, Viara R; Rippe, Ralph C A; Tiemeier, Henning; Jaddoe, Vincent W V; Verhulst, Frank C; van IJzendoorn, Marinus H; Bakermans-Kranenburg, Marian J

    2016-08-01

    In a longitudinal cohort study, we investigated the interplay of harsh parenting and genetic variation across a set of functionally related dopamine genes, in association with children's externalizing behavior. This is one of the first studies to employ gene-based and gene-set approaches in tests of Gene by Environment (G × E) effects on complex behavior. This approach can offer an important alternative or complement to candidate gene and genome-wide environmental interaction (GWEI) studies in the search for genetic variation underlying individual differences in behavior. Genetic variants in 12 autosomal dopaminergic genes were available in an ethnically homogenous part of a population-based cohort. Harsh parenting was assessed with maternal (n = 1881) and paternal (n = 1710) reports at age 3. Externalizing behavior was assessed with the Child Behavior Checklist (CBCL) at age 5 (71 ± 3.7 months). We conducted gene-set analyses of the association between variation in dopaminergic genes and externalizing behavior, stratified for harsh parenting. The association was statistically significant or approached significance for children without harsh parenting experiences, but was absent in the group with harsh parenting. Similarly, significant associations between single genes and externalizing behavior were only found in the group without harsh parenting. Effect sizes in the groups with and without harsh parenting did not differ significantly. Gene-environment interaction tests were conducted for individual genetic variants, resulting in two significant interaction effects (rs1497023 and rs4922132) after correction for multiple testing. Our findings are suggestive of G × E interplay, with associations between dopamine genes and externalizing behavior present in children without harsh parenting, but not in children with harsh parenting experiences. Harsh parenting may overrule the role of genetic factors in externalizing behavior. Gene-based and gene-set

  2. MADS goes genomic in conifers: towards determining the ancestral set of MADS-box genes in seed plants.

    Science.gov (United States)

    Gramzow, Lydia; Weilandt, Lisa; Theißen, Günter

    2014-11-01

    MADS-box genes comprise a gene family coding for transcription factors. This gene family expanded greatly during land plant evolution such that the number of MADS-box genes ranges from one or two in green algae to around 100 in angiosperms. Given the crucial functions of MADS-box genes for nearly all aspects of plant development, the expansion of this gene family probably contributed to the increasing complexity of plants. However, the expansion of MADS-box genes during one important step of land plant evolution, namely the origin of seed plants, remains poorly understood due to the previous lack of whole-genome data for gymnosperms. The newly available genome sequences of Picea abies, Picea glauca and Pinus taeda were used to identify the complete set of MADS-box genes in these conifers. In addition, MADS-box genes were identified in the growing number of transcriptomes available for gymnosperms. With these datasets, phylogenies were constructed to determine the ancestral set of MADS-box genes of seed plants and to infer the ancestral functions of these genes. Type I MADS-box genes are under-represented in gymnosperms and only a minimum of two Type I MADS-box genes have been present in the most recent common ancestor (MRCA) of seed plants. In contrast, a large number of Type II MADS-box genes were found in gymnosperms. The MRCA of extant seed plants probably possessed at least 11-14 Type II MADS-box genes. In gymnosperms two duplications of Type II MADS-box genes were found, such that the MRCA of extant gymnosperms had at least 14-16 Type II MADS-box genes. The implied ancestral set of MADS-box genes for seed plants shows simplicity for Type I MADS-box genes and remarkable complexity for Type II MADS-box genes in terms of phylogeny and putative functions. The analysis of transcriptome data reveals that gymnosperm MADS-box genes are expressed in a great variety of tissues, indicating diverse roles of MADS-box genes for the development of gymnosperms. This study is

  3. Systems-based biological concordance and predictive reproducibility of gene set discovery methods in cardiovascular disease.

    Science.gov (United States)

    Azuaje, Francisco; Zheng, Huiru; Camargo, Anyela; Wang, Haiying

    2011-08-01

    The discovery of novel disease biomarkers is a crucial challenge for translational bioinformatics. Demonstration of both their classification power and reproducibility across independent datasets are essential requirements to assess their potential clinical relevance. Small datasets and multiplicity of putative biomarker sets may explain lack of predictive reproducibility. Studies based on pathway-driven discovery approaches have suggested that, despite such discrepancies, the resulting putative biomarkers tend to be implicated in common biological processes. Investigations of this problem have been mainly focused on datasets derived from cancer research. We investigated the predictive and functional concordance of five methods for discovering putative biomarkers in four independently-generated datasets from the cardiovascular disease domain. A diversity of biosignatures was identified by the different methods. However, we found strong biological process concordance between them, especially in the case of methods based on gene set analysis. With a few exceptions, we observed lack of classification reproducibility using independent datasets. Partial overlaps between our putative sets of biomarkers and the primary studies exist. Despite the observed limitations, pathway-driven or gene set analysis can predict potentially novel biomarkers and can jointly point to biomedically-relevant underlying molecular mechanisms. Copyright © 2011 Elsevier Inc. All rights reserved.

  4. DNMT1 is associated with cell cycle and DNA replication gene sets in diffuse large B-cell lymphoma.

    Science.gov (United States)

    Loo, Suet Kee; Ab Hamid, Suzina Sheikh; Musa, Mustaffa; Wong, Kah Keng

    2018-01-01

    Dysregulation of DNA (cytosine-5)-methyltransferase 1 (DNMT1) is associated with the pathogenesis of various types of cancer. It has been previously shown that DNMT1 is frequently expressed in diffuse large B-cell lymphoma (DLBCL), however its functions remain to be elucidated in the disease. In this study, we gene expression profiled (GEP) shRNA targeting DNMT1(shDNMT1)-treated germinal center B-cell-like DLBCL (GCB-DLBCL)-derived cell line (i.e. HT) compared with non-silencing shRNA (control shRNA)-treated HT cells. Independent gene set enrichment analysis (GSEA) performed using GEPs of shRNA-treated HT cells and primary GCB-DLBCL cases derived from two publicly-available datasets (i.e. GSE10846 and GSE31312) produced three separate lists of enriched gene sets for each gene sets collection from Molecular Signatures Database (MSigDB). Subsequent Venn analysis identified 268, 145 and six consensus gene sets from analyzing gene sets in C2 collection (curated gene sets), C5 sub-collection [gene sets from gene ontology (GO) biological process ontology] and Hallmark collection, respectively to be enriched in positive correlation with DNMT1 expression profiles in shRNA-treated HT cells, GSE10846 and GSE31312 datasets [false discovery rate (FDR) 0.8) with DNMT1 expression and significantly downregulated (log fold-change <-1.35; p<0.05) following DNMT1 silencing in HT cells. These results suggest the involvement of DNMT1 in the activation of cell cycle and DNA replication in DLBCL cells. Copyright © 2017 Elsevier GmbH. All rights reserved.

  5. Coverage and characteristics of the Affymetrix GeneChip Human Mapping 100K SNP set.

    Directory of Open Access Journals (Sweden)

    2006-05-01

    Full Text Available Improvements in technology have made it possible to conduct genome-wide association mapping at costs within reach of academic investigators, and experiments are currently being conducted with a variety of high-throughput platforms. To provide an appropriate context for interpreting results of such studies, we summarize here results of an investigation of one of the first of these technologies to be publicly available, the Affymetrix GeneChip Human Mapping 100K set of single nucleotide polymorphisms (SNPs. In a systematic analysis of the pattern and distribution of SNPs in the Mapping 100K set, we find that SNPs in this set are undersampled from coding regions (both nonsynonymous and synonymous and oversampled from regions outside genes, relative to SNPs in the overall HapMap database. In addition, we utilize a novel multilocus linkage disequilibrium (LD coefficient based on information content (analogous to the information content scores commonly used for linkage mapping that is equivalent to the familiar measure r2 in the special case of two loci. Using this approach, we are able to summarize for any subset of markers, such as the Affymetrix Mapping 100K set, the information available for association mapping in that subset, relative to the information available in the full set of markers included in the HapMap, and highlight circumstances in which this multilocus measure of LD provides substantial additional insight about the haplotype structure in a region over pairwise measures of LD.

  6. Beyond main effects of gene-sets: harsh parenting moderates the association between a dopamine gene-set and child externalizing behavior

    NARCIS (Netherlands)

    J. Windhorst (Judith); V. Mileva-Seitz (Viara); R.C.A. Rippe (Ralph C.A.); H.W. Tiemeier (Henning); V.W.V. Jaddoe (Vincent); F.C. Verhulst (Frank); M.H. van IJzendoorn (Rien); M.J. Bakermans-Kranenburg (Marian)

    2016-01-01

    textabstractBackground: In a longitudinal cohort study, we investigated the interplay of harsh parenting and genetic variation across a set of functionally related dopamine genes, in association with children's externalizing behavior. This is one of the first studies to employ gene-based and

  7. A reference gene set for sex pheromone biosynthesis and degradation genes from the diamondback moth, Plutella xylostella, based on genome and transcriptome digital gene expression analyses.

    Science.gov (United States)

    He, Peng; Zhang, Yun-Fei; Hong, Duan-Yang; Wang, Jun; Wang, Xing-Liang; Zuo, Ling-Hua; Tang, Xian-Fu; Xu, Wei-Ming; He, Ming

    2017-03-01

    Female moths synthesize species-specific sex pheromone components and release them to attract male moths, which depend on precise sex pheromone chemosensory system to locate females. Two types of genes involved in the sex pheromone biosynthesis and degradation pathways play essential roles in this important moth behavior. To understand the function of genes in the sex pheromone pathway, this study investigated the genome-wide and digital gene expression of sex pheromone biosynthesis and degradation genes in various adult tissues in the diamondback moth (DBM), Plutella xylostella, which is a notorious vegetable pest worldwide. A massive transcriptome data (at least 39.04 Gb) was generated by sequencing 6 adult tissues including male antennae, female antennae, heads, legs, abdomen and female pheromone glands from DBM by using Illumina 4000 next-generation sequencing and mapping to a published DBM genome. Bioinformatics analysis yielded a total of 89,332 unigenes among which 87 transcripts were putatively related to seven gene families in the sex pheromone biosynthesis pathway. Among these, seven [two desaturases (DES), three fatty acyl-CoA reductases (FAR) one acetyltransferase (ACT) and one alcohol dehydrogenase (AD)] were mainly expressed in the pheromone glands with likely function in the three essential sex pheromone biosynthesis steps: desaturation, reduction, and esterification. We also identified 210 odorant-degradation related genes (including sex pheromone-degradation related genes) from seven major enzyme groups. Among these genes, 100 genes are new identified and two aldehyde oxidases (AOXs), one aldehyde dehydrogenase (ALDH), five carboxyl/cholinesterases (CCEs), five UDP-glycosyltransferases (UGTs), eight cytochrome P450 (CYP) and three glutathione S-transferases (GSTs) displayed more robust expression in the antennae, and thus are proposed to participate in the degradation of sex pheromone components and plant volatiles. To date, this is the most

  8. Expression map of a complete set of gustatory receptor genes in chemosensory organs of Bombyx mori.

    Science.gov (United States)

    Guo, Huizhen; Cheng, Tingcai; Chen, Zhiwei; Jiang, Liang; Guo, Youbing; Liu, Jianqiu; Li, Shenglong; Taniai, Kiyoko; Asaoka, Kiyoshi; Kadono-Okuda, Keiko; Arunkumar, Kallare P; Wu, Jiaqi; Kishino, Hirohisa; Zhang, Huijie; Seth, Rakesh K; Gopinathan, Karumathil P; Montagné, Nicolas; Jacquin-Joly, Emmanuelle; Goldsmith, Marian R; Xia, Qingyou; Mita, Kazuei

    2017-03-01

    Most lepidopteran species are herbivores, and interaction with host plants affects their gene expression and behavior as well as their genome evolution. Gustatory receptors (Grs) are expected to mediate host plant selection, feeding, oviposition and courtship behavior. However, due to their high diversity, sequence divergence and extremely low level of expression it has been difficult to identify precisely a complete set of Grs in Lepidoptera. By manual annotation and BAC sequencing, we improved annotation of 43 gene sequences compared with previously reported Grs in the most studied lepidopteran model, the silkworm, Bombyx mori, and identified 7 new tandem copies of BmGr30 on chromosome 7, bringing the total number of BmGrs to 76. Among these, we mapped 68 genes to chromosomes in a newly constructed chromosome distribution map and 8 genes to scaffolds; we also found new evidence for large clusters of BmGrs, especially from the bitter receptor family. RNA-seq analysis of diverse BmGr expression patterns in chemosensory organs of larvae and adults enabled us to draw a precise organ specific map of BmGr expression. Interestingly, most of the clustered genes were expressed in the same tissues and more than half of the genes were expressed in larval maxillae, larval thoracic legs and adult legs. For example, BmGr63 showed high expression levels in all organs in both larval and adult stages. By contrast, some genes showed expression limited to specific developmental stages or organs and tissues. BmGr19 was highly expressed in larval chemosensory organs (especially antennae and thoracic legs), the single exon genes BmGr53 and BmGr67 were expressed exclusively in larval tissues, the BmGr27-BmGr31 gene cluster on chr7 displayed a high expression level limited to adult legs and the candidate CO 2 receptor BmGr2 was highly expressed in adult antennae, where few other Grs were expressed. Transcriptional analysis of the Grs in B. mori provides a valuable new reference for

  9. Identification of a core set of rhizobial infection genes using data from single cell-types

    Directory of Open Access Journals (Sweden)

    Da-Song eChen

    2015-07-01

    Full Text Available Genome-wide expression studies on nodulation have varied in their scale from entire root systems to dissected nodules or root sections containing nodule primordia. More recently efforts have focused on developing methods for isolation of root hairs from infected plants and the application of laser-capture microdissection technology to nodules. Here we analyze two published data sets to identify a core set of infection genes that are expressed in the nodule and in root hairs during infection. Among the genes identified were those encoding phenylpropanoid biosynthesis enzymes including Chalcone-O-Methyltransferase which is required for the production of the potent Nod gene inducer 4’,4-dihydroxy-2-methoxychalcone. A promoter-GUS analysis in transgenic hairy roots for two genes encoding Chalcone-O-Methyltransferase isoforms revealed their expression in rhizobially infected root hairs and the nodule infection zone but not in the nitrogen fixation zone. We also describe a group of Rhizobially Induced Peroxidases whose expression overlaps with the production of superoxide in rhizobially infected root hairs and in nodules and roots. Finally, we identify a cohort of co-regulated transcription factors as candidate regulators of these processes.

  10. Gene Set Analyses of Genome-Wide Association Studies on 49 Quantitative Traits Measured in a Single Genetic Epidemiology Dataset

    Directory of Open Access Journals (Sweden)

    Jihye Kim

    2013-09-01

    Full Text Available Gene set analysis is a powerful tool for interpreting a genome-wide association study result and is gaining popularity these days. Comparison of the gene sets obtained for a variety of traits measured from a single genetic epidemiology dataset may give insights into the biological mechanisms underlying these traits. Based on the previously published single nucleotide polymorphism (SNP genotype data on 8,842 individuals enrolled in the Korea Association Resource project, we performed a series of systematic genome-wide association analyses for 49 quantitative traits of basic epidemiological, anthropometric, or blood chemistry parameters. Each analysis result was subjected to subsequent gene set analyses based on Gene Ontology (GO terms using gene set analysis software, GSA-SNP, identifying a set of GO terms significantly associated to each trait (pcorr < 0.05. Pairwise comparison of the traits in terms of the semantic similarity in their GO sets revealed surprising cases where phenotypically uncorrelated traits showed high similarity in terms of biological pathways. For example, the pH level was related to 7 other traits that showed low phenotypic correlations with it. A literature survey implies that these traits may be regulated partly by common pathways that involve neuronal or nerve systems.

  11. Candidate genes for COPD in two large data sets.

    Science.gov (United States)

    Bakke, P S; Zhu, G; Gulsvik, A; Kong, X; Agusti, A G N; Calverley, P M A; Donner, C F; Levy, R D; Make, B J; Paré, P D; Rennard, S I; Vestbo, J; Wouters, E F M; Anderson, W; Lomas, D A; Silverman, E K; Pillai, S G

    2011-02-01

    Lack of reproducibility of findings has been a criticism of genetic association studies on complex diseases, such as chronic obstructive pulmonary disease (COPD). We selected 257 polymorphisms of 16 genes with reported or potential relationships to COPD and genotyped these variants in a case-control study that included 953 COPD cases and 956 control subjects. We explored the association of these polymorphisms to three COPD phenotypes: a COPD binary phenotype and two quantitative traits (post-bronchodilator forced expiratory volume in 1 s (FEV₁) % predicted and FEV₁/forced vital capacity (FVC)). The polymorphisms significantly associated to these phenotypes in this first study were tested in a second, family-based study that included 635 pedigrees with 1,910 individuals. Significant associations to the binary COPD phenotype in both populations were seen for STAT1 (rs13010343) and NFKBIB/SIRT2 (rs2241704) (p<0.05). Single-nucleotide polymorphisms rs17467825 and rs1155563 of the GC gene were significantly associated with FEV₁ % predicted and FEV₁/FVC, respectively, in both populations (p<0.05). This study has replicated associations to COPD phenotypes in the STAT1, NFKBIB/SIRT2 and GC genes in two independent populations, the associations of the former two genes representing novel findings.

  12. Identification of Human HK Genes and Gene Expression Regulation Study in Cancer from Transcriptomics Data Analysis

    Science.gov (United States)

    Zhang, Zhang; Liu, Jingxing; Wu, Jiayan; Yu, Jun

    2013-01-01

    The regulation of gene expression is essential for eukaryotes, as it drives the processes of cellular differentiation and morphogenesis, leading to the creation of different cell types in multicellular organisms. RNA-Sequencing (RNA-Seq) provides researchers with a powerful toolbox for characterization and quantification of transcriptome. Many different human tissue/cell transcriptome datasets coming from RNA-Seq technology are available on public data resource. The fundamental issue here is how to develop an effective analysis method to estimate expression pattern similarities between different tumor tissues and their corresponding normal tissues. We define the gene expression pattern from three directions: 1) expression breadth, which reflects gene expression on/off status, and mainly concerns ubiquitously expressed genes; 2) low/high or constant/variable expression genes, based on gene expression level and variation; and 3) the regulation of gene expression at the gene structure level. The cluster analysis indicates that gene expression pattern is higher related to physiological condition rather than tissue spatial distance. Two sets of human housekeeping (HK) genes are defined according to cell/tissue types, respectively. To characterize the gene expression pattern in gene expression level and variation, we firstly apply improved K-means algorithm and a gene expression variance model. We find that cancer-associated HK genes (a HK gene is specific in cancer group, while not in normal group) are expressed higher and more variable in cancer condition than in normal condition. Cancer-associated HK genes prefer to AT-rich genes, and they are enriched in cell cycle regulation related functions and constitute some cancer signatures. The expression of large genes is also avoided in cancer group. These studies will help us understand which cell type-specific patterns of gene expression differ among different cell types, and particularly for cancer. PMID:23382867

  13. A gene network bioinformatics analysis for pemphigoid autoimmune blistering diseases.

    Science.gov (United States)

    Barone, Antonio; Toti, Paolo; Giuca, Maria Rita; Derchi, Giacomo; Covani, Ugo

    2015-07-01

    In this theoretical study, a text mining search and clustering analysis of data related to genes potentially involved in human pemphigoid autoimmune blistering diseases (PAIBD) was performed using web tools to create a gene/protein interaction network. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database was employed to identify a final set of PAIBD-involved genes and to calculate the overall significant interactions among genes: for each gene, the weighted number of links, or WNL, was registered and a clustering procedure was performed using the WNL analysis. Genes were ranked in class (leader, B, C, D and so on, up to orphans). An ontological analysis was performed for the set of 'leader' genes. Using the above-mentioned data network, 115 genes represented the final set; leader genes numbered 7 (intercellular adhesion molecule 1 (ICAM-1), interferon gamma (IFNG), interleukin (IL)-2, IL-4, IL-6, IL-8 and tumour necrosis factor (TNF)), class B genes were 13, whereas the orphans were 24. The ontological analysis attested that the molecular action was focused on extracellular space and cell surface, whereas the activation and regulation of the immunity system was widely involved. Despite the limited knowledge of the present pathologic phenomenon, attested by the presence of 24 genes revealing no protein-protein direct or indirect interactions, the network showed significant pathways gathered in several subgroups: cellular components, molecular functions, biological processes and the pathologic phenomenon obtained from the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database. The molecular basis for PAIBD was summarised and expanded, which will perhaps give researchers promising directions for the identification of new therapeutic targets.

  14. Transcriptome-wide selection of a reliable set of reference genes for gene expression studies in potato cyst nematodes (Globodera spp.).

    Science.gov (United States)

    Sabeh, Michael; Duceppe, Marc-Olivier; St-Arnaud, Marc; Mimee, Benjamin

    2018-01-01

    Relative gene expression analyses by qRT-PCR (quantitative reverse transcription PCR) require an internal control to normalize the expression data of genes of interest and eliminate the unwanted variation introduced by sample preparation. A perfect reference gene should have a constant expression level under all the experimental conditions. However, the same few housekeeping genes selected from the literature or successfully used in previous unrelated experiments are often routinely used in new conditions without proper validation of their stability across treatments. The advent of RNA-Seq and the availability of public datasets for numerous organisms are opening the way to finding better reference genes for expression studies. Globodera rostochiensis is a plant-parasitic nematode that is particularly yield-limiting for potato. The aim of our study was to identify a reliable set of reference genes to study G. rostochiensis gene expression. Gene expression levels from an RNA-Seq database were used to identify putative reference genes and were validated with qRT-PCR analysis. Three genes, GR, PMP-3, and aaRS, were found to be very stable within the experimental conditions of this study and are proposed as reference genes for future work.

  15. Comparative analysis among several cross section sets

    International Nuclear Information System (INIS)

    Caldeira, A.D.

    1983-01-01

    Critical parameters were calculated using the one dimensional multigroup transport theory for several cross section sets. Calculations have been performed for water mixtures of uranium metal, plutonium metal and uranium-thorium oxide, and for metallics systems, to determine the critical dimensions of geometries (sphere and cylinder). For this aim, the following cross section sets were employed: 1) multigroup cross section sets obtained from the GAMTEC-II code; 2) the HANSEN-ROACH cross section sets; 3) cross section sets from the ENDF/B-IV, processed by the NJOY code. Finally, we have also calculated the corresponding critical radius using the one dimensional multigroup transport DTF-IV code. The numerical results agree within a few percent with the critical values obtained in the literature (where the greatest discrepancy occured in the critical dimensions of water mixtures calculated with the values generated by the NJOY code), a very good results in comparison with similar works. (Author) [pt

  16. Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data

    NARCIS (Netherlands)

    Hettne, K.M.; Boorsma, A.; Dartel, D.A. van; Goeman, J.J.; Jong, E. de; Piersma, A.H.; Stierum, R.H.; Kleinjans, J.C.; Kors, J.A.

    2013-01-01

    BACKGROUND: Availability of chemical response-specific lists of genes (gene sets) for pharmacological and/or toxic effect prediction for compounds is limited. We hypothesize that more gene sets can be created by next-generation text mining (next-gen TM), and that these can be used with gene set

  17. Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data

    NARCIS (Netherlands)

    Hettne, K.M.; Boorsma, A.; Dartel, van D.A.M.; Goeman, J.J.; Jong, de E.; Piersma, A.H.; Stierum, R.H.; Kleinjans, J.C.; Kors, J.A.

    2013-01-01

    Background: Availability of chemical response-specific lists of genes (gene sets) for pharmacological and/or toxic effect prediction for compounds is limited. We hypothesize that more gene sets can be created by next-generation text mining (next-gen TM), and that these can be used with gene set

  18. Gene Circuit Analysis of the Terminal Gap Gene huckebein

    Science.gov (United States)

    Ashyraliyev, Maksat; Siggens, Ken; Janssens, Hilde; Blom, Joke; Akam, Michael; Jaeger, Johannes

    2009-01-01

    The early embryo of Drosophila melanogaster provides a powerful model system to study the role of genes in pattern formation. The gap gene network constitutes the first zygotic regulatory tier in the hierarchy of the segmentation genes involved in specifying the position of body segments. Here, we use an integrative, systems-level approach to investigate the regulatory effect of the terminal gap gene huckebein (hkb) on gap gene expression. We present quantitative expression data for the Hkb protein, which enable us to include hkb in gap gene circuit models. Gap gene circuits are mathematical models of gene networks used as computational tools to extract regulatory information from spatial expression data. This is achieved by fitting the model to gap gene expression patterns, in order to obtain estimates for regulatory parameters which predict a specific network topology. We show how considering variability in the data combined with analysis of parameter determinability significantly improves the biological relevance and consistency of the approach. Our models are in agreement with earlier results, which they extend in two important respects: First, we show that Hkb is involved in the regulation of the posterior hunchback (hb) domain, but does not have any other essential function. Specifically, Hkb is required for the anterior shift in the posterior border of this domain, which is now reproduced correctly in our models. Second, gap gene circuits presented here are able to reproduce mutants of terminal gap genes, while previously published models were unable to reproduce any null mutants correctly. As a consequence, our models now capture the expression dynamics of all posterior gap genes and some variational properties of the system correctly. This is an important step towards a better, quantitative understanding of the developmental and evolutionary dynamics of the gap gene network. PMID:19876378

  19. Music analysis and point-set compression

    DEFF Research Database (Denmark)

    Meredith, David

    2015-01-01

    COSIATEC, SIATECCompress and Forth’s algorithm are point-set compression algorithms developed for discovering repeated patterns in music, such as themes and motives that would be of interest to a music analyst. To investigate their effectiveness and versatility, these algorithms were evaluated...... on three analytical tasks that depend on the discovery of repeated patterns: classifying folk song melodies into tune families, discovering themes and sections in polyphonic music, and discovering subject and countersubject entries in fugues. Each algorithm computes a compressed encoding of a point......-set representation of a musical object in the form of a list of compact patterns, each pattern being given with a set of vectors indicating its occurrences. However, the algorithms adopt different strategies in their attempts to discover encodings that maximize compression.The best-performing algorithm on the folk...

  20. Rice Transcriptome Analysis to Identify Possible Herbicide Quinclorac Detoxification Genes

    Directory of Open Access Journals (Sweden)

    Wenying eXu

    2015-09-01

    Full Text Available Quinclorac is a highly selective auxin-type herbicide, and is widely used in the effective control of barnyard grass in paddy rice fields, improving the world’s rice yield. The herbicide mode of action of quinclorac has been proposed and hormone interactions affect quinclorac signaling. Because of widespread use, quinclorac may be transported outside rice fields with the drainage waters, leading to soil and water pollution and environmental health problems.In this study, we used 57K Affymetrix rice whole-genome array to identify quinclorac signaling response genes to study the molecular mechanisms of action and detoxification of quinclorac in rice plants. Overall, 637 probe sets were identified with differential expression levels under either 6 or 24 h of quinclorac treatment. Auxin-related genes such as GH3 and OsIAAs responded to quinclorac treatment. Gene Ontology analysis showed that genes of detoxification-related family genes were significantly enriched, including cytochrome P450, GST, UGT, and ABC and drug transporter genes. Moreover, real-time RT-PCR analysis showed that top candidate P450 families such as CYP81, CYP709C and CYP72A genes were universally induced by different herbicides. Some Arabidopsis genes for the same P450 family were up-regulated under quinclorac treatment.We conduct rice whole-genome GeneChip analysis and the first global identification of quinclorac response genes. This work may provide potential markers for detoxification of quinclorac and biomonitors of environmental chemical pollution.

  1. Blockchain Transaction Analysis Using Dominant Sets

    OpenAIRE

    Awan , Malik ,; Cortesi , Agostino

    2017-01-01

    Part 4: Engineering of Enterprise Software Products; International audience; Blockchain is an emerging backbone technology behind different crypto-currencies. It can also be used for other purposes and areas. There are different scalability issues associated with blockchain. It is important to know the in depth structure of blockchain by identifying common behaviors of the transactions and the effect of these behaviors on the nodes of the network. Dominant set approach can categorize the bloc...

  2. Nonlinear canonical correlation analysis with k sets of variables

    NARCIS (Netherlands)

    van der Burg, Eeke; de Leeuw, Jan

    1987-01-01

    The multivariate technique OVERALS is introduced as a non-linear generalization of canonical correlation analysis (CCA). First, two sets CCA is introduced. Two sets CCA is a technique that computes linear combinations of sets of variables that correlate in an optimal way. Two sets CCA is then

  3. A novel joint analysis framework improves identification of differentially expressed genes in cross disease transcriptomic analysis

    Directory of Open Access Journals (Sweden)

    Wenyi Qin

    2018-02-01

    Full Text Available Abstract Motivation Detecting differentially expressed (DE genes between disease and normal control group is one of the most common analyses in genome-wide transcriptomic data. Since most studies don’t have a lot of samples, researchers have used meta-analysis to group different datasets for the same disease. Even then, in many cases the statistical power is still not enough. Taking into account the fact that many diseases share the same disease genes, it is desirable to design a statistical framework that can identify diseases’ common and specific DE genes simultaneously to improve the identification power. Results We developed a novel empirical Bayes based mixture model to identify DE genes in specific study by leveraging the shared information across multiple different disease expression data sets. The effectiveness of joint analysis was demonstrated through comprehensive simulation studies and two real data applications. The simulation results showed that our method consistently outperformed single data set analysis and two other meta-analysis methods in identification power. In real data analysis, overall our method demonstrated better identification power in detecting DE genes and prioritized more disease related genes and disease related pathways than single data set analysis. Over 150% more disease related genes are identified by our method in application to Huntington’s disease. We expect that our method would provide researchers a new way of utilizing available data sets from different diseases when sample size of the focused disease is limited.

  4. Comparative Genomic Analysis of Soybean Flowering Genes

    Science.gov (United States)

    Jung, Chol-Hee; Wong, Chui E.; Singh, Mohan B.; Bhalla, Prem L.

    2012-01-01

    Flowering is an important agronomic trait that determines crop yield. Soybean is a major oilseed legume crop used for human and animal feed. Legumes have unique vegetative and floral complexities. Our understanding of the molecular basis of flower initiation and development in legumes is limited. Here, we address this by using a computational approach to examine flowering regulatory genes in the soybean genome in comparison to the most studied model plant, Arabidopsis. For this comparison, a genome-wide analysis of orthologue groups was performed, followed by an in silico gene expression analysis of the identified soybean flowering genes. Phylogenetic analyses of the gene families highlighted the evolutionary relationships among these candidates. Our study identified key flowering genes in soybean and indicates that the vernalisation and the ambient-temperature pathways seem to be the most variant in soybean. A comparison of the orthologue groups containing flowering genes indicated that, on average, each Arabidopsis flowering gene has 2-3 orthologous copies in soybean. Our analysis highlighted that the CDF3, VRN1, SVP, AP3 and PIF3 genes are paralogue-rich genes in soybean. Furthermore, the genome mapping of the soybean flowering genes showed that these genes are scattered randomly across the genome. A paralogue comparison indicated that the soybean genes comprising the largest orthologue group are clustered in a 1.4 Mb region on chromosome 16 of soybean. Furthermore, a comparison with the undomesticated soybean (Glycine soja) revealed that there are hundreds of SNPs that are associated with putative soybean flowering genes and that there are structural variants that may affect the genes of the light-signalling and ambient-temperature pathways in soybean. Our study provides a framework for the soybean flowering pathway and insights into the relationship and evolution of flowering genes between a short-day soybean and the long-day plant, Arabidopsis. PMID:22679494

  5. Analysis of the Westland Data Set

    Science.gov (United States)

    Wen, Fang; Willett, Peter; Deb, Somnath

    2001-01-01

    The "Westland" set of empirical accelerometer helicopter data with seeded and labeled faults is analyzed with the aim of condition monitoring. The autoregressive (AR) coefficients from a simple linear model encapsulate a great deal of information in a relatively few measurements; and it has also been found that augmentation of these by harmonic and other parameters call improve classification significantly. Several techniques have been explored, among these restricted Coulomb energy (RCE) networks, learning vector quantization (LVQ), Gaussian mixture classifiers and decision trees. A problem with these approaches, and in common with many classification paradigms, is that augmentation of the feature dimension can degrade classification ability. Thus, we also introduce the Bayesian data reduction algorithm (BDRA), which imposes a Dirichlet prior oil training data and is thus able to quantify probability of error in all exact manner, such that features may be discarded or coarsened appropriately.

  6. Development of a set of SNP markers present in expressed genes of the apple.

    Science.gov (United States)

    Chagné, David; Gasic, Ksenija; Crowhurst, Ross N; Han, Yuepeng; Bassett, Heather C; Bowatte, Deepa R; Lawrence, Timothy J; Rikkerink, Erik H A; Gardiner, Susan E; Korban, Schuyler S

    2008-11-01

    Molecular markers associated with gene coding regions are useful tools for bridging functional and structural genomics. Due to their high abundance in plant genomes, single nucleotide polymorphisms (SNPs) are present within virtually all genomic regions, including most coding sequences. The objective of this study was to develop a set of SNPs for the apple by taking advantage of the wealth of genomics resources available for the apple, including a large collection of expressed sequenced tags (ESTs). Using bioinformatics tools, a search for SNPs within an EST database of approximately 350,000 sequences developed from a variety of apple accessions was conducted. This resulted in the identification of a total of 71,482 putative SNPs. As the apple genome is reported to be an ancient polyploid, attempts were made to verify whether those SNPs detected in silico were attributable either to allelic polymorphisms or to gene duplication or paralogous or homeologous sequence variations. To this end, a set of 464 PCR primer pairs was designed, PCR was amplified using two subsets of plants, and the PCR products were sequenced. The SNPs retrieved from these sequences were then mapped onto apple genetic maps, including a newly constructed map of a Royal Gala x A689-24 cross and a Malling 9 x Robusta 5, map using a bin mapping strategy. The SNP genotyping was performed using the high-resolution melting (HRM) technique. A total of 93 new markers containing 210 coding SNPs were successfully mapped. This new set of SNP markers for the apple offers new opportunities for understanding the genetic control of important horticultural traits using quantitative trait loci (QTL) or linkage disequilibrium analysis. These also serve as useful markers for aligning physical and genetic maps, and as potential transferable markers across the Rosaceae family.

  7. Joint mapping of genes and conditions via multidimensional unfolding analysis

    Directory of Open Access Journals (Sweden)

    Engelen Kristof

    2007-06-01

    Full Text Available Abstract Background Microarray compendia profile the expression of genes in a number of experimental conditions. Such data compendia are useful not only to group genes and conditions based on their similarity in overall expression over profiles but also to gain information on more subtle relations between genes and conditions. Getting a clear visual overview of all these patterns in a single easy-to-grasp representation is a useful preliminary analysis step: We propose to use for this purpose an advanced exploratory method, called multidimensional unfolding. Results We present a novel algorithm for multidimensional unfolding that overcomes both general problems and problems that are specific for the analysis of gene expression data sets. Applying the algorithm to two publicly available microarray compendia illustrates its power as a tool for exploratory data analysis: The unfolding analysis of a first data set resulted in a two-dimensional representation which clearly reveals temporal regulation patterns for the genes and a meaningful structure for the time points, while the analysis of a second data set showed the algorithm's ability to go beyond a mere identification of those genes that discriminate between different patient or tissue types. Conclusion Multidimensional unfolding offers a useful tool for preliminary explorations of microarray data: By relying on an easy-to-grasp low-dimensional geometric framework, relations among genes, among conditions and between genes and conditions are simultaneously represented in an accessible way which may reveal interesting patterns in the data. An additional advantage of the method is that it can be applied to the raw data without necessitating the choice of suitable genewise transformations of the data.

  8. Genomic determinants of sporulation in Bacilli and Clostridia: towards the minimal set of sporulation-specific genes.

    Science.gov (United States)

    Galperin, Michael Y; Mekhedov, Sergei L; Puigbo, Pere; Smirnov, Sergey; Wolf, Yuri I; Rigden, Daniel J

    2012-11-01

    Three classes of low-G+C Gram-positive bacteria (Firmicutes), Bacilli, Clostridia and Negativicutes, include numerous members that are capable of producing heat-resistant endospores. Spore-forming firmicutes include many environmentally important organisms, such as insect pathogens and cellulose-degrading industrial strains, as well as human pathogens responsible for such diseases as anthrax, botulism, gas gangrene and tetanus. In the best-studied model organism Bacillus subtilis, sporulation involves over 500 genes, many of which are conserved among other bacilli and clostridia. This work aimed to define the genomic requirements for sporulation through an analysis of the presence of sporulation genes in various firmicutes, including those with smaller genomes than B. subtilis. Cultivable spore-formers were found to have genomes larger than 2300 kb and encompass over 2150 protein-coding genes of which 60 are orthologues of genes that are apparently essential for sporulation in B. subtilis. Clostridial spore-formers lack, among others, spoIIB, sda, spoVID and safA genes and have non-orthologous displacements of spoIIQ and spoIVFA, suggesting substantial differences between bacilli and clostridia in the engulfment and spore coat formation steps. Many B. subtilis sporulation genes, particularly those encoding small acid-soluble spore proteins and spore coat proteins, were found only in the family Bacillaceae, or even in a subset of Bacillus spp. Phylogenetic profiles of sporulation genes, compiled in this work, confirm the presence of a common sporulation gene core, but also illuminate the diversity of the sporulation processes within various lineages. These profiles should help further experimental studies of uncharacterized widespread sporulation genes, which would ultimately allow delineation of the minimal set(s) of sporulation-specific genes in Bacilli and Clostridia. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.

  9. Dynamic association rules for gene expression data analysis.

    Science.gov (United States)

    Chen, Shu-Chuan; Tsai, Tsung-Hsien; Chung, Cheng-Han; Li, Wen-Hsiung

    2015-10-14

    The purpose of gene expression analysis is to look for the association between regulation of gene expression levels and phenotypic variations. This association based on gene expression profile has been used to determine whether the induction/repression of genes correspond to phenotypic variations including cell regulations, clinical diagnoses and drug development. Statistical analyses on microarray data have been developed to resolve gene selection issue. However, these methods do not inform us of causality between genes and phenotypes. In this paper, we propose the dynamic association rule algorithm (DAR algorithm) which helps ones to efficiently select a subset of significant genes for subsequent analysis. The DAR algorithm is based on association rules from market basket analysis in marketing. We first propose a statistical way, based on constructing a one-sided confidence interval and hypothesis testing, to determine if an association rule is meaningful. Based on the proposed statistical method, we then developed the DAR algorithm for gene expression data analysis. The method was applied to analyze four microarray datasets and one Next Generation Sequencing (NGS) dataset: the Mice Apo A1 dataset, the whole genome expression dataset of mouse embryonic stem cells, expression profiling of the bone marrow of Leukemia patients, Microarray Quality Control (MAQC) data set and the RNA-seq dataset of a mouse genomic imprinting study. A comparison of the proposed method with the t-test on the expression profiling of the bone marrow of Leukemia patients was conducted. We developed a statistical way, based on the concept of confidence interval, to determine the minimum support and minimum confidence for mining association relationships among items. With the minimum support and minimum confidence, one can find significant rules in one single step. The DAR algorithm was then developed for gene expression data analysis. Four gene expression datasets showed that the proposed

  10. Mechanism-based biomarker gene sets for glutathione depletion-related hepatotoxicity in rats

    International Nuclear Information System (INIS)

    Gao Weihua; Mizukawa, Yumiko; Nakatsu, Noriyuki; Minowa, Yosuke; Yamada, Hiroshi; Ohno, Yasuo; Urushidani, Tetsuro

    2010-01-01

    Chemical-induced glutathione depletion is thought to be caused by two types of toxicological mechanisms: PHO-type glutathione depletion [glutathione conjugated with chemicals such as phorone (PHO) or diethyl maleate (DEM)], and BSO-type glutathione depletion [i.e., glutathione synthesis inhibited by chemicals such as L-buthionine-sulfoximine (BSO)]. In order to identify mechanism-based biomarker gene sets for glutathione depletion in rat liver, male SD rats were treated with various chemicals including PHO (40, 120 and 400 mg/kg), DEM (80, 240 and 800 mg/kg), BSO (150, 450 and 1500 mg/kg), and bromobenzene (BBZ, 10, 100 and 300 mg/kg). Liver samples were taken 3, 6, 9 and 24 h after administration and examined for hepatic glutathione content, physiological and pathological changes, and gene expression changes using Affymetrix GeneChip Arrays. To identify differentially expressed probe sets in response to glutathione depletion, we focused on the following two courses of events for the two types of mechanisms of glutathione depletion: a) gene expression changes occurring simultaneously in response to glutathione depletion, and b) gene expression changes after glutathione was depleted. The gene expression profiles of the identified probe sets for the two types of glutathione depletion differed markedly at times during and after glutathione depletion, whereas Srxn1 was markedly increased for both types as glutathione was depleted, suggesting that Srxn1 is a key molecule in oxidative stress related to glutathione. The extracted probe sets were refined and verified using various compounds including 13 additional positive or negative compounds, and they established two useful marker sets. One contained three probe sets (Akr7a3, Trib3 and Gstp1) that could detect conjugation-type glutathione depletors any time within 24 h after dosing, and the other contained 14 probe sets that could detect glutathione depletors by any mechanism. These two sets, with appropriate scoring

  11. Genome-Wide Detection and Analysis of Multifunctional Genes

    Science.gov (United States)

    Pritykin, Yuri; Ghersi, Dario; Singh, Mona

    2015-01-01

    Many genes can play a role in multiple biological processes or molecular functions. Identifying multifunctional genes at the genome-wide level and studying their properties can shed light upon the complexity of molecular events that underpin cellular functioning, thereby leading to a better understanding of the functional landscape of the cell. However, to date, genome-wide analysis of multifunctional genes (and the proteins they encode) has been limited. Here we introduce a computational approach that uses known functional annotations to extract genes playing a role in at least two distinct biological processes. We leverage functional genomics data sets for three organisms—H. sapiens, D. melanogaster, and S. cerevisiae—and show that, as compared to other annotated genes, genes involved in multiple biological processes possess distinct physicochemical properties, are more broadly expressed, tend to be more central in protein interaction networks, tend to be more evolutionarily conserved, and are more likely to be essential. We also find that multifunctional genes are significantly more likely to be involved in human disorders. These same features also hold when multifunctionality is defined with respect to molecular functions instead of biological processes. Our analysis uncovers key features about multifunctional genes, and is a step towards a better genome-wide understanding of gene multifunctionality. PMID:26436655

  12. A novel CpG island set identifies tissue-specific methylation at developmental gene loci.

    Directory of Open Access Journals (Sweden)

    Robert Illingworth

    2008-01-01

    Full Text Available CpG islands (CGIs are dense clusters of CpG sequences that punctuate the CpG-deficient human genome and associate with many gene promoters. As CGIs also differ from bulk chromosomal DNA by their frequent lack of cytosine methylation, we devised a CGI enrichment method based on nonmethylated CpG affinity chromatography. The resulting library was sequenced to define a novel human blood CGI set that includes many that are not detected by current algorithms. Approximately half of CGIs were associated with annotated gene transcription start sites, the remainder being intra- or intergenic. Using an array representing over 17,000 CGIs, we established that 6%-8% of CGIs are methylated in genomic DNA of human blood, brain, muscle, and spleen. Inter- and intragenic CGIs are preferentially susceptible to methylation. CGIs showing tissue-specific methylation were overrepresented at numerous genetic loci that are essential for development, including HOX and PAX family members. The findings enable a comprehensive analysis of the roles played by CGI methylation in normal and diseased human tissues.

  13. Gene Expression Signature in Endemic Osteoarthritis by Microarray Analysis

    Directory of Open Access Journals (Sweden)

    Xi Wang

    2015-05-01

    Full Text Available Kashin-Beck Disease (KBD is an endemic osteochondropathy with an unknown pathogenesis. Diagnosis of KBD is effective only in advanced cases, which eliminates the possibility of early treatment and leads to an inevitable exacerbation of symptoms. Therefore, we aim to identify an accurate blood-based gene signature for the detection of KBD. Previously published gene expression profile data on cartilage and peripheral blood mononuclear cells (PBMCs from adults with KBD were compared to select potential target genes. Microarray analysis was conducted to evaluate the expression of the target genes in a cohort of 100 KBD patients and 100 healthy controls. A gene expression signature was identified using a training set, which was subsequently validated using an independent test set with a minimum redundancy maximum relevance (mRMR algorithm and support vector machine (SVM algorithm. Fifty unique genes were differentially expressed between KBD patients and healthy controls. A 20-gene signature was identified that distinguished between KBD patients and controls with 90% accuracy, 85% sensitivity, and 95% specificity. This study identified a 20-gene signature that accurately distinguishes between patients with KBD and controls using peripheral blood samples. These results promote the further development of blood-based genetic biomarkers for detection of KBD.

  14. Serial analysis of gene expression (SAGE)

    NARCIS (Netherlands)

    van Ruissen, Fred; Baas, Frank

    2007-01-01

    In 1995, serial analysis of gene expression (SAGE) was developed as a versatile tool for gene expression studies. SAGE technology does not require pre-existing knowledge of the genome that is being examined and therefore SAGE can be applied to many different model systems. In this chapter, the SAGE

  15. Accurate Gene Expression-Based Biodosimetry Using a Minimal Set of Human Gene Transcripts

    Energy Technology Data Exchange (ETDEWEB)

    Tucker, James D., E-mail: jtucker@biology.biosci.wayne.edu [Department of Biological Sciences, Wayne State University, Detroit, Michigan (United States); Joiner, Michael C. [Department of Radiation Oncology, Wayne State University, Detroit, Michigan (United States); Thomas, Robert A.; Grever, William E.; Bakhmutsky, Marina V. [Department of Biological Sciences, Wayne State University, Detroit, Michigan (United States); Chinkhota, Chantelle N.; Smolinski, Joseph M. [Department of Electrical and Computer Engineering, Wayne State University, Detroit, Michigan (United States); Divine, George W. [Department of Public Health Sciences, Henry Ford Hospital, Detroit, Michigan (United States); Auner, Gregory W. [Department of Electrical and Computer Engineering, Wayne State University, Detroit, Michigan (United States)

    2014-03-15

    Purpose: Rapid and reliable methods for conducting biological dosimetry are a necessity in the event of a large-scale nuclear event. Conventional biodosimetry methods lack the speed, portability, ease of use, and low cost required for triaging numerous victims. Here we address this need by showing that polymerase chain reaction (PCR) on a small number of gene transcripts can provide accurate and rapid dosimetry. The low cost and relative ease of PCR compared with existing dosimetry methods suggest that this approach may be useful in mass-casualty triage situations. Methods and Materials: Human peripheral blood from 60 adult donors was acutely exposed to cobalt-60 gamma rays at doses of 0 (control) to 10 Gy. mRNA expression levels of 121 selected genes were obtained 0.5, 1, and 2 days after exposure by reverse-transcriptase real-time PCR. Optimal dosimetry at each time point was obtained by stepwise regression of dose received against individual gene transcript expression levels. Results: Only 3 to 4 different gene transcripts, ASTN2, CDKN1A, GDF15, and ATM, are needed to explain ≥0.87 of the variance (R{sup 2}). Receiver-operator characteristics, a measure of sensitivity and specificity, of 0.98 for these statistical models were achieved at each time point. Conclusions: The actual and predicted radiation doses agree very closely up to 6 Gy. Dosimetry at 8 and 10 Gy shows some effect of saturation, thereby slightly diminishing the ability to quantify higher exposures. Analyses of these gene transcripts may be advantageous for use in a field-portable device designed to assess exposures in mass casualty situations or in clinical radiation emergencies.

  16. A comparison of cosegregation analysis methods for the clinical setting.

    Science.gov (United States)

    Rañola, John Michael O; Liu, Quanhui; Rosenthal, Elisabeth A; Shirts, Brian H

    2018-04-01

    Quantitative cosegregation analysis can help evaluate the pathogenicity of genetic variants. However, genetics professionals without statistical training often use simple methods, reporting only qualitative findings. We evaluate the potential utility of quantitative cosegregation in the clinical setting by comparing three methods. One thousand pedigrees each were simulated for benign and pathogenic variants in BRCA1 and MLH1 using United States historical demographic data to produce pedigrees similar to those seen in the clinic. These pedigrees were analyzed using two robust methods, full likelihood Bayes factors (FLB) and cosegregation likelihood ratios (CSLR), and a simpler method, counting meioses. Both FLB and CSLR outperform counting meioses when dealing with pathogenic variants, though counting meioses is not far behind. For benign variants, FLB and CSLR greatly outperform as counting meioses is unable to generate evidence for benign variants. Comparing FLB and CSLR, we find that the two methods perform similarly, indicating that quantitative results from either of these methods could be combined in multifactorial calculations. Combining quantitative information will be important as isolated use of cosegregation in single families will yield classification for less than 1% of variants. To encourage wider use of robust cosegregation analysis, we present a website ( http://www.analyze.myvariant.org ) which implements the CSLR, FLB, and Counting Meioses methods for ATM, BRCA1, BRCA2, CHEK2, MEN1, MLH1, MSH2, MSH6, and PMS2. We also present an R package, CoSeg, which performs the CSLR analysis on any gene with user supplied parameters. Future variant classification guidelines should allow nuanced inclusion of cosegregation evidence against pathogenicity.

  17. Identification and Validation of a New Set of Five Genes for Prediction of Risk in Early Breast Cancer

    Directory of Open Access Journals (Sweden)

    Giorgio Mustacchi

    2013-05-01

    Full Text Available Molecular tests predicting the outcome of breast cancer patients based on gene expression levels can be used to assist in making treatment decisions after consideration of conventional markers. In this study we identified a subset of 20 mRNA differentially regulated in breast cancer analyzing several publicly available array gene expression data using R/Bioconductor package. Using RTqPCR we evaluate 261 consecutive invasive breast cancer cases not selected for age, adjuvant treatment, nodal and estrogen receptor status from paraffin embedded sections. The biological samples dataset was split into a training (137 cases and a validation set (124 cases. The gene signature was developed on the training set and a multivariate stepwise Cox analysis selected five genes independently associated with DFS: FGF18 (HR = 1.13, p = 0.05, BCL2 (HR = 0.57, p = 0.001, PRC1 (HR = 1.51, p = 0.001, MMP9 (HR = 1.11, p = 0.08, SERF1a (HR = 0.83, p = 0.007. These five genes were combined into a linear score (signature weighted according to the coefficients of the Cox model, as: 0.125FGF18 − 0.560BCL2 + 0.409PRC1 + 0.104MMP9 − 0.188SERF1A (HR = 2.7, 95% CI = 1.9–4.0, p < 0.001. The signature was then evaluated on the validation set assessing the discrimination ability by a Kaplan Meier analysis, using the same cut offs classifying patients at low, intermediate or high risk of disease relapse as defined on the training set (p < 0.001. Our signature, after a further clinical validation, could be proposed as prognostic signature for disease free survival in breast cancer patients where the indication for adjuvant chemotherapy added to endocrine treatment is uncertain.

  18. SIGNATURE: A workbench for gene expression signature analysis

    Directory of Open Access Journals (Sweden)

    Chang Jeffrey T

    2011-11-01

    Full Text Available Abstract Background The biological phenotype of a cell, such as a characteristic visual image or behavior, reflects activities derived from the expression of collections of genes. As such, an ability to measure the expression of these genes provides an opportunity to develop more precise and varied sets of phenotypes. However, to use this approach requires computational methods that are difficult to implement and apply, and thus there is a critical need for intelligent software tools that can reduce the technical burden of the analysis. Tools for gene expression analyses are unusually difficult to implement in a user-friendly way because their application requires a combination of biological data curation, statistical computational methods, and database expertise. Results We have developed SIGNATURE, a web-based resource that simplifies gene expression signature analysis by providing software, data, and protocols to perform the analysis successfully. This resource uses Bayesian methods for processing gene expression data coupled with a curated database of gene expression signatures, all carried out within a GenePattern web interface for easy use and access. Conclusions SIGNATURE is available for public use at http://genepattern.genome.duke.edu/signature/.

  19. Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data

    NARCIS (Netherlands)

    K.M. Hettne (Kristina); J. Boorsma (Jeffrey); D.A.M. van Dartel (Dorien A M); J.J. Goeman (Jelle); E.C. de Jong (Esther); A.H. Piersma (Aldert); R.H. Stierum (Rob); J. Kleinjans (Jos); J.A. Kors (Jan)

    2013-01-01

    textabstractBackground: Availability of chemical response-specific lists of genes (gene sets) for pharmacological and/or toxic effect prediction for compounds is limited. We hypothesize that more gene sets can be created by next-generation text mining (next-gen TM), and that these can be used with

  20. Identification of noise in linear data sets by factor analysis

    International Nuclear Information System (INIS)

    Roscoe, B.A.; Hopke, Ph.K.

    1982-01-01

    A technique which has the ability to identify bad data points, after the data has been generated, is classical factor analysis. The ability of classical factor analysis to identify two different types of data errors make it ideally suited for scanning large data sets. Since the results yielded by factor analysis indicate correlations between parameters, one must know something about the nature of the data set and the analytical techniques used to obtain it to confidentially isolate errors. (author)

  1. Rice Multi-Gene Analysis

    Indian Academy of Sciences (India)

    gdyang

    Click here for a legend that explains the icons and colors in the image below. Click here to ..... PARE Data. We display these signature images when the abundance view option is set to "Individual ..... These data comprise two libraries from the ...

  2. Delve: A Data Set Retrieval and Document Analysis System

    KAUST Repository

    Akujuobi, Uchenna Thankgod

    2017-12-29

    Academic search engines (e.g., Google scholar or Microsoft academic) provide a medium for retrieving various information on scholarly documents. However, most of these popular scholarly search engines overlook the area of data set retrieval, which should provide information on relevant data sets used for academic research. Due to the increasing volume of publications, it has become a challenging task to locate suitable data sets on a particular research area for benchmarking or evaluations. We propose Delve, a web-based system for data set retrieval and document analysis. This system is different from other scholarly search engines as it provides a medium for both data set retrieval and real time visual exploration and analysis of data sets and documents.

  3. Analysis of operation events for HFETR emergency diesel generator set

    International Nuclear Information System (INIS)

    Li Zhiqiang; Ji Xifang; Deng Hong

    2015-01-01

    By the statistic analysis of the historical failure data of the emergency diesel generator set, the specific mode, the attribute, and the direct and root origin for each failure are reviewed and summarized. Considering the current status of the emergency diesel generator set, the preventive measures and solutions in terms of operation, handling and maintenance are proposed, and the potential events for the emergency diesel generator set are analyzed. (authors)

  4. Principles of gene microarray data analysis.

    Science.gov (United States)

    Mocellin, Simone; Rossi, Carlo Riccardo

    2007-01-01

    The development of several gene expression profiling methods, such as comparative genomic hybridization (CGH), differential display, serial analysis of gene expression (SAGE), and gene microarray, together with the sequencing of the human genome, has provided an opportunity to monitor and investigate the complex cascade of molecular events leading to tumor development and progression. The availability of such large amounts of information has shifted the attention of scientists towards a nonreductionist approach to biological phenomena. High throughput technologies can be used to follow changing patterns of gene expression over time. Among them, gene microarray has become prominent because it is easier to use, does not require large-scale DNA sequencing, and allows for the parallel quantification of thousands of genes from multiple samples. Gene microarray technology is rapidly spreading worldwide and has the potential to drastically change the therapeutic approach to patients affected with tumor. Therefore, it is of paramount importance for both researchers and clinicians to know the principles underlying the analysis of the huge amount of data generated with microarray technology.

  5. Identification of a novel set of genes reflecting different in vivo invasive patterns of human GBM cells

    Directory of Open Access Journals (Sweden)

    Monticone Massimiliano

    2012-08-01

    Full Text Available Abstract Background Most patients affected by Glioblastoma multiforme (GBM, grade IV glioma experience a recurrence of the disease because of the spreading of tumor cells beyond surgical boundaries. Unveiling mechanisms causing this process is a logic goal to impair the killing capacity of GBM cells by molecular targeting. We noticed that our long-term GBM cultures, established from different patients, may display two categories/types of growth behavior in an orthotopic xenograft model: expansion of the tumor mass and formation of tumor branches/nodules (nodular like, NL-type or highly diffuse single tumor cell infiltration (HD-type. Methods We determined by DNA microarrays the gene expression profiles of three NL-type and three HD-type long-term GBM cultures. Subsequently, individual genes with different expression levels between the two groups were identified using Significance Analysis of Microarrays (SAM. Real time RT-PCR, immunofluorescence and immunoblot analyses, were performed for a selected subgroup of regulated gene products to confirm the results obtained by the expression analysis. Results Here, we report the identification of a set of 34 differentially expressed genes in the two types of GBM cultures. Twenty-three of these genes encode for proteins localized to the plasma membrane and 9 of these for proteins are involved in the process of cell adhesion. Conclusions This study suggests the participation in the diffuse infiltrative/invasive process of GBM cells within the CNS of a novel set of genes coding for membrane-associated proteins, which should be thus susceptible to an inhibition strategy by specific targeting. Massimiliano Monticone and Antonio Daga contributed equally to this work

  6. Identification of a novel set of genes reflecting different in vivo invasive patterns of human GBM cells.

    Science.gov (United States)

    Monticone, Massimiliano; Daga, Antonio; Candiani, Simona; Romeo, Francesco; Mirisola, Valentina; Viaggi, Silvia; Melloni, Ilaria; Pedemonte, Simona; Zona, Gianluigi; Giaretti, Walter; Pfeffer, Ulrich; Castagnola, Patrizio

    2012-08-17

    Most patients affected by Glioblastoma multiforme (GBM, grade IV glioma) experience a recurrence of the disease because of the spreading of tumor cells beyond surgical boundaries. Unveiling mechanisms causing this process is a logic goal to impair the killing capacity of GBM cells by molecular targeting.We noticed that our long-term GBM cultures, established from different patients, may display two categories/types of growth behavior in an orthotopic xenograft model: expansion of the tumor mass and formation of tumor branches/nodules (nodular like, NL-type) or highly diffuse single tumor cell infiltration (HD-type). We determined by DNA microarrays the gene expression profiles of three NL-type and three HD-type long-term GBM cultures. Subsequently, individual genes with different expression levels between the two groups were identified using Significance Analysis of Microarrays (SAM). Real time RT-PCR, immunofluorescence and immunoblot analyses, were performed for a selected subgroup of regulated gene products to confirm the results obtained by the expression analysis. Here, we report the identification of a set of 34 differentially expressed genes in the two types of GBM cultures. Twenty-three of these genes encode for proteins localized to the plasma membrane and 9 of these for proteins are involved in the process of cell adhesion. This study suggests the participation in the diffuse infiltrative/invasive process of GBM cells within the CNS of a novel set of genes coding for membrane-associated proteins, which should be thus susceptible to an inhibition strategy by specific targeting.Massimiliano Monticone and Antonio Daga contributed equally to this work.

  7. Identification of a novel set of genes reflecting different in vivo invasive patterns of human GBM cells

    International Nuclear Information System (INIS)

    Monticone, Massimiliano; Giaretti, Walter; Pfeffer, Ulrich; Daga, Antonio; Candiani, Simona; Romeo, Francesco; Mirisola, Valentina; Viaggi, Silvia; Melloni, Ilaria; Pedemonte, Simona; Zona, Gianluigi

    2012-01-01

    Most patients affected by Glioblastoma multiforme (GBM, grade IV glioma) experience a recurrence of the disease because of the spreading of tumor cells beyond surgical boundaries. Unveiling mechanisms causing this process is a logic goal to impair the killing capacity of GBM cells by molecular targeting. We noticed that our long-term GBM cultures, established from different patients, may display two categories/types of growth behavior in an orthotopic xenograft model: expansion of the tumor mass and formation of tumor branches/nodules (nodular like, NL-type) or highly diffuse single tumor cell infiltration (HD-type). We determined by DNA microarrays the gene expression profiles of three NL-type and three HD-type long-term GBM cultures. Subsequently, individual genes with different expression levels between the two groups were identified using Significance Analysis of Microarrays (SAM). Real time RT-PCR, immunofluorescence and immunoblot analyses, were performed for a selected subgroup of regulated gene products to confirm the results obtained by the expression analysis. Here, we report the identification of a set of 34 differentially expressed genes in the two types of GBM cultures. Twenty-three of these genes encode for proteins localized to the plasma membrane and 9 of these for proteins are involved in the process of cell adhesion. This study suggests the participation in the diffuse infiltrative/invasive process of GBM cells within the CNS of a novel set of genes coding for membrane-associated proteins, which should be thus susceptible to an inhibition strategy by specific targeting. Massimiliano Monticone and Antonio Daga contributed equally to this work

  8. Adaptable Value-Set Analysis for Low-Level Code

    OpenAIRE

    Brauer, Jörg; Hansen, René Rydhof; Kowalewski, Stefan; Larsen, Kim G.; Olesen, Mads Chr.

    2012-01-01

    This paper presents a framework for binary code analysis that uses only SAT-based algorithms. Within the framework, incremental SAT solving is used to perform a form of weakly relational value-set analysis in a novel way, connecting the expressiveness of the value sets to computational complexity. Another key feature of our framework is that it translates the semantics of binary code into an intermediate representation. This allows for a straightforward translation of the program semantics in...

  9. Mining tissue specificity, gene connectivity and disease association to reveal a set of genes that modify the action of disease causing genes

    Directory of Open Access Journals (Sweden)

    Reverter Antonio

    2008-09-01

    Full Text Available Abstract Background The tissue specificity of gene expression has been linked to a number of significant outcomes including level of expression, and differential rates of polymorphism, evolution and disease association. Recent studies have also shown the importance of exploring differential gene connectivity and sequence conservation in the identification of disease-associated genes. However, no study relates gene interactions with tissue specificity and disease association. Methods We adopted an a priori approach making as few assumptions as possible to analyse the interplay among gene-gene interactions with tissue specificity and its subsequent likelihood of association with disease. We mined three large datasets comprising expression data drawn from massively parallel signature sequencing across 32 tissues, describing a set of 55,606 true positive interactions for 7,197 genes, and microarray expression results generated during the profiling of systemic inflammation, from which 126,543 interactions among 7,090 genes were reported. Results Amongst the myriad of complex relationships identified between expression, disease, connectivity and tissue specificity, some interesting patterns emerged. These include elevated rates of expression and network connectivity in housekeeping and disease-associated tissue-specific genes. We found that disease-associated genes are more likely to show tissue specific expression and most frequently interact with other disease genes. Using the thresholds defined in these observations, we develop a guilt-by-association algorithm and discover a group of 112 non-disease annotated genes that predominantly interact with disease-associated genes, impacting on disease outcomes. Conclusion We conclude that parameters such as tissue specificity and network connectivity can be used in combination to identify a group of genes, not previously confirmed as disease causing, that are involved in interactions with disease causing

  10. Can survival prediction be improved by merging gene expression data sets?

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    Haleh Yasrebi

    Full Text Available BACKGROUND: High-throughput gene expression profiling technologies generating a wealth of data, are increasingly used for characterization of tumor biopsies for clinical trials. By applying machine learning algorithms to such clinically documented data sets, one hopes to improve tumor diagnosis, prognosis, as well as prediction of treatment response. However, the limited number of patients enrolled in a single trial study limits the power of machine learning approaches due to over-fitting. One could partially overcome this limitation by merging data from different studies. Nevertheless, such data sets differ from each other with regard to technical biases, patient selection criteria and follow-up treatment. It is therefore not clear at all whether the advantage of increased sample size outweighs the disadvantage of higher heterogeneity of merged data sets. Here, we present a systematic study to answer this question specifically for breast cancer data sets. We use survival prediction based on Cox regression as an assay to measure the added value of merged data sets. RESULTS: Using time-dependent Receiver Operating Characteristic-Area Under the Curve (ROC-AUC and hazard ratio as performance measures, we see in overall no significant improvement or deterioration of survival prediction with merged data sets as compared to individual data sets. This apparently was due to the fact that a few genes with strong prognostic power were not available on all microarray platforms and thus were not retained in the merged data sets. Surprisingly, we found that the overall best performance was achieved with a single-gene predictor consisting of CYB5D1. CONCLUSIONS: Merging did not deteriorate performance on average despite (a The diversity of microarray platforms used. (b The heterogeneity of patients cohorts. (c The heterogeneity of breast cancer disease. (d Substantial variation of time to death or relapse. (e The reduced number of genes in the merged data

  11. Identification of self-consistent modulons from bacterial microarray expression data with the help of structured regulon gene sets

    KAUST Repository

    Permina, Elizaveta A.; Medvedeva, Yulia; Baeck, Pia M.; Hegde, Shubhada R.; Mande, Shekhar C.; Makeev, Vsevolod J.

    2013-01-01

    interactions helps to evaluate parameters for regulatory subnetwork inference. We suggest a procedure for modulon construction where a seed regulon is iteratively updated with genes having expression patterns similar to those for regulon member genes. A set

  12. Quantitative modeling of gene networks of biological systems using fuzzy Petri nets and fuzzy sets

    Directory of Open Access Journals (Sweden)

    Raed I. Hamed

    2018-01-01

    Full Text Available Quantitative demonstrating of organic frameworks has turned into an essential computational methodology in the configuration of novel and investigation of existing natural frameworks. Be that as it may, active information that portrays the framework's elements should be known keeping in mind the end goal to get pertinent results with the routine displaying strategies. This information is frequently robust or even difficult to get. Here, we exhibit a model of quantitative fuzzy rational demonstrating approach that can adapt to obscure motor information and hence deliver applicable results despite the fact that dynamic information is fragmented or just dubiously characterized. Besides, the methodology can be utilized as a part of the blend with the current cutting edge quantitative demonstrating strategies just in specific parts of the framework, i.e., where the data are absent. The contextual analysis of the methodology suggested in this paper is performed on the model of nine-quality genes. We propose a kind of FPN model in light of fuzzy sets to manage the quantitative modeling of biological systems. The tests of our model appear that the model is practical and entirely powerful for information impersonation and thinking of fuzzy expert frameworks.

  13. Association between expression of random gene sets and survival is evident in multiple cancer types and may be explained by sub-classification

    Science.gov (United States)

    2018-01-01

    One of the goals of cancer research is to identify a set of genes that cause or control disease progression. However, although multiple such gene sets were published, these are usually in very poor agreement with each other, and very few of the genes proved to be functional therapeutic targets. Furthermore, recent findings from a breast cancer gene-expression cohort showed that sets of genes selected randomly can be used to predict survival with a much higher probability than expected. These results imply that many of the genes identified in breast cancer gene expression analysis may not be causal of cancer progression, even though they can still be highly predictive of prognosis. We performed a similar analysis on all the cancer types available in the cancer genome atlas (TCGA), namely, estimating the predictive power of random gene sets for survival. Our work shows that most cancer types exhibit the property that random selections of genes are more predictive of survival than expected. In contrast to previous work, this property is not removed by using a proliferation signature, which implies that proliferation may not always be the confounder that drives this property. We suggest one possible solution in the form of data-driven sub-classification to reduce this property significantly. Our results suggest that the predictive power of random gene sets may be used to identify the existence of sub-classes in the data, and thus may allow better understanding of patient stratification. Furthermore, by reducing the observed bias this may allow more direct identification of biologically relevant, and potentially causal, genes. PMID:29470520

  14. Biclustering methods: biological relevance and application in gene expression analysis.

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    Ali Oghabian

    Full Text Available DNA microarray technologies are used extensively to profile the expression levels of thousands of genes under various conditions, yielding extremely large data-matrices. Thus, analyzing this information and extracting biologically relevant knowledge becomes a considerable challenge. A classical approach for tackling this challenge is to use clustering (also known as one-way clustering methods where genes (or respectively samples are grouped together based on the similarity of their expression profiles across the set of all samples (or respectively genes. An alternative approach is to develop biclustering methods to identify local patterns in the data. These methods extract subgroups of genes that are co-expressed across only a subset of samples and may feature important biological or medical implications. In this study we evaluate 13 biclustering and 2 clustering (k-means and hierarchical methods. We use several approaches to compare their performance on two real gene expression data sets. For this purpose we apply four evaluation measures in our analysis: (1 we examine how well the considered (biclustering methods differentiate various sample types; (2 we evaluate how well the groups of genes discovered by the (biclustering methods are annotated with similar Gene Ontology categories; (3 we evaluate the capability of the methods to differentiate genes that are known to be specific to the particular sample types we study and (4 we compare the running time of the algorithms. In the end, we conclude that as long as the samples are well defined and annotated, the contamination of the samples is limited, and the samples are well replicated, biclustering methods such as Plaid and SAMBA are useful for discovering relevant subsets of genes and samples.

  15. BACFIRE, Minimal Cut Sets Common Cause Failure Fault Tree Analysis

    International Nuclear Information System (INIS)

    Fussell, J.B.

    1983-01-01

    1 - Description of problem or function: BACFIRE, designed to aid in common cause failure analysis, searches among the basic events of a minimal cut set of the system logic model for common potential causes of failure. The potential cause of failure is called a qualitative failure characteristics. The algorithm searches qualitative failure characteristics (that are part of the program input) of the basic events contained in a set to find those characteristics common to all basic events. This search is repeated for all cut sets input to the program. Common cause failure analysis is thereby performed without inclusion of secondary failure in the system logic model. By using BACFIRE, a common cause failure analysis can be added to an existing system safety and reliability analysis. 2 - Method of solution: BACFIRE searches the qualitative failure characteristics of the basic events contained in the fault tree minimal cut set to find those characteristics common to all basic events by either of two criteria. The first criterion can be met if all the basic events in a minimal cut set are associated by a condition which alone may increase the probability of multiple component malfunction. The second criterion is met if all the basic events in a minimal cut set are susceptible to the same secondary failure cause and are located in the same domain for that cause of secondary failure. 3 - Restrictions on the complexity of the problem - Maxima of: 1001 secondary failure maps, 101 basic events, 10 cut sets

  16. Bioinformatic Analysis of Strawberry GSTF12 Gene

    Science.gov (United States)

    Wang, Xiran; Jiang, Leiyu; Tang, Haoru

    2018-01-01

    GSTF12 has always been known as a key factor of proanthocyanins accumulate in plant testa. Through bioinformatics analysis of the nucleotide and encoded protein sequence of GSTF12, it is more advantageous to the study of genes related to anthocyanin biosynthesis accumulation pathway. Therefore, we chosen GSTF12 gene of 11 kinds species, downloaded their nucleotide and protein sequence from NCBI as the research object, found strawberry GSTF12 gene via bioinformation analyse, constructed phylogenetic tree. At the same time, we analysed the strawberry GSTF12 gene of physical and chemical properties and its protein structure and so on. The phylogenetic tree showed that Strawberry and petunia were closest relative. By the protein prediction, we found that the protein owed one proper signal peptide without obvious transmembrane regions.

  17. Platform dependence of inference on gene-wise and gene-set involvement in human lung development

    Directory of Open Access Journals (Sweden)

    Kho Alvin T

    2009-06-01

    Full Text Available Abstract Background With the recent development of microarray technologies, the comparability of gene expression data obtained from different platforms poses an important problem. We evaluated two widely used platforms, Affymetrix U133 Plus 2.0 and the Illumina HumanRef-8 v2 Expression Bead Chips, for comparability in a biological system in which changes may be subtle, namely fetal lung tissue as a function of gestational age. Results We performed the comparison via sequence-based probe matching between the two platforms. "Significance grouping" was defined as a measure of comparability. Using both expression correlation and significance grouping as measures of comparability, we demonstrated that despite overall cross-platform differences at the single gene level, increased correlation between the two platforms was found in genes with higher expression level, higher probe overlap, and lower p-value. We also demonstrated that biological function as determined via KEGG pathways or GO categories is more consistent across platforms than single gene analysis. Conclusion We conclude that while the comparability of the platforms at the single gene level may be increased by increasing sample size, they are highly comparable ontologically even for subtle differences in a relatively small sample size. Biologically relevant inference should therefore be reproducible across laboratories using different platforms.

  18. Reduced Set of Virulence Genes Allows High Accuracy Prediction of Bacterial Pathogenicity in Humans

    Science.gov (United States)

    Iraola, Gregorio; Vazquez, Gustavo; Spangenberg, Lucía; Naya, Hugo

    2012-01-01

    Although there have been great advances in understanding bacterial pathogenesis, there is still a lack of integrative information about what makes a bacterium a human pathogen. The advent of high-throughput sequencing technologies has dramatically increased the amount of completed bacterial genomes, for both known human pathogenic and non-pathogenic strains; this information is now available to investigate genetic features that determine pathogenic phenotypes in bacteria. In this work we determined presence/absence patterns of different virulence-related genes among more than finished bacterial genomes from both human pathogenic and non-pathogenic strains, belonging to different taxonomic groups (i.e: Actinobacteria, Gammaproteobacteria, Firmicutes, etc.). An accuracy of 95% using a cross-fold validation scheme with in-fold feature selection is obtained when classifying human pathogens and non-pathogens. A reduced subset of highly informative genes () is presented and applied to an external validation set. The statistical model was implemented in the BacFier v1.0 software (freely available at ), that displays not only the prediction (pathogen/non-pathogen) and an associated probability for pathogenicity, but also the presence/absence vector for the analyzed genes, so it is possible to decipher the subset of virulence genes responsible for the classification on the analyzed genome. Furthermore, we discuss the biological relevance for bacterial pathogenesis of the core set of genes, corresponding to eight functional categories, all with evident and documented association with the phenotypes of interest. Also, we analyze which functional categories of virulence genes were more distinctive for pathogenicity in each taxonomic group, which seems to be a completely new kind of information and could lead to important evolutionary conclusions. PMID:22916122

  19. Diallel analysis of fruit set in Hevea brasiliensis Muell. Arg.

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    Kenneth O. Omokhafe

    2007-03-01

    Full Text Available The clonal and inter-clonal combining ability of fruit set in a Hevea brasiliensis four-parent diallel mating was evaluated using a randomized complete block experimental design with three replicates. Twelve main and reciprocal crosses were hand pollinated and percentage fruit set was recorded. The raw data were subjected to an arc-sine transformation for analysis of variance, and general and specific combining ability. There was significant variation of each of fruit set for the various crosses, general combining ability and reciprocal effect. The breeding implications of these results are also discussed.

  20. Analysis of gene evolution and metabolic pathways using the Candida Gene Order Browser

    LENUS (Irish Health Repository)

    Fitzpatrick, David A

    2010-05-10

    Abstract Background Candida species are the most common cause of opportunistic fungal infection worldwide. Recent sequencing efforts have provided a wealth of Candida genomic data. We have developed the Candida Gene Order Browser (CGOB), an online tool that aids comparative syntenic analyses of Candida species. CGOB incorporates all available Candida clade genome sequences including two Candida albicans isolates (SC5314 and WO-1) and 8 closely related species (Candida dubliniensis, Candida tropicalis, Candida parapsilosis, Lodderomyces elongisporus, Debaryomyces hansenii, Pichia stipitis, Candida guilliermondii and Candida lusitaniae). Saccharomyces cerevisiae is also included as a reference genome. Results CGOB assignments of homology were manually curated based on sequence similarity and synteny. In total CGOB includes 65617 genes arranged into 13625 homology columns. We have also generated improved Candida gene sets by merging\\/removing partial genes in each genome. Interrogation of CGOB revealed that the majority of tandemly duplicated genes are under strong purifying selection in all Candida species. We identified clusters of adjacent genes involved in the same metabolic pathways (such as catabolism of biotin, galactose and N-acetyl glucosamine) and we showed that some clusters are species or lineage-specific. We also identified one example of intron gain in C. albicans. Conclusions Our analysis provides an important resource that is now available for the Candida community. CGOB is available at http:\\/\\/cgob.ucd.ie.

  1. Evaluation of reference genes for gene expression analysis using quantitative RT-PCR in Azospirillum brasilense.

    Science.gov (United States)

    McMillan, Mary; Pereg, Lily

    2014-01-01

    Azospirillum brasilense is a nitrogen fixing bacterium that has been shown to have various beneficial effects on plant growth and yield. Under normal conditions A. brasilense exists in a motile flagellated form, which, under starvation or stress conditions, can undergo differentiation into an encapsulated, cyst-like form. Quantitative RT-PCR can be used to analyse changes in gene expression during this differentiation process. The accuracy of quantification of mRNA levels by qRT-PCR relies on the normalisation of data against stably expressed reference genes. No suitable set of reference genes has yet been described for A. brasilense. Here we evaluated the expression of ten candidate reference genes (16S rRNA, gapB, glyA, gyrA, proC, pykA, recA, recF, rpoD, and tpiA) in wild-type and mutant A. brasilense strains under different culture conditions, including conditions that induce differentiation. Analysis with the software programs BestKeeper, NormFinder and GeNorm indicated that gyrA, glyA and recA are the most stably expressed reference genes in A. brasilense. The results also suggested that the use of two reference genes (gyrA and glyA) is sufficient for effective normalisation of qRT-PCR data.

  2. Evaluation of reference genes for gene expression analysis using quantitative RT-PCR in Azospirillum brasilense.

    Directory of Open Access Journals (Sweden)

    Mary McMillan

    Full Text Available Azospirillum brasilense is a nitrogen fixing bacterium that has been shown to have various beneficial effects on plant growth and yield. Under normal conditions A. brasilense exists in a motile flagellated form, which, under starvation or stress conditions, can undergo differentiation into an encapsulated, cyst-like form. Quantitative RT-PCR can be used to analyse changes in gene expression during this differentiation process. The accuracy of quantification of mRNA levels by qRT-PCR relies on the normalisation of data against stably expressed reference genes. No suitable set of reference genes has yet been described for A. brasilense. Here we evaluated the expression of ten candidate reference genes (16S rRNA, gapB, glyA, gyrA, proC, pykA, recA, recF, rpoD, and tpiA in wild-type and mutant A. brasilense strains under different culture conditions, including conditions that induce differentiation. Analysis with the software programs BestKeeper, NormFinder and GeNorm indicated that gyrA, glyA and recA are the most stably expressed reference genes in A. brasilense. The results also suggested that the use of two reference genes (gyrA and glyA is sufficient for effective normalisation of qRT-PCR data.

  3. Identification of the Core Set of Carbon-Associated Genes in a Bioenergy Grassland Soil.

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    Adina Howe

    Full Text Available Despite the central role of soil microbial communities in global carbon (C cycling, little is known about soil microbial community structure and even less about their metabolic pathways. Efforts to characterize soil communities often focus on identifying differences in gene content across environmental gradients, but an alternative question is what genes are similar in soils. These genes may indicate critical species or potential functions that are required in all soils. Here we identified the "core" set of C cycling sequences widely present in multiple soil metagenomes from a fertilized prairie (FP. Of 226,887 sequences associated with known enzymes involved in the synthesis, metabolism, and transport of carbohydrates, 843 were identified to be consistently prevalent across four replicate soil metagenomes. This core metagenome was functionally and taxonomically diverse, representing five enzyme classes and 99 enzyme families within the CAZy database. Though it only comprised 0.4% of all CAZy-associated genes identified in FP metagenomes, the core was found to be comprised of functions similar to those within cumulative soils. The FP CAZy-associated core sequences were present in multiple publicly available soil metagenomes and most similar to soils sharing geographic proximity. In soil ecosystems, where high diversity remains a key challenge for metagenomic investigations, these core genes represent a subset of critical functions necessary for carbohydrate metabolism, which can be targeted to evaluate important C fluxes in these and other similar soils.

  4. Genome-Wide Temporal Expression Profiling in Caenorhabditis elegans Identifies a Core Gene Set Related to Long-Term Memory.

    Science.gov (United States)

    Freytag, Virginie; Probst, Sabine; Hadziselimovic, Nils; Boglari, Csaba; Hauser, Yannick; Peter, Fabian; Gabor Fenyves, Bank; Milnik, Annette; Demougin, Philippe; Vukojevic, Vanja; de Quervain, Dominique J-F; Papassotiropoulos, Andreas; Stetak, Attila

    2017-07-12

    The identification of genes related to encoding, storage, and retrieval of memories is a major interest in neuroscience. In the current study, we analyzed the temporal gene expression changes in a neuronal mRNA pool during an olfactory long-term associative memory (LTAM) in Caenorhabditis elegans hermaphrodites. Here, we identified a core set of 712 (538 upregulated and 174 downregulated) genes that follows three distinct temporal peaks demonstrating multiple gene regulation waves in LTAM. Compared with the previously published positive LTAM gene set (Lakhina et al., 2015), 50% of the identified upregulated genes here overlap with the previous dataset, possibly representing stimulus-independent memory-related genes. On the other hand, the remaining genes were not previously identified in positive associative memory and may specifically regulate aversive LTAM. Our results suggest a multistep gene activation process during the formation and retrieval of long-term memory and define general memory-implicated genes as well as conditioning-type-dependent gene sets. SIGNIFICANCE STATEMENT The identification of genes regulating different steps of memory is of major interest in neuroscience. Identification of common memory genes across different learning paradigms and the temporal activation of the genes are poorly studied. Here, we investigated the temporal aspects of Caenorhabditis elegans gene expression changes using aversive olfactory associative long-term memory (LTAM) and identified three major gene activation waves. Like in previous studies, aversive LTAM is also CREB dependent, and CREB activity is necessary immediately after training. Finally, we define a list of memory paradigm-independent core gene sets as well as conditioning-dependent genes. Copyright © 2017 the authors 0270-6474/17/376661-12$15.00/0.

  5. Integrative analysis of genome-wide gene copy number changes and gene expression in non-small cell lung cancer.

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    Verena Jabs

    Full Text Available Non-small cell lung cancer (NSCLC represents a genomically unstable cancer type with extensive copy number aberrations. The relationship of gene copy number alterations and subsequent mRNA levels has only fragmentarily been described. The aim of this study was to conduct a genome-wide analysis of gene copy number gains and corresponding gene expression levels in a clinically well annotated NSCLC patient cohort (n = 190 and their association with survival. While more than half of all analyzed gene copy number-gene expression pairs showed statistically significant correlations (10,296 of 18,756 genes, high correlations, with a correlation coefficient >0.7, were obtained only in a subset of 301 genes (1.6%, including KRAS, EGFR and MDM2. Higher correlation coefficients were associated with higher copy number and expression levels. Strong correlations were frequently based on few tumors with high copy number gains and correspondingly increased mRNA expression. Among the highly correlating genes, GO groups associated with posttranslational protein modifications were particularly frequent, including ubiquitination and neddylation. In a meta-analysis including 1,779 patients we found that survival associated genes were overrepresented among highly correlating genes (61 of the 301 highly correlating genes, FDR adjusted p<0.05. Among them are the chaperone CCT2, the core complex protein NUP107 and the ubiquitination and neddylation associated protein CAND1. In conclusion, in a comprehensive analysis we described a distinct set of highly correlating genes. These genes were found to be overrepresented among survival-associated genes based on gene expression in a large collection of publicly available datasets.

  6. Comparative modular analysis of gene expression in vertebrate organs

    Directory of Open Access Journals (Sweden)

    Piasecka Barbara

    2012-03-01

    Full Text Available Abstract Background The degree of conservation of gene expression between homologous organs largely remains an open question. Several recent studies reported some evidence in favor of such conservation. Most studies compute organs' similarity across all orthologous genes, whereas the expression level of many genes are not informative about organ specificity. Results Here, we use a modularization algorithm to overcome this limitation through the identification of inter-species co-modules of organs and genes. We identify such co-modules using mouse and human microarray expression data. They are functionally coherent both in terms of genes and of organs from both organisms. We show that a large proportion of genes belonging to the same co-module are orthologous between mouse and human. Moreover, their zebrafish orthologs also tend to be expressed in the corresponding homologous organs. Notable exceptions to the general pattern of conservation are the testis and the olfactory bulb. Interestingly, some co-modules consist of single organs, while others combine several functionally related organs. For instance, amygdala, cerebral cortex, hypothalamus and spinal cord form a clearly discernible unit of expression, both in mouse and human. Conclusions Our study provides a new framework for comparative analysis which will be applicable also to other sets of large-scale phenotypic data collected across different species.

  7. Comparative modular analysis of gene expression in vertebrate organs.

    Science.gov (United States)

    Piasecka, Barbara; Kutalik, Zoltán; Roux, Julien; Bergmann, Sven; Robinson-Rechavi, Marc

    2012-03-29

    The degree of conservation of gene expression between homologous organs largely remains an open question. Several recent studies reported some evidence in favor of such conservation. Most studies compute organs' similarity across all orthologous genes, whereas the expression level of many genes are not informative about organ specificity. Here, we use a modularization algorithm to overcome this limitation through the identification of inter-species co-modules of organs and genes. We identify such co-modules using mouse and human microarray expression data. They are functionally coherent both in terms of genes and of organs from both organisms. We show that a large proportion of genes belonging to the same co-module are orthologous between mouse and human. Moreover, their zebrafish orthologs also tend to be expressed in the corresponding homologous organs. Notable exceptions to the general pattern of conservation are the testis and the olfactory bulb. Interestingly, some co-modules consist of single organs, while others combine several functionally related organs. For instance, amygdala, cerebral cortex, hypothalamus and spinal cord form a clearly discernible unit of expression, both in mouse and human. Our study provides a new framework for comparative analysis which will be applicable also to other sets of large-scale phenotypic data collected across different species.

  8. Stochastic biological response to radiation. Comprehensive analysis of gene expression

    International Nuclear Information System (INIS)

    Inoue, Tohru; Hirabayashi, Yoko

    2012-01-01

    Authors explain that the radiation effect on biological system is stochastic along the law of physics, differing from chemical effect, using instances of Cs-137 gamma-ray (GR) and benzene (BZ) exposures to mice and of resultant comprehensive analyses of gene expression. Single GR irradiation is done with Gamma Cell 40 (CSR) to C57BL/6 or C3H/He mouse at 0, 0.6 and 3 Gy. BE is given orally at 150 mg/kg/day for 5 days x 2 weeks. Bone marrow cells are sampled 1 month after the exposure. Comprehensive gene expression is analyzed by Gene Chip Mouse Genome 430 2.0 Array (Affymetrix) and data are processed by programs like case normalization, statistics, network generation, functional analysis etc. GR irradiation brings about changes of gene expression, which are classifiable in common genes variable commonly on the dose change and stochastic genes variable stochastically within each dose: e.g., with Welch-t-test, significant differences are between 0/3 Gy (dose-specific difference, 455 pbs (probe set), in stochastic 2113 pbs), 0/0.6 Gy (267 in 1284 pbs) and 0.6/3 Gy (532 pbs); and with one-way analysis of variation (ANOVA) and hierarchial/dendrographic analyses, 520 pbs are shown to involve the dose-dependent 226 and dose-specific 294 pbs. It is also shown that at 3 Gy, expression of common genes are rather suppressed, including those related to the proliferation/apoptosis of B/T cells, and of stochastic genes, related to cell division/signaling. Ven diagram of the common genes of above 520 pbs, stochastic 2113 pbs at 3 Gy and 1284 pbs at 0.6 Gy shows the overlapping genes 29, 2 and 4, respectively, indicating only 35 pbs are overlapping in total. Network analysis of changes by GR shows the rather high expression of genes around hub of cAMP response element binding protein (CREB) at 0.6 Gy, and rather variable expression around CREB hub/suppressed expression of kinesin hub at 3 Gy; in the network by BZ exposure, unchanged or low expression around p53 hub and suppression

  9. Variable precision rough set for multiple decision attribute analysis

    Institute of Scientific and Technical Information of China (English)

    Lai; Kin; Keung

    2008-01-01

    A variable precision rough set (VPRS) model is used to solve the multi-attribute decision analysis (MADA) problem with multiple conflicting decision attributes and multiple condition attributes. By introducing confidence measures and a β-reduct, the VPRS model can rationally solve the conflicting decision analysis problem with multiple decision attributes and multiple condition attributes. For illustration, a medical diagnosis example is utilized to show the feasibility of the VPRS model in solving the MADA...

  10. A static analysis tool set for assembler code verification

    International Nuclear Information System (INIS)

    Dhodapkar, S.D.; Bhattacharjee, A.K.; Sen, Gopa

    1991-01-01

    Software Verification and Validation (V and V) is an important step in assuring reliability and quality of the software. The verification of program source code forms an important part of the overall V and V activity. The static analysis tools described here are useful in verification of assembler code. The tool set consists of static analysers for Intel 8086 and Motorola 68000 assembly language programs. The analysers examine the program source code and generate information about control flow within the program modules, unreachable code, well-formation of modules, call dependency between modules etc. The analysis of loops detects unstructured loops and syntactically infinite loops. Software metrics relating to size and structural complexity are also computed. This report describes the salient features of the design, implementation and the user interface of the tool set. The outputs generated by the analyser are explained using examples taken from some projects analysed by this tool set. (author). 7 refs., 17 figs

  11. A Molecular Iodine Spectral Data Set for Rovibronic Analysis

    Science.gov (United States)

    Williamson, J. Charles; Kuntzleman, Thomas S.; Kafader, Rachael A.

    2013-01-01

    A data set of 7,381 molecular iodine vapor rovibronic transitions between the X and B electronic states has been prepared for an advanced undergraduate spectroscopic analysis project. Students apply standard theoretical techniques to these data and determine the values of three X-state constants (image omitted) and four B-state constants (image…

  12. Large scale gene expression meta-analysis reveals tissue-specific, sex-biased gene expression in humans

    Directory of Open Access Journals (Sweden)

    Benjamin Mayne

    2016-10-01

    Full Text Available The severity and prevalence of many diseases are known to differ between the sexes. Organ specific sex-biased gene expression may underpin these and other sexually dimorphic traits. To further our understanding of sex differences in transcriptional regulation, we performed meta-analyses of sex biased gene expression in multiple human tissues. We analysed 22 publicly available human gene expression microarray data sets including over 2500 samples from 15 different tissues and 9 different organs. Briefly, by using an inverse-variance method we determined the effect size difference of gene expression between males and females. We found the greatest sex differences in gene expression in the brain, specifically in the anterior cingulate cortex, (1818 genes, followed by the heart (375 genes, kidney (224 genes, colon (218 genes and thyroid (163 genes. More interestingly, we found different parts of the brain with varying numbers and identity of sex-biased genes, indicating that specific cortical regions may influence sexually dimorphic traits. The majority of sex-biased genes in other tissues such as the bladder, liver, lungs and pancreas were on the sex chromosomes or involved in sex hormone production. On average in each tissue, 32% of autosomal genes that were expressed in a sex-biased fashion contained androgen or estrogen hormone response elements. Interestingly, across all tissues, we found approximately two-thirds of autosomal genes that were sex-biased were not under direct influence of sex hormones. To our knowledge this is the largest analysis of sex-biased gene expression in human tissues to date. We identified many sex-biased genes that were not under the direct influence of sex chromosome genes or sex hormones. These may provide targets for future development of sex-specific treatments for diseases.

  13. Ultrahigh-dimensional variable selection method for whole-genome gene-gene interaction analysis

    Directory of Open Access Journals (Sweden)

    Ueki Masao

    2012-05-01

    Full Text Available Abstract Background Genome-wide gene-gene interaction analysis using single nucleotide polymorphisms (SNPs is an attractive way for identification of genetic components that confers susceptibility of human complex diseases. Individual hypothesis testing for SNP-SNP pairs as in common genome-wide association study (GWAS however involves difficulty in setting overall p-value due to complicated correlation structure, namely, the multiple testing problem that causes unacceptable false negative results. A large number of SNP-SNP pairs than sample size, so-called the large p small n problem, precludes simultaneous analysis using multiple regression. The method that overcomes above issues is thus needed. Results We adopt an up-to-date method for ultrahigh-dimensional variable selection termed the sure independence screening (SIS for appropriate handling of numerous number of SNP-SNP interactions by including them as predictor variables in logistic regression. We propose ranking strategy using promising dummy coding methods and following variable selection procedure in the SIS method suitably modified for gene-gene interaction analysis. We also implemented the procedures in a software program, EPISIS, using the cost-effective GPGPU (General-purpose computing on graphics processing units technology. EPISIS can complete exhaustive search for SNP-SNP interactions in standard GWAS dataset within several hours. The proposed method works successfully in simulation experiments and in application to real WTCCC (Wellcome Trust Case–control Consortium data. Conclusions Based on the machine-learning principle, the proposed method gives powerful and flexible genome-wide search for various patterns of gene-gene interaction.

  14. The first set of EST resource for gene discovery and marker development in pigeonpea (Cajanus cajan L.

    Directory of Open Access Journals (Sweden)

    Byregowda Munishamappa

    2010-03-01

    .8% in molecular function. Further, 19 genes were identified differentially expressed between FW- responsive genotypes and 20 between SMD- responsive genotypes. Generated ESTs were compiled together with 908 ESTs available in public domain, at the time of analysis, and a set of 5,085 unigenes were defined that were used for identification of molecular markers in pigeonpea. For instance, 3,583 simple sequence repeat (SSR motifs were identified in 1,365 unigenes and 383 primer pairs were designed. Assessment of a set of 84 primer pairs on 40 elite pigeonpea lines showed polymorphism with 15 (28.8% markers with an average of four alleles per marker and an average polymorphic information content (PIC value of 0.40. Similarly, in silico mining of 133 contigs with ≥ 5 sequences detected 102 single nucleotide polymorphisms (SNPs in 37 contigs. As an example, a set of 10 contigs were used for confirming in silico predicted SNPs in a set of four genotypes using wet lab experiments. Occurrence of SNPs were confirmed for all the 6 contigs for which scorable and sequenceable amplicons were generated. PCR amplicons were not obtained in case of 4 contigs. Recognition sites for restriction enzymes were identified for 102 SNPs in 37 contigs that indicates possibility of assaying SNPs in 37 genes using cleaved amplified polymorphic sequences (CAPS assay. Conclusion The pigeonpea EST dataset generated here provides a transcriptomic resource for gene discovery and development of functional markers associated with biotic stress resistance. Sequence analyses of this dataset have showed conservation of a considerable number of pigeonpea transcripts across legume and model plant species analysed as well as some putative pigeonpea specific genes. Validation of identified biotic stress responsive genes should provide candidate genes for allele mining as well as candidate markers for molecular breeding.

  15. Comparative genome analysis of PHB gene family reveals deep evolutionary origins and diverse gene function.

    Science.gov (United States)

    Di, Chao; Xu, Wenying; Su, Zhen; Yuan, Joshua S

    2010-10-07

    PHB (Prohibitin) gene family is involved in a variety of functions important for different biological processes. PHB genes are ubiquitously present in divergent species from prokaryotes to eukaryotes. Human PHB genes have been found to be associated with various diseases. Recent studies by our group and others have shown diverse function of PHB genes in plants for development, senescence, defence, and others. Despite the importance of the PHB gene family, no comprehensive gene family analysis has been carried to evaluate the relatedness of PHB genes across different species. In order to better guide the gene function analysis and understand the evolution of the PHB gene family, we therefore carried out the comparative genome analysis of the PHB genes across different kingdoms. The relatedness, motif distribution, and intron/exon distribution all indicated that PHB genes is a relatively conserved gene family. The PHB genes can be classified into 5 classes and each class have a very deep evolutionary origin. The PHB genes within the class maintained the same motif patterns during the evolution. With Arabidopsis as the model species, we found that PHB gene intron/exon structure and domains are also conserved during the evolution. Despite being a conserved gene family, various gene duplication events led to the expansion of the PHB genes. Both segmental and tandem gene duplication were involved in Arabidopsis PHB gene family expansion. However, segmental duplication is predominant in Arabidopsis. Moreover, most of the duplicated genes experienced neofunctionalization. The results highlighted that PHB genes might be involved in important functions so that the duplicated genes are under the evolutionary pressure to derive new function. PHB gene family is a conserved gene family and accounts for diverse but important biological functions based on the similar molecular mechanisms. The highly diverse biological function indicated that more research needs to be carried out

  16. Gene expression analysis of flax seed development

    Science.gov (United States)

    2011-01-01

    Background Flax, Linum usitatissimum L., is an important crop whose seed oil and stem fiber have multiple industrial applications. Flax seeds are also well-known for their nutritional attributes, viz., omega-3 fatty acids in the oil and lignans and mucilage from the seed coat. In spite of the importance of this crop, there are few molecular resources that can be utilized toward improving seed traits. Here, we describe flax embryo and seed development and generation of comprehensive genomic resources for the flax seed. Results We describe a large-scale generation and analysis of expressed sequences in various tissues. Collectively, the 13 libraries we have used provide a broad representation of genes active in developing embryos (globular, heart, torpedo, cotyledon and mature stages) seed coats (globular and torpedo stages) and endosperm (pooled globular to torpedo stages) and genes expressed in flowers, etiolated seedlings, leaves, and stem tissue. A total of 261,272 expressed sequence tags (EST) (GenBank accessions LIBEST_026995 to LIBEST_027011) were generated. These EST libraries included transcription factor genes that are typically expressed at low levels, indicating that the depth is adequate for in silico expression analysis. Assembly of the ESTs resulted in 30,640 unigenes and 82% of these could be identified on the basis of homology to known and hypothetical genes from other plants. When compared with fully sequenced plant genomes, the flax unigenes resembled poplar and castor bean more than grape, sorghum, rice or Arabidopsis. Nearly one-fifth of these (5,152) had no homologs in sequences reported for any organism, suggesting that this category represents genes that are likely unique to flax. Digital analyses revealed gene expression dynamics for the biosynthesis of a number of important seed constituents during seed development. Conclusions We have developed a foundational database of expressed sequences and collection of plasmid clones that comprise

  17. Gene expression analysis of flax seed development

    Directory of Open Access Journals (Sweden)

    Sharpe Andrew

    2011-04-01

    Full Text Available Abstract Background Flax, Linum usitatissimum L., is an important crop whose seed oil and stem fiber have multiple industrial applications. Flax seeds are also well-known for their nutritional attributes, viz., omega-3 fatty acids in the oil and lignans and mucilage from the seed coat. In spite of the importance of this crop, there are few molecular resources that can be utilized toward improving seed traits. Here, we describe flax embryo and seed development and generation of comprehensive genomic resources for the flax seed. Results We describe a large-scale generation and analysis of expressed sequences in various tissues. Collectively, the 13 libraries we have used provide a broad representation of genes active in developing embryos (globular, heart, torpedo, cotyledon and mature stages seed coats (globular and torpedo stages and endosperm (pooled globular to torpedo stages and genes expressed in flowers, etiolated seedlings, leaves, and stem tissue. A total of 261,272 expressed sequence tags (EST (GenBank accessions LIBEST_026995 to LIBEST_027011 were generated. These EST libraries included transcription factor genes that are typically expressed at low levels, indicating that the depth is adequate for in silico expression analysis. Assembly of the ESTs resulted in 30,640 unigenes and 82% of these could be identified on the basis of homology to known and hypothetical genes from other plants. When compared with fully sequenced plant genomes, the flax unigenes resembled poplar and castor bean more than grape, sorghum, rice or Arabidopsis. Nearly one-fifth of these (5,152 had no homologs in sequences reported for any organism, suggesting that this category represents genes that are likely unique to flax. Digital analyses revealed gene expression dynamics for the biosynthesis of a number of important seed constituents during seed development. Conclusions We have developed a foundational database of expressed sequences and collection of plasmid

  18. Genomic Analysis Reveals Contrasting PIFq Contribution to Diurnal Rhythmic Gene Expression in PIF-Induced and -Repressed Genes.

    Science.gov (United States)

    Martin, Guiomar; Soy, Judit; Monte, Elena

    2016-01-01

    Members of the PIF quartet (PIFq; PIF1, PIF3, PIF4, and PIF5) collectively contribute to induce growth in Arabidopsis seedlings under short day (SD) conditions, specifically promoting elongation at dawn. Their action involves the direct regulation of growth-related and hormone-associated genes. However, a comprehensive definition of the PIFq-regulated transcriptome under SD is still lacking. We have recently shown that SD and free-running (LL) conditions correspond to "growth" and "no growth" conditions, respectively, correlating with greater abundance of PIF protein in SD. Here, we present a genomic analysis whereby we first define SD-regulated genes at dawn compared to LL in the wild type, followed by identification of those SD-regulated genes whose expression depends on the presence of PIFq. By using this sequential strategy, we have identified 349 PIF/SD-regulated genes, approximately 55% induced and 42% repressed by both SD and PIFq. Comparison with available databases indicates that PIF/SD-induced and PIF/SD-repressed sets are differently phased at dawn and mid-morning, respectively. In addition, we found that whereas rhythmicity of the PIF/SD-induced gene set is lost in LL, most PIF/SD-repressed genes keep their rhythmicity in LL, suggesting differential regulation of both gene sets by the circadian clock. Moreover, we also uncovered distinct overrepresented functions in the induced and repressed gene sets, in accord with previous studies in other examined PIF-regulated processes. Interestingly, promoter analyses showed that, whereas PIF/SD-induced genes are enriched in direct PIF targets, PIF/SD-repressed genes are mostly indirectly regulated by the PIFs and might be more enriched in ABA-regulated genes.

  19. Genome-wide identification, characterization and phylogenetic analysis of 50 catfish ATP-binding cassette (ABC) transporter genes.

    Science.gov (United States)

    Liu, Shikai; Li, Qi; Liu, Zhanjiang

    2013-01-01

    Although a large set of full-length transcripts was recently assembled in catfish, annotation of large gene families, especially those with duplications, is still a great challenge. Most often, complexities in annotation cause mis-identification and thereby much confusion in the scientific literature. As such, detailed phylogenetic analysis and/or orthology analysis are required for annotation of genes involved in gene families. The ATP-binding cassette (ABC) transporter gene superfamily is a large gene family that encodes membrane proteins that transport a diverse set of substrates across membranes, playing important roles in protecting organisms from diverse environment. In this work, we identified a set of 50 ABC transporters in catfish genome. Phylogenetic analysis allowed their identification and annotation into seven subfamilies, including 9 ABCA genes, 12 ABCB genes, 12 ABCC genes, 5 ABCD genes, 2 ABCE genes, 4 ABCF genes and 6 ABCG genes. Most ABC transporters are conserved among vertebrates, though cases of recent gene duplications and gene losses do exist. Gene duplications in catfish were found for ABCA1, ABCB3, ABCB6, ABCC5, ABCD3, ABCE1, ABCF2 and ABCG2. The whole set of catfish ABC transporters provide the essential genomic resources for future biochemical, toxicological and physiological studies of ABC drug efflux transporters. The establishment of orthologies should allow functional inferences with the information from model species, though the function of lineage-specific genes can be distinct because of specific living environment with different selection pressure.

  20. Cluster analysis by optimal decomposition of induced fuzzy sets

    Energy Technology Data Exchange (ETDEWEB)

    Backer, E

    1978-01-01

    Nonsupervised pattern recognition is addressed and the concept of fuzzy sets is explored in order to provide the investigator (data analyst) additional information supplied by the pattern class membership values apart from the classical pattern class assignments. The basic ideas behind the pattern recognition problem, the clustering problem, and the concept of fuzzy sets in cluster analysis are discussed, and a brief review of the literature of the fuzzy cluster analysis is given. Some mathematical aspects of fuzzy set theory are briefly discussed; in particular, a measure of fuzziness is suggested. The optimization-clustering problem is characterized. Then the fundamental idea behind affinity decomposition is considered. Next, further analysis takes place with respect to the partitioning-characterization functions. The iterative optimization procedure is then addressed. The reclassification function is investigated and convergence properties are examined. Finally, several experiments in support of the method suggested are described. Four object data sets serve as appropriate test cases. 120 references, 70 figures, 11 tables. (RWR)

  1. Serial Expression Analysis: a web tool for the analysis of serial gene expression data

    Science.gov (United States)

    Nueda, Maria José; Carbonell, José; Medina, Ignacio; Dopazo, Joaquín; Conesa, Ana

    2010-01-01

    Serial transcriptomics experiments investigate the dynamics of gene expression changes associated with a quantitative variable such as time or dosage. The statistical analysis of these data implies the study of global and gene-specific expression trends, the identification of significant serial changes, the comparison of expression profiles and the assessment of transcriptional changes in terms of cellular processes. We have created the SEA (Serial Expression Analysis) suite to provide a complete web-based resource for the analysis of serial transcriptomics data. SEA offers five different algorithms based on univariate, multivariate and functional profiling strategies framed within a user-friendly interface and a project-oriented architecture to facilitate the analysis of serial gene expression data sets from different perspectives. SEA is available at sea.bioinfo.cipf.es. PMID:20525784

  2. Seismic analysis for conceptual design of HCCR TBM-set

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Dong Won, E-mail: dwlee@kaeri.re.kr [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Park, Seong Dae; Jin, Hyung Gon; Lee, Eo Hwak; Kim, Suk-Kwon; Yoon, Jae Sung [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Shin, Kyu In [Gentec Co., Daejeon, Republic of Korea (Korea, Republic of); Cho, Seungyon [National Fusion Research Institute, Daejeon (Korea, Republic of)

    2016-11-01

    Highlights: • The seismic analysis of KO HCCR TBM-set are performed. • The seismic envents like SL-1, SL-2, and SMHV are selected and evaluated with FEM code (ANSYS). • The results of the stresses and deformations are confirmed to meet the design criteria. - Abstract: Using the conceptual design of the Korean helium cooled ceramic reflector (HCCR) test blanket module (TBM) including the TBM-shield for testing in ITER, a seismic analysis is performed. According to the ITER TBM port plug (TBM PP) system load specifications, seismic events are selected as SL-1 (seismic level-1), SL-2 (seismic level-2), and SMHV (seismes maximaux historiquement vraisemblables, Maximum Histroically Probable Earthquakes). In a modal analysis a total of 50 modes are obtained. Then, a spectra response analysis for each seismic event is carried out using ANSYS based on the modal analysis results. For each event, the obtained Tresca stress is evaluated to confirm the design integrity, by comparing the resulting stress to the design criteria. The Tresca strain and displacement are also estimated for the HCCR TBM-set. From the analysis, it was concluded that the maximum stresses by the seismic events meet the design criteria, and the displacements are lower than the designed gap from the TBM PP frame. The results are provided to a load combination analysis.

  3. Multiscale Embedded Gene Co-expression Network Analysis.

    Directory of Open Access Journals (Sweden)

    Won-Min Song

    2015-11-01

    Full Text Available Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3, the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA by: i introducing quality control of co-expression similarities, ii parallelizing embedded network construction, and iii developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs. We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA. MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

  4. Multiscale Embedded Gene Co-expression Network Analysis.

    Science.gov (United States)

    Song, Won-Min; Zhang, Bin

    2015-11-01

    Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

  5. Prediction potential of candidate biomarker sets identified and validated on gene expression data from multiple datasets

    Directory of Open Access Journals (Sweden)

    Karacali Bilge

    2007-10-01

    Full Text Available Abstract Background Independently derived expression profiles of the same biological condition often have few genes in common. In this study, we created populations of expression profiles from publicly available microarray datasets of cancer (breast, lymphoma and renal samples linked to clinical information with an iterative machine learning algorithm. ROC curves were used to assess the prediction error of each profile for classification. We compared the prediction error of profiles correlated with molecular phenotype against profiles correlated with relapse-free status. Prediction error of profiles identified with supervised univariate feature selection algorithms were compared to profiles selected randomly from a all genes on the microarray platform and b a list of known disease-related genes (a priori selection. We also determined the relevance of expression profiles on test arrays from independent datasets, measured on either the same or different microarray platforms. Results Highly discriminative expression profiles were produced on both simulated gene expression data and expression data from breast cancer and lymphoma datasets on the basis of ER and BCL-6 expression, respectively. Use of relapse-free status to identify profiles for prognosis prediction resulted in poorly discriminative decision rules. Supervised feature selection resulted in more accurate classifications than random or a priori selection, however, the difference in prediction error decreased as the number of features increased. These results held when decision rules were applied across-datasets to samples profiled on the same microarray platform. Conclusion Our results show that many gene sets predict molecular phenotypes accurately. Given this, expression profiles identified using different training datasets should be expected to show little agreement. In addition, we demonstrate the difficulty in predicting relapse directly from microarray data using supervised machine

  6. Integrative sparse principal component analysis of gene expression data.

    Science.gov (United States)

    Liu, Mengque; Fan, Xinyan; Fang, Kuangnan; Zhang, Qingzhao; Ma, Shuangge

    2017-12-01

    In the analysis of gene expression data, dimension reduction techniques have been extensively adopted. The most popular one is perhaps the PCA (principal component analysis). To generate more reliable and more interpretable results, the SPCA (sparse PCA) technique has been developed. With the "small sample size, high dimensionality" characteristic of gene expression data, the analysis results generated from a single dataset are often unsatisfactory. Under contexts other than dimension reduction, integrative analysis techniques, which jointly analyze the raw data of multiple independent datasets, have been developed and shown to outperform "classic" meta-analysis and other multidatasets techniques and single-dataset analysis. In this study, we conduct integrative analysis by developing the iSPCA (integrative SPCA) method. iSPCA achieves the selection and estimation of sparse loadings using a group penalty. To take advantage of the similarity across datasets and generate more accurate results, we further impose contrasted penalties. Different penalties are proposed to accommodate different data conditions. Extensive simulations show that iSPCA outperforms the alternatives under a wide spectrum of settings. The analysis of breast cancer and pancreatic cancer data further shows iSPCA's satisfactory performance. © 2017 WILEY PERIODICALS, INC.

  7. Analysis of baseline gene expression levels from ...

    Science.gov (United States)

    The use of gene expression profiling to predict chemical mode of action would be enhanced by better characterization of variance due to individual, environmental, and technical factors. Meta-analysis of microarray data from untreated or vehicle-treated animals within the control arm of toxicogenomics studies has yielded useful information on baseline fluctuations in gene expression. A dataset of control animal microarray expression data was assembled by a working group of the Health and Environmental Sciences Institute's Technical Committee on the Application of Genomics in Mechanism Based Risk Assessment in order to provide a public resource for assessments of variability in baseline gene expression. Data from over 500 Affymetrix microarrays from control rat liver and kidney were collected from 16 different institutions. Thirty-five biological and technical factors were obtained for each animal, describing a wide range of study characteristics, and a subset were evaluated in detail for their contribution to total variability using multivariate statistical and graphical techniques. The study factors that emerged as key sources of variability included gender, organ section, strain, and fasting state. These and other study factors were identified as key descriptors that should be included in the minimal information about a toxicogenomics study needed for interpretation of results by an independent source. Genes that are the most and least variable, gender-selectiv

  8. Analysis of breast cancer metastasis candidate genes from next generation-sequencing via systematic functional genomics

    DEFF Research Database (Denmark)

    Blomstrøm, Monica Marie

    2016-01-01

    several growth modulators and invasion modulators were identified and independently validated. These candidates revealed a group of genes with metastasis-related functions in vitro that are involved in RNA-related processes, such as RNA-processing. Moreover, a general feature was that proliferation......) and non-CSCs. The main goal of this project was to functionally characterize a set of candidate genes recovered from next-generation sequencing analysis for their role in breast cancer metastasis formation. The starting gene set comprised 104 gene variants; i.e. 57 wildtype and 47 mutated variants. During...

  9. When is hub gene selection better than standard meta-analysis?

    Science.gov (United States)

    Langfelder, Peter; Mischel, Paul S; Horvath, Steve

    2013-01-01

    Since hub nodes have been found to play important roles in many networks, highly connected hub genes are expected to play an important role in biology as well. However, the empirical evidence remains ambiguous. An open question is whether (or when) hub gene selection leads to more meaningful gene lists than a standard statistical analysis based on significance testing when analyzing genomic data sets (e.g., gene expression or DNA methylation data). Here we address this question for the special case when multiple genomic data sets are available. This is of great practical importance since for many research questions multiple data sets are publicly available. In this case, the data analyst can decide between a standard statistical approach (e.g., based on meta-analysis) and a co-expression network analysis approach that selects intramodular hubs in consensus modules. We assess the performance of these two types of approaches according to two criteria. The first criterion evaluates the biological insights gained and is relevant in basic research. The second criterion evaluates the validation success (reproducibility) in independent data sets and often applies in clinical diagnostic or prognostic applications. We compare meta-analysis with consensus network analysis based on weighted correlation network analysis (WGCNA) in three comprehensive and unbiased empirical studies: (1) Finding genes predictive of lung cancer survival, (2) finding methylation markers related to age, and (3) finding mouse genes related to total cholesterol. The results demonstrate that intramodular hub gene status with respect to consensus modules is more useful than a meta-analysis p-value when identifying biologically meaningful gene lists (reflecting criterion 1). However, standard meta-analysis methods perform as good as (if not better than) a consensus network approach in terms of validation success (criterion 2). The article also reports a comparison of meta-analysis techniques applied to

  10. When Is Hub Gene Selection Better than Standard Meta-Analysis?

    Science.gov (United States)

    Langfelder, Peter; Mischel, Paul S.; Horvath, Steve

    2013-01-01

    Since hub nodes have been found to play important roles in many networks, highly connected hub genes are expected to play an important role in biology as well. However, the empirical evidence remains ambiguous. An open question is whether (or when) hub gene selection leads to more meaningful gene lists than a standard statistical analysis based on significance testing when analyzing genomic data sets (e.g., gene expression or DNA methylation data). Here we address this question for the special case when multiple genomic data sets are available. This is of great practical importance since for many research questions multiple data sets are publicly available. In this case, the data analyst can decide between a standard statistical approach (e.g., based on meta-analysis) and a co-expression network analysis approach that selects intramodular hubs in consensus modules. We assess the performance of these two types of approaches according to two criteria. The first criterion evaluates the biological insights gained and is relevant in basic research. The second criterion evaluates the validation success (reproducibility) in independent data sets and often applies in clinical diagnostic or prognostic applications. We compare meta-analysis with consensus network analysis based on weighted correlation network analysis (WGCNA) in three comprehensive and unbiased empirical studies: (1) Finding genes predictive of lung cancer survival, (2) finding methylation markers related to age, and (3) finding mouse genes related to total cholesterol. The results demonstrate that intramodular hub gene status with respect to consensus modules is more useful than a meta-analysis p-value when identifying biologically meaningful gene lists (reflecting criterion 1). However, standard meta-analysis methods perform as good as (if not better than) a consensus network approach in terms of validation success (criterion 2). The article also reports a comparison of meta-analysis techniques applied to

  11. When is hub gene selection better than standard meta-analysis?

    Directory of Open Access Journals (Sweden)

    Peter Langfelder

    Full Text Available Since hub nodes have been found to play important roles in many networks, highly connected hub genes are expected to play an important role in biology as well. However, the empirical evidence remains ambiguous. An open question is whether (or when hub gene selection leads to more meaningful gene lists than a standard statistical analysis based on significance testing when analyzing genomic data sets (e.g., gene expression or DNA methylation data. Here we address this question for the special case when multiple genomic data sets are available. This is of great practical importance since for many research questions multiple data sets are publicly available. In this case, the data analyst can decide between a standard statistical approach (e.g., based on meta-analysis and a co-expression network analysis approach that selects intramodular hubs in consensus modules. We assess the performance of these two types of approaches according to two criteria. The first criterion evaluates the biological insights gained and is relevant in basic research. The second criterion evaluates the validation success (reproducibility in independent data sets and often applies in clinical diagnostic or prognostic applications. We compare meta-analysis with consensus network analysis based on weighted correlation network analysis (WGCNA in three comprehensive and unbiased empirical studies: (1 Finding genes predictive of lung cancer survival, (2 finding methylation markers related to age, and (3 finding mouse genes related to total cholesterol. The results demonstrate that intramodular hub gene status with respect to consensus modules is more useful than a meta-analysis p-value when identifying biologically meaningful gene lists (reflecting criterion 1. However, standard meta-analysis methods perform as good as (if not better than a consensus network approach in terms of validation success (criterion 2. The article also reports a comparison of meta-analysis techniques

  12. Separate enrichment analysis of pathways for up- and downregulated genes.

    Science.gov (United States)

    Hong, Guini; Zhang, Wenjing; Li, Hongdong; Shen, Xiaopei; Guo, Zheng

    2014-03-06

    Two strategies are often adopted for enrichment analysis of pathways: the analysis of all differentially expressed (DE) genes together or the analysis of up- and downregulated genes separately. However, few studies have examined the rationales of these enrichment analysis strategies. Using both microarray and RNA-seq data, we show that gene pairs with functional links in pathways tended to have positively correlated expression levels, which could result in an imbalance between the up- and downregulated genes in particular pathways. We then show that the imbalance could greatly reduce the statistical power for finding disease-associated pathways through the analysis of all-DE genes. Further, using gene expression profiles from five types of tumours, we illustrate that the separate analysis of up- and downregulated genes could identify more pathways that are really pertinent to phenotypic difference. In conclusion, analysing up- and downregulated genes separately is more powerful than analysing all of the DE genes together.

  13. Setting Standards for Medically-Based Running Analysis

    Science.gov (United States)

    Vincent, Heather K.; Herman, Daniel C.; Lear-Barnes, Leslie; Barnes, Robert; Chen, Cong; Greenberg, Scott; Vincent, Kevin R.

    2015-01-01

    Setting standards for medically based running analyses is necessary to ensure that runners receive a high-quality service from practitioners. Medical and training history, physical and functional tests, and motion analysis of running at self-selected and faster speeds are key features of a comprehensive analysis. Self-reported history and movement symmetry are critical factors that require follow-up therapy or long-term management. Pain or injury is typically the result of a functional deficit above or below the site along the kinematic chain. PMID:25014394

  14. Stochastic analysis in discrete and continuous settings with normal martingales

    CERN Document Server

    Privault, Nicolas

    2009-01-01

    This volume gives a unified presentation of stochastic analysis for continuous and discontinuous stochastic processes, in both discrete and continuous time. It is mostly self-contained and accessible to graduate students and researchers having already received a basic training in probability. The simultaneous treatment of continuous and jump processes is done in the framework of normal martingales; that includes the Brownian motion and compensated Poisson processes as specific cases. In particular, the basic tools of stochastic analysis (chaos representation, gradient, divergence, integration by parts) are presented in this general setting. Applications are given to functional and deviation inequalities and mathematical finance.

  15. Reconstruction of gene regulatory modules from RNA silencing of IFN-α modulators: experimental set-up and inference method.

    Science.gov (United States)

    Grassi, Angela; Di Camillo, Barbara; Ciccarese, Francesco; Agnusdei, Valentina; Zanovello, Paola; Amadori, Alberto; Finesso, Lorenzo; Indraccolo, Stefano; Toffolo, Gianna Maria

    2016-03-12

    Inference of gene regulation from expression data may help to unravel regulatory mechanisms involved in complex diseases or in the action of specific drugs. A challenging task for many researchers working in the field of systems biology is to build up an experiment with a limited budget and produce a dataset suitable to reconstruct putative regulatory modules worth of biological validation. Here, we focus on small-scale gene expression screens and we introduce a novel experimental set-up and a customized method of analysis to make inference on regulatory modules starting from genetic perturbation data, e.g. knockdown and overexpression data. To illustrate the utility of our strategy, it was applied to produce and analyze a dataset of quantitative real-time RT-PCR data, in which interferon-α (IFN-α) transcriptional response in endothelial cells is investigated by RNA silencing of two candidate IFN-α modulators, STAT1 and IFIH1. A putative regulatory module was reconstructed by our method, revealing an intriguing feed-forward loop, in which STAT1 regulates IFIH1 and they both negatively regulate IFNAR1. STAT1 regulation on IFNAR1 was object of experimental validation at the protein level. Detailed description of the experimental set-up and of the analysis procedure is reported, with the intent to be of inspiration for other scientists who want to realize similar experiments to reconstruct gene regulatory modules starting from perturbations of possible regulators. Application of our approach to the study of IFN-α transcriptional response modulators in endothelial cells has led to many interesting novel findings and new biological hypotheses worth of validation.

  16. An ancient dental gene set governs development and continuous regeneration of teeth in sharks.

    Science.gov (United States)

    Rasch, Liam J; Martin, Kyle J; Cooper, Rory L; Metscher, Brian D; Underwood, Charlie J; Fraser, Gareth J

    2016-07-15

    The evolution of oral teeth is considered a major contributor to the overall success of jawed vertebrates. This is especially apparent in cartilaginous fishes including sharks and rays, which develop elaborate arrays of highly specialized teeth, organized in rows and retain the capacity for life-long regeneration. Perpetual regeneration of oral teeth has been either lost or highly reduced in many other lineages including important developmental model species, so cartilaginous fishes are uniquely suited for deep comparative analyses of tooth development and regeneration. Additionally, sharks and rays can offer crucial insights into the characters of the dentition in the ancestor of all jawed vertebrates. Despite this, tooth development and regeneration in chondrichthyans is poorly understood and remains virtually uncharacterized from a developmental genetic standpoint. Using the emerging chondrichthyan model, the catshark (Scyliorhinus spp.), we characterized the expression of genes homologous to those known to be expressed during stages of early dental competence, tooth initiation, morphogenesis, and regeneration in bony vertebrates. We have found that expression patterns of several genes from Hh, Wnt/β-catenin, Bmp and Fgf signalling pathways indicate deep conservation over ~450 million years of tooth development and regeneration. We describe how these genes participate in the initial emergence of the shark dentition and how they are redeployed during regeneration of successive tooth generations. We suggest that at the dawn of the vertebrate lineage, teeth (i) were most likely continuously regenerative structures, and (ii) utilised a core set of genes from members of key developmental signalling pathways that were instrumental in creating a dental legacy redeployed throughout vertebrate evolution. These data lay the foundation for further experimental investigations utilizing the unique regenerative capacity of chondrichthyan models to answer evolutionary

  17. Knowledge Enrichment Analysis for Human Tissue- Specific Genes Uncover New Biological Insights

    Directory of Open Access Journals (Sweden)

    Gong Xiu-Jun

    2012-06-01

    Full Text Available The expression and regulation of genes in different tissues are fundamental questions to be answered in biology. Knowledge enrichment analysis for tissue specific (TS and housekeeping (HK genes may help identify their roles in biological process or diseases and gain new biological insights.In this paper, we performed the knowledge enrichment analysis for 17,343 genes in 84 human tissues using Gene Set Enrichment Analysis (GSEA and Hypergeometric Analysis (HA against three biological ontologies: Gene Ontology (GO, KEGG pathways and Disease Ontology (DO respectively.The analyses results demonstrated that the functions of most gene groups are consistent with their tissue origins. Meanwhile three interesting new associations for HK genes and the skeletal muscle tissuegenes are found. Firstly, Hypergeometric analysis against KEGG database for HK genes disclosed that three disease terms (Parkinson’s disease, Huntington’s disease, Alzheimer’s disease are intensively enriched.Secondly, Hypergeometric analysis against the KEGG database for Skeletal Muscle tissue genes shows that two cardiac diseases of “Hypertrophic cardiomyopathy (HCM” and “Arrhythmogenic right ventricular cardiomyopathy (ARVC” are heavily enriched, which are also considered as no relationship with skeletal functions.Thirdly, “Prostate cancer” is intensively enriched in Hypergeometric analysis against the disease ontology (DO for the Skeletal Muscle tissue genes, which is a much unexpected phenomenon.

  18. A comparative analysis of biclustering algorithms for gene expression data

    Science.gov (United States)

    Eren, Kemal; Deveci, Mehmet; Küçüktunç, Onur; Çatalyürek, Ümit V.

    2013-01-01

    The need to analyze high-dimension biological data is driving the development of new data mining methods. Biclustering algorithms have been successfully applied to gene expression data to discover local patterns, in which a subset of genes exhibit similar expression levels over a subset of conditions. However, it is not clear which algorithms are best suited for this task. Many algorithms have been published in the past decade, most of which have been compared only to a small number of algorithms. Surveys and comparisons exist in the literature, but because of the large number and variety of biclustering algorithms, they are quickly outdated. In this article we partially address this problem of evaluating the strengths and weaknesses of existing biclustering methods. We used the BiBench package to compare 12 algorithms, many of which were recently published or have not been extensively studied. The algorithms were tested on a suite of synthetic data sets to measure their performance on data with varying conditions, such as different bicluster models, varying noise, varying numbers of biclusters and overlapping biclusters. The algorithms were also tested on eight large gene expression data sets obtained from the Gene Expression Omnibus. Gene Ontology enrichment analysis was performed on the resulting biclusters, and the best enrichment terms are reported. Our analyses show that the biclustering method and its parameters should be selected based on the desired model, whether that model allows overlapping biclusters, and its robustness to noise. In addition, we observe that the biclustering algorithms capable of finding more than one model are more successful at capturing biologically relevant clusters. PMID:22772837

  19. Identification of sparsely distributed clusters of cis-regulatory elements in sets of co-expressed genes

    OpenAIRE

    Kreiman, Gabriel

    2004-01-01

    Sequence information and high‐throughput methods to measure gene expression levels open the door to explore transcriptional regulation using computational tools. Combinatorial regulation and sparseness of regulatory elements throughout the genome allow organisms to control the spatial and temporal patterns of gene expression. Here we study the organization of cis‐regulatory elements in sets of co‐regulated genes. We build an algorithm to search for combinations of transcription factor binding...

  20. Human factors and fuzzy set theory for safety analysis

    International Nuclear Information System (INIS)

    Nishiwaki, Y.

    1987-01-01

    Human reliability and performance is affected by many factors: medical, physiological and psychological, etc. The uncertainty involved in human factors may not necessarily be probabilistic, but fuzzy. Therefore, it is important to develop a theory by which both the non-probabilistic uncertainties, or fuzziness, of human factors and the probabilistic properties of machines can be treated consistently. In reality, randomness and fuzziness are sometimes mixed. From the mathematical point of view, probabilistic measures may be considered a special case of fuzzy measures. Therefore, fuzzy set theory seems to be an effective tool for analysing man-machine systems. The concept 'failure possibility' based on fuzzy sets is suggested as an approach to safety analysis and fault diagnosis of a large complex system. Fuzzy measures and fuzzy integrals are introduced and their possible applications are also discussed. (author)

  1. Analysis of Doppler effect with JAERI-Fast set

    International Nuclear Information System (INIS)

    Takano, Hideki; Matsui, Yasushi.

    1977-07-01

    Temperature dependence of group cross sections in the JAERI-Fast set versions I, IR, II and IIR has been tested from the analysis of Doppler experiments performed with two different methods. One is Doppler reactivity measurement for the whole core of SEFOR assembly, and the other sample Doppler reactivity measurement for natural UO 2 in FCA assemblies V-1, V-2, VI-1 and VI-2, ZPR-6-7, ZPR-3-47, and ZPPR-2 and 3. Doppler effects were calculated with one- and two-dimensional diffusion 1-st order perturbation code DOPP2D. The results calculated with the JAERI-Fast set versions II and IIR are in good agreement with the experimental ones. In these calculation, resonance heterogeneity effect, stainless-stell buffer effect and plate heterogeneity effect are considered, and these effects contribute significantly to Doppler effect. (auth.)

  2. A Strategy for Identifying Quantitative Trait Genes Using Gene Expression Analysis and Causal Analysis

    Directory of Open Access Journals (Sweden)

    Akira Ishikawa

    2017-11-01

    Full Text Available Large numbers of quantitative trait loci (QTL affecting complex diseases and other quantitative traits have been reported in humans and model animals. However, the genetic architecture of these traits remains elusive due to the difficulty in identifying causal quantitative trait genes (QTGs for common QTL with relatively small phenotypic effects. A traditional strategy based on techniques such as positional cloning does not always enable identification of a single candidate gene for a QTL of interest because it is difficult to narrow down a target genomic interval of the QTL to a very small interval harboring only one gene. A combination of gene expression analysis and statistical causal analysis can greatly reduce the number of candidate genes. This integrated approach provides causal evidence that one of the candidate genes is a putative QTG for the QTL. Using this approach, I have recently succeeded in identifying a single putative QTG for resistance to obesity in mice. Here, I outline the integration approach and discuss its usefulness using my studies as an example.

  3. A Strategy for Identifying Quantitative Trait Genes Using Gene Expression Analysis and Causal Analysis.

    Science.gov (United States)

    Ishikawa, Akira

    2017-11-27

    Large numbers of quantitative trait loci (QTL) affecting complex diseases and other quantitative traits have been reported in humans and model animals. However, the genetic architecture of these traits remains elusive due to the difficulty in identifying causal quantitative trait genes (QTGs) for common QTL with relatively small phenotypic effects. A traditional strategy based on techniques such as positional cloning does not always enable identification of a single candidate gene for a QTL of interest because it is difficult to narrow down a target genomic interval of the QTL to a very small interval harboring only one gene. A combination of gene expression analysis and statistical causal analysis can greatly reduce the number of candidate genes. This integrated approach provides causal evidence that one of the candidate genes is a putative QTG for the QTL. Using this approach, I have recently succeeded in identifying a single putative QTG for resistance to obesity in mice. Here, I outline the integration approach and discuss its usefulness using my studies as an example.

  4. In-silico gene co-expression network analysis in Paracoccidioides brasiliensis with reference to haloacid dehalogenase superfamily hydrolase gene

    Directory of Open Access Journals (Sweden)

    Raghunath Satpathy

    2015-01-01

    Full Text Available Context: Paracoccidioides brasiliensis, a dimorphic fungus is the causative agent of paracoccidioidomycosis, a disease globally affecting millions of people. The haloacid dehalogenase (HAD superfamily hydrolases enzyme in the fungi, in particular, is known to be responsible in the pathogenesis by adhering to the tissue. Hence, identification of novel drug targets is essential. Aims: In-silico based identification of co-expressed genes along with HAD superfamily hydrolase in P. brasiliensis during the morphogenesis from mycelium to yeast to identify possible genes as drug targets. Materials and Methods: In total, four datasets were retrieved from the NCBI-gene expression omnibus (GEO database, each containing 4340 genes, followed by gene filtration expression of the data set. Further co-expression (CE study was performed individually and then a combination these genes were visualized in the Cytoscape 2. 8.3. Statistical Analysis Used: Mean and standard deviation value of the HAD superfamily hydrolase gene was obtained from the expression data and this value was subsequently used for the CE calculation purpose by selecting specific correlation power and filtering threshold. Results: The 23 genes that were thus obtained are common with respect to the HAD superfamily hydrolase gene. A significant network was selected from the Cytoscape network visualization that contains total 7 genes out of which 5 genes, which do not have significant protein hits, obtained from gene annotation of the expressed sequence tags by BLAST X. For all the protein PSI-BLAST was performed against human genome to find the homology. Conclusions: The gene co-expression network was obtained with respect to HAD superfamily dehalogenase gene in P. Brasiliensis.

  5. An analysis of strategic price setting in retail gasoline markets

    Science.gov (United States)

    Jaureguiberry, Florencia

    This dissertation studies price-setting behavior in the retail gasoline industry. The main questions addressed are: How important is a retail station's brand and proximity to competitors when retail stations set price? How do retailers adjust their pricing when they cater to consumers who are less aware of competing options or have less discretion over where they purchase gasoline? These questions are explored in two separate analyses using a unique datasets containing retail pricing behavior of stations in California and in 24 different metropolitan areas. The evidence suggests that brand and location generate local market power for gasoline stations. After controlling for market and station characteristics, the analysis finds a spread of 11 cents per gallon between the highest and the lowest priced retail gasoline brands. The analysis also indicates that when the nearest competitor is located over 2 miles away as opposed to next door, consumers will pay an additional 1 cent per gallon of gasoline. In order to quantify the significance of local market power, data for stations located near major airport rental car locations are utilized. The presumption here is that rental car users are less aware or less sensitive to fueling options near the rental car return location and are to some extent "captured consumers". Retailers located near rental car locations have incentives to adjust their pricing strategies to exploit this. The analysis of pricing near rental car locations indicates that retailers charge prices that are 4 cent per gallon higher than other stations in the same metropolitan area. This analysis is of interest to regulators who are concerned with issues of consolidation, market power, and pricing in the retail gasoline industry. This dissertation concludes with a discussion of the policy implications of the empirical analysis.

  6. Radiological error: analysis, standard setting, targeted instruction and teamworking

    International Nuclear Information System (INIS)

    FitzGerald, Richard

    2005-01-01

    Diagnostic radiology does not have objective benchmarks for acceptable levels of missed diagnoses [1]. Until now, data collection of radiological discrepancies has been very time consuming. The culture within the specialty did not encourage it. However, public concern about patient safety is increasing. There have been recent innovations in compiling radiological interpretive discrepancy rates which may facilitate radiological standard setting. However standard setting alone will not optimise radiologists' performance or patient safety. We must use these new techniques in radiological discrepancy detection to stimulate greater knowledge sharing, targeted instruction and teamworking among radiologists. Not all radiological discrepancies are errors. Radiological discrepancy programmes must not be abused as an instrument for discrediting individual radiologists. Discrepancy rates must not be distorted as a weapon in turf battles. Radiological errors may be due to many causes and are often multifactorial. A systems approach to radiological error is required. Meaningful analysis of radiological discrepancies and errors is challenging. Valid standard setting will take time. Meanwhile, we need to develop top-up training, mentoring and rehabilitation programmes. (orig.)

  7. Fuzzy-Set Based Sentiment Analysis of Big Social Data

    DEFF Research Database (Denmark)

    Mukkamala, Raghava Rao; Hussain, Abid; Vatrapu, Ravi

    2014-01-01

    Computational approaches to social media analytics are largely limited to graph theoretical approaches such as social network analysis (SNA) informed by the social philosophical approach of relational sociology. There are no other unified modelling approaches to social data that integrate...... the conceptual, formal, software, analytical and empirical realms. In this paper, we first present and discuss a theory and conceptual model of social data. Second, we outline a formal model based on fuzzy set theory and describe the operational semantics of the formal model with a real-world social data example...... from Facebook. Third, we briefly present and discuss the Social Data Analytics Tool (SODATO) that realizes the conceptual model in software and provisions social data analysis based on the conceptual and formal models. Fourth, we use SODATO to fetch social data from the facebook wall of a global brand...

  8. Cartilage-selective genes identified in genome-scale analysis of non-cartilage and cartilage gene expression

    Directory of Open Access Journals (Sweden)

    Cohn Zachary A

    2007-06-01

    Full Text Available Abstract Background Cartilage plays a fundamental role in the development of the human skeleton. Early in embryogenesis, mesenchymal cells condense and differentiate into chondrocytes to shape the early skeleton. Subsequently, the cartilage anlagen differentiate to form the growth plates, which are responsible for linear bone growth, and the articular chondrocytes, which facilitate joint function. However, despite the multiplicity of roles of cartilage during human fetal life, surprisingly little is known about its transcriptome. To address this, a whole genome microarray expression profile was generated using RNA isolated from 18–22 week human distal femur fetal cartilage and compared with a database of control normal human tissues aggregated at UCLA, termed Celsius. Results 161 cartilage-selective genes were identified, defined as genes significantly expressed in cartilage with low expression and little variation across a panel of 34 non-cartilage tissues. Among these 161 genes were cartilage-specific genes such as cartilage collagen genes and 25 genes which have been associated with skeletal phenotypes in humans and/or mice. Many of the other cartilage-selective genes do not have established roles in cartilage or are novel, unannotated genes. Quantitative RT-PCR confirmed the unique pattern of gene expression observed by microarray analysis. Conclusion Defining the gene expression pattern for cartilage has identified new genes that may contribute to human skeletogenesis as well as provided further candidate genes for skeletal dysplasias. The data suggest that fetal cartilage is a complex and transcriptionally active tissue and demonstrate that the set of genes selectively expressed in the tissue has been greatly underestimated.

  9. A fuzzy set preference model for market share analysis

    Science.gov (United States)

    Turksen, I. B.; Willson, Ian A.

    1992-01-01

    Consumer preference models are widely used in new product design, marketing management, pricing, and market segmentation. The success of new products depends on accurate market share prediction and design decisions based on consumer preferences. The vague linguistic nature of consumer preferences and product attributes, combined with the substantial differences between individuals, creates a formidable challenge to marketing models. The most widely used methodology is conjoint analysis. Conjoint models, as currently implemented, represent linguistic preferences as ratio or interval-scaled numbers, use only numeric product attributes, and require aggregation of individuals for estimation purposes. It is not surprising that these models are costly to implement, are inflexible, and have a predictive validity that is not substantially better than chance. This affects the accuracy of market share estimates. A fuzzy set preference model can easily represent linguistic variables either in consumer preferences or product attributes with minimal measurement requirements (ordinal scales), while still estimating overall preferences suitable for market share prediction. This approach results in flexible individual-level conjoint models which can provide more accurate market share estimates from a smaller number of more meaningful consumer ratings. Fuzzy sets can be incorporated within existing preference model structures, such as a linear combination, using the techniques developed for conjoint analysis and market share estimation. The purpose of this article is to develop and fully test a fuzzy set preference model which can represent linguistic variables in individual-level models implemented in parallel with existing conjoint models. The potential improvements in market share prediction and predictive validity can substantially improve management decisions about what to make (product design), for whom to make it (market segmentation), and how much to make (market share

  10. A plastome primer set for comprehensive quantitative real time RT-PCR analysis of Zea mays: a starter primer set for other Poaceae species

    Directory of Open Access Journals (Sweden)

    Dunn Sade N

    2008-06-01

    Full Text Available Abstract Background Quantitative Real Time RT-PCR (q2(RTPCR is a maturing technique which gives researchers the ability to quantify and compare very small amounts of nucleic acids. Primer design and optimization is an essential yet time consuming aspect of using q2(RTPCR. In this paper we describe the design and empirical optimization of primers to amplify and quantify plastid RNAs from Zea mays that are robust enough to use with other closely related species. Results Primers were designed and successfully optimized for 57 of the 104 reported genes in the maize plastome plus two nuclear genes. All 59 primer pairs produced single amplicons after end-point reverse transcriptase polymerase chain reactions (RT-PCR as visualized on agarose gels and subsequently verified by q2(RTPCR. Primer pairs were divided into several categories based on the optimization requirements or the uniqueness of the target gene. An in silico test suggested the majority of the primer sets should work with other members of the Poaceae family. An in vitro test of the primer set on two unsequenced species (Panicum virgatum and Miscanthus sinensis supported this assumption by successfully producing single amplicons for each primer pair. Conclusion Due to the highly conserved chloroplast genome in plant families it is possible to utilize primer pairs designed against one genomic sequence to detect the presence and abundance of plastid genes or transcripts from genomes that have yet to be sequenced. Analysis of steady state transcription of vital system genes is a necessary requirement to comprehensively elucidate gene expression in any organism. The primer pairs reported in this paper were designed for q2(RTPCR of maize chloroplast genes but should be useful for other members of the Poaceae family. Both in silico and in vitro data are presented to support this assumption.

  11. MOCUS, Minimal Cut Sets and Minimal Path Sets from Fault Tree Analysis

    International Nuclear Information System (INIS)

    Fussell, J.B.; Henry, E.B.; Marshall, N.H.

    1976-01-01

    1 - Description of problem or function: From a description of the Boolean failure logic of a system, called a fault tree, and control parameters specifying the minimal cut set length to be obtained MOCUS determines the system failure modes, or minimal cut sets, and the system success modes, or minimal path sets. 2 - Method of solution: MOCUS uses direct resolution of the fault tree into the cut and path sets. The algorithm used starts with the main failure of interest, the top event, and proceeds to basic independent component failures, called primary events, to resolve the fault tree to obtain the minimal sets. A key point of the algorithm is that an and gate alone always increases the number of path sets; an or gate alone always increases the number of cut sets and increases the size of path sets. Other types of logic gates must be described in terms of and and or logic gates. 3 - Restrictions on the complexity of the problem: Output from MOCUS can include minimal cut and path sets for up to 20 gates

  12. Gene expression meta-analysis identifies chromosomal regions involved in ovarian cancer survival

    DEFF Research Database (Denmark)

    Thomassen, Mads; Jochumsen, Kirsten M; Mogensen, Ole

    2009-01-01

    the relation of gene expression and chromosomal position to identify chromosomal regions of importance for early recurrence of ovarian cancer. By use of *Gene Set Enrichment Analysis*, we have ranked chromosomal regions according to their association to survival. Over-representation analysis including 1...... using death (P = 0.015) and recurrence (P = 0.002) as outcome. The combined mutation score is strongly associated to upregulation of several growth factor pathways....

  13. Canonical correlation analysis for gene-based pleiotropy discovery.

    Directory of Open Access Journals (Sweden)

    Jose A Seoane

    2014-10-01

    Full Text Available Genome-wide association studies have identified a wealth of genetic variants involved in complex traits and multifactorial diseases. There is now considerable interest in testing variants for association with multiple phenotypes (pleiotropy and for testing multiple variants for association with a single phenotype (gene-based association tests. Such approaches can increase statistical power by combining evidence for association over multiple phenotypes or genetic variants respectively. Canonical Correlation Analysis (CCA measures the correlation between two sets of multidimensional variables, and thus offers the potential to combine these two approaches. To apply CCA, we must restrict the number of attributes relative to the number of samples. Hence we consider modules of genetic variation that can comprise a gene, a pathway or another biologically relevant grouping, and/or a set of phenotypes. In order to do this, we use an attribute selection strategy based on a binary genetic algorithm. Applied to a UK-based prospective cohort study of 4286 women (the British Women's Heart and Health Study, we find improved statistical power in the detection of previously reported genetic associations, and identify a number of novel pleiotropic associations between genetic variants and phenotypes. New discoveries include gene-based association of NSF with triglyceride levels and several genes (ACSM3, ERI2, IL18RAP, IL23RAP and NRG1 with left ventricular hypertrophy phenotypes. In multiple-phenotype analyses we find association of NRG1 with left ventricular hypertrophy phenotypes, fibrinogen and urea and pleiotropic relationships of F7 and F10 with Factor VII, Factor IX and cholesterol levels.

  14. Reporter gene bioassays in environmental analysis.

    Science.gov (United States)

    Köhler, S; Belkin, S; Schmid, R D

    2000-01-01

    In parallel to the continuous development of increasingly more sophisticated physical and chemical analytical technologies for the detection of environmental pollutants, there is a progressively more urgent need also for bioassays which report not only on the presence of a chemical but also on its bioavailability and its biological effects. As a partial fulfillment of that need, there has been a rapid development of biosensors based on genetically engineered bacteria. Such microorganisms typically combine a promoter-operator, which acts as the sensing element, with reporter gene(s) coding for easily detectable proteins. These sensors have the ability to detect global parameters such as stress conditions, toxicity or DNA-damaging agents as well as specific organic and inorganic compounds. The systems described in this review, designed to detect different groups of target chemicals, vary greatly in their detection limits, specificity, response times and more. These variations reflect on their potential applicability which, for most of the constructs described, is presently rather limited. Nevertheless, present trends promise that additional improvements will make microbial biosensors an important tool for future environmental analysis.

  15. Gene expression analysis identifies global gene dosage sensitivity in cancer

    DEFF Research Database (Denmark)

    Fehrmann, Rudolf S. N.; Karjalainen, Juha M.; Krajewska, Malgorzata

    2015-01-01

    Many cancer-associated somatic copy number alterations (SCNAs) are known. Currently, one of the challenges is to identify the molecular downstream effects of these variants. Although several SCNAs are known to change gene expression levels, it is not clear whether each individual SCNA affects gen...

  16. A dual origin of the Xist gene from a protein-coding gene and a set of transposable elements.

    Directory of Open Access Journals (Sweden)

    Eugeny A Elisaphenko

    2008-06-01

    Full Text Available X-chromosome inactivation, which occurs in female eutherian mammals is controlled by a complex X-linked locus termed the X-inactivation center (XIC. Previously it was proposed that genes of the XIC evolved, at least in part, as a result of pseudogenization of protein-coding genes. In this study we show that the key XIC gene Xist, which displays fragmentary homology to a protein-coding gene Lnx3, emerged de novo in early eutherians by integration of mobile elements which gave rise to simple tandem repeats. The Xist gene promoter region and four out of ten exons found in eutherians retain homology to exons of the Lnx3 gene. The remaining six Xist exons including those with simple tandem repeats detectable in their structure have similarity to different transposable elements. Integration of mobile elements into Xist accompanies the overall evolution of the gene and presumably continues in contemporary eutherian species. Additionally we showed that the combination of remnants of protein-coding sequences and mobile elements is not unique to the Xist gene and is found in other XIC genes producing non-coding nuclear RNA.

  17. RNA amplification for successful gene profiling analysis

    Directory of Open Access Journals (Sweden)

    Wang Ena

    2005-07-01

    Full Text Available Abstract The study of clinical samples is often limited by the amount of material available to study. While proteins cannot be multiplied in their natural form, DNA and RNA can be amplified from small specimens and used for high-throughput analyses. Therefore, genetic studies offer the best opportunity to screen for novel insights of human pathology when little material is available. Precise estimates of DNA copy numbers in a given specimen are necessary. However, most studies investigate static variables such as the genetic background of patients or mutations within pathological specimens without a need to assess proportionality of expression among different genes throughout the genome. Comparative genomic hybridization of DNA samples represents a crude exception to this rule since genomic amplification or deletion is compared among different specimens directly. For gene expression analysis, however, it is critical to accurately estimate the proportional expression of distinct RNA transcripts since such proportions directly govern cell function by modulating protein expression. Furthermore, comparative estimates of relative RNA expression at different time points portray the response of cells to environmental stimuli, indirectly informing about broader biological events affecting a particular tissue in physiological or pathological conditions. This cognitive reaction of cells is similar to the detection of electroencephalographic patterns which inform about the status of the brain in response to external stimuli. As our need to understand human pathophysiology at the global level increases, the development and refinement of technologies for high fidelity messenger RNA amplification have become the focus of increasing interest during the past decade. The need to increase the abundance of RNA has been met not only for gene specific amplification, but, most importantly for global transcriptome wide, unbiased amplification. Now gene

  18. Identification of cytokinin-responsive genes using microarray meta-analysis and RNA-Seq in Arabidopsis.

    Science.gov (United States)

    Bhargava, Apurva; Clabaugh, Ivory; To, Jenn P; Maxwell, Bridey B; Chiang, Yi-Hsuan; Schaller, G Eric; Loraine, Ann; Kieber, Joseph J

    2013-05-01

    Cytokinins are N(6)-substituted adenine derivatives that play diverse roles in plant growth and development. We sought to define a robust set of genes regulated by cytokinin as well as to query the response of genes not represented on microarrays. To this end, we performed a meta-analysis of microarray data from a variety of cytokinin-treated samples and used RNA-seq to examine cytokinin-regulated gene expression in Arabidopsis (Arabidopsis thaliana). Microarray meta-analysis using 13 microarray experiments combined with empirically defined filtering criteria identified a set of 226 genes differentially regulated by cytokinin, a subset of which has previously been validated by other methods. RNA-seq validated about 73% of the up-regulated genes identified by this meta-analysis. In silico promoter analysis indicated an overrepresentation of type-B Arabidopsis response regulator binding elements, consistent with the role of type-B Arabidopsis response regulators as primary mediators of cytokinin-responsive gene expression. RNA-seq analysis identified 73 cytokinin-regulated genes that were not represented on the ATH1 microarray. Representative genes were verified using quantitative reverse transcription-polymerase chain reaction and NanoString analysis. Analysis of the genes identified reveals a substantial effect of cytokinin on genes encoding proteins involved in secondary metabolism, particularly those acting in flavonoid and phenylpropanoid biosynthesis, as well as in the regulation of redox state of the cell, particularly a set of glutaredoxin genes. Novel splicing events were found in members of some gene families that are known to play a role in cytokinin signaling or metabolism. The genes identified in this analysis represent a robust set of cytokinin-responsive genes that are useful in the analysis of cytokinin function in plants.

  19. Engineering design and analysis of Indian LLCB TBM set

    Energy Technology Data Exchange (ETDEWEB)

    Ranjithkumar, S., E-mail: ranjith@ipr.res.in [Institute for Plasma Research, Bhat, Gandhinagar 382428, Gujarat (India); Sharma, Deepak; Chaudhuri, Paritosh; Danani, Chandan; Kumar, E. Rajendra [Institute for Plasma Research, Bhat, Gandhinagar 382428, Gujarat (India); Khan, Istiyak; Bhattacharya, Sujay; Vyas, K.N. [Bhabha Atomic Research Centre, Trombay, Mumbai 400085 (India)

    2016-11-01

    India is developing Lead Lithium cooled Ceramic Breeder (LLCB) Test Blanket Module (TBM) for testing in ITER for the validation of fusion blanket design tools, tritium breeding performance and high grade heat extraction capability relevant to Indian DEMO. LLCB TBM is designed to withstand various ITER loads and its combinations, like thermal, mechanical including the high pressure coolant loads, electromagnetic loads during plasma disruption and seismic loading conditions. LLCB TBM system is designed in compliance with ITER Safety requirements and guidelines. A few challenging part in the design includes the design of helium cooled First Wall (FW) and back plates, the attachment system between TBM and the shield block to withstand loads for all the ITER operational modes, routing of high temperature process pipes between TBM and the shield block, interfaces between process pipes and the connecting flange, design of manifolds of different process fluids etc. Analysis has been performed on the LLCB TBM set using a Finite Element code, ANSYS. Relevant codes and standards, namely the French code RCC-MR, has been followed for the design analysis. The details of the analysis and further plans and proposals for improvement in design will be discussed in this paper.

  20. MALDI-TOF mass spectrometry for quantitative gene expression analysis of acid responses in Staphylococcus aureus.

    Science.gov (United States)

    Rode, Tone Mari; Berget, Ingunn; Langsrud, Solveig; Møretrø, Trond; Holck, Askild

    2009-07-01

    Microorganisms are constantly exposed to new and altered growth conditions, and respond by changing gene expression patterns. Several methods for studying gene expression exist. During the last decade, the analysis of microarrays has been one of the most common approaches applied for large scale gene expression studies. A relatively new method for gene expression analysis is MassARRAY, which combines real competitive-PCR and MALDI-TOF (matrix-assisted laser desorption/ionization time-of-flight) mass spectrometry. In contrast to microarray methods, MassARRAY technology is suitable for analysing a larger number of samples, though for a smaller set of genes. In this study we compare the results from MassARRAY with microarrays on gene expression responses of Staphylococcus aureus exposed to acid stress at pH 4.5. RNA isolated from the same stress experiments was analysed using both the MassARRAY and the microarray methods. The MassARRAY and microarray methods showed good correlation. Both MassARRAY and microarray estimated somewhat lower fold changes compared with quantitative real-time PCR (qRT-PCR). The results confirmed the up-regulation of the urease genes in acidic environments, and also indicated the importance of metal ion regulation. This study shows that the MassARRAY technology is suitable for gene expression analysis in prokaryotes, and has advantages when a set of genes is being analysed for an organism exposed to many different environmental conditions.

  1. Analysis of the complement and molecular evolution of tRNA genes in cow

    Directory of Open Access Journals (Sweden)

    Barris Wesley C

    2009-04-01

    Full Text Available Abstract Background Detailed information regarding the number and organization of transfer RNA (tRNA genes at the genome level is becoming readily available with the increase of DNA sequencing of whole genomes. However the identification of functional tRNA genes is challenging for species that have large numbers of repetitive elements containing tRNA derived sequences, such as Bos taurus. Reliable identification and annotation of entire sets of tRNA genes allows the evolution of tRNA genes to be understood on a genomic scale. Results In this study, we explored the B. taurus genome using bioinformatics and comparative genomics approaches to catalogue and analyze cow tRNA genes. The initial analysis of the cow genome using tRNAscan-SE identified 31,868 putative tRNA genes and 189,183 pseudogenes, where 28,830 of the 31,868 predicted tRNA genes were classified as repetitive elements by the RepeatMasker program. We then used comparative genomics to further discriminate between functional tRNA genes and tRNA-derived sequences for the remaining set of 3,038 putative tRNA genes. For our analysis, we used the human, chimpanzee, mouse, rat, horse, dog, chicken and fugu genomes to predict that the number of active tRNA genes in cow lies in the vicinity of 439. Of this set, 150 tRNA genes were 100% identical in their sequences across all nine vertebrate genomes studied. Using clustering analyses, we identified a new tRNA-GlyCCC subfamily present in all analyzed mammalian genomes. We suggest that this subfamily originated from an ancestral tRNA-GlyGCC gene via a point mutation prior to the radiation of the mammalian lineages. Lastly, in a separate analysis we created phylogenetic profiles for each putative cow tRNA gene using a representative set of genomes to gain an overview of common evolutionary histories of tRNA genes. Conclusion The use of a combination of bioinformatics and comparative genomics approaches has allowed the confident identification of a

  2. Analysis method set up to check against adulterated export honey

    International Nuclear Information System (INIS)

    Lyon, G.L.

    2001-01-01

    Over the past few years, North America has experienced occasional problems with the adulteration of honey, mainly by additions of other, cheaper sugar to increase bulk and lower production costs. The main addition was usually high fructose corn syrup, which had a similar chemical composition to that of honey. As a consequence of this type of adulteration, a method for its detection was developed using isotope ratio mass spectroscopy (IRMS). This was later refined to be more sensitive and is now specified as an Official Test. The Institute of Geological and Nuclear Sciences has now set up the analysis method to the international criteria at the Rafter Stable Isotope Laboratory in Lower Hutt. 2 refs

  3. Mutation analysis of the preproghrelin gene

    DEFF Research Database (Denmark)

    Larsen, Lesli H; Gjesing, Anette P; Sørensen, Thorkild I A

    2005-01-01

    To investigate the preproghrelin gene for variants and their association with obesity and type 2 diabetes.......To investigate the preproghrelin gene for variants and their association with obesity and type 2 diabetes....

  4. GSHR, a Web-Based Platform Provides Gene Set-Level Analyses of Hormone Responses in Arabidopsis

    Directory of Open Access Journals (Sweden)

    Xiaojuan Ran

    2018-01-01

    Full Text Available Phytohormones regulate diverse aspects of plant growth and environmental responses. Recent high-throughput technologies have promoted a more comprehensive profiling of genes regulated by different hormones. However, these omics data generally result in large gene lists that make it challenging to interpret the data and extract insights into biological significance. With the rapid accumulation of theses large-scale experiments, especially the transcriptomic data available in public databases, a means of using this information to explore the transcriptional networks is needed. Different platforms have different architectures and designs, and even similar studies using the same platform may obtain data with large variances because of the highly dynamic and flexible effects of plant hormones; this makes it difficult to make comparisons across different studies and platforms. Here, we present a web server providing gene set-level analyses of Arabidopsis thaliana hormone responses. GSHR collected 333 RNA-seq and 1,205 microarray datasets from the Gene Expression Omnibus, characterizing transcriptomic changes in Arabidopsis in response to phytohormones including abscisic acid, auxin, brassinosteroids, cytokinins, ethylene, gibberellins, jasmonic acid, salicylic acid, and strigolactones. These data were further processed and organized into 1,368 gene sets regulated by different hormones or hormone-related factors. By comparing input gene lists to these gene sets, GSHR helped to identify gene sets from the input gene list regulated by different phytohormones or related factors. Together, GSHR links prior information regarding transcriptomic changes induced by hormones and related factors to newly generated data and facilities cross-study and cross-platform comparisons; this helps facilitate the mining of biologically significant information from large-scale datasets. The GSHR is freely available at http://bioinfo.sibs.ac.cn/GSHR/.

  5. A random set scoring model for prioritization of disease candidate genes using protein complexes and data-mining of GeneRIF, OMIM and PubMed records.

    Science.gov (United States)

    Jiang, Li; Edwards, Stefan M; Thomsen, Bo; Workman, Christopher T; Guldbrandtsen, Bernt; Sørensen, Peter

    2014-09-24

    Prioritizing genetic variants is a challenge because disease susceptibility loci are often located in genes of unknown function or the relationship with the corresponding phenotype is unclear. A global data-mining exercise on the biomedical literature can establish the phenotypic profile of genes with respect to their connection to disease phenotypes. The importance of protein-protein interaction networks in the genetic heterogeneity of common diseases or complex traits is becoming increasingly recognized. Thus, the development of a network-based approach combined with phenotypic profiling would be useful for disease gene prioritization. We developed a random-set scoring model and implemented it to quantify phenotype relevance in a network-based disease gene-prioritization approach. We validated our approach based on different gene phenotypic profiles, which were generated from PubMed abstracts, OMIM, and GeneRIF records. We also investigated the validity of several vocabulary filters and different likelihood thresholds for predicted protein-protein interactions in terms of their effect on the network-based gene-prioritization approach, which relies on text-mining of the phenotype data. Our method demonstrated good precision and sensitivity compared with those of two alternative complex-based prioritization approaches. We then conducted a global ranking of all human genes according to their relevance to a range of human diseases. The resulting accurate ranking of known causal genes supported the reliability of our approach. Moreover, these data suggest many promising novel candidate genes for human disorders that have a complex mode of inheritance. We have implemented and validated a network-based approach to prioritize genes for human diseases based on their phenotypic profile. We have devised a powerful and transparent tool to identify and rank candidate genes. Our global gene prioritization provides a unique resource for the biological interpretation of data

  6. Performance of single and concatenated sets of mitochondrial genes at inferring metazoan relationships relative to full mitogenome data.

    Directory of Open Access Journals (Sweden)

    Justin C Havird

    Full Text Available Mitochondrial (mt genes are some of the most popular and widely-utilized genetic loci in phylogenetic studies of metazoan taxa. However, their linked nature has raised questions on whether using the entire mitogenome for phylogenetics is overkill (at best or pseudoreplication (at worst. Moreover, no studies have addressed the comparative phylogenetic utility of mitochondrial genes across individual lineages within the entire Metazoa. To comment on the phylogenetic utility of individual mt genes as well as concatenated subsets of genes, we analyzed mitogenomic data from 1865 metazoan taxa in 372 separate lineages spanning genera to subphyla. Specifically, phylogenies inferred from these datasets were statistically compared to ones generated from all 13 mt protein-coding (PC genes (i.e., the "supergene" set to determine which single genes performed "best" at, and the minimum number of genes required to, recover the "supergene" topology. Surprisingly, the popular marker COX1 performed poorest, while ND5, ND4, and ND2 were most likely to reproduce the "supergene" topology. Averaged across all lineages, the longest ∼2 mt PC genes were sufficient to recreate the "supergene" topology, although this average increased to ∼5 genes for datasets with 40 or more taxa. Furthermore, concatenation of the three "best" performing mt PC genes outperformed that of the three longest mt PC genes (i.e, ND5, COX1, and ND4. Taken together, while not all mt PC genes are equally interchangeable in phylogenetic studies of the metazoans, some subset can serve as a proxy for the 13 mt PC genes. However, the exact number and identity of these genes is specific to the lineage in question and cannot be applied indiscriminately across the Metazoa.

  7. Two new loci and gene sets related to sex determination and cancer progression are associated with susceptibility to testicular germ cell tumor.

    Science.gov (United States)

    Kristiansen, Wenche; Karlsson, Robert; Rounge, Trine B; Whitington, Thomas; Andreassen, Bettina K; Magnusson, Patrik K; Fosså, Sophie D; Adami, Hans-Olov; Turnbull, Clare; Haugen, Trine B; Grotmol, Tom; Wiklund, Fredrik

    2015-07-15

    Genome-wide association (GWA) studies have reported 19 distinct susceptibility loci for testicular germ cell tumor (TGCT). A GWA study for TGCT was performed by genotyping 610 240 single-nucleotide polymorphisms (SNPs) in 1326 cases and 6687 controls from Sweden and Norway. No novel genome-wide significant associations were observed in this discovery stage. We put forward 27 SNPs from 15 novel regions and 12 SNPs previously reported, for replication in 710 case-parent triads and 289 cases and 290 controls. Predefined biological pathways and processes, in addition to a custom-built sex-determination gene set, were subject to enrichment analyses using Meta-Analysis Gene Set Enrichment of Variant Associations (M) and Improved Gene Set Enrichment Analysis for Genome-wide Association Study (I). In the combined meta-analysis, we observed genome-wide significant association for rs7501939 on chromosome 17q12 (OR = 0.78, 95% CI = 0.72-0.84, P = 1.1 × 10(-9)) and rs2195987 on chromosome 19p12 (OR = 0.76, 95% CI: 0.69-0.84, P = 3.2 × 10(-8)). The marker rs7501939 on chromosome 17q12 is located in an intron of the HNF1B gene, encoding a member of the homeodomain-containing superfamily of transcription factors. The sex-determination gene set (false discovery rate, FDRM cancer and apoptosis, was associated with TGCT (FDR utero are implicated in the development of TGCT. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  8. A gene pathway analysis highlights the role of cellular adhesion molecules in multiple sclerosis susceptibility

    DEFF Research Database (Denmark)

    Damotte, V; Guillot-Noel, L; Patsopoulos, N A

    2014-01-01

    adhesion molecule (CAMs) biological pathway using Cytoscape software. This network is a strong candidate, as it is involved in the crossing of the blood-brain barrier by the T cells, an early event in MS pathophysiology, and is used as an efficient therapeutic target. We drew up a list of 76 genes...... in interaction with other genes as a group. Pathway analysis is an alternative way to highlight such group of genes. Using SNP association P-values from eight multiple sclerosis (MS) GWAS data sets, we performed a candidate pathway analysis for MS susceptibility by considering genes interacting in the cell...... belonging to the CAM network. We highlighted 64 networks enriched with CAM genes with low P-values. Filtering by a percentage of CAM genes up to 50% and rejecting enriched signals mainly driven by transcription factors, we highlighted five networks associated with MS susceptibility. One of them, constituted...

  9. Comparative analysis through probability distributions of a data set

    Science.gov (United States)

    Cristea, Gabriel; Constantinescu, Dan Mihai

    2018-02-01

    In practice, probability distributions are applied in such diverse fields as risk analysis, reliability engineering, chemical engineering, hydrology, image processing, physics, market research, business and economic research, customer support, medicine, sociology, demography etc. This article highlights important aspects of fitting probability distributions to data and applying the analysis results to make informed decisions. There are a number of statistical methods available which can help us to select the best fitting model. Some of the graphs display both input data and fitted distributions at the same time, as probability density and cumulative distribution. The goodness of fit tests can be used to determine whether a certain distribution is a good fit. The main used idea is to measure the "distance" between the data and the tested distribution, and compare that distance to some threshold values. Calculating the goodness of fit statistics also enables us to order the fitted distributions accordingly to how good they fit to data. This particular feature is very helpful for comparing the fitted models. The paper presents a comparison of most commonly used goodness of fit tests as: Kolmogorov-Smirnov, Anderson-Darling, and Chi-Squared. A large set of data is analyzed and conclusions are drawn by visualizing the data, comparing multiple fitted distributions and selecting the best model. These graphs should be viewed as an addition to the goodness of fit tests.

  10. Safety analysis and synthesis using fuzzy sets and evidential reasoning

    International Nuclear Information System (INIS)

    Wang, J.; Yang, J.B.; Sen, P.

    1995-01-01

    This paper presents a new methodology for safety analysis and synthesis of a complex engineering system with a structure that is capable of being decomposed into a hierarchy of levels. In this methodology, fuzzy set theory is used to describe each failure event and an evidential reasoning approach is then employed to synthesise the information thus produced to assess the safety of the whole system. Three basic parameters--failure likelihood, consequence severity and failure consequence probability, are used to analyse a failure event. These three parameters are described by linguistic variables which are characterised by a membership function to the defined categories. As safety can also be clearly described by linguistic variables referred to as the safety expressions, the obtained fuzzy safety score can be mapped back to the safety expressions which are characterised by membership functions over the same categories. This mapping results in the identification of the safety of each failure event in terms of the degree to which the fuzzy safety score belongs to each of the safety expressions. Such degrees represent the uncertainty in safety evaluations and can be synthesised using an evidential reasoning approach so that the safety of the whole system can be evaluated in terms of these safety expressions. Finally, a practical engineering example is presented to demonstrate the proposed safety analysis and synthesis methodology

  11. Virtual collaboration in the online educational setting: a concept analysis.

    Science.gov (United States)

    Breen, Henny

    2013-01-01

    This study was designed to explore the concept of virtual collaboration within the context of an online learning environment in an academic setting. Rodgers' method of evolutionary concept analysis was used to provide a contextual view of the concept to identify attributes, antecedents, and consequences of virtual collaboration. Commonly used terms to describe virtual collaboration are collaborative and cooperative learning, group work, group interaction, group learning, and teamwork. A constructivist pedagogy, group-based process with a shared purpose, support, and web-based technology is required for virtual collaboration to take place. Consequences of virtual collaboration are higher order thinking and learning to work with others. A comprehensive definition of virtual collaboration is offered as an outcome of this analysis. Clarification of virtual collaboration prior to using it as a pedagogical tool in the online learning environment will enhance nursing education with the changes in nursing curriculum being implemented today. Further research is recommended to describe the developmental stages of the collaborative process among nursing students in online education and how virtual collaboration facilitates collaboration in practice. © 2013 Wiley Periodicals, Inc.

  12. Genome-wide analysis of the WRKY gene family in physic nut (Jatropha curcas L.).

    Science.gov (United States)

    Xiong, Wangdan; Xu, Xueqin; Zhang, Lin; Wu, Pingzhi; Chen, Yaping; Li, Meiru; Jiang, Huawu; Wu, Guojiang

    2013-07-25

    The WRKY proteins, which contain highly conserved WRKYGQK amino acid sequences and zinc-finger-like motifs, constitute a large family of transcription factors in plants. They participate in diverse physiological and developmental processes. WRKY genes have been identified and characterized in a number of plant species. We identified a total of 58 WRKY genes (JcWRKY) in the genome of the physic nut (Jatropha curcas L.). On the basis of their conserved WRKY domain sequences, all of the JcWRKY proteins could be assigned to one of the previously defined groups, I-III. Phylogenetic analysis of JcWRKY genes with Arabidopsis and rice WRKY genes, and separately with castor bean WRKY genes, revealed no evidence of recent gene duplication in JcWRKY gene family. Analysis of transcript abundance of JcWRKY gene products were tested in different tissues under normal growth condition. In addition, 47 WRKY genes responded to at least one abiotic stress (drought, salinity, phosphate starvation and nitrogen starvation) in individual tissues (leaf, root and/or shoot cortex). Our study provides a useful reference data set as the basis for cloning and functional analysis of physic nut WRKY genes. Copyright © 2013 Elsevier B.V. All rights reserved.

  13. Identifying key genes in rheumatoid arthritis by weighted gene co-expression network analysis.

    Science.gov (United States)

    Ma, Chunhui; Lv, Qi; Teng, Songsong; Yu, Yinxian; Niu, Kerun; Yi, Chengqin

    2017-08-01

    This study aimed to identify rheumatoid arthritis (RA) related genes based on microarray data using the WGCNA (weighted gene co-expression network analysis) method. Two gene expression profile datasets GSE55235 (10 RA samples and 10 healthy controls) and GSE77298 (16 RA samples and seven healthy controls) were downloaded from Gene Expression Omnibus database. Characteristic genes were identified using metaDE package. WGCNA was used to find disease-related networks based on gene expression correlation coefficients, and module significance was defined as the average gene significance of all genes used to assess the correlation between the module and RA status. Genes in the disease-related gene co-expression network were subject to functional annotation and pathway enrichment analysis using Database for Annotation Visualization and Integrated Discovery. Characteristic genes were also mapped to the Connectivity Map to screen small molecules. A total of 599 characteristic genes were identified. For each dataset, characteristic genes in the green, red and turquoise modules were most closely associated with RA, with gene numbers of 54, 43 and 79, respectively. These genes were enriched in totally enriched in 17 Gene Ontology terms, mainly related to immune response (CD97, FYB, CXCL1, IKBKE, CCR1, etc.), inflammatory response (CD97, CXCL1, C3AR1, CCR1, LYZ, etc.) and homeostasis (C3AR1, CCR1, PLN, CCL19, PPT1, etc.). Two small-molecule drugs sanguinarine and papaverine were predicted to have a therapeutic effect against RA. Genes related to immune response, inflammatory response and homeostasis presumably have critical roles in RA pathogenesis. Sanguinarine and papaverine have a potential therapeutic effect against RA. © 2017 Asia Pacific League of Associations for Rheumatology and John Wiley & Sons Australia, Ltd.

  14. The SET1 Complex Selects Actively Transcribed Target Genes via Multivalent Interaction with CpG Island Chromatin.

    Science.gov (United States)

    Brown, David A; Di Cerbo, Vincenzo; Feldmann, Angelika; Ahn, Jaewoo; Ito, Shinsuke; Blackledge, Neil P; Nakayama, Manabu; McClellan, Michael; Dimitrova, Emilia; Turberfield, Anne H; Long, Hannah K; King, Hamish W; Kriaucionis, Skirmantas; Schermelleh, Lothar; Kutateladze, Tatiana G; Koseki, Haruhiko; Klose, Robert J

    2017-09-05

    Chromatin modifications and the promoter-associated epigenome are important for the regulation of gene expression. However, the mechanisms by which chromatin-modifying complexes are targeted to the appropriate gene promoters in vertebrates and how they influence gene expression have remained poorly defined. Here, using a combination of live-cell imaging and functional genomics, we discover that the vertebrate SET1 complex is targeted to actively transcribed gene promoters through CFP1, which engages in a form of multivalent chromatin reading that involves recognition of non-methylated DNA and histone H3 lysine 4 trimethylation (H3K4me3). CFP1 defines SET1 complex occupancy on chromatin, and its multivalent interactions are required for the SET1 complex to place H3K4me3. In the absence of CFP1, gene expression is perturbed, suggesting that normal targeting and function of the SET1 complex are central to creating an appropriately functioning vertebrate promoter-associated epigenome. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  15. The SET1 Complex Selects Actively Transcribed Target Genes via Multivalent Interaction with CpG Island Chromatin

    Directory of Open Access Journals (Sweden)

    David A. Brown

    2017-09-01

    Full Text Available Chromatin modifications and the promoter-associated epigenome are important for the regulation of gene expression. However, the mechanisms by which chromatin-modifying complexes are targeted to the appropriate gene promoters in vertebrates and how they influence gene expression have remained poorly defined. Here, using a combination of live-cell imaging and functional genomics, we discover that the vertebrate SET1 complex is targeted to actively transcribed gene promoters through CFP1, which engages in a form of multivalent chromatin reading that involves recognition of non-methylated DNA and histone H3 lysine 4 trimethylation (H3K4me3. CFP1 defines SET1 complex occupancy on chromatin, and its multivalent interactions are required for the SET1 complex to place H3K4me3. In the absence of CFP1, gene expression is perturbed, suggesting that normal targeting and function of the SET1 complex are central to creating an appropriately functioning vertebrate promoter-associated epigenome.

  16. A tandem sequence motif acts as a distance-dependent enhancer in a set of genes involved in translation by binding the proteins NonO and SFPQ

    Directory of Open Access Journals (Sweden)

    Roepcke Stefan

    2011-12-01

    Full Text Available Abstract Background Bioinformatic analyses of expression control sequences in promoters of co-expressed or functionally related genes enable the discovery of common regulatory sequence motifs that might be involved in co-ordinated gene expression. By studying promoter sequences of the human ribosomal protein genes we recently identified a novel highly specific Localized Tandem Sequence Motif (LTSM. In this work we sought to identify additional genes and LTSM-binding proteins to elucidate potential regulatory mechanisms. Results Genome-wide analyses allowed finding a considerable number of additional LTSM-positive genes, the products of which are involved in translation, among them, translation initiation and elongation factors, and 5S rRNA. Electromobility shift assays then showed specific signals demonstrating the binding of protein complexes to LTSM in ribosomal protein gene promoters. Pull-down assays with LTSM-containing oligonucleotides and subsequent mass spectrometric analysis identified the related multifunctional nucleotide binding proteins NonO and SFPQ in the binding complex. Functional characterization then revealed that LTSM enhances the transcriptional activity of the promoters in dependency of the distance from the transcription start site. Conclusions Our data demonstrate the power of bioinformatic analyses for the identification of biologically relevant sequence motifs. LTSM and the here found LTSM-binding proteins NonO and SFPQ were discovered through a synergistic combination of bioinformatic and biochemical methods and are regulators of the expression of a set of genes of the translational apparatus in a distance-dependent manner.

  17. Bridging cancer biology with the clinic: relative expression of a GRHL2-mediated gene-set pair predicts breast cancer metastasis.

    Directory of Open Access Journals (Sweden)

    Xinan Yang

    Full Text Available Identification and characterization of crucial gene target(s that will allow focused therapeutics development remains a challenge. We have interrogated the putative therapeutic targets associated with the transcription factor Grainy head-like 2 (GRHL2, a critical epithelial regulatory factor. We demonstrate the possibility to define the molecular functions of critical genes in terms of their personalized expression profiles, allowing appropriate functional conclusions to be derived. A novel methodology, relative expression analysis with gene-set pairs (RXA-GSP, is designed to explore the potential clinical utility of cancer-biology discovery. Observing that Grhl2-overexpression leads to increased metastatic potential in vitro, we established a model assuming Grhl2-induced or -inhibited genes confer poor or favorable prognosis respectively for cancer metastasis. Training on public gene expression profiles of 995 breast cancer patients, this method prioritized one gene-set pair (GRHL2, CDH2, FN1, CITED2, MKI67 versus CTNNB1 and CTNNA3 from all 2717 possible gene-set pairs (GSPs. The identified GSP significantly dichotomized 295 independent patients for metastasis-free survival (log-rank tested p = 0.002; severe empirical p = 0.035. It also showed evidence of clinical prognostication in another independent 388 patients collected from three studies (log-rank tested p = 3.3e-6. This GSP is independent of most traditional prognostic indicators, and is only significantly associated with the histological grade of breast cancer (p = 0.0017, a GRHL2-associated clinical character (p = 6.8e-6, Spearman correlation, suggesting that this GSP is reflective of GRHL2-mediated events. Furthermore, a literature review indicates the therapeutic potential of the identified genes. This research demonstrates a novel strategy to integrate both biological experiments and clinical gene expression profiles for extracting and elucidating the genomic

  18. Using Variable Precision Rough Set for Selection and Classification of Biological Knowledge Integrated in DNA Gene Expression

    Directory of Open Access Journals (Sweden)

    Calvo-Dmgz D.

    2012-12-01

    Full Text Available DNA microarrays have contributed to the exponential growth of genomic and experimental data in the last decade. This large amount of gene expression data has been used by researchers seeking diagnosis of diseases like cancer using machine learning methods. In turn, explicit biological knowledge about gene functions has also grown tremendously over the last decade. This work integrates explicit biological knowledge, provided as gene sets, into the classication process by means of Variable Precision Rough Set Theory (VPRS. The proposed model is able to highlight which part of the provided biological knowledge has been important for classification. This paper presents a novel model for microarray data classification which is able to incorporate prior biological knowledge in the form of gene sets. Based on this knowledge, we transform the input microarray data into supergenes, and then we apply rough set theory to select the most promising supergenes and to derive a set of easy interpretable classification rules. The proposed model is evaluated over three breast cancer microarrays datasets obtaining successful results compared to classical classification techniques. The experimental results shows that there are not significat differences between our model and classical techniques but it is able to provide a biological-interpretable explanation of how it classifies new samples.

  19. Pathway-based analysis of a melanoma genome-wide association study: analysis of genes related to tumour-immunosuppression.

    Directory of Open Access Journals (Sweden)

    Nils Schoof

    Full Text Available Systemic immunosuppression is a risk factor for melanoma, and sunburn-induced immunosuppression is thought to be causal. Genes in immunosuppression pathways are therefore candidate melanoma-susceptibility genes. If variants within these genes individually have a small effect on disease risk, the association may be undetected in genome-wide association (GWA studies due to low power to reach a high significance level. Pathway-based approaches have been suggested as a method of incorporating a priori knowledge into the analysis of GWA studies. In this study, the association of 1113 single nucleotide polymorphisms (SNPs in 43 genes (39 genomic regions related to immunosuppression have been analysed using a gene-set approach in 1539 melanoma cases and 3917 controls from the GenoMEL consortium GWA study. The association between melanoma susceptibility and the whole set of tumour-immunosuppression genes, and also predefined functional subgroups of genes, was considered. The analysis was based on a measure formed by summing the evidence from the most significant SNP in each gene, and significance was evaluated empirically by case-control label permutation. An association was found between melanoma and the complete set of genes (p(emp=0.002, as well as the subgroups related to the generation of tolerogenic dendritic cells (p(emp=0.006 and secretion of suppressive factors (p(emp=0.0004, thus providing preliminary evidence of involvement of tumour-immunosuppression gene polymorphisms in melanoma susceptibility. The analysis was repeated on a second phase of the GenoMEL study, which showed no evidence of an association. As one of the first attempts to replicate a pathway-level association, our results suggest that low power and heterogeneity may present challenges.

  20. Selection and validation of a set of reliable reference genes for quantitative RT-PCR studies in the brain of the Cephalopod Mollusc Octopus vulgaris

    Directory of Open Access Journals (Sweden)

    Biffali Elio

    2009-07-01

    Full Text Available Abstract Background Quantitative real-time polymerase chain reaction (RT-qPCR is valuable for studying the molecular events underlying physiological and behavioral phenomena. Normalization of real-time PCR data is critical for a reliable mRNA quantification. Here we identify reference genes to be utilized in RT-qPCR experiments to normalize and monitor the expression of target genes in the brain of the cephalopod mollusc Octopus vulgaris, an invertebrate. Such an approach is novel for this taxon and of advantage in future experiments given the complexity of the behavioral repertoire of this species when compared with its relatively simple neural organization. Results We chose 16S, and 18S rRNA, actB, EEF1A, tubA and ubi as candidate reference genes (housekeeping genes, HKG. The expression of 16S and 18S was highly variable and did not meet the requirements of candidate HKG. The expression of the other genes was almost stable and uniform among samples. We analyzed the expression of HKG into two different set of animals using tissues taken from the central nervous system (brain parts and mantle (here considered as control tissue by BestKeeper, geNorm and NormFinder. We found that HKG expressions differed considerably with respect to brain area and octopus samples in an HKG-specific manner. However, when the mantle is treated as control tissue and the entire central nervous system is considered, NormFinder revealed tubA and ubi as the most suitable HKG pair. These two genes were utilized to evaluate the relative expression of the genes FoxP, creb, dat and TH in O. vulgaris. Conclusion We analyzed the expression profiles of some genes here identified for O. vulgaris by applying RT-qPCR analysis for the first time in cephalopods. We validated candidate reference genes and found the expression of ubi and tubA to be the most appropriate to evaluate the expression of target genes in the brain of different octopuses. Our results also underline the

  1. Inferring gene expression dynamics via functional regression analysis

    Directory of Open Access Journals (Sweden)

    Leng Xiaoyan

    2008-01-01

    Full Text Available Abstract Background Temporal gene expression profiles characterize the time-dynamics of expression of specific genes and are increasingly collected in current gene expression experiments. In the analysis of experiments where gene expression is obtained over the life cycle, it is of interest to relate temporal patterns of gene expression associated with different developmental stages to each other to study patterns of long-term developmental gene regulation. We use tools from functional data analysis to study dynamic changes by relating temporal gene expression profiles of different developmental stages to each other. Results We demonstrate that functional regression methodology can pinpoint relationships that exist between temporary gene expression profiles for different life cycle phases and incorporates dimension reduction as needed for these high-dimensional data. By applying these tools, gene expression profiles for pupa and adult phases are found to be strongly related to the profiles of the same genes obtained during the embryo phase. Moreover, one can distinguish between gene groups that exhibit relationships with positive and others with negative associations between later life and embryonal expression profiles. Specifically, we find a positive relationship in expression for muscle development related genes, and a negative relationship for strictly maternal genes for Drosophila, using temporal gene expression profiles. Conclusion Our findings point to specific reactivation patterns of gene expression during the Drosophila life cycle which differ in characteristic ways between various gene groups. Functional regression emerges as a useful tool for relating gene expression patterns from different developmental stages, and avoids the problems with large numbers of parameters and multiple testing that affect alternative approaches.

  2. A Comprehensive Classification and Evolutionary Analysis of Plant Homeobox Genes

    OpenAIRE

    Mukherjee, Krishanu; Brocchieri, Luciano; B?rglin, Thomas R.

    2009-01-01

    The full complement of homeobox transcription factor sequences, including genes and pseudogenes, was determined from the analysis of 10 complete genomes from flowering plants, moss, Selaginella, unicellular green algae, and red algae. Our exhaustive genome-wide searches resulted in the discovery in each class of a greater number of homeobox genes than previously reported. All homeobox genes can be unambiguously classified by sequence evolutionary analysis into 14 distinct classes also charact...

  3. Microarray analysis reveals key genes and pathways in Tetralogy of Fallot

    Science.gov (United States)

    He, Yue-E; Qiu, Hui-Xian; Jiang, Jian-Bing; Wu, Rong-Zhou; Xiang, Ru-Lian; Zhang, Yuan-Hai

    2017-01-01

    The aim of the present study was to identify key genes that may be involved in the pathogenesis of Tetralogy of Fallot (TOF) using bioinformatics methods. The GSE26125 microarray dataset, which includes cardiovascular tissue samples derived from 16 children with TOF and five healthy age-matched control infants, was downloaded from the Gene Expression Omnibus database. Differential expression analysis was performed between TOF and control samples to identify differentially expressed genes (DEGs) using Student's t-test, and the R/limma package, with a log2 fold-change of >2 and a false discovery rate of <0.01 set as thresholds. The biological functions of DEGs were analyzed using the ToppGene database. The ReactomeFIViz application was used to construct functional interaction (FI) networks, and the genes in each module were subjected to pathway enrichment analysis. The iRegulon plugin was used to identify transcription factors predicted to regulate the DEGs in the FI network, and the gene-transcription factor pairs were then visualized using Cytoscape software. A total of 878 DEGs were identified, including 848 upregulated genes and 30 downregulated genes. The gene FI network contained seven function modules, which were all comprised of upregulated genes. Genes enriched in Module 1 were enriched in the following three neurological disorder-associated signaling pathways: Parkinson's disease, Alzheimer's disease and Huntington's disease. Genes in Modules 0, 3 and 5 were dominantly enriched in pathways associated with ribosomes and protein translation. The Xbox binding protein 1 transcription factor was demonstrated to be involved in the regulation of genes encoding the subunits of cytoplasmic and mitochondrial ribosomes, as well as genes involved in neurodegenerative disorders. Therefore, dysfunction of genes involved in signaling pathways associated with neurodegenerative disorders, ribosome function and protein translation may contribute to the pathogenesis of TOF

  4. The map-1 gene family in root-knot nematodes, Meloidogyne spp.: a set of taxonomically restricted genes specific to clonal species.

    Directory of Open Access Journals (Sweden)

    Iva Tomalova

    Full Text Available Taxonomically restricted genes (TRGs, i.e., genes that are restricted to a limited subset of phylogenetically related organisms, may be important in adaptation. In parasitic organisms, TRG-encoded proteins are possible determinants of the specificity of host-parasite interactions. In the root-knot nematode (RKN Meloidogyne incognita, the map-1 gene family encodes expansin-like proteins that are secreted into plant tissues during parasitism, thought to act as effectors to promote successful root infection. MAP-1 proteins exhibit a modular architecture, with variable number and arrangement of 58 and 13-aa domains in their central part. Here, we address the evolutionary origins of this gene family using a combination of bioinformatics and molecular biology approaches. Map-1 genes were solely identified in one single member of the phylum Nematoda, i.e., the genus Meloidogyne, and not detected in any other nematode, thus indicating that the map-1 gene family is indeed a TRG family. A phylogenetic analysis of the distribution of map-1 genes in RKNs further showed that these genes are specifically present in species that reproduce by mitotic parthenogenesis, with the exception of M. floridensis, and could not be detected in RKNs reproducing by either meiotic parthenogenesis or amphimixis. These results highlight the divergence between mitotic and meiotic RKN species as a critical transition in the evolutionary history of these parasites. Analysis of the sequence conservation and organization of repeated domains in map-1 genes suggests that gene duplication(s together with domain loss/duplication have contributed to the evolution of the map-1 family, and that some strong selection mechanism may be acting upon these genes to maintain their functional role(s in the specificity of the plant-RKN interactions.

  5. Analysis of multiplex gene expression maps obtained by voxelation.

    Science.gov (United States)

    An, Li; Xie, Hongbo; Chin, Mark H; Obradovic, Zoran; Smith, Desmond J; Megalooikonomou, Vasileios

    2009-04-29

    Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological disease. Researchers have previously used voxelation in combination with microarrays for acquisition of genome-wide atlases of expression patterns in the mouse brain. On the other hand, some work has been performed on studying gene functions, without taking into account the location information of a gene's expression in a mouse brain. In this paper, we present an approach for identifying the relation between gene expression maps obtained by voxelation and gene functions. To analyze the dataset, we chose typical genes as queries and aimed at discovering similar gene groups. Gene similarity was determined by using the wavelet features extracted from the left and right hemispheres averaged gene expression maps, and by the Euclidean distance between each pair of feature vectors. We also performed a multiple clustering approach on the gene expression maps, combined with hierarchical clustering. Among each group of similar genes and clusters, the gene function similarity was measured by calculating the average gene function distances in the gene ontology structure. By applying our methodology to find similar genes to certain target genes we were able to improve our understanding of gene expression patterns and gene functions. By applying the clustering analysis method, we obtained significant clusters, which have both very similar gene expression maps and very similar gene functions respectively to their corresponding gene ontologies. The cellular component ontology resulted in prominent clusters expressed in cortex and corpus callosum. The molecular function ontology gave prominent clusters in cortex, corpus callosum and hypothalamus. The biological process ontology resulted in clusters in cortex, hypothalamus and choroid plexus. Clusters from all three ontologies combined were most prominently expressed in cortex and corpus callosum. The experimental

  6. Analysis of multiplex gene expression maps obtained by voxelation

    Directory of Open Access Journals (Sweden)

    Smith Desmond J

    2009-04-01

    Full Text Available Abstract Background Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological disease. Researchers have previously used voxelation in combination with microarrays for acquisition of genome-wide atlases of expression patterns in the mouse brain. On the other hand, some work has been performed on studying gene functions, without taking into account the location information of a gene's expression in a mouse brain. In this paper, we present an approach for identifying the relation between gene expression maps obtained by voxelation and gene functions. Results To analyze the dataset, we chose typical genes as queries and aimed at discovering similar gene groups. Gene similarity was determined by using the wavelet features extracted from the left and right hemispheres averaged gene expression maps, and by the Euclidean distance between each pair of feature vectors. We also performed a multiple clustering approach on the gene expression maps, combined with hierarchical clustering. Among each group of similar genes and clusters, the gene function similarity was measured by calculating the average gene function distances in the gene ontology structure. By applying our methodology to find similar genes to certain target genes we were able to improve our understanding of gene expression patterns and gene functions. By applying the clustering analysis method, we obtained significant clusters, which have both very similar gene expression maps and very similar gene functions respectively to their corresponding gene ontologies. The cellular component ontology resulted in prominent clusters expressed in cortex and corpus callosum. The molecular function ontology gave prominent clusters in cortex, corpus callosum and hypothalamus. The biological process ontology resulted in clusters in cortex, hypothalamus and choroid plexus. Clusters from all three ontologies combined were most prominently expressed in

  7. Multifractal analysis for the historic set in topological dynamical systems

    International Nuclear Information System (INIS)

    Zhou, Xiaoyao; Chen, Ercai

    2013-01-01

    In this paper the historic set is divided into different level sets and we use topological pressure to describe the size of these level sets. We give an application of these results to dimension theory. Our primary focus is using topological pressure to describe the relative multifractal spectrum of ergodic averages and to give a positive answer to the conjecture posed by Olsen (2003 J. Math. Pures Appl. 82 1591–649). (paper)

  8. Gene prediction validation and functional analysis of redundant pathways

    DEFF Research Database (Denmark)

    Sønderkær, Mads

    2011-01-01

    have employed a large mRNA-seq data set to improve and validate ab initio predicted gene models. This direct experimental evidence also provides reliable determinations of UTR regions and polyadenylation sites, which are not easily predicted in plants. Furthermore, once an annotated genome sequence...... is available, gene expression by mRNA-Seq enables acquisition of a more complete overview of gene isoform usage in complex enzymatic pathways enabling the identification of key genes. Metabolism in potatoes This information is useful e.g. for crop improvement based on manipulation of agronomically important...

  9. Automated Detection of Cancer Associated Genes Using a Combined Fuzzy-Rough-Set-Based F-Information and Water Swirl Algorithm of Human Gene Expression Data.

    Directory of Open Access Journals (Sweden)

    Pugalendhi Ganesh Kumar

    Full Text Available This study describes a novel approach to reducing the challenges of highly nonlinear multiclass gene expression values for cancer diagnosis. To build a fruitful system for cancer diagnosis, in this study, we introduced two levels of gene selection such as filtering and embedding for selection of potential genes and the most relevant genes associated with cancer, respectively. The filter procedure was implemented by developing a fuzzy rough set (FR-based method for redefining the criterion function of f-information (FI to identify the potential genes without discretizing the continuous gene expression values. The embedded procedure is implemented by means of a water swirl algorithm (WSA, which attempts to optimize the rule set and membership function required to classify samples using a fuzzy-rule-based multiclassification system (FRBMS. Two novel update equations are proposed in WSA, which have better exploration and exploitation abilities while designing a self-learning FRBMS. The efficiency of our new approach was evaluated on 13 multicategory and 9 binary datasets of cancer gene expression. Additionally, the performance of the proposed FRFI-WSA method in designing an FRBMS was compared with existing methods for gene selection and optimization such as genetic algorithm (GA, particle swarm optimization (PSO, and artificial bee colony algorithm (ABC on all the datasets. In the global cancer map with repeated measurements (GCM_RM dataset, the FRFI-WSA showed the smallest number of 16 most relevant genes associated with cancer using a minimal number of 26 compact rules with the highest classification accuracy (96.45%. In addition, the statistical validation used in this study revealed that the biological relevance of the most relevant genes associated with cancer and their linguistics detected by the proposed FRFI-WSA approach are better than those in the other methods. The simple interpretable rules with most relevant genes and effectively

  10. Identification of self-consistent modulons from bacterial microarray expression data with the help of structured regulon gene sets

    KAUST Repository

    Permina, Elizaveta A.

    2013-01-01

    Identification of bacterial modulons from series of gene expression measurements on microarrays is a principal problem, especially relevant for inadequately studied but practically important species. Usage of a priori information on regulatory interactions helps to evaluate parameters for regulatory subnetwork inference. We suggest a procedure for modulon construction where a seed regulon is iteratively updated with genes having expression patterns similar to those for regulon member genes. A set of genes essential for a regulon is used to control modulon updating. Essential genes for a regulon were selected as a subset of regulon genes highly related by different measures to each other. Using Escherichia coli as a model, we studied how modulon identification depends on the data, including the microarray experiments set, the adopted relevance measure and the regulon itself. We have found that results of modulon identification are highly dependent on all parameters studied and thus the resulting modulon varies substantially depending on the identification procedure. Yet, modulons that were identified correctly displayed higher stability during iterations, which allows developing a procedure for reliable modulon identification in the case of less studied species where the known regulatory interactions are sparse. Copyright © 2013 Taylor & Francis.

  11. The Schizophrenia-Associated BRD1 Gene Regulates Behavior, Neurotransmission, and Expression of Schizophrenia Risk Enriched Gene Sets in Mice.

    Science.gov (United States)

    Qvist, Per; Christensen, Jane Hvarregaard; Vardya, Irina; Rajkumar, Anto Praveen; Mørk, Arne; Paternoster, Veerle; Füchtbauer, Ernst-Martin; Pallesen, Jonatan; Fryland, Tue; Dyrvig, Mads; Hauberg, Mads Engel; Lundsberg, Birgitte; Fejgin, Kim; Nyegaard, Mette; Jensen, Kimmo; Nyengaard, Jens Randel; Mors, Ole; Didriksen, Michael; Børglum, Anders Dupont

    2017-07-01

    The schizophrenia-associated BRD1 gene encodes a transcriptional regulator whose comprehensive chromatin interactome is enriched with schizophrenia risk genes. However, the biology underlying the disease association of BRD1 remains speculative. This study assessed the transcriptional drive of a schizophrenia-associated BRD1 risk variant in vitro. Accordingly, to examine the effects of reduced Brd1 expression, we generated a genetically modified Brd1 +/- mouse and subjected it to behavioral, electrophysiological, molecular, and integrative genomic analyses with focus on schizophrenia-relevant parameters. Brd1 +/- mice displayed cerebral histone H3K14 hypoacetylation and a broad range of behavioral changes with translational relevance to schizophrenia. These behaviors were accompanied by striatal dopamine/serotonin abnormalities and cortical excitation-inhibition imbalances involving loss of parvalbumin immunoreactive interneurons. RNA-sequencing analyses of cortical and striatal micropunches from Brd1 +/- and wild-type mice revealed differential expression of genes enriched for schizophrenia risk, including several schizophrenia genome-wide association study risk genes (e.g., calcium channel subunits [Cacna1c and Cacnb2], cholinergic muscarinic receptor 4 [Chrm4)], dopamine receptor D 2 [Drd2], and transcription factor 4 [Tcf4]). Integrative analyses further found differentially expressed genes to cluster in functional networks and canonical pathways associated with mental illness and molecular signaling processes (e.g., glutamatergic, monoaminergic, calcium, cyclic adenosine monophosphate [cAMP], dopamine- and cAMP-regulated neuronal phosphoprotein 32 kDa [DARPP-32], and cAMP responsive element binding protein signaling [CREB]). Our study bridges the gap between genetic association and pathogenic effects and yields novel insights into the unfolding molecular changes in the brain of a new schizophrenia model that incorporates genetic risk at three levels: allelic

  12. EXP-PAC: providing comparative analysis and storage of next generation gene expression data.

    Science.gov (United States)

    Church, Philip C; Goscinski, Andrzej; Lefèvre, Christophe

    2012-07-01

    Microarrays and more recently RNA sequencing has led to an increase in available gene expression data. How to manage and store this data is becoming a key issue. In response we have developed EXP-PAC, a web based software package for storage, management and analysis of gene expression and sequence data. Unique to this package is SQL based querying of gene expression data sets, distributed normalization of raw gene expression data and analysis of gene expression data across experiments and species. This package has been populated with lactation data in the international milk genomic consortium web portal (http://milkgenomics.org/). Source code is also available which can be hosted on a Windows, Linux or Mac APACHE server connected to a private or public network (http://mamsap.it.deakin.edu.au/~pcc/Release/EXP_PAC.html). Copyright © 2012 Elsevier Inc. All rights reserved.

  13. Integrative analysis of copy number alteration and gene expression profiling in ovarian clear cell adenocarcinoma.

    Science.gov (United States)

    Sung, Chang Ohk; Choi, Chel Hun; Ko, Young-Hyeh; Ju, Hyunjeong; Choi, Yoon-La; Kim, Nyunsu; Kang, So Young; Ha, Sang Yun; Choi, Kyusam; Bae, Duk-Soo; Lee, Jeong-Won; Kim, Tae-Joong; Song, Sang Yong; Kim, Byoung-Gie

    2013-05-01

    Ovarian clear cell adenocarcinoma (Ov-CCA) is a distinctive subtype of ovarian epithelial carcinoma. In this study, we performed array comparative genomic hybridization (aCGH) and paired gene expression microarray of 19 fresh-frozen samples and conducted integrative analysis. For the copy number alterations, significantly amplified regions (false discovery rate [FDR] q genes demonstrating frequent copy number alterations (>25% of samples) that correlated with gene expression (FDR genes were mainly located on 8p11.21, 8p21.2-p21.3, 8q22.1, 8q24.3, 17q23.2-q23.3, 19p13.3, and 19p13.11. Among the regions, 8q24.3 was found to contain the most genes (30 of 94 genes) including PTK2. The 8q24.3 region was indicated as the most significant region, as supported by copy number, GISTIC, and integrative analysis. Pathway analysis using differentially expressed genes on 8q24.3 revealed several major nodes, including PTK2. In conclusion, we identified a set of 94 candidate genes with frequent copy number alterations that correlated with gene expression. Specific chromosomal alterations, such as the 8q24.3 gain containing PTK2, could be a therapeutic target in a subset of Ov-CCAs. Copyright © 2013. Published by Elsevier Inc.

  14. MINER: exploratory analysis of gene interaction networks by machine learning from expression data

    Directory of Open Access Journals (Sweden)

    Sivieng Jane

    2009-12-01

    Full Text Available Abstract Background The reconstruction of gene regulatory networks from high-throughput "omics" data has become a major goal in the modelling of living systems. Numerous approaches have been proposed, most of which attempt only "one-shot" reconstruction of the whole network with no intervention from the user, or offer only simple correlation analysis to infer gene dependencies. Results We have developed MINER (Microarray Interactive Network Exploration and Representation, an application that combines multivariate non-linear tree learning of individual gene regulatory dependencies, visualisation of these dependencies as both trees and networks, and representation of known biological relationships based on common Gene Ontology annotations. MINER allows biologists to explore the dependencies influencing the expression of individual genes in a gene expression data set in the form of decision, model or regression trees, using their domain knowledge to guide the exploration and formulate hypotheses. Multiple trees can then be summarised in the form of a gene network diagram. MINER is being adopted by several of our collaborators and has already led to the discovery of a new significant regulatory relationship with subsequent experimental validation. Conclusion Unlike most gene regulatory network inference methods, MINER allows the user to start from genes of interest and build the network gene-by-gene, incorporating domain expertise in the process. This approach has been used successfully with RNA microarray data but is applicable to other quantitative data produced by high-throughput technologies such as proteomics and "next generation" DNA sequencing.

  15. Gene expression meta-analysis identifies metastatic pathways and transcription factors in breast cancer

    International Nuclear Information System (INIS)

    Thomassen, Mads; Tan, Qihua; Kruse, Torben A

    2008-01-01

    Metastasis is believed to progress in several steps including different pathways but the determination and understanding of these mechanisms is still fragmentary. Microarray analysis of gene expression patterns in breast tumors has been used to predict outcome in recent studies. Besides classification of outcome, these global expression patterns may reflect biological mechanisms involved in metastasis of breast cancer. Our purpose has been to investigate pathways and transcription factors involved in metastasis by use of gene expression data sets. We have analyzed 8 publicly available gene expression data sets. A global approach, 'gene set enrichment analysis' as well as an approach focusing on a subset of significantly differently regulated genes, GenMAPP, has been applied to rank pathway gene sets according to differential regulation in metastasizing tumors compared to non-metastasizing tumors. Meta-analysis has been used to determine overrepresentation of pathways and transcription factors targets, concordant deregulated in metastasizing breast tumors, in several data sets. The major findings are up-regulation of cell cycle pathways and a metabolic shift towards glucose metabolism reflected in several pathways in metastasizing tumors. Growth factor pathways seem to play dual roles; EGF and PDGF pathways are decreased, while VEGF and sex-hormone pathways are increased in tumors that metastasize. Furthermore, migration, proteasome, immune system, angiogenesis, DNA repair and several signal transduction pathways are associated to metastasis. Finally several transcription factors e.g. E2F, NFY, and YY1 are identified as being involved in metastasis. By pathway meta-analysis many biological mechanisms beyond major characteristics such as proliferation are identified. Transcription factor analysis identifies a number of key factors that support central pathways. Several previously proposed treatment targets are identified and several new pathways that may

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

    Directory of Open Access Journals (Sweden)

    Eils Roland

    2005-11-01

    Full Text Available Abstract 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

  17. Microarray meta-analysis to explore abiotic stress-specific gene expression patterns in Arabidopsis.

    Science.gov (United States)

    Shen, Po-Chih; Hour, Ai-Ling; Liu, Li-Yu Daisy

    2017-12-01

    Abiotic stresses are the major limiting factors that affect plant growth, development, yield and final quality. Deciphering the underlying mechanisms of plants' adaptations to stresses using few datasets might overlook the different aspects of stress tolerance in plants, which might be simultaneously and consequently operated in the system. Fortunately, the accumulated microarray expression data offer an opportunity to infer abiotic stress-specific gene expression patterns through meta-analysis. In this study, we propose to combine microarray gene expression data under control, cold, drought, heat, and salt conditions and determined modules (gene sets) of genes highly associated with each other according to the observed expression data. By analyzing the expression variations of the Eigen genes from different conditions, we had identified two, three, and five gene modules as cold-, heat-, and salt-specific modules, respectively. Most of the cold- or heat-specific modules were differentially expressed to a particular degree in shoot samples, while most of the salt-specific modules were differentially expressed to a particular degree in root samples. A gene ontology (GO) analysis on the stress-specific modules suggested that the gene modules exclusively enriched stress-related GO terms and that different genes under the same GO terms may be alternatively disturbed in different conditions. The gene regulatory events for two genes, DREB1A and DEAR1, in the cold-specific gene module had also been validated, as evidenced through the literature search. Our protocols study the specificity of the gene modules that were specifically activated under a particular type of abiotic stress. The biplot can also assist to visualize the stress-specific gene modules. In conclusion, our approach has the potential to further elucidate mechanisms in plants and beneficial for future experiments design under different abiotic stresses.

  18. DAVID Knowledgebase: a gene-centered database integrating heterogeneous gene annotation resources to facilitate high-throughput gene functional analysis

    Directory of Open Access Journals (Sweden)

    Baseler Michael W

    2007-11-01

    Full Text Available Abstract Background Due to the complex and distributed nature of biological research, our current biological knowledge is spread over many redundant annotation databases maintained by many independent groups. Analysts usually need to visit many of these bioinformatics databases in order to integrate comprehensive annotation information for their genes, which becomes one of the bottlenecks, particularly for the analytic task associated with a large gene list. Thus, a highly centralized and ready-to-use gene-annotation knowledgebase is in demand for high throughput gene functional analysis. Description The DAVID Knowledgebase is built around the DAVID Gene Concept, a single-linkage method to agglomerate tens of millions of gene/protein identifiers from a variety of public genomic resources into DAVID gene clusters. The grouping of such identifiers improves the cross-reference capability, particularly across NCBI and UniProt systems, enabling more than 40 publicly available functional annotation sources to be comprehensively integrated and centralized by the DAVID gene clusters. The simple, pair-wise, text format files which make up the DAVID Knowledgebase are freely downloadable for various data analysis uses. In addition, a well organized web interface allows users to query different types of heterogeneous annotations in a high-throughput manner. Conclusion The DAVID Knowledgebase is designed to facilitate high throughput gene functional analysis. For a given gene list, it not only provides the quick accessibility to a wide range of heterogeneous annotation data in a centralized location, but also enriches the level of biological information for an individual gene. Moreover, the entire DAVID Knowledgebase is freely downloadable or searchable at http://david.abcc.ncifcrf.gov/knowledgebase/.

  19. Inverse bifurcation analysis: application to simple gene systems

    Directory of Open Access Journals (Sweden)

    Schuster Peter

    2006-07-01

    Full Text Available Abstract Background Bifurcation analysis has proven to be a powerful method for understanding the qualitative behavior of gene regulatory networks. In addition to the more traditional forward problem of determining the mapping from parameter space to the space of model behavior, the inverse problem of determining model parameters to result in certain desired properties of the bifurcation diagram provides an attractive methodology for addressing important biological problems. These include understanding how the robustness of qualitative behavior arises from system design as well as providing a way to engineer biological networks with qualitative properties. Results We demonstrate that certain inverse bifurcation problems of biological interest may be cast as optimization problems involving minimal distances of reference parameter sets to bifurcation manifolds. This formulation allows for an iterative solution procedure based on performing a sequence of eigen-system computations and one-parameter continuations of solutions, the latter being a standard capability in existing numerical bifurcation software. As applications of the proposed method, we show that the problem of maximizing regions of a given qualitative behavior as well as the reverse engineering of bistable gene switches can be modelled and efficiently solved.

  20. Delve: A Data Set Retrieval and Document Analysis System

    KAUST Repository

    Akujuobi, Uchenna Thankgod; Zhang, Xiangliang

    2017-01-01

    Academic search engines (e.g., Google scholar or Microsoft academic) provide a medium for retrieving various information on scholarly documents. However, most of these popular scholarly search engines overlook the area of data set retrieval, which

  1. Nitrate-induced genes in tomato roots. Array analysis reveals novel genes that may play a role in nitrogen nutrition.

    Science.gov (United States)

    Wang, Y H; Garvin, D F; Kochian, L V

    2001-09-01

    A subtractive tomato (Lycopersicon esculentum) root cDNA library enriched in genes up-regulated by changes in plant mineral status was screened with labeled mRNA from roots of both nitrate-induced and mineral nutrient-deficient (-nitrogen [N], -phosphorus, -potassium [K], -sulfur, -magnesium, -calcium, -iron, -zinc, and -copper) tomato plants. A subset of cDNAs was selected from this library based on mineral nutrient-related changes in expression. Additional cDNAs were selected from a second mineral-deficient tomato root library based on sequence homology to known genes. These selection processes yielded a set of 1,280 mineral nutrition-related cDNAs that were arrayed on nylon membranes for further analysis. These high-density arrays were hybridized with mRNA from tomato plants exposed to nitrate at different time points after N was withheld for 48 h, for plants that were grown on nitrate/ammonium for 5 weeks prior to the withholding of N. One hundred-fifteen genes were found to be up-regulated by nitrate resupply. Among these genes were several previously identified as nitrate responsive, including nitrate transporters, nitrate and nitrite reductase, and metabolic enzymes such as transaldolase, transketolase, malate dehydrogenase, asparagine synthetase, and histidine decarboxylase. We also identified 14 novel nitrate-inducible genes, including: (a) water channels, (b) root phosphate and K(+) transporters, (c) genes potentially involved in transcriptional regulation, (d) stress response genes, and (e) ribosomal protein genes. In addition, both families of nitrate transporters were also found to be inducible by phosphate, K, and iron deficiencies. The identification of these novel nitrate-inducible genes is providing avenues of research that will yield new insights into the molecular basis of plant N nutrition, as well as possible networking between the regulation of N, phosphorus, and K nutrition.

  2. Explorative data analysis of MCL reveals gene expression networks implicated in survival and prognosis supported by explorative CGH analysis

    International Nuclear Information System (INIS)

    Blenk, Steffen; Engelmann, Julia C; Pinkert, Stefan; Weniger, Markus; Schultz, Jörg; Rosenwald, Andreas; Müller-Hermelink, Hans K; Müller, Tobias; Dandekar, Thomas

    2008-01-01

    Mantle cell lymphoma (MCL) is an incurable B cell lymphoma and accounts for 6% of all non-Hodgkin's lymphomas. On the genetic level, MCL is characterized by the hallmark translocation t(11;14) that is present in most cases with few exceptions. Both gene expression and comparative genomic hybridization (CGH) data vary considerably between patients with implications for their prognosis. We compare patients over and below the median of survival. Exploratory principal component analysis of gene expression data showed that the second principal component correlates well with patient survival. Explorative analysis of CGH data shows the same correlation. On chromosome 7 and 9 specific genes and bands are delineated which improve prognosis prediction independent of the previously described proliferation signature. We identify a compact survival predictor of seven genes for MCL patients. After extensive re-annotation using GEPAT, we established protein networks correlating with prognosis. Well known genes (CDC2, CCND1) and further proliferation markers (WEE1, CDC25, aurora kinases, BUB1, PCNA, E2F1) form a tight interaction network, but also non-proliferative genes (SOCS1, TUBA1B CEBPB) are shown to be associated with prognosis. Furthermore we show that aggressive MCL implicates a gene network shift to higher expressed genes in late cell cycle states and refine the set of non-proliferative genes implicated with bad prognosis in MCL. The results from explorative data analysis of gene expression and CGH data are complementary to each other. Including further tests such as Wilcoxon rank test we point both to proliferative and non-proliferative gene networks implicated in inferior prognosis of MCL and identify suitable markers both in gene expression and CGH data

  3. A comparative analysis of soft computing techniques for gene prediction.

    Science.gov (United States)

    Goel, Neelam; Singh, Shailendra; Aseri, Trilok Chand

    2013-07-01

    The rapid growth of genomic sequence data for both human and nonhuman species has made analyzing these sequences, especially predicting genes in them, very important and is currently the focus of many research efforts. Beside its scientific interest in the molecular biology and genomics community, gene prediction is of considerable importance in human health and medicine. A variety of gene prediction techniques have been developed for eukaryotes over the past few years. This article reviews and analyzes the application of certain soft computing techniques in gene prediction. First, the problem of gene prediction and its challenges are described. These are followed by different soft computing techniques along with their application to gene prediction. In addition, a comparative analysis of different soft computing techniques for gene prediction is given. Finally some limitations of the current research activities and future research directions are provided. Copyright © 2013 Elsevier Inc. All rights reserved.

  4. A resampling-based meta-analysis for detection of differential gene expression in breast cancer

    International Nuclear Information System (INIS)

    Gur-Dedeoglu, Bala; Konu, Ozlen; Kir, Serkan; Ozturk, Ahmet Rasit; Bozkurt, Betul; Ergul, Gulusan; Yulug, Isik G

    2008-01-01

    Accuracy in the diagnosis of breast cancer and classification of cancer subtypes has improved over the years with the development of well-established immunohistopathological criteria. More recently, diagnostic gene-sets at the mRNA expression level have been tested as better predictors of disease state. However, breast cancer is heterogeneous in nature; thus extraction of differentially expressed gene-sets that stably distinguish normal tissue from various pathologies poses challenges. Meta-analysis of high-throughput expression data using a collection of statistical methodologies leads to the identification of robust tumor gene expression signatures. A resampling-based meta-analysis strategy, which involves the use of resampling and application of distribution statistics in combination to assess the degree of significance in differential expression between sample classes, was developed. Two independent microarray datasets that contain normal breast, invasive ductal carcinoma (IDC), and invasive lobular carcinoma (ILC) samples were used for the meta-analysis. Expression of the genes, selected from the gene list for classification of normal breast samples and breast tumors encompassing both the ILC and IDC subtypes were tested on 10 independent primary IDC samples and matched non-tumor controls by real-time qRT-PCR. Other existing breast cancer microarray datasets were used in support of the resampling-based meta-analysis. The two independent microarray studies were found to be comparable, although differing in their experimental methodologies (Pearson correlation coefficient, R = 0.9389 and R = 0.8465 for ductal and lobular samples, respectively). The resampling-based meta-analysis has led to the identification of a highly stable set of genes for classification of normal breast samples and breast tumors encompassing both the ILC and IDC subtypes. The expression results of the selected genes obtained through real-time qRT-PCR supported the meta-analysis results. The

  5. A resampling-based meta-analysis for detection of differential gene expression in breast cancer

    Directory of Open Access Journals (Sweden)

    Ergul Gulusan

    2008-12-01

    Full Text Available Abstract Background Accuracy in the diagnosis of breast cancer and classification of cancer subtypes has improved over the years with the development of well-established immunohistopathological criteria. More recently, diagnostic gene-sets at the mRNA expression level have been tested as better predictors of disease state. However, breast cancer is heterogeneous in nature; thus extraction of differentially expressed gene-sets that stably distinguish normal tissue from various pathologies poses challenges. Meta-analysis of high-throughput expression data using a collection of statistical methodologies leads to the identification of robust tumor gene expression signatures. Methods A resampling-based meta-analysis strategy, which involves the use of resampling and application of distribution statistics in combination to assess the degree of significance in differential expression between sample classes, was developed. Two independent microarray datasets that contain normal breast, invasive ductal carcinoma (IDC, and invasive lobular carcinoma (ILC samples were used for the meta-analysis. Expression of the genes, selected from the gene list for classification of normal breast samples and breast tumors encompassing both the ILC and IDC subtypes were tested on 10 independent primary IDC samples and matched non-tumor controls by real-time qRT-PCR. Other existing breast cancer microarray datasets were used in support of the resampling-based meta-analysis. Results The two independent microarray studies were found to be comparable, although differing in their experimental methodologies (Pearson correlation coefficient, R = 0.9389 and R = 0.8465 for ductal and lobular samples, respectively. The resampling-based meta-analysis has led to the identification of a highly stable set of genes for classification of normal breast samples and breast tumors encompassing both the ILC and IDC subtypes. The expression results of the selected genes obtained through real

  6. A reference gene set for sex pheromone biosynthesis and degradation genes from the diamondback moth, Plutella xylostella, based on genome and transcriptome digital gene expression analyses

    OpenAIRE

    He, Peng; Zhang, Yun-Fei; Hong, Duan-Yang; Wang, Jun; Wang, Xing-Liang; Zuo, Ling-Hua; Tang, Xian-Fu; Xu, Wei-Ming; He, Ming

    2017-01-01

    Background Female moths synthesize species-specific sex pheromone components and release them to attract male moths, which depend on precise sex pheromone chemosensory system to locate females. Two types of genes involved in the sex pheromone biosynthesis and degradation pathways play essential roles in this important moth behavior. To understand the function of genes in the sex pheromone pathway, this study investigated the genome-wide and digital gene expression of sex pheromone biosynthesi...

  7. Serial analysis of gene expression (SAGE) in rat liver regeneration

    International Nuclear Information System (INIS)

    Cimica, Velasco; Batusic, Danko; Haralanova-Ilieva, Borislava; Chen, Yonglong; Hollemann, Thomas; Pieler, Tomas; Ramadori, Giuliano

    2007-01-01

    We have applied serial analysis of gene expression for studying the molecular mechanism of the rat liver regeneration in the model of 70% partial hepatectomy. We generated three SAGE libraries from a normal control liver (NL library: 52,343 tags), from a sham control operated liver (Sham library: 51,028 tags), and from a regenerating liver (PH library: 53,061 tags). By SAGE bioinformatics analysis we identified 40 induced genes and 20 repressed genes during the liver regeneration. We verified temporal expression of such genes by real time PCR during the regeneration process and we characterized 13 induced genes and 3 repressed genes. We found connective tissue growth factor transcript and protein induced very early at 4 h after PH operation before hepatocytes proliferation is triggered. Our study suggests CTGF as a growth factor signaling mediator that could be involved directly in the mechanism of liver regeneration induction

  8. GeneLab Analysis Working Group Kick-Off Meeting

    Science.gov (United States)

    Costes, Sylvain V.

    2018-01-01

    Goals to achieve for GeneLab AWG - GL vision - Review of GeneLab AWG charter Timeline and milestones for 2018 Logistics - Monthly Meeting - Workshop - Internship - ASGSR Introduction of team leads and goals of each group Introduction of all members Q/A Three-tier Client Strategy to Democratize Data Physiological changes, pathway enrichment, differential expression, normalization, processing metadata, reproducibility, Data federation/integration with heterogeneous bioinformatics external databases The GLDS currently serves over 100 omics investigations to the biomedical community via open access. In order to expand the scope of metadata record searches via the GLDS, we designed a metadata warehouse that collects and updates metadata records from external systems housing similar data. To demonstrate the capabilities of federated search and retrieval of these data, we imported metadata records from three open-access data systems into the GLDS metadata warehouse: NCBI's Gene Expression Omnibus (GEO), EBI's PRoteomics IDEntifications (PRIDE) repository, and the Metagenomics Analysis server (MG-RAST). Each of these systems defines metadata for omics data sets differently. One solution to bridge such differences is to employ a common object model (COM) to which each systems' representation of metadata can be mapped. Warehoused metadata records are then transformed at ETL to this single, common representation. Queries generated via the GLDS are then executed against the warehouse, and matching records are shown in the COM representation (Fig. 1). While this approach is relatively straightforward to implement, the volume of the data in the omics domain presents challenges in dealing with latency and currency of records. Furthermore, the lack of a coordinated has been federated data search for and retrieval of these kinds of data across other open-access systems, so that users are able to conduct biological meta-investigations using data from a variety of sources. Such meta

  9. Digital gene expression analysis of gene expression differences within Brassica diploids and allopolyploids.

    Science.gov (United States)

    Jiang, Jinjin; Wang, Yue; Zhu, Bao; Fang, Tingting; Fang, Yujie; Wang, Youping

    2015-01-27

    Brassica includes many successfully cultivated crop species of polyploid origin, either by ancestral genome triplication or by hybridization between two diploid progenitors, displaying complex repetitive sequences and transposons. The U's triangle, which consists of three diploids and three amphidiploids, is optimal for the analysis of complicated genomes after polyploidization. Next-generation sequencing enables the transcriptome profiling of polyploids on a global scale. We examined the gene expression patterns of three diploids (Brassica rapa, B. nigra, and B. oleracea) and three amphidiploids (B. napus, B. juncea, and B. carinata) via digital gene expression analysis. In total, the libraries generated between 5.7 and 6.1 million raw reads, and the clean tags of each library were mapped to 18547-21995 genes of B. rapa genome. The unambiguous tag-mapped genes in the libraries were compared. Moreover, the majority of differentially expressed genes (DEGs) were explored among diploids as well as between diploids and amphidiploids. Gene ontological analysis was performed to functionally categorize these DEGs into different classes. The Kyoto Encyclopedia of Genes and Genomes analysis was performed to assign these DEGs into approximately 120 pathways, among which the metabolic pathway, biosynthesis of secondary metabolites, and peroxisomal pathway were enriched. The non-additive genes in Brassica amphidiploids were analyzed, and the results indicated that orthologous genes in polyploids are frequently expressed in a non-additive pattern. Methyltransferase genes showed differential expression pattern in Brassica species. Our results provided an understanding of the transcriptome complexity of natural Brassica species. The gene expression changes in diploids and allopolyploids may help elucidate the morphological and physiological differences among Brassica species.

  10. Effects of in ovo electroporation on endogenous gene expression: genome-wide analysis

    Directory of Open Access Journals (Sweden)

    Chambers David

    2011-04-01

    Full Text Available Abstract Background In ovo electroporation is a widely used technique to study gene function in developmental biology. Despite the widespread acceptance of this technique, no genome-wide analysis of the effects of in ovo electroporation, principally the current applied across the tissue and exogenous vector DNA introduced, on endogenous gene expression has been undertaken. Here, the effects of electric current and expression of a GFP-containing construct, via electroporation into the midbrain of Hamburger-Hamilton stage 10 chicken embryos, are analysed by microarray. Results Both current alone and in combination with exogenous DNA expression have a small but reproducible effect on endogenous gene expression, changing the expression of the genes represented on the array by less than 0.1% (current and less than 0.5% (current + DNA, respectively. The subset of genes regulated by electric current and exogenous DNA span a disparate set of cellular functions. However, no genes involved in the regional identity were affected. In sharp contrast to this, electroporation of a known transcription factor, Dmrt5, caused a much greater change in gene expression. Conclusions These findings represent the first systematic genome-wide analysis of the effects of in ovo electroporation on gene expression during embryonic development. The analysis reveals that this process has minimal impact on the genetic basis of cell fate specification. Thus, the study demonstrates the validity of the in ovo electroporation technique to study gene function and expression during development. Furthermore, the data presented here can be used as a resource to refine the set of transcriptional responders in future in ovo electroporation studies of specific gene function.

  11. Bayesian models and meta analysis for multiple tissue gene expression data following corticosteroid administration

    Directory of Open Access Journals (Sweden)

    Kelemen Arpad

    2008-08-01

    Full Text Available Abstract Background This paper addresses key biological problems and statistical issues in the analysis of large gene expression data sets that describe systemic temporal response cascades to therapeutic doses in multiple tissues such as liver, skeletal muscle, and kidney from the same animals. Affymetrix time course gene expression data U34A are obtained from three different tissues including kidney, liver and muscle. Our goal is not only to find the concordance of gene in different tissues, identify the common differentially expressed genes over time and also examine the reproducibility of the findings by integrating the results through meta analysis from multiple tissues in order to gain a significant increase in the power of detecting differentially expressed genes over time and to find the differential differences of three tissues responding to the drug. Results and conclusion Bayesian categorical model for estimating the proportion of the 'call' are used for pre-screening genes. Hierarchical Bayesian Mixture Model is further developed for the identifications of differentially expressed genes across time and dynamic clusters. Deviance information criterion is applied to determine the number of components for model comparisons and selections. Bayesian mixture model produces the gene-specific posterior probability of differential/non-differential expression and the 95% credible interval, which is the basis for our further Bayesian meta-inference. Meta-analysis is performed in order to identify commonly expressed genes from multiple tissues that may serve as ideal targets for novel treatment strategies and to integrate the results across separate studies. We have found the common expressed genes in the three tissues. However, the up/down/no regulations of these common genes are different at different time points. Moreover, the most differentially expressed genes were found in the liver, then in kidney, and then in muscle.

  12. Bioinformatics analysis of RNA-seq data revealed critical genes in colon adenocarcinoma.

    Science.gov (United States)

    Xi, W-D; Liu, Y-J; Sun, X-B; Shan, J; Yi, L; Zhang, T-T

    2017-07-01

    RNA-seq data of colon adenocarcinoma (COAD) were analyzed with bioinformatics tools to discover critical genes in the disease. Relevant small molecule drugs, transcription factors (TFs) and microRNAs (miRNAs) were also investigated. RNA-seq data of COAD were downloaded from The Cancer Genome Atlas (TCGA). Differential analysis was performed with package edgeR. False positive discovery (FDR) 1 were set as the cut-offs to screen out differentially expressed genes (DEGs). Gene coexpression network was constructed with package Ebcoexpress. GO enrichment analysis was performed for the DEGs in the gene coexpression network with DAVID. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was also performed for the genes with KOBASS 2.0. Modules were identified with MCODE of Cytoscape. Relevant small molecules drugs were predicted by Connectivity map. Relevant miRNAs and TFs were searched by WebGestalt. A total of 457 DEGs, including 255 up-regulated and 202 down-regulated genes, were identified from 437 COAD and 39 control samples. A gene coexpression network was constructed containing 40 DEGs and 101 edges. The genes were mainly associated with collagen fibril organization, extracellular matrix organization and translation. Two modules were identified from the gene coexpression network, which were implicated in muscle contraction and extracellular matrix organization, respectively. Several critical genes were disclosed, such as MYH11, COL5A2 and ribosomal proteins. Nine relevant small molecule drugs were identified, such as scriptaid and STOCK1N-35874. Accordingly, a total of 17 TFs and 10 miRNAs related to COAD were acquired, such as ETS2, NFAT, AP4, miR-124A, MiR-9, miR-96 and let-7. Several critical genes and relevant drugs, TFs and miRNAs were revealed in COAD. These findings could advance the understanding of the disease and benefit therapy development.

  13. Prediction of operon-like gene clusters in the Arabidopsis thaliana genome based on co-expression analysis of neighboring genes.

    Science.gov (United States)

    Wada, Masayoshi; Takahashi, Hiroki; Altaf-Ul-Amin, Md; Nakamura, Kensuke; Hirai, Masami Y; Ohta, Daisaku; Kanaya, Shigehiko

    2012-07-15

    Operon-like arrangements of genes occur in eukaryotes ranging from yeasts and filamentous fungi to nematodes, plants, and mammals. In plants, several examples of operon-like gene clusters involved in metabolic pathways have recently been characterized, e.g. the cyclic hydroxamic acid pathways in maize, the avenacin biosynthesis gene clusters in oat, the thalianol pathway in Arabidopsis thaliana, and the diterpenoid momilactone cluster in rice. Such operon-like gene clusters are defined by their co-regulation or neighboring positions within immediate vicinity of chromosomal regions. A comprehensive analysis of the expression of neighboring genes therefore accounts a crucial step to reveal the complete set of operon-like gene clusters within a genome. Genome-wide prediction of operon-like gene clusters should contribute to functional annotation efforts and provide novel insight into evolutionary aspects acquiring certain biological functions as well. We predicted co-expressed gene clusters by comparing the Pearson correlation coefficient of neighboring genes and randomly selected gene pairs, based on a statistical method that takes false discovery rate (FDR) into consideration for 1469 microarray gene expression datasets of A. thaliana. We estimated that A. thaliana contains 100 operon-like gene clusters in total. We predicted 34 statistically significant gene clusters consisting of 3 to 22 genes each, based on a stringent FDR threshold of 0.1. Functional relationships among genes in individual clusters were estimated by sequence similarity and functional annotation of genes. Duplicated gene pairs (determined based on BLAST with a cutoff of EOperon-like clusters tend to include genes encoding bio-machinery associated with ribosomes, the ubiquitin/proteasome system, secondary metabolic pathways, lipid and fatty-acid metabolism, and the lipid transfer system. Copyright © 2012 Elsevier B.V. All rights reserved.

  14. Impact of Docosahexaenoic Acid on Gene Expression during Osteoclastogenesis in Vitro—A Comprehensive Analysis

    Directory of Open Access Journals (Sweden)

    Ikuo Morita

    2013-08-01

    Full Text Available Polyunsaturated fatty acids (PUFAs, especially n-3 polyunsaturated fatty acids, docosahexaenoic acid (DHA and eicosapentaenoic acid (EPA, are known to protect against inflammation-induced bone loss in chronic inflammatory diseases, such as rheumatoid arthritis, periodontitis and osteoporosis. We previously reported that DHA, not EPA, inhibited osteoclastogenesis induced by the receptor activator of nuclear factor-κB ligand (sRANKL in vitro. In this study, we performed gene expression analysis using microarrays to identify genes affected by the DHA treatment during osteoclastogenesis. DHA strongly inhibited osteoclastogenesis at the late stage. Among the genes upregulated by the sRANKL treatment, 4779 genes were downregulated by DHA and upregulated by the EPA treatment. Gene ontology analysis identified sets of genes related to cell motility, cell adhesion, cell-cell signaling and cell morphogenesis. Quantitative PCR analysis confirmed that DC-STAMP, an essential gene for the cell fusion process in osteoclastogenesis, and other osteoclast-related genes, such as Siglec-15, Tspan7 and Mst1r, were inhibited by DHA.

  15. Suppression subtractive hybridization and comparative expression analysis to identify developmentally regulated genes in filamentous fungi.

    Science.gov (United States)

    Gesing, Stefan; Schindler, Daniel; Nowrousian, Minou

    2013-09-01

    Ascomycetes differentiate four major morphological types of fruiting bodies (apothecia, perithecia, pseudothecia and cleistothecia) that are derived from an ancestral fruiting body. Thus, fruiting body differentiation is most likely controlled by a set of common core genes. One way to identify such genes is to search for genes with evolutionary conserved expression patterns. Using suppression subtractive hybridization (SSH), we selected differentially expressed transcripts in Pyronema confluens (Pezizales) by comparing two cDNA libraries specific for sexual and for vegetative development, respectively. The expression patterns of selected genes from both libraries were verified by quantitative real time PCR. Expression of several corresponding homologous genes was found to be conserved in two members of the Sordariales (Sordaria macrospora and Neurospora crassa), a derived group of ascomycetes that is only distantly related to the Pezizales. Knockout studies with N. crassa orthologues of differentially regulated genes revealed a functional role during fruiting body development for the gene NCU05079, encoding a putative MFS peptide transporter. These data indicate conserved gene expression patterns and a functional role of the corresponding genes during fruiting body development; such genes are candidates of choice for further functional analysis. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  16. Bioinformatics analysis and detection of gelatinase encoded gene in Lysinibacillussphaericus

    Science.gov (United States)

    Repin, Rul Aisyah Mat; Mutalib, Sahilah Abdul; Shahimi, Safiyyah; Khalid, Rozida Mohd.; Ayob, Mohd. Khan; Bakar, Mohd. Faizal Abu; Isa, Mohd Noor Mat

    2016-11-01

    In this study, we performed bioinformatics analysis toward genome sequence of Lysinibacillussphaericus (L. sphaericus) to determine gene encoded for gelatinase. L. sphaericus was isolated from soil and gelatinase species-specific bacterium to porcine and bovine gelatin. This bacterium offers the possibility of enzymes production which is specific to both species of meat, respectively. The main focus of this research is to identify the gelatinase encoded gene within the bacteria of L. Sphaericus using bioinformatics analysis of partially sequence genome. From the research study, three candidate gene were identified which was, gelatinase candidate gene 1 (P1), NODE_71_length_93919_cov_158.931839_21 which containing 1563 base pair (bp) in size with 520 amino acids sequence; Secondly, gelatinase candidate gene 2 (P2), NODE_23_length_52851_cov_190.061386_17 which containing 1776 bp in size with 591 amino acids sequence; and Thirdly, gelatinase candidate gene 3 (P3), NODE_106_length_32943_cov_169.147919_8 containing 1701 bp in size with 566 amino acids sequence. Three pairs of oligonucleotide primers were designed and namely as, F1, R1, F2, R2, F3 and R3 were targeted short sequences of cDNA by PCR. The amplicons were reliably results in 1563 bp in size for candidate gene P1 and 1701 bp in size for candidate gene P3. Therefore, the results of bioinformatics analysis of L. Sphaericus resulting in gene encoded gelatinase were identified.

  17. Gene Expression Analysis of Four Radiation-resistant Bacteria

    OpenAIRE

    Gao, Na; Ma, Bin-Guang; Zhang, Yu-Sheng; Song, Qin; Chen, Ling-Ling; Zhang, Hong-Yu

    2009-01-01

    To investigate the general radiation-resistant mechanisms of bacteria, bioinformatic method was employed to predict highly expressed genes for four radiation-resistant bacteria, i.e. Deinococcus geothermalis (D. geo), Deinococcus radiodurans (D. rad), Kineococcus radiotolerans (K. rad) and Rubrobacter xylanophilus (R. xyl). It is revealed that most of the three reference gene sets, i.e. ribosomal proteins, transcription factors and major chaperones, are generally highly expressed in the four ...

  18. Gastric Cancer Associated Genes Identified by an Integrative Analysis of Gene Expression Data

    Directory of Open Access Journals (Sweden)

    Bing Jiang

    2017-01-01

    Full Text Available Gastric cancer is one of the most severe complex diseases with high morbidity and mortality in the world. The molecular mechanisms and risk factors for this disease are still not clear since the cancer heterogeneity caused by different genetic and environmental factors. With more and more expression data accumulated nowadays, we can perform integrative analysis for these data to understand the complexity of gastric cancer and to identify consensus players for the heterogeneous cancer. In the present work, we screened the published gene expression data and analyzed them with integrative tool, combined with pathway and gene ontology enrichment investigation. We identified several consensus differentially expressed genes and these genes were further confirmed with literature mining; at last, two genes, that is, immunoglobulin J chain and C-X-C motif chemokine ligand 17, were screened as novel gastric cancer associated genes. Experimental validation is proposed to further confirm this finding.

  19. A comparative study of three different gene expression analysis methods.

    Science.gov (United States)

    Choe, Jae Young; Han, Hyung Soo; Lee, Seon Duk; Lee, Hanna; Lee, Dong Eun; Ahn, Jae Yun; Ryoo, Hyun Wook; Seo, Kang Suk; Kim, Jong Kun

    2017-12-04

    TNF-α regulates immune cells and acts as an endogenous pyrogen. Reverse transcription polymerase chain reaction (RT-PCR) is one of the most commonly used methods for gene expression analysis. Among the alternatives to PCR, loop-mediated isothermal amplification (LAMP) shows good potential in terms of specificity and sensitivity. However, few studies have compared RT-PCR and LAMP for human gene expression analysis. Therefore, in the present study, we compared one-step RT-PCR, two-step RT-LAMP and one-step RT-LAMP for human gene expression analysis. We compared three gene expression analysis methods using the human TNF-α gene as a biomarker from peripheral blood cells. Total RNA from the three selected febrile patients were subjected to the three different methods of gene expression analysis. In the comparison of three gene expression analysis methods, the detection limit of both one-step RT-PCR and one-step RT-LAMP were the same, while that of two-step RT-LAMP was inferior. One-step RT-LAMP takes less time, and the experimental result is easy to determine. One-step RT-LAMP is a potentially useful and complementary tool that is fast and reasonably sensitive. In addition, one-step RT-LAMP could be useful in environments lacking specialized equipment or expertise.

  20. Identification of a set of endogenous reference genes for miRNA expression studies in Parkinson's disease blood samples.

    Science.gov (United States)

    Serafin, Alice; Foco, Luisa; Blankenburg, Hagen; Picard, Anne; Zanigni, Stefano; Zanon, Alessandra; Pramstaller, Peter P; Hicks, Andrew A; Schwienbacher, Christine

    2014-10-10

    Research on microRNAs (miRNAs) is becoming an increasingly attractive field, as these small RNA molecules are involved in several physiological functions and diseases. To date, only few studies have assessed the expression of blood miRNAs related to Parkinson's disease (PD) using microarray and quantitative real-time PCR (qRT-PCR). Measuring miRNA expression involves normalization of qRT-PCR data using endogenous reference genes for calibration, but their choice remains a delicate problem with serious impact on the resulting expression levels. The aim of the present study was to evaluate the suitability of a set of commonly used small RNAs as normalizers and to identify which of these miRNAs might be considered reliable reference genes in qRT-PCR expression analyses on PD blood samples. Commonly used reference genes snoRNA RNU24, snRNA RNU6B, snoRNA Z30 and miR-103a-3p were selected from the literature. We then analyzed the effect of using these genes as reference, alone or in any possible combination, on the measured expression levels of the target genes miR-30b-5p and miR-29a-3p, which have been previously reported to be deregulated in PD blood samples. We identified RNU24 and Z30 as a reliable and stable pair of reference genes in PD blood samples.

  1. A set of vectors for introduction of antibiotic resistance genes by in vitro Cre-mediated recombination

    Directory of Open Access Journals (Sweden)

    Vassetzky Yegor S

    2008-12-01

    Full Text Available Abstract Background Introduction of new antibiotic resistance genes in the plasmids of interest is a frequent task in molecular cloning practice. Classical approaches involving digestion with restriction endonucleases and ligation are time-consuming. Findings We have created a set of insertion vectors (pINS carrying genes that provide resistance to various antibiotics (puromycin, blasticidin and G418 and containing a loxP site. Each vector (pINS-Puro, pINS-Blast or pINS-Neo contains either a chloramphenicol or a kanamycin resistance gene and is unable to replicate in most E. coli strains as it contains a conditional R6Kγ replication origin. Introduction of the antibiotic resistance genes into the vector of interest is achieved by Cre-mediated recombination between the replication-incompetent pINS and a replication-competent target vector. The recombination mix is then transformed into E. coli and selected by the resistance marker (kanamycin or chloramphenicol present in pINS, which allows to recover the recombinant plasmids with 100% efficiency. Conclusion Here we propose a simple strategy that allows to introduce various antibiotic-resistance genes into any plasmid containing a replication origin, an ampicillin resistance gene and a loxP site.

  2. Health care priority setting in Norway a multicriteria decision analysis

    NARCIS (Netherlands)

    Defechereux, T.; Paolucci, F.; Mirelman, A.; Youngkong, S.; Botten, G.; Hagen, T.P.; Niessen, L.W.

    2012-01-01

    BACKGROUND: Priority setting in population health is increasingly based on explicitly formulated values. The Patients Rights Act of the Norwegian tax-based health service guaranties all citizens health care in case of a severe illness, a proven health benefit, and proportionality between need and

  3. A HIERARCHICAL SET OF MODELS FOR SPECIES RESPONSE ANALYSIS

    NARCIS (Netherlands)

    HUISMAN, J; OLFF, H; FRESCO, LFM

    Variation in the abundance of species in space and/or time can be caused by a wide range of underlying processes. Before such causes can be analysed we need simple mathematical models which can describe the observed response patterns. For this purpose a hierarchical set of models is presented. These

  4. A hierarchical set of models for species response analysis

    NARCIS (Netherlands)

    Huisman, J.; Olff, H.; Fresco, L.F.M.

    1993-01-01

    Variation in the abundance of species in space and/or time can be caused by a wide range of underlying processes. Before such causes can be analysed we need simple mathematical models which can describe the observed response patterns. For this purpose a hierarchical set of models is presented. These

  5. Automotive RF immunity test set-up analysis

    NARCIS (Netherlands)

    Coenen, M.J.; Pues, H.; Bousquet, T.; Gillon, R.; Gielen, G.; Baric, A.

    2011-01-01

    Though the automotive RF emission and RF immunity requirements are highly justifiable, the application of those requirements in an non-intended manner leads to false conclusions and unnecessary redesigns for the electronics involved. When the test results become too dependent upon the test set-up

  6. Shrinkage Approach for Gene Expression Data Analysis

    Czech Academy of Sciences Publication Activity Database

    Haman, Jiří; Valenta, Zdeněk; Kalina, Jan

    2013-01-01

    Roč. 1, č. 1 (2013), s. 65-65 ISSN 1805-8698. [EFMI 2013 Special Topic Conference. 17.04.2013-19.04.2013, Prague] Institutional support: RVO:67985807 Keywords : shrinkage estimation * covariance matrix * high dimensional data * gene expression Subject RIV: IN - Informatics, Computer Science

  7. GenePublisher: automated analysis of DNA microarray data

    DEFF Research Database (Denmark)

    Knudsen, Steen; Workman, Christopher; Sicheritz-Ponten, T.

    2003-01-01

    GenePublisher, a system for automatic analysis of data from DNA microarray experiments, has been implemented with a web interface at http://www.cbs.dtu.dk/services/GenePublisher. Raw data are uploaded to the server together with aspecification of the data. The server performs normalization...

  8. Genomewide identification and expression analysis of the ARF gene ...

    Indian Academy of Sciences (India)

    Figure 1. Phylogenetic relation of apple ARF genes. The phylogenetic tree was constructed based on a complete protein sequence align- ment of MdARFs by the neighbour-joining method with bootstrapping analysis (1000 replicates). The scale bar represents 0.05 amino acid substitutions per site. Paralogous gene pairs ...

  9. Microarray analysis of the gene expression profile in triethylene ...

    African Journals Online (AJOL)

    Microarray analysis of the gene expression profile in triethylene glycol dimethacrylate-treated human dental pulp cells. ... Conclusions: Our results suggest that TEGDMA can change the many functions of hDPCs through large changes in gene expression levels and complex interactions with different signaling pathways.

  10. Validation of suitable reference genes for quantitative gene expression analysis in Panax ginseng

    Directory of Open Access Journals (Sweden)

    Meizhen eWang

    2016-01-01

    Full Text Available Reverse transcription-qPCR (RT-qPCR has become a popular method for gene expression studies. Its results require data normalization by housekeeping genes. No single gene is proved to be stably expressed under all experimental conditions. Therefore, systematic evaluation of reference genes is necessary. With the aim to identify optimum reference genes for RT-qPCR analysis of gene expression in different tissues of Panax ginseng and the seedlings grown under heat stress, we investigated the expression stability of eight candidate reference genes, including elongation factor 1-beta (EF1-β, elongation factor 1-gamma (EF1-γ, eukaryotic translation initiation factor 3G (IF3G, eukaryotic translation initiation factor 3B (IF3B, actin (ACT, actin11 (ACT11, glyceraldehyde-3-phosphate dehydrogenase (GAPDH and cyclophilin ABH-like protein (CYC, using four widely used computational programs: geNorm, Normfinder, BestKeeper, and the comparative ΔCt method. The results were then integrated using the web-based tool RefFinder. As a result, EF1-γ, IF3G and EF1-β were the three most stable genes in different tissues of P. ginseng, while IF3G, ACT11 and GAPDH were the top three-ranked genes in seedlings treated with heat. Using three better reference genes alone or in combination as internal control, we examined the expression profiles of MAR, a multiple function-associated mRNA-like non-coding RNA (mlncRNA in P. ginseng. Taken together, we recommended EF1-γ/IF3G and IF3G/ACT11 as the suitable pair of reference genes for RT-qPCR analysis of gene expression in different tissues of P. ginseng and the seedlings grown under heat stress, respectively. The results serve as a foundation for future studies on P. ginseng functional genomics.

  11. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool.

    Science.gov (United States)

    Chen, Edward Y; Tan, Christopher M; Kou, Yan; Duan, Qiaonan; Wang, Zichen; Meirelles, Gabriela Vaz; Clark, Neil R; Ma'ayan, Avi

    2013-04-15

    System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement. Here, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. We applied Enrichr to analyze nine cancer cell lines by comparing their enrichment signatures to the enrichment signatures of matched normal tissues. We observed a common pattern of up regulation of the polycomb group PRC2 and enrichment for the histone mark H3K27me3 in many cancer cell lines, as well as alterations in Toll-like receptor and interlukin signaling in K562 cells when compared with normal myeloid CD33+ cells. Such analyses provide global visualization of critical differences between normal tissues and cancer cell lines but can be applied to many other scenarios. Enrichr is an easy to use intuitive enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr.

  12. Network-based differential gene expression analysis suggests cell cycle related genes regulated by E2F1 underlie the molecular difference between smoker and non-smoker lung adenocarcinoma

    Science.gov (United States)

    2013-01-01

    Background Differential gene expression (DGE) analysis is commonly used to reveal the deregulated molecular mechanisms of complex diseases. However, traditional DGE analysis (e.g., the t test or the rank sum test) tests each gene independently without considering interactions between them. Top-ranked differentially regulated genes prioritized by the analysis may not directly relate to the coherent molecular changes underlying complex diseases. Joint analyses of co-expression and DGE have been applied to reveal the deregulated molecular modules underlying complex diseases. Most of these methods consist of separate steps: first to identify gene-gene relationships under the studied phenotype then to integrate them with gene expression changes for prioritizing signature genes, or vice versa. It is warrant a method that can simultaneously consider gene-gene co-expression strength and corresponding expression level changes so that both types of information can be leveraged optimally. Results In this paper, we develop a gene module based method for differential gene expression analysis, named network-based differential gene expression (nDGE) analysis, a one-step integrative process for prioritizing deregulated genes and grouping them into gene modules. We demonstrate that nDGE outperforms existing methods in prioritizing deregulated genes and discovering deregulated gene modules using simulated data sets. When tested on a series of smoker and non-smoker lung adenocarcinoma data sets, we show that top differentially regulated genes identified by the rank sum test in different sets are not consistent while top ranked genes defined by nDGE in different data sets significantly overlap. nDGE results suggest that a differentially regulated gene module, which is enriched for cell cycle related genes and E2F1 targeted genes, plays a role in the molecular differences between smoker and non-smoker lung adenocarcinoma. Conclusions In this paper, we develop nDGE to prioritize

  13. In silico analysis of miRNA-mediated gene regulation in OCA and OA genes.

    Science.gov (United States)

    Kamaraj, Balu; Gopalakrishnan, Chandrasekhar; Purohit, Rituraj

    2014-12-01

    Albinism is an autosomal recessive genetic disorder due to low secretion of melanin. The oculocutaneous albinism (OCA) and ocular albinism (OA) genes are responsible for melanin production and also act as a potential targets for miRNAs. The role of miRNA is to inhibit the protein synthesis partially or completely by binding with the 3'UTR of the mRNA thus regulating gene expression. In this analysis, we predicted the genetic variation that occurred in 3'UTR of the transcript which can be a reason for low melanin production thus causing albinism. The single nucleotide polymorphisms (SNPs) in 3'UTR cause more new binding sites for miRNA which binds with mRNA which leads to inhibit the translation process either partially or completely. The SNPs in the mRNA of OCA and OA genes can create new binding sites for miRNA which may control the gene expression and lead to hypopigmentation. We have developed a computational procedure to determine the SNPs in the 3'UTR region of mRNA of OCA (TYR, OCA2, TYRP1 and SLC45A2) and OA (GPR143) genes which will be a potential cause for albinism. We identified 37 SNPs in five genes that are predicted to create 87 new binding sites on mRNA, which may lead to abrogation of the translation process. Expression analysis confirms that these genes are highly expressed in skin and eye regions. It is well supported by enrichment analysis that these genes are mainly involved in eye pigmentation and melanin biosynthesis process. The network analysis also shows how the genes are interacting and expressing in a complex network. This insight provides clue to wet-lab researches to understand the expression pattern of OCA and OA genes and binding phenomenon of mRNA and miRNA upon mutation, which is responsible for inhibition of translation process at genomic levels.

  14. Screening Key Genes Associated with the Development and Progression of Non-small Cell Lung Cancer Based on Gene-enrichment Analysis and Meta-analysis

    Directory of Open Access Journals (Sweden)

    Wenwu HE

    2012-07-01

    Full Text Available Background and objective Non-small cell lung cancer (NSCLC is one of the most common malignant tumors; however, its causes are still not completely understood. This study was designed to screen the key genes and pathways related to NSCLC occurrence and development and to establish the scientific foundation for the genetic mechanisms and targeted therapy of NSCLC. Methods Both gene set-enrichment analysis (GSEA and meta-analysis (meta were used to screen the critical pathways and genes that might be corretacted with the development and progression of lung cancer at the transcription level. Results Using the GSEA and meta methods, focal adhesion and regulation of actin cytoskeleton were determined to be the more prominent overlapping significant pathways. In the focal adhesion pathway, 31 genes were statistically significant (P<0.05, whereas in the regulation of actin cytoskeleton pathway, 32 genes were statistically significant (P<0.05. Conclusion The focal adhesion and the regulation of actin cytoskeleton pathways might play important roles in the occurrence and development of NSCLC. Further studies are needed to determine the biological function for the positiue genes.

  15. Identification of a cis-regulatory element by transient analysis of co-ordinately regulated genes

    Directory of Open Access Journals (Sweden)

    Allan Andrew C

    2008-07-01

    Full Text Available Abstract Background Transcription factors (TFs co-ordinately regulate target genes that are dispersed throughout the genome. This co-ordinate regulation is achieved, in part, through the interaction of transcription factors with conserved cis-regulatory motifs that are in close proximity to the target genes. While much is known about the families of transcription factors that regulate gene expression in plants, there are few well characterised cis-regulatory motifs. In Arabidopsis, over-expression of the MYB transcription factor PAP1 (PRODUCTION OF ANTHOCYANIN PIGMENT 1 leads to transgenic plants with elevated anthocyanin levels due to the co-ordinated up-regulation of genes in the anthocyanin biosynthetic pathway. In addition to the anthocyanin biosynthetic genes, there are a number of un-associated genes that also change in expression level. This may be a direct or indirect consequence of the over-expression of PAP1. Results Oligo array analysis of PAP1 over-expression Arabidopsis plants identified genes co-ordinately up-regulated in response to the elevated expression of this transcription factor. Transient assays on the promoter regions of 33 of these up-regulated genes identified eight promoter fragments that were transactivated by PAP1. Bioinformatic analysis on these promoters revealed a common cis-regulatory motif that we showed is required for PAP1 dependent transactivation. Conclusion Co-ordinated gene regulation by individual transcription factors is a complex collection of both direct and indirect effects. Transient transactivation assays provide a rapid method to identify direct target genes from indirect target genes. Bioinformatic analysis of the promoters of these direct target genes is able to locate motifs that are common to this sub-set of promoters, which is impossible to identify with the larger set of direct and indirect target genes. While this type of analysis does not prove a direct interaction between protein and DNA

  16. Identification and Analysis of the SET-Domain Family in Silkworm, Bombyx mori

    Directory of Open Access Journals (Sweden)

    Hailong Zhao

    2015-01-01

    Full Text Available As an important economic insect, Bombyx mori is also a useful model organism for lepidopteran insect. SET-domain-containing proteins belong to a group of enzymes named after a common domain that utilizes the cofactor S-adenosyl-L-methionine (SAM to achieve methylation of its substrates. Many SET-domain-containing proteins have been shown to display catalytic activity towards particular lysine residues on histones, but emerging evidence also indicates that various nonhistone proteins are specifically targeted by this clade of enzymes. To explore their diverse functions of SET-domain superfamily in insect, we identified, cloned, and analyzed the SET-domains proteins in silkworm, Bombyx mori. Firstly, 24 genes containing SET domain from silkworm genome were characterized and 17 of them belonged to six subfamilies of SUV39, SET1, SET2, SUV4-20, EZ, and SMYD. Secondly, SET domains of silkworm SET-domain family were intraspecifically and interspecifically conserved, especially for the catalytic core “NHSC” motif, substrate binding site, and catalytic site in the SET domain. Lastly, further analyses indicated that silkworm SET-domain gene BmSu(var3-9 owned different characterization and expression profiles compared to other invertebrates. Overall, our results provide a new insight into the functional and evolutionary features of SET-domain family.

  17. Vector optimization set-valued and variational analysis

    CERN Document Server

    Chen, Guang-ya; Yang, Xiaogi

    2005-01-01

    This book is devoted to vector or multiple criteria approaches in optimization. Topics covered include: vector optimization, vector variational inequalities, vector variational principles, vector minmax inequalities and vector equilibrium problems. In particular, problems with variable ordering relations and set-valued mappings are treated. The nonlinear scalarization method is extensively used throughout the book to deal with various vector-related problems. The results presented are original and should be interesting to researchers and graduates in applied mathematics and operations research

  18. Mean-Variance Analysis in a Multiperiod Setting

    OpenAIRE

    Frauendorfer, Karl; Siede, Heiko

    1997-01-01

    Similar to the classical Markowitz approach it is possible to apply a mean-variance criterion to a multiperiod setting to obtain efficient portfolios. To represent the stochastic dynamic characteristics necessary for modelling returns a process of asset returns is discretized with respect to time and space and summarized in a scenario tree. The resulting optimization problem is solved by means of stochastic multistage programming. The optimal solutions show equivalent structural properties as...

  19. Long SAGE analysis of genes differentially expressed in the midgut ...

    African Journals Online (AJOL)

    Long SAGE analysis of genes differentially expressed in the midgut and silk gland between the sexes of the silkwormBombyx mori. Liping Gan, Ying Wang, Jian Xi, Yanshan Niu, Hongyou Qin, Yanghu Sima, Shiqing Xu ...

  20. AnGeLi: A Tool for the Analysis of Gene Lists from Fission Yeast

    Directory of Open Access Journals (Sweden)

    Danny A Bitton

    2015-11-01

    Full Text Available Genome-wide assays and screens typically result in large lists of genes or proteins. Enrichments of functional or other biological properties within such lists can provide valuable insights and testable hypotheses. To systematically detect these enrichments can be challenging and time-consuming, because relevant data to compare against query gene lists are spread over many different sources. We have developed AnGeLi (Analysis of Gene Lists, an intuitive, integrated web-tool for comprehensive and customized interrogation of gene lists from the fission yeast, Schizosaccharomyces pombe. AnGeLi searches for significant enrichments among multiple qualitative and quantitative information sources, including gene and phenotype ontologies, genetic and protein interactions, numerous features of genes, transcripts, translation, and proteins such as copy numbers, chromosomal positions, genetic diversity, RNA polymerase II and ribosome occupancy, localization, conservation, half-lives, domains and molecular weight among others, as well as diverse sets of genes that are co-regulated or lead to the same phenotypes when mutated. AnGeLi uses robust statistics which can be tailored to specific needs. It also provides the option to upload user-defined gene sets to compare against the query list. Through an integrated data submission form, AnGeLi encourages the community to contribute additional curated gene lists to further increase the usefulness of this resource and to get the most from the ever increasing large-scale experiments. AnGeLi offers a rigorous yet flexible statistical analysis platform for rich insights into functional enrichments and biological context for query gene lists, thus providing a powerful exploratory tool through which S. pombe researchers can uncover fresh perspectives and unexpected connections from genomic data. AnGeLi is freely available at: www.bahlerlab.info/AnGeLi

  1. Improving probe set selection for microbial community analysis by leveraging taxonomic information of training sequences

    Directory of Open Access Journals (Sweden)

    Jiang Tao

    2011-10-01

    Full Text Available Abstract Background Population levels of microbial phylotypes can be examined using a hybridization-based method that utilizes a small set of computationally-designed DNA probes targeted to a gene common to all. Our previous algorithm attempts to select a set of probes such that each training sequence manifests a unique theoretical hybridization pattern (a binary fingerprint to a probe set. It does so without taking into account similarity between training gene sequences or their putative taxonomic classifications, however. We present an improved algorithm for probe set selection that utilizes the available taxonomic information of training gene sequences and attempts to choose probes such that the resultant binary fingerprints cluster into real taxonomic groups. Results Gene sequences manifesting identical fingerprints with probes chosen by the new algorithm are more likely to be from the same taxonomic group than probes chosen by the previous algorithm. In cases where they are from different taxonomic groups, underlying DNA sequences of identical fingerprints are more similar to each other in probe sets made with the new versus the previous algorithm. Complete removal of large taxonomic groups from training data does not greatly decrease the ability of probe sets to distinguish those groups. Conclusions Probe sets made from the new algorithm create fingerprints that more reliably cluster into biologically meaningful groups. The method can readily distinguish microbial phylotypes that were excluded from the training sequences, suggesting novel microbes can also be detected.

  2. Improving probe set selection for microbial community analysis by leveraging taxonomic information of training sequences.

    Science.gov (United States)

    Ruegger, Paul M; Della Vedova, Gianluca; Jiang, Tao; Borneman, James

    2011-10-10

    Population levels of microbial phylotypes can be examined using a hybridization-based method that utilizes a small set of computationally-designed DNA probes targeted to a gene common to all. Our previous algorithm attempts to select a set of probes such that each training sequence manifests a unique theoretical hybridization pattern (a binary fingerprint) to a probe set. It does so without taking into account similarity between training gene sequences or their putative taxonomic classifications, however. We present an improved algorithm for probe set selection that utilizes the available taxonomic information of training gene sequences and attempts to choose probes such that the resultant binary fingerprints cluster into real taxonomic groups. Gene sequences manifesting identical fingerprints with probes chosen by the new algorithm are more likely to be from the same taxonomic group than probes chosen by the previous algorithm. In cases where they are from different taxonomic groups, underlying DNA sequences of identical fingerprints are more similar to each other in probe sets made with the new versus the previous algorithm. Complete removal of large taxonomic groups from training data does not greatly decrease the ability of probe sets to distinguish those groups. Probe sets made from the new algorithm create fingerprints that more reliably cluster into biologically meaningful groups. The method can readily distinguish microbial phylotypes that were excluded from the training sequences, suggesting novel microbes can also be detected.

  3. Global gene expression analysis for evaluation and design of biomaterials

    Directory of Open Access Journals (Sweden)

    Nobutaka Hanagata, Taro Takemura and Takashi Minowa

    2010-01-01

    Full Text Available Comprehensive gene expression analysis using DNA microarrays has become a widespread technique in molecular biological research. In the biomaterials field, it is used to evaluate the biocompatibility or cellular toxicity of metals, polymers and ceramics. Studies in this field have extracted differentially expressed genes in the context of differences in cellular responses among multiple materials. Based on these genes, the effects of materials on cells at the molecular level have been examined. Expression data ranging from several to tens of thousands of genes can be obtained from DNA microarrays. For this reason, several tens or hundreds of differentially expressed genes are often present in different materials. In this review, we outline the principles of DNA microarrays, and provide an introduction to methods of extracting information which is useful for evaluating and designing biomaterials from comprehensive gene expression data.

  4. Global gene expression analysis for evaluation and design of biomaterials

    International Nuclear Information System (INIS)

    Hanagata, Nobutaka; Takemura, Taro; Minowa, Takashi

    2010-01-01

    Comprehensive gene expression analysis using DNA microarrays has become a widespread technique in molecular biological research. In the biomaterials field, it is used to evaluate the biocompatibility or cellular toxicity of metals, polymers and ceramics. Studies in this field have extracted differentially expressed genes in the context of differences in cellular responses among multiple materials. Based on these genes, the effects of materials on cells at the molecular level have been examined. Expression data ranging from several to tens of thousands of genes can be obtained from DNA microarrays. For this reason, several tens or hundreds of differentially expressed genes are often present in different materials. In this review, we outline the principles of DNA microarrays, and provide an introduction to methods of extracting information which is useful for evaluating and designing biomaterials from comprehensive gene expression data. (topical review)

  5. Genetic investigation of 100 heart genes in sudden unexplained death victims in a forensic setting

    DEFF Research Database (Denmark)

    Christiansen, Sofie Lindgren; Hertz, Christin Løth; Ferrero, Laura

    2016-01-01

    indicate that broad genetic investigation of SUD victims increases the diagnostic outcome, and the investigation should comprise genes involved in both cardiomyopathies and cardiac channelopathies.European Journal of Human Genetics advance online publication, 21 September 2016; doi:10.1038/ejhg.2016.118....

  6. Bioinformatic analysis of the nucleotide binding site-encoding disease-resistance genes in foxtail millet (Setaria italica (L.) Beauv.).

    Science.gov (United States)

    Zhu, Y B; Xie, X Q; Li, Z Y; Bai, H; Dong, L; Dong, Z P; Dong, J G

    2014-08-28

    The nucleotide-binding site (NBS) disease-resistance genes are the largest category of plant disease-resistance gene analogs. The complete set of disease-resistant candidate genes, which encode the NBS sequence, was filtered in the genomes of two varieties of foxtail millet (Yugu1 and 'Zhang gu'). This study investigated a number of characteristics of the putative NBS genes, such as structural diversity and phylogenetic relationships. A total of 269 and 281 NBS-coding sequences were identified in Yugu1 and 'Zhang gu', respectively. When the two databases were compared, 72 genes were found to be identical and 164 genes showed more than 90% similarity. Physical positioning and gene family analysis of the NBS disease-resistance genes in the genome revealed that the number of genes on each chromosome was similar in both varieties. The eighth chromosome contained the largest number of genes and the ninth chromosome contained the lowest number of genes. Exactly 34 gene clusters containing the 161 genes were found in the Yugu1 genome, with each cluster containing 4.7 genes on average. In comparison, the 'Zhang gu' genome possessed 28 gene clusters, which had 151 genes, with an average of 5.4 genes in each cluster. The largest gene cluster, located on the eighth chromosome, contained 12 genes in the Yugu1 database, whereas it contained 16 genes in the 'Zhang gu' database. The classification results showed that the CC-NBS-LRR gene made up the largest part of each chromosome in the two databases. Two TIR-NBS genes were also found in the Yugu1 genome.

  7. Gene expression profiling of resting and activated vascular smooth muscle cells by serial analysis of gene expression and clustering analysis

    NARCIS (Netherlands)

    Beauchamp, Nicholas J.; van Achterberg, Tanja A. E.; Engelse, Marten A.; Pannekoek, Hans; de Vries, Carlie J. M.

    2003-01-01

    Migration and proliferation of vascular smooth muscle cells (SMCs) are key events in atherosclerosis. However, little is known about alterations in gene expression upon transition of the quiescent, contractile SMC to the proliferative SMC. We performed serial analysis of gene expression (SAGE) of

  8. Microarray gene expression profiling and analysis in renal cell carcinoma

    Directory of Open Access Journals (Sweden)

    Sadhukhan Provash

    2004-06-01

    Full Text Available Abstract Background Renal cell carcinoma (RCC is the most common cancer in adult kidney. The accuracy of current diagnosis and prognosis of the disease and the effectiveness of the treatment for the disease are limited by the poor understanding of the disease at the molecular level. To better understand the genetics and biology of RCC, we profiled the expression of 7,129 genes in both clear cell RCC tissue and cell lines using oligonucleotide arrays. Methods Total RNAs isolated from renal cell tumors, adjacent normal tissue and metastatic RCC cell lines were hybridized to affymatrix HuFL oligonucleotide arrays. Genes were categorized into different functional groups based on the description of the Gene Ontology Consortium and analyzed based on the gene expression levels. Gene expression profiles of the tissue and cell line samples were visualized and classified by singular value decomposition. Reverse transcription polymerase chain reaction was performed to confirm the expression alterations of selected genes in RCC. Results Selected genes were annotated based on biological processes and clustered into functional groups. The expression levels of genes in each group were also analyzed. Seventy-four commonly differentially expressed genes with more than five-fold changes in RCC tissues were identified. The expression alterations of selected genes from these seventy-four genes were further verified using reverse transcription polymerase chain reaction (RT-PCR. Detailed comparison of gene expression patterns in RCC tissue and RCC cell lines shows significant differences between the two types of samples, but many important expression patterns were preserved. Conclusions This is one of the initial studies that examine the functional ontology of a large number of genes in RCC. Extensive annotation, clustering and analysis of a large number of genes based on the gene functional ontology revealed many interesting gene expression patterns in RCC. Most

  9. Gene targeting associated with the radiation sensitivity in squamous cell carcinoma by using microarray analysis

    International Nuclear Information System (INIS)

    Nimura, Yoshinori; Kumagai, Ken; Kouzu, Yoshinao; Higo, Morihiro; Kato, Yoshikuni; Seki, Naohiko; Yamada, Shigeru

    2005-01-01

    In order to identify a set of genes related to radiation sensitivity of squamous cell carcinoma (SCC) and establish a predictive method, we compared expression profiles of radio-sensitive/radio-resistant SCC cell lines, using the in-house cDNA microarray consisting of 2,201 human genes derived from full-length enriched SCC cDNA libraries and the Human oligo chip 30 K (Hitachi Software Engineering). Surviving fractions (SF) after irradiation of heavy iron were calculated by colony formation assay. Three pairs (TE2-TE13, YES5-YES6, and HSC3-HSC2), sensitive (SF1 0.6), were selected for the microarray analysis. The results of cDNA microarray analysis showed that 20 genes in resistant cell lines and 5 genes in sensitive cell lines were up regulated more than 1.5-fold compared with sensitive and resistant cell lines respectively. Fourteen out of 25 genes were confirmed the gene expression profiles by real-time polymerase chain reaction (PCR). Twenty-seven genes identified by Human oligo chip 30 K are candidate for the markers to distinguish radio-sensitive from radio-resistant. These results suggest that the isolated 27 genes are the candidates that might be used as specific molecular markers to predict radiation sensitivity. (author)

  10. Comparative genomics identification of a novel set of temporally regulated hedgehog target genes in the retina.

    Science.gov (United States)

    McNeill, Brian; Perez-Iratxeta, Carol; Mazerolle, Chantal; Furimsky, Marosh; Mishina, Yuji; Andrade-Navarro, Miguel A; Wallace, Valerie A

    2012-03-01

    The hedgehog (Hh) signaling pathway is involved in numerous developmental and adult processes with many links to cancer. In vertebrates, the activity of the Hh pathway is mediated primarily through three Gli transcription factors (Gli1, 2 and 3) that can serve as transcriptional activators or repressors. The identification of Gli target genes is essential for the understanding of the Hh-mediated processes. We used a comparative genomics approach using the mouse and human genomes to identify 390 genes that contained conserved Gli binding sites. RT-qPCR validation of 46 target genes in E14.5 and P0.5 retinal explants revealed that Hh pathway activation resulted in the modulation of 30 of these targets, 25 of which demonstrated a temporal regulation. Further validation revealed that the expression of Bok, FoxA1, Sox8 and Wnt7a was dependent upon Sonic Hh (Shh) signaling in the retina and their regulation is under positive and negative controls by Gli2 and Gli3, respectively. We also show using chromatin immunoprecipitation that Gli2 binds to the Sox8 promoter, suggesting that Sox8 is an Hh-dependent direct target of Gli2. Finally, we demonstrate that the Hh pathway also modulates the expression of Sox9 and Sox10, which together with Sox8 make up the SoxE group. Previously, it has been shown that Hh and SoxE group genes promote Müller glial cell development in the retina. Our data are consistent with the possibility for a role of SoxE group genes downstream of Hh signaling on Müller cell development. Crown Copyright © 2012. Published by Elsevier Inc. All rights reserved.

  11. The imprinted brain: how genes set the balance between autism and psychosis.

    Science.gov (United States)

    Badcock, Christopher

    2011-06-01

    The imprinted brain theory proposes that autism spectrum disorder (ASD) represents a paternal bias in the expression of imprinted genes. This is reflected in a preference for mechanistic cognition and in the corresponding mentalistic deficits symptomatic of ASD. Psychotic spectrum disorder (PSD) would correspondingly result from an imbalance in favor of maternal and/or X-chromosome gene expression. If differences in gene expression were reflected locally in the human brain as mouse models and other evidence suggests they are, ASD would represent not so much an 'extreme male brain' as an extreme paternal one, with PSD correspondingly representing an extreme maternal brain. To the extent that copy number variation resembles imprinting and aneuploidy in nullifying or multiplying the expression of particular genes, it has been found to conform to the diametric model of mental illness peculiar to the imprinted brain theory. The fact that nongenetic factors such as nutrition in pregnancy can mimic and/or interact with imprinted gene expression suggests that the theory might even be able to explain the notable effect of maternal starvation on the risk of PSD - not to mention the 'autism epidemic' of modern affluent societies. Finally, the theory suggests that normality represents balanced cognition, and that genius is an extraordinary extension of cognitive configuration in both mentalistic and mechanistic directions. Were it to be proven correct, the imprinted brain theory would represent one of the biggest single advances in our understanding of the mind and of mental illness that has ever taken place, and would revolutionize psychiatric diagnosis, prevention and treatment - not to mention our understanding of epigenomics.

  12. Detecting Corporate Social Media Crises on Facebook Using Social Set Analysis

    DEFF Research Database (Denmark)

    Mukkamala, Raghava Rao; Iskou Sørensen, Jannie; Hussain, Abid

    2015-01-01

    social media crises using social set analysis-an approach to computational social based on associational sociology and set theory. Based on a conceptual and formal model of social data, we conduct social set analysis of the facebook wall data of four diferent Danish companies. Findings show...

  13. In Silico Analysis of FMR1 Gene Missense SNPs.

    Science.gov (United States)

    Tekcan, Akin

    2016-06-01

    The FMR1 gene, a member of the fragile X-related gene family, is responsible for fragile X syndrome (FXS). Missense single-nucleotide polymorphisms (SNPs) are responsible for many complex diseases. The effect of FMR1 gene missense SNPs is unknown. The aim of this study, using in silico techniques, was to analyze all known missense mutations that can affect the functionality of the FMR1 gene, leading to mental retardation (MR) and FXS. Data on the human FMR1 gene were collected from the Ensembl database (release 81), National Centre for Biological Information dbSNP Short Genetic Variations database, 1000 Genomes Browser, and NHLBI Exome Sequencing Project Exome Variant Server. In silico analysis was then performed. One hundred-twenty different missense SNPs of the FMR1 gene were determined. Of these, 11.66 % of the FMR1 gene missense SNPs were in highly conserved domains, and 83.33 % were in domains with high variety. The results of the in silico prediction analysis showed that 31.66 % of the FMR1 gene SNPs were disease related and that 50 % of SNPs had a pathogenic effect. The results of the structural and functional analysis revealed that although the R138Q mutation did not seem to have a damaging effect on the protein, the G266E and I304N SNPs appeared to disturb the interaction between the domains and affect the function of the protein. This is the first study to analyze all missense SNPs of the FMR1 gene. The results indicate the applicability of a bioinformatics approach to FXS and other FMR1-related diseases. I think that the analysis of FMR1 gene missense SNPs using bioinformatics methods would help diagnosis of FXS and other FMR1-related diseases.

  14. Bone Metastasis in Advanced Breast Cancer: Analysis of Gene Expression Microarray.

    Science.gov (United States)

    Cosphiadi, Irawan; Atmakusumah, Tubagus D; Siregar, Nurjati C; Muthalib, Abdul; Harahap, Alida; Mansyur, Muchtarruddin

    2018-03-08

    Approximately 30% to 40% of breast cancer recurrences involve bone metastasis (BM). Certain genes have been linked to BM; however, none have been able to predict bone involvement. In this study, we analyzed gene expression profiles in advanced breast cancer patients to elucidate genes that can be used to predict BM. A total of 92 advanced breast cancer patients, including 46 patients with BM and 46 patients without BM, were identified for this study. Immunohistochemistry and gene expression analysis was performed on 81 formalin-fixed paraffin-embedded samples. Data were collected through medical records, and gene expression of 200 selected genes compiled from 6 previous studies was performed using NanoString nCounter. Genetic expression profiles showed that 22 genes were significantly differentially expressed between breast cancer patients with metastasis in bone and other organs (BM+) and non-BM, whereas subjects with only BM showed 17 significantly differentially expressed genes. The following genes were associated with an increasing incidence of BM in the BM+ group: estrogen receptor 1 (ESR1), GATA binding protein 3 (GATA3), and melanophilin with an area under the curve (AUC) of 0.804. In the BM group, the following genes were associated with an increasing incidence of BM: ESR1, progesterone receptor, B-cell lymphoma 2, Rab escort protein, N-acetyltransferase 1, GATA3, annexin A9, and chromosome 9 open reading frame 116. ESR1 and GATA3 showed an increased strength of association with an AUC of 0.928. A combination of the identified 3 genes in BM+ and 8 genes in BM showed better prediction than did each individual gene, and this combination can be used as a training set. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

  15. Evaluation of Housekeeping Genes for Quantitative Real-Time PCR Analysis of Bradysia odoriphaga (Diptera: Sciaridae

    Directory of Open Access Journals (Sweden)

    Caihua Shi

    2016-07-01

    Full Text Available The soil insect Bradysia odoriphaga (Diptera: Sciaridae causes substantial damage to Chinese chive. Suitable reference genes in B. odoriphaga (Bradysia odoriphaga have yet to be identified for normalizing target gene expression among samples by quantitative real-time PCR (qRT-PCR. This study was focused on identifying the expression stability of 12 candidate housekeeping genes in B. odoriphaga under various experiment conditions. The final stability ranking of 12 housekeeping genes was obtained with RefFinder, and the most suitable number of reference genes was analyzed by GeNorm. The results revealed that the most appropriate sets of internal controls were RPS15, RPL18, and RPS18 across developmental phases; RPS15, RPL28, and GAPDH across temperatures; RPS15 and RPL18 across pesticide treatments; RSP5, RPS18, and SDHA across photoperiods; ACTb, RPS18, and RPS15 across diets; RPS13 and RPL28 across populations; and RPS15, ACTb, and RPS18 across all samples. The use of the most suitable reference genes versus an arbitrarily selected reference gene resulted in significant differences in the analysis of a target gene expression. HSP23 in B. odoriphaga was found to be up-regulated under low temperatures. These results will contribute to the standardization of qRT-PCR and will also be valuable for further research on gene function in B. odoriphaga.

  16. Gene expression patterns during the larval development of European sea bass (dicentrarchus labrax) by microarray analysis.

    Science.gov (United States)

    Darias, M J; Zambonino-Infante, J L; Hugot, K; Cahu, C L; Mazurais, D

    2008-01-01

    During the larval period, marine teleosts undergo very fast growth and dramatic changes in morphology, metabolism, and behavior to accomplish their metamorphosis into juvenile fish. Regulation of gene expression is widely thought to be a key mechanism underlying the management of the biological processes required for harmonious development over this phase of life. To provide an overall analysis of gene expression in the whole body during sea bass larval development, we monitored the expression of 6,626 distinct genes at 10 different points in time between 7 and 43 days post-hatching (dph) by using heterologous hybridization of a rainbow trout cDNA microarray. The differentially expressed genes (n = 485) could be grouped into two categories: genes that were generally up-expressed early, between 7 and 23 dph, and genes up-expressed between 25 and 43 dph. Interestingly, among the genes regulated during the larval period, those related to organogenesis, energy pathways, biosynthesis, and digestion were over-represented compared with total set of analyzed genes. We discuss the quantitative regulation of whole-body contents of these specific transcripts with regard to the ontogenesis and maturation of essential functions that take place over larval development. Our study is the first utilization of a transcriptomic approach in sea bass and reveals dynamic changes in gene expression patterns in relation to marine finfish larval development.

  17. The set of prime numbers: Multiscale analysis and numeric accelerators

    International Nuclear Information System (INIS)

    Iovane, Gerardo

    2009-01-01

    In this work, we show that the prime numbers follow a multiscale distribution. Indeed they can be classified thanks to tree structures, which are expressed in terms of two maximal subsets of N and using multilayer selection rules, acting on these sets of prime candidates. Consequently, the prime numbers follow a specific deterministic rules. Indeed, a numeric accelerator for generating primes can be realized in terms of the above mentioned specific rules. From the comparison with the Fibonacci numbers a beautiful harmony comes in terms of the Golden Mean which is relevant to high energy physics and E-Infinity theory too.

  18. Combining Shapley value and statistics to the analysis of gene expression data in children exposed to air pollution

    Directory of Open Access Journals (Sweden)

    Kleinjans Jos

    2008-09-01

    Full Text Available Abstract Background In gene expression analysis, statistical tests for differential gene expression provide lists of candidate genes having, individually, a sufficiently low p-value. However, the interpretation of each single p-value within complex systems involving several interacting genes is problematic. In parallel, in the last sixty years, game theory has been applied to political and social problems to assess the power of interacting agents in forcing a decision and, more recently, to represent the relevance of genes in response to certain conditions. Results In this paper we introduce a Bootstrap procedure to test the null hypothesis that each gene has the same relevance between two conditions, where the relevance is represented by the Shapley value of a particular coalitional game defined on a microarray data-set. This method, which is called Comparative Analysis of Shapley value (shortly, CASh, is applied to data concerning the gene expression in children differentially exposed to air pollution. The results provided by CASh are compared with the results from a parametric statistical test for testing differential gene expression. Both lists of genes provided by CASh and t-test are informative enough to discriminate exposed subjects on the basis of their gene expression profiles. While many genes are selected in common by CASh and the parametric test, it turns out that the biological interpretation of the differences between these two selections is more interesting, suggesting a different interpretation of the main biological pathways in gene expression regulation for exposed individuals. A simulation study suggests that CASh offers more power than t-test for the detection of differential gene expression variability. Conclusion CASh is successfully applied to gene expression analysis of a data-set where the joint expression behavior of genes may be critical to characterize the expression response to air pollution. We demonstrate a

  19. Combining Shapley value and statistics to the analysis of gene expression data in children exposed to air pollution.

    Science.gov (United States)

    Moretti, Stefano; van Leeuwen, Danitsja; Gmuender, Hans; Bonassi, Stefano; van Delft, Joost; Kleinjans, Jos; Patrone, Fioravante; Merlo, Domenico Franco

    2008-09-02

    In gene expression analysis, statistical tests for differential gene expression provide lists of candidate genes having, individually, a sufficiently low p-value. However, the interpretation of each single p-value within complex systems involving several interacting genes is problematic. In parallel, in the last sixty years, game theory has been applied to political and social problems to assess the power of interacting agents in forcing a decision and, more recently, to represent the relevance of genes in response to certain conditions. In this paper we introduce a Bootstrap procedure to test the null hypothesis that each gene has the same relevance between two conditions, where the relevance is represented by the Shapley value of a particular coalitional game defined on a microarray data-set. This method, which is called Comparative Analysis of Shapley value (shortly, CASh), is applied to data concerning the gene expression in children differentially exposed to air pollution. The results provided by CASh are compared with the results from a parametric statistical test for testing differential gene expression. Both lists of genes provided by CASh and t-test are informative enough to discriminate exposed subjects on the basis of their gene expression profiles. While many genes are selected in common by CASh and the parametric test, it turns out that the biological interpretation of the differences between these two selections is more interesting, suggesting a different interpretation of the main biological pathways in gene expression regulation for exposed individuals. A simulation study suggests that CASh offers more power than t-test for the detection of differential gene expression variability. CASh is successfully applied to gene expression analysis of a data-set where the joint expression behavior of genes may be critical to characterize the expression response to air pollution. We demonstrate a synergistic effect between coalitional games and statistics that

  20. Genome-wide analysis of immune system genes by EST profiling

    Science.gov (United States)

    Giallourakis, Cosmas; Benita, Yair; Molinie, Benoit; Cao, Zhifang; Despo, Orion; Pratt, Henry E.; Zukerberg, Lawrence R.; Daly, Mark J.; Rioux, John D.; Xavier, Ramnik J.

    2013-01-01

    Profiling studies of mRNA and miRNA, particularly microarray-based studies, have been extensively used to create compendia of genes that are preferentially expressed in the immune system. In some instances, functional studies have been subsequently pursued. Recent efforts such as ENCODE have demonstrated the benefit of coupling RNA-Seq analysis with information from expressed sequence tags (ESTs) for transcriptomic analysis. However, the full characterization and identification of transcripts that function as modulators of human immune responses remains incomplete. In this study, we demonstrate that an integrated analysis of human ESTs provides a robust platform to identify the immune transcriptome. Beyond recovering a reference set of immune-enriched genes and providing large-scale cross-validation of previous microarray studies, we discovered hundreds of novel genes preferentially expressed in the immune system, including non-coding RNAs. As a result, we have established the Immunogene database, representing an integrated EST “road map” of gene expression in human immune cells, which can be used to further investigate the function of coding and non-coding genes in the immune system. Using this approach, we have uncovered a unique metabolic gene signature of human macrophages and identified PRDM15 as a novel overexpressed gene in human lymphomas. Thus we demonstrate the utility of EST profiling as a basis for further deconstruction of physiologic and pathologic immune processes. PMID:23616578

  1. Comparative genome analysis of entomopathogenic fungi reveals a complex set of secreted proteins.

    Science.gov (United States)

    Staats, Charley Christian; Junges, Angela; Guedes, Rafael Lucas Muniz; Thompson, Claudia Elizabeth; de Morais, Guilherme Loss; Boldo, Juliano Tomazzoni; de Almeida, Luiz Gonzaga Paula; Andreis, Fábio Carrer; Gerber, Alexandra Lehmkuhl; Sbaraini, Nicolau; da Paixão, Rana Louise de Andrade; Broetto, Leonardo; Landell, Melissa; Santi, Lucélia; Beys-da-Silva, Walter Orlando; Silveira, Carolina Pereira; Serrano, Thaiane Rispoli; de Oliveira, Eder Silva; Kmetzsch, Lívia; Vainstein, Marilene Henning; de Vasconcelos, Ana Tereza Ribeiro; Schrank, Augusto

    2014-09-29

    Metarhizium anisopliae is an entomopathogenic fungus used in the biological control of some agricultural insect pests, and efforts are underway to use this fungus in the control of insect-borne human diseases. A large repertoire of proteins must be secreted by M. anisopliae to cope with the various available nutrients as this fungus switches through different lifestyles, i.e., from a saprophytic, to an infectious, to a plant endophytic stage. To further evaluate the predicted secretome of M. anisopliae, we employed genomic and transcriptomic analyses, coupled with phylogenomic analysis, focusing on the identification and characterization of secreted proteins. We determined the M. anisopliae E6 genome sequence and compared this sequence to other entomopathogenic fungi genomes. A robust pipeline was generated to evaluate the predicted secretomes of M. anisopliae and 15 other filamentous fungi, leading to the identification of a core of secreted proteins. Transcriptomic analysis using the tick Rhipicephalus microplus cuticle as an infection model during two periods of infection (48 and 144 h) allowed the identification of several differentially expressed genes. This analysis concluded that a large proportion of the predicted secretome coding genes contained altered transcript levels in the conditions analyzed in this study. In addition, some specific secreted proteins from Metarhizium have an evolutionary history similar to orthologs found in Beauveria/Cordyceps. This similarity suggests that a set of secreted proteins has evolved to participate in entomopathogenicity. The data presented represents an important step to the characterization of the role of secreted proteins in the virulence and pathogenicity of M. anisopliae.

  2. Comparative genomics of Mycoplasma: analysis of conserved essential genes and diversity of the pan-genome.

    Directory of Open Access Journals (Sweden)

    Wei Liu

    Full Text Available Mycoplasma, the smallest self-replicating organism with a minimal metabolism and little genomic redundancy, is expected to be a close approximation to the minimal set of genes needed to sustain bacterial life. This study employs comparative evolutionary analysis of twenty Mycoplasma genomes to gain an improved understanding of essential genes. By analyzing the core genome of mycoplasmas, we finally revealed the conserved essential genes set for mycoplasma survival. Further analysis showed that the core genome set has many characteristics in common with experimentally identified essential genes. Several key genes, which are related to DNA replication and repair and can be disrupted in transposon mutagenesis studies, may be critical for bacteria survival especially over long period natural selection. Phylogenomic reconstructions based on 3,355 homologous groups allowed robust estimation of phylogenetic relatedness among mycoplasma strains. To obtain deeper insight into the relative roles of molecular evolution in pathogen adaptation to their hosts, we also analyzed the positive selection pressures on particular sites and lineages. There appears to be an approximate correlation between the divergence of species and the level of positive selection detected in corresponding lineages.

  3. Supervised learning with restricted training sets: a generating functional analysis

    Energy Technology Data Exchange (ETDEWEB)

    Heimel, J.A.F.; Coolen, A.C.C. [Department of Mathematics, King' s College London, Strand, London (United Kingdom)

    2001-10-26

    We study the dynamics of supervised on-line learning of realizable tasks in feed-forward neural networks. We focus on the regime where the number of examples used for training is proportional to the number of input channels N. Using generating functional techniques from spin glass theory, we are able to average over the composition of the training set and transform the problem for N{yields}{infinity} to an effective single pattern system described completely by the student autocovariance, the student-teacher overlap and the student response function with exact closed equations. Our method applies to arbitrary learning rules, i.e., not necessarily of a gradient-descent type. The resulting exact macroscopic dynamical equations can be integrated without finite-size effects up to any degree of accuracy, but their main value is in providing an exact and simple starting point for analytical approximation schemes. Finally, we show how, in the region of absent anomalous response and using the hypothesis that (as in detailed balance systems) the short-time part of the various operators can be transformed away, one can describe the stationary state of the network successfully by a set of coupled equations involving only four scalar order parameters. (author)

  4. An efficient parallel stochastic simulation method for analysis of nonviral gene delivery systems

    KAUST Repository

    Kuwahara, Hiroyuki

    2011-01-01

    Gene therapy has a great potential to become an effective treatment for a wide variety of diseases. One of the main challenges to make gene therapy practical in clinical settings is the development of efficient and safe mechanisms to deliver foreign DNA molecules into the nucleus of target cells. Several computational and experimental studies have shown that the design process of synthetic gene transfer vectors can be greatly enhanced by computational modeling and simulation. This paper proposes a novel, effective parallelization of the stochastic simulation algorithm (SSA) for pharmacokinetic models that characterize the rate-limiting, multi-step processes of intracellular gene delivery. While efficient parallelizations of the SSA are still an open problem in a general setting, the proposed parallel simulation method is able to substantially accelerate the next reaction selection scheme and the reaction update scheme in the SSA by exploiting and decomposing the structures of stochastic gene delivery models. This, thus, makes computationally intensive analysis such as parameter optimizations and gene dosage control for specific cell types, gene vectors, and transgene expression stability substantially more practical than that could otherwise be with the standard SSA. Here, we translated the nonviral gene delivery model based on mass-action kinetics by Varga et al. [Molecular Therapy, 4(5), 2001] into a more realistic model that captures intracellular fluctuations based on stochastic chemical kinetics, and as a case study we applied our parallel simulation to this stochastic model. Our results show that our simulation method is able to increase the efficiency of statistical analysis by at least 50% in various settings. © 2011 ACM.

  5. Gene profile analysis of osteoblast genes differentially regulated by histone deacetylase inhibitors

    Directory of Open Access Journals (Sweden)

    Lamblin Anne-Francoise

    2007-10-01

    Full Text Available Abstract Background Osteoblast differentiation requires the coordinated stepwise expression of multiple genes. Histone deacetylase inhibitors (HDIs accelerate the osteoblast differentiation process by blocking the activity of histone deacetylases (HDACs, which alter gene expression by modifying chromatin structure. We previously demonstrated that HDIs and HDAC3 shRNAs accelerate matrix mineralization and the expression of osteoblast maturation genes (e.g. alkaline phosphatase, osteocalcin. Identifying other genes that are differentially regulated by HDIs might identify new pathways that contribute to osteoblast differentiation. Results To identify other osteoblast genes that are altered early by HDIs, we incubated MC3T3-E1 preosteoblasts with HDIs (trichostatin A, MS-275, or valproic acid for 18 hours in osteogenic conditions. The promotion of osteoblast differentiation by HDIs in this experiment was confirmed by osteogenic assays. Gene expression profiles relative to vehicle-treated cells were assessed by microarray analysis with Affymetrix GeneChip 430 2.0 arrays. The regulation of several genes by HDIs in MC3T3-E1 cells and primary osteoblasts was verified by quantitative real-time PCR. Nine genes were differentially regulated by at least two-fold after exposure to each of the three HDIs and six were verified by PCR in osteoblasts. Four of the verified genes (solute carrier family 9 isoform 3 regulator 1 (Slc9a3r1, sorbitol dehydrogenase 1, a kinase anchor protein, and glutathione S-transferase alpha 4 were induced. Two genes (proteasome subunit, beta type 10 and adaptor-related protein complex AP-4 sigma 1 were suppressed. We also identified eight growth factors and growth factor receptor genes that are significantly altered by each of the HDIs, including Frizzled related proteins 1 and 4, which modulate the Wnt signaling pathway. Conclusion This study identifies osteoblast genes that are regulated early by HDIs and indicates pathways that

  6. Reliability analysis of a sensitive and independent stabilometry parameter set.

    Science.gov (United States)

    Nagymáté, Gergely; Orlovits, Zsanett; Kiss, Rita M

    2018-01-01

    Recent studies have suggested reduced independent and sensitive parameter sets for stabilometry measurements based on correlation and variance analyses. However, the reliability of these recommended parameter sets has not been studied in the literature or not in every stance type used in stabilometry assessments, for example, single leg stances. The goal of this study is to evaluate the test-retest reliability of different time-based and frequency-based parameters that are calculated from the center of pressure (CoP) during bipedal and single leg stance for 30- and 60-second measurement intervals. Thirty healthy subjects performed repeated standing trials in a bipedal stance with eyes open and eyes closed conditions and in a single leg stance with eyes open for 60 seconds. A force distribution measuring plate was used to record the CoP. The reliability of the CoP parameters was characterized by using the intraclass correlation coefficient (ICC), standard error of measurement (SEM), minimal detectable change (MDC), coefficient of variation (CV) and CV compliance rate (CVCR). Based on the ICC, SEM and MDC results, many parameters yielded fair to good reliability values, while the CoP path length yielded the highest reliability (smallest ICC > 0.67 (0.54-0.79), largest SEM% = 19.2%). Usually, frequency type parameters and extreme value parameters yielded poor reliability values. There were differences in the reliability of the maximum CoP velocity (better with 30 seconds) and mean power frequency (better with 60 seconds) parameters between the different sampling intervals.

  7. Reliability analysis of a sensitive and independent stabilometry parameter set

    Science.gov (United States)

    Nagymáté, Gergely; Orlovits, Zsanett

    2018-01-01

    Recent studies have suggested reduced independent and sensitive parameter sets for stabilometry measurements based on correlation and variance analyses. However, the reliability of these recommended parameter sets has not been studied in the literature or not in every stance type used in stabilometry assessments, for example, single leg stances. The goal of this study is to evaluate the test-retest reliability of different time-based and frequency-based parameters that are calculated from the center of pressure (CoP) during bipedal and single leg stance for 30- and 60-second measurement intervals. Thirty healthy subjects performed repeated standing trials in a bipedal stance with eyes open and eyes closed conditions and in a single leg stance with eyes open for 60 seconds. A force distribution measuring plate was used to record the CoP. The reliability of the CoP parameters was characterized by using the intraclass correlation coefficient (ICC), standard error of measurement (SEM), minimal detectable change (MDC), coefficient of variation (CV) and CV compliance rate (CVCR). Based on the ICC, SEM and MDC results, many parameters yielded fair to good reliability values, while the CoP path length yielded the highest reliability (smallest ICC > 0.67 (0.54–0.79), largest SEM% = 19.2%). Usually, frequency type parameters and extreme value parameters yielded poor reliability values. There were differences in the reliability of the maximum CoP velocity (better with 30 seconds) and mean power frequency (better with 60 seconds) parameters between the different sampling intervals. PMID:29664938

  8. Gene Ontology-Based Analysis of Zebrafish Omics Data Using the Web Tool Comparative Gene Ontology.

    Science.gov (United States)

    Ebrahimie, Esmaeil; Fruzangohar, Mario; Moussavi Nik, Seyyed Hani; Newman, Morgan

    2017-10-01

    Gene Ontology (GO) analysis is a powerful tool in systems biology, which uses a defined nomenclature to annotate genes/proteins within three categories: "Molecular Function," "Biological Process," and "Cellular Component." GO analysis can assist in revealing functional mechanisms underlying observed patterns in transcriptomic, genomic, and proteomic data. The already extensive and increasing use of zebrafish for modeling genetic and other diseases highlights the need to develop a GO analytical tool for this organism. The web tool Comparative GO was originally developed for GO analysis of bacterial data in 2013 ( www.comparativego.com ). We have now upgraded and elaborated this web tool for analysis of zebrafish genetic data using GOs and annotations from the Gene Ontology Consortium.

  9. Cyber-Bullying in School Settings: A Research Citation Analysis

    Science.gov (United States)

    Piotrowski, Chris

    2011-01-01

    Research on the topic of cyber-bullying has proliferated over the past decade, particularly on its impact on school-aged children. Thus, it would be of interest to examine the scope and extent of research interest in the topic in scholarly publications. This paper reports on a reference citation analysis of the database PsycINFO, using…

  10. System Biology Approach: Gene Network Analysis for Muscular Dystrophy.

    Science.gov (United States)

    Censi, Federica; Calcagnini, Giovanni; Mattei, Eugenio; Giuliani, Alessandro

    2018-01-01

    Phenotypic changes at different organization levels from cell to entire organism are associated to changes in the pattern of gene expression. These changes involve the entire genome expression pattern and heavily rely upon correlation patterns among genes. The classical approach used to analyze gene expression data builds upon the application of supervised statistical techniques to detect genes differentially expressed among two or more phenotypes (e.g., normal vs. disease). The use of an a posteriori, unsupervised approach based on principal component analysis (PCA) and the subsequent construction of gene correlation networks can shed a light on unexpected behaviour of gene regulation system while maintaining a more naturalistic view on the studied system.In this chapter we applied an unsupervised method to discriminate DMD patient and controls. The genes having the highest absolute scores in the discrimination between the groups were then analyzed in terms of gene expression networks, on the basis of their mutual correlation in the two groups. The correlation network structures suggest two different modes of gene regulation in the two groups, reminiscent of important aspects of DMD pathogenesis.

  11. Network graph analysis of gene-gene interactions in genome-wide association study data.

    Science.gov (United States)

    Lee, Sungyoung; Kwon, Min-Seok; Park, Taesung

    2012-12-01

    Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs). For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR) is one of the powerful and efficient methods for detecting high-order gene-gene (GxG) interactions. However, the biological interpretation of GxG interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified GxG interactions. The proposed network graph analysis consists of three steps. The first step is for performing GxG interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified GxG interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE) data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform GxG interaction analysis of body mass index (BMI). Our network graph analysis successfully showed that many identified GxG interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of GxG interactions.

  12. Selection of Housekeeping Genes for Transgene Expression Analysis in Eucommia ulmoides Oliver Using Real-Time RT-PCR

    Directory of Open Access Journals (Sweden)

    Ren Chen

    2010-01-01

    Full Text Available In order to select appropriate housekeeping genes for accurate calibration of experimental variations in real-time (RT- PCR results in transgene expression analysis, particularly with respect to the influence of transgene on stability of endogenous housekeeping gene expression in transgenic plants, we outline a reliable strategy to identify the optimal housekeeping genes from a set of candidates by combining statistical analyses of their (RT- PCR amplification efficiency, gene expression stability, and transgene influences. We used the strategy to select two genes, ACTα and EF1α, from 10 candidate housekeeping genes, as the optimal housekeeping genes to evaluate transgenic Eucommia ulmoides Oliver root lines overexpressing IPPI or FPPS1 genes, which are involved in isoprenoid biosynthesis.

  13. Analysis of pan-genome to identify the core genes and essential genes of Brucella spp.

    Science.gov (United States)

    Yang, Xiaowen; Li, Yajie; Zang, Juan; Li, Yexia; Bie, Pengfei; Lu, Yanli; Wu, Qingmin

    2016-04-01

    Brucella spp. are facultative intracellular pathogens, that cause a contagious zoonotic disease, that can result in such outcomes as abortion or sterility in susceptible animal hosts and grave, debilitating illness in humans. For deciphering the survival mechanism of Brucella spp. in vivo, 42 Brucella complete genomes from NCBI were analyzed for the pan-genome and core genome by identification of their composition and function of Brucella genomes. The results showed that the total 132,143 protein-coding genes in these genomes were divided into 5369 clusters. Among these, 1710 clusters were associated with the core genome, 1182 clusters with strain-specific genes and 2477 clusters with dispensable genomes. COG analysis indicated that 44 % of the core genes were devoted to metabolism, which were mainly responsible for energy production and conversion (COG category C), and amino acid transport and metabolism (COG category E). Meanwhile, approximately 35 % of the core genes were in positive selection. In addition, 1252 potential essential genes were predicted in the core genome by comparison with a prokaryote database of essential genes. The results suggested that the core genes in Brucella genomes are relatively conservation, and the energy and amino acid metabolism play a more important role in the process of growth and reproduction in Brucella spp. This study might help us to better understand the mechanisms of Brucella persistent infection and provide some clues for further exploring the gene modules of the intracellular survival in Brucella spp.

  14. Analysis of gene and protein name synonyms in Entrez Gene and UniProtKB resources

    KAUST Repository

    Arkasosy, Basil

    2013-05-11

    Ambiguity in texts is a well-known problem: words can carry several meanings, and hence, can be read and interpreted differently. This is also true in the biological literature; names of biological concepts, such as genes and proteins, might be ambiguous, referring in some cases to more than one gene or one protein, or in others, to both genes and proteins at the same time. Public biological databases give a very useful insight about genes and proteins information, including their names. In this study, we made a thorough analysis of the nomenclatures of genes and proteins in two data sources and for six different species. We developed an automated process that parses, extracts, processes and stores information available in two major biological databases: Entrez Gene and UniProtKB. We analysed gene and protein synonyms, their types, frequencies, and the ambiguities within a species, in between data sources and cross-species. We found that at least 40% of the cross-species ambiguities are caused by names that are already ambiguous within the species. Our study shows that from the six species we analysed (Homo Sapiens, Mus Musculus, Arabidopsis Thaliana, Oryza Sativa, Bacillus Subtilis and Pseudomonas Fluorescens), rice (Oriza Sativa) has the best naming model in Entrez Gene database, with low ambiguities between data sources and cross-species.

  15. Linking Advanced Visualization and MATLAB for the Analysis of 3D Gene Expression Data

    Energy Technology Data Exchange (ETDEWEB)

    Ruebel, Oliver; Keranen, Soile V.E.; Biggin, Mark; Knowles, David W.; Weber, Gunther H.; Hagen, Hans; Hamann, Bernd; Bethel, E. Wes

    2011-03-30

    Three-dimensional gene expression PointCloud data generated by the Berkeley Drosophila Transcription Network Project (BDTNP) provides quantitative information about the spatial and temporal expression of genes in early Drosophila embryos at cellular resolution. The BDTNP team visualizes and analyzes Point-Cloud data using the software application PointCloudXplore (PCX). To maximize the impact of novel, complex data sets, such as PointClouds, the data needs to be accessible to biologists and comprehensible to developers of analysis functions. We address this challenge by linking PCX and Matlab via a dedicated interface, thereby providing biologists seamless access to advanced data analysis functions and giving bioinformatics researchers the opportunity to integrate their analysis directly into the visualization application. To demonstrate the usefulness of this approach, we computationally model parts of the expression pattern of the gene even skipped using a genetic algorithm implemented in Matlab and integrated into PCX via our Matlab interface.

  16. Risk analysis and priority setting for environmental policy

    International Nuclear Information System (INIS)

    Travis, C.C.

    1991-01-01

    There is a growing realization that the demand for funding to correct our nation's environmental problems will soon outstrip available resources. In the hazardous waste area alone, the estimated cost of remediating Superfund sites ranges from $32 billion to $80 billion. Numerous other areas of competing for these same financial resources. These include ozone depletion, global warming, the protection of endangered species and wetlands, toxic air pollution, carcinogenic pesticides, and urban smog. In response to this imbalance in the supply and demand for national funds, several political constituencies are calling for the use of risk assessment as a tool in the prioritization of research and budget needs. Comparative risk analysis offers a logical framework in which to organize information about complex environmental problems. Risk analysis allows policy analysts to make resource allocation decisions on the basis of scientific judgement rather than political expediency

  17. Genome-wide Analysis of Gene Regulation

    DEFF Research Database (Denmark)

    Chen, Yun

    to protein: through epigenetic modifications, transcription regulators or post-transcriptional controls. The following papers concern several layers of gene regulation with questions answered by different HTS approaches. Genome-wide screening of epigenetic changes by ChIP-seq allowed us to study both spatial...... and temporal alterations of histone modifications (Papers I and II). Coupling the data with machine learning approaches, we established a prediction framework to assess the most informative histone marks as well as their most influential nucleosome positions in predicting the promoter usages. (Papers I...... they regulated or if the sites had global elevated usage rates by multiple TFs. Using RNA-seq, 5’end-seq in combination with depletion of 5’exonuclease as well as nonsensemediated decay (NMD) factors, we systematically analyzed NMD substrates as well as their degradation intermediates in human cells (Paper V...

  18. Health care priority setting in Norway a multicriteria decision analysis

    Directory of Open Access Journals (Sweden)

    Defechereux Thierry

    2012-02-01

    Full Text Available Abstract Background Priority setting in population health is increasingly based on explicitly formulated values. The Patients Rights Act of the Norwegian tax-based health service guaranties all citizens health care in case of a severe illness, a proven health benefit, and proportionality between need and treatment. This study compares the values of the country's health policy makers with these three official principles. Methods In total 34 policy makers participated in a discrete choice experiment, weighting the relative value of six policy criteria. We used multi-variate logistic regression with selection as dependent valuable to derive odds ratios for each criterion. Next, we constructed a composite league table - based on the sum score for the probability of selection - to rank potential interventions in five major disease areas. Results The group considered cost effectiveness, large individual benefits and severity of disease as the most important criteria in decision making. Priority interventions are those related to cardiovascular diseases and respiratory diseases. Less attractive interventions rank those related to mental health. Conclusions Norwegian policy makers' values are in agreement with principles formulated in national health laws. Multi-criteria decision approaches may provide a tool to support explicit allocation decisions.

  19. Health care priority setting in Norway a multicriteria decision analysis.

    Science.gov (United States)

    Defechereux, Thierry; Paolucci, Francesco; Mirelman, Andrew; Youngkong, Sitaporn; Botten, Grete; Hagen, Terje P; Niessen, Louis W

    2012-02-15

    Priority setting in population health is increasingly based on explicitly formulated values. The Patients Rights Act of the Norwegian tax-based health service guaranties all citizens health care in case of a severe illness, a proven health benefit, and proportionality between need and treatment. This study compares the values of the country's health policy makers with these three official principles. In total 34 policy makers participated in a discrete choice experiment, weighting the relative value of six policy criteria. We used multi-variate logistic regression with selection as dependent valuable to derive odds ratios for each criterion. Next, we constructed a composite league table - based on the sum score for the probability of selection - to rank potential interventions in five major disease areas. The group considered cost effectiveness, large individual benefits and severity of disease as the most important criteria in decision making. Priority interventions are those related to cardiovascular diseases and respiratory diseases. Less attractive interventions rank those related to mental health. Norwegian policy makers' values are in agreement with principles formulated in national health laws. Multi-criteria decision approaches may provide a tool to support explicit allocation decisions.

  20. Selection and validation of reference genes for gene expression analysis in switchgrass (Panicum virgatum using quantitative real-time RT-PCR.

    Directory of Open Access Journals (Sweden)

    Jacinta Gimeno

    Full Text Available Switchgrass (Panicum virgatum has received a lot of attention as a forage and bioenergy crop during the past few years. Gene expression studies are in progress to improve new traits and develop new cultivars. Quantitative real time PCR (qRT-PCR has emerged as an important technique to study gene expression analysis. For accurate and reliable results, normalization of data with reference genes is essential. In this work, we evaluate the stability of expression of genes to use as reference for qRT-PCR in the grass P. virgatum. Eleven candidate reference genes, including eEF-1α, UBQ6, ACT12, TUB6, eIF-4a, GAPDH, SAMDC, TUA6, CYP5, U2AF, and FTSH4, were validated for qRT-PCR normalization in different plant tissues and under different stress conditions. The expression stability of these genes was verified by the use of two distinct algorithms, geNorm and NormFinder. Differences were observed after comparison of the ranking of the candidate reference genes identified by both programs but eEF-1α, eIF-4a, CYP5 and U2AF are ranked as the most stable genes in the samples sets under study. Both programs discard the use of SAMDC and TUA6 for normalization. Validation of the reference genes proposed by geNorm and NormFinder were performed by normalization of transcript abundance of a group of target genes in different samples. Results show similar expression patterns when the best reference genes selected by both programs were used but differences were detected in the transcript abundance of the target genes. Based on the above research, we recommend the use of different statistical algorithms to identify the best reference genes for expression data normalization. The best genes selected in this study will help to improve the quality of gene expression data in a wide variety of samples in switchgrass.

  1. Prioritization of epilepsy associated candidate genes by convergent analysis.

    Science.gov (United States)

    Jia, Peilin; Ewers, Jeffrey M; Zhao, Zhongming

    2011-02-24

    Epilepsy is a severe neurological disorder affecting a large number of individuals, yet the underlying genetic risk factors for epilepsy remain unclear. Recent studies have revealed several recurrent copy number variations (CNVs) that are more likely to be associated with epilepsy. The responsible gene(s) within these regions have yet to be definitively linked to the disorder, and the implications of their interactions are not fully understood. Identification of these genes may contribute to a better pathological understanding of epilepsy, and serve to implicate novel therapeutic targets for further research. In this study, we examined genes within heterozygous deletion regions identified in a recent large-scale study, encompassing a diverse spectrum of epileptic syndromes. By integrating additional protein-protein interaction data, we constructed subnetworks for these CNV-region genes and also those previously studied for epilepsy. We observed 20 genes common to both networks, primarily concentrated within a small molecular network populated by GABA receptor, BDNF/MAPK signaling, and estrogen receptor genes. From among the hundreds of genes in the initial networks, these were designated by convergent evidence for their likely association with epilepsy. Importantly, the identified molecular network was found to contain complex interrelationships, providing further insight into epilepsy's underlying pathology. We further performed pathway enrichment and crosstalk analysis and revealed a functional map which indicates the significant enrichment of closely related neurological, immune, and kinase regulatory pathways. The convergent framework we proposed here provides a unique and powerful approach to screening and identifying promising disease genes out of typically hundreds to thousands of genes in disease-related CNV-regions. Our network and pathway analysis provides important implications for the underlying molecular mechanisms for epilepsy. The strategy can be

  2. Prioritization of epilepsy associated candidate genes by convergent analysis.

    Directory of Open Access Journals (Sweden)

    Peilin Jia

    2011-02-01

    Full Text Available Epilepsy is a severe neurological disorder affecting a large number of individuals, yet the underlying genetic risk factors for epilepsy remain unclear. Recent studies have revealed several recurrent copy number variations (CNVs that are more likely to be associated with epilepsy. The responsible gene(s within these regions have yet to be definitively linked to the disorder, and the implications of their interactions are not fully understood. Identification of these genes may contribute to a better pathological understanding of epilepsy, and serve to implicate novel therapeutic targets for further research.In this study, we examined genes within heterozygous deletion regions identified in a recent large-scale study, encompassing a diverse spectrum of epileptic syndromes. By integrating additional protein-protein interaction data, we constructed subnetworks for these CNV-region genes and also those previously studied for epilepsy. We observed 20 genes common to both networks, primarily concentrated within a small molecular network populated by GABA receptor, BDNF/MAPK signaling, and estrogen receptor genes. From among the hundreds of genes in the initial networks, these were designated by convergent evidence for their likely association with epilepsy. Importantly, the identified molecular network was found to contain complex interrelationships, providing further insight into epilepsy's underlying pathology. We further performed pathway enrichment and crosstalk analysis and revealed a functional map which indicates the significant enrichment of closely related neurological, immune, and kinase regulatory pathways.The convergent framework we proposed here provides a unique and powerful approach to screening and identifying promising disease genes out of typically hundreds to thousands of genes in disease-related CNV-regions. Our network and pathway analysis provides important implications for the underlying molecular mechanisms for epilepsy. The

  3. UMD-USHbases: a comprehensive set of databases to record and analyse pathogenic mutations and unclassified variants in seven Usher syndrome causing genes.

    Science.gov (United States)

    Baux, David; Faugère, Valérie; Larrieu, Lise; Le Guédard-Méreuze, Sandie; Hamroun, Dalil; Béroud, Christophe; Malcolm, Sue; Claustres, Mireille; Roux, Anne-Françoise

    2008-08-01

    Using the Universal Mutation Database (UMD) software, we have constructed "UMD-USHbases", a set of relational databases of nucleotide variations for seven genes involved in Usher syndrome (MYO7A, CDH23, PCDH15, USH1C, USH1G, USH3A and USH2A). Mutations in the Usher syndrome type I causing genes are also recorded in non-syndromic hearing loss cases and mutations in USH2A in non-syndromic retinitis pigmentosa. Usher syndrome provides a particular challenge for molecular diagnostics because of the clinical and molecular heterogeneity. As many mutations are missense changes, and all the genes also contain apparently non-pathogenic polymorphisms, well-curated databases are crucial for accurate interpretation of pathogenicity. Tools are provided to assess the pathogenicity of mutations, including conservation of amino acids and analysis of splice-sites. Reference amino acid alignments are provided. Apparently non-pathogenic variants in patients with Usher syndrome, at both the nucleotide and amino acid level, are included. The UMD-USHbases currently contain more than 2,830 entries including disease causing mutations, unclassified variants or non-pathogenic polymorphisms identified in over 938 patients. In addition to data collected from 89 publications, 15 novel mutations identified in our laboratory are recorded in MYO7A (6), CDH23 (8), or PCDH15 (1) genes. Information is given on the relative involvement of the seven genes, the number and distribution of variants in each gene. UMD-USHbases give access to a software package that provides specific routines and optimized multicriteria research and sorting tools. These databases should assist clinicians and geneticists seeking information about mutations responsible for Usher syndrome.

  4. Basis set expansion for inverse problems in plasma diagnostic analysis

    Science.gov (United States)

    Jones, B.; Ruiz, C. L.

    2013-07-01

    A basis set expansion method [V. Dribinski, A. Ossadtchi, V. A. Mandelshtam, and H. Reisler, Rev. Sci. Instrum. 73, 2634 (2002)], 10.1063/1.1482156 is applied to recover physical information about plasma radiation sources from instrument data, which has been forward transformed due to the nature of the measurement technique. This method provides a general approach for inverse problems, and we discuss two specific examples relevant to diagnosing fast z pinches on the 20-25 MA Z machine [M. E. Savage, L. F. Bennett, D. E. Bliss, W. T. Clark, R. S. Coats, J. M. Elizondo, K. R. LeChien, H. C. Harjes, J. M. Lehr, J. E. Maenchen, D. H. McDaniel, M. F. Pasik, T. D. Pointon, A. C. Owen, D. B. Seidel, D. L. Smith, B. S. Stoltzfus, K. W. Struve, W. A. Stygar, L. K. Warne, J. R. Woodworth, C. W. Mendel, K. R. Prestwich, R. W. Shoup, D. L. Johnson, J. P. Corley, K. C. Hodge, T. C. Wagoner, and P. E. Wakeland, in Proceedings of the Pulsed Power Plasma Sciences Conference (IEEE, 2007), p. 979]. First, Abel inversion of time-gated, self-emission x-ray images from a wire array implosion is studied. Second, we present an approach for unfolding neutron time-of-flight measurements from a deuterium gas puff z pinch to recover information about emission time history and energy distribution. Through these examples, we discuss how noise in the measured data limits the practical resolution of the inversion, and how the method handles discontinuities in the source function and artifacts in the projected image. We add to the method a propagation of errors calculation for estimating uncertainties in the inverted solution.

  5. Basis set expansion for inverse problems in plasma diagnostic analysis

    Energy Technology Data Exchange (ETDEWEB)

    Jones, B.; Ruiz, C. L. [Sandia National Laboratories, PO Box 5800, Albuquerque, New Mexico 87185 (United States)

    2013-07-15

    A basis set expansion method [V. Dribinski, A. Ossadtchi, V. A. Mandelshtam, and H. Reisler, Rev. Sci. Instrum. 73, 2634 (2002)] is applied to recover physical information about plasma radiation sources from instrument data, which has been forward transformed due to the nature of the measurement technique. This method provides a general approach for inverse problems, and we discuss two specific examples relevant to diagnosing fast z pinches on the 20–25 MA Z machine [M. E. Savage, L. F. Bennett, D. E. Bliss, W. T. Clark, R. S. Coats, J. M. Elizondo, K. R. LeChien, H. C. Harjes, J. M. Lehr, J. E. Maenchen, D. H. McDaniel, M. F. Pasik, T. D. Pointon, A. C. Owen, D. B. Seidel, D. L. Smith, B. S. Stoltzfus, K. W. Struve, W. A. Stygar, L. K. Warne, J. R. Woodworth, C. W. Mendel, K. R. Prestwich, R. W. Shoup, D. L. Johnson, J. P. Corley, K. C. Hodge, T. C. Wagoner, and P. E. Wakeland, in Proceedings of the Pulsed Power Plasma Sciences Conference (IEEE, 2007), p. 979]. First, Abel inversion of time-gated, self-emission x-ray images from a wire array implosion is studied. Second, we present an approach for unfolding neutron time-of-flight measurements from a deuterium gas puff z pinch to recover information about emission time history and energy distribution. Through these examples, we discuss how noise in the measured data limits the practical resolution of the inversion, and how the method handles discontinuities in the source function and artifacts in the projected image. We add to the method a propagation of errors calculation for estimating uncertainties in the inverted solution.

  6. Analysis of gene expression during odontogenic differentiation of cultured human dental pulp cells

    Directory of Open Access Journals (Sweden)

    Min-Seock Seo

    2012-08-01

    Full Text Available Objectives We analyzed gene-expression profiles after 14 day odontogenic induction of human dental pulp cells (DPCs using a DNA microarray and sought candidate genes possibly associated with mineralization. Materials and Methods Induced human dental pulp cells were obtained by culturing DPCs in odontogenic induction medium (OM for 14 day. Cells exposed to normal culture medium were used as controls. Total RNA was extracted from cells and analyzed by microarray analysis and the key results were confirmed selectively by reverse-transcriptase polymerase chain reaction (RT-PCR. We also performed a gene set enrichment analysis (GSEA of the microarray data. Results Six hundred and five genes among the 47,320 probes on the BeadChip differed by a factor of more than two-fold in the induced cells. Of these, 217 genes were upregulated, and 388 were down-regulated. GSEA revealed that in the induced cells, genes implicated in Apoptosis and Signaling by wingless MMTV integration (Wnt were significantly upregulated. Conclusions Genes implicated in Apoptosis and Signaling by Wnt are highly connected to the differentiation of dental pulp cells into odontoblast.

  7. The impact of ACE gene polymorphism on the incidence and phenotype of sarcoidosis in rural and urban settings.

    Science.gov (United States)

    Kieszko, Robert; Krawczyk, Paweł; Powrózek, Tomasz; Szudy-Szczyrek, Aneta; Szczyrek, Michał; Homa, Iwona; Daniluk, Jadwiga; Milanowski, Janusz

    2016-12-01

    Sarcoidosis is a multisystem granulomatous disease of unknown etiology. Current theory on the etiology of this disease involves participation of genetic factors and unknown antigens present in the patients' environment. The aim of the study was to evaluate the prevalence of different polymorphic forms of the ACE gene in healthy individuals and sarcoidosis patients, and to estimate the risk of sarcoidosis in carriers of different ACE genotypes living in rural and urban settings. The study group included 180 patients with pulmonary sarcoidosis. Assessment of the disease was based on clinical features, laboratory and imaging examinations, as well as bronchoscopy with bronchoalveolar lavage (BAL). ACE gene polymorphism was examined in DNA isolated from peripheral blood or BAL fluid (BALF) leukocytes. Incidence of sarcoidosis was not influenced by gender, age or place of residence of the patients. There were no differences in the frequency of particular genotypes in patients with sarcoidosis and in healthy individuals. The risk of disease did not depend on the ACE gene polymorphism. There were no differences in the frequencies of the different genotypes and alleles of the ACE gene in patients with sarcoidosis divided by gender, age and place of residence or by clinical manifestation of sarcoidosis. Our results do not support the previous concept which suggested a higher incidence of sarcoidosis in individuals living in rural areas and in carriers of selected ACE genotypes. It is possible that this is related to the changing environment of rural areas, increasing urbanization and pollution.

  8. Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks.

    Science.gov (United States)

    Haibe-Kains, Benjamin; Olsen, Catharina; Djebbari, Amira; Bontempi, Gianluca; Correll, Mick; Bouton, Christopher; Quackenbush, John

    2012-01-01

    Genomics provided us with an unprecedented quantity of data on the genes that are activated or repressed in a wide range of phenotypes. We have increasingly come to recognize that defining the networks and pathways underlying these phenotypes requires both the integration of multiple data types and the development of advanced computational methods to infer relationships between the genes and to estimate the predictive power of the networks through which they interact. To address these issues we have developed Predictive Networks (PN), a flexible, open-source, web-based application and data services framework that enables the integration, navigation, visualization and analysis of gene interaction networks. The primary goal of PN is to allow biomedical researchers to evaluate experimentally derived gene lists in the context of large-scale gene interaction networks. The PN analytical pipeline involves two key steps. The first is the collection of a comprehensive set of known gene interactions derived from a variety of publicly available sources. The second is to use these 'known' interactions together with gene expression data to infer robust gene networks. The PN web application is accessible from http://predictivenetworks.org. The PN code base is freely available at https://sourceforge.net/projects/predictivenets/.

  9. Sequence comparison and phylogenetic analysis of core gene of ...

    African Journals Online (AJOL)

    Phylogenetic analysis suggests that our sequences are clustered with sequences reported from Japan. This is the first phylogenetic analysis of HCV core gene from Pakistani population. Our sequences and sequences from Japan are grouped into same cluster in the phylogenetic tree. Sequence comparison and ...

  10. Molecular responses and expression analysis of genes in a ...

    African Journals Online (AJOL)

    STORAGESEVER

    2009-06-17

    Jun 17, 2009 ... Molecular responses and expression analysis of genes in a xerophytic desert shrub Haloxylon ammodendron .... physiological determination and cDNA-AFLP analysis, three groups of seeds were sowed in pots with sand and .... HaDR27. U. 234. PDR-like ABC transporter. AT1G59870. HaDR28. U. 135.

  11. Heat Stress and Lipopolysaccharide Stimulation of Chicken Macrophage-Like Cell Line Activates Expression of Distinct Sets of Genes.

    Directory of Open Access Journals (Sweden)

    Anna Slawinska

    Full Text Available Acute heat stress requires immediate adjustment of the stressed individual to sudden changes of ambient temperatures. Chickens are particularly sensitive to heat stress due to development of insufficient physiological mechanisms to mitigate its effects. One of the symptoms of heat stress is endotoxemia that results from release of the lipopolysaccharide (LPS from the guts. Heat-related cytotoxicity is mitigated by the innate immune system, which is comprised mostly of phagocytic cells such as monocytes and macrophages. The objective of this study was to analyze the molecular responses of the chicken macrophage-like HD11 cell line to combined heat stress and lipopolysaccharide treatment in vitro. The cells were heat-stressed and then allowed a temperature-recovery period, during which the gene expression was investigated. LPS was added to the cells to mimic the heat-stress-related endotoxemia. Semi high-throughput gene expression analysis was used to study a gene panel comprised of heat shock proteins, stress-related genes, signaling molecules and immune response genes. HD11 cell line responded to heat stress with increased mRNA abundance of the HSP25, HSPA2 and HSPH1 chaperones as well as DNAJA4 and DNAJB6 co-chaperones. The anti-apoptotic gene BAG3 was also highly up-regulated, providing evidence that the cells expressed pro-survival processes. The immune response of the HD11 cell line to LPS in the heat stress environment (up-regulation of CCL4, CCL5, IL1B, IL8 and iNOS was higher than in thermoneutral conditions. However, the peak in the transcriptional regulation of the immune genes was after two hours of temperature-recovery. Therefore, we propose the potential influence of the extracellular heat shock proteins not only in mitigating effects of abiotic stress but also in triggering the higher level of the immune responses. Finally, use of correlation networks for the data analysis aided in discovering subtle differences in the gene

  12. GWATCH: a web platform for automated gene association discovery analysis

    Science.gov (United States)

    2014-01-01

    Background As genome-wide sequence analyses for complex human disease determinants are expanding, it is increasingly necessary to develop strategies to promote discovery and validation of potential disease-gene associations. Findings Here we present a dynamic web-based platform – GWATCH – that automates and facilitates four steps in genetic epidemiological discovery: 1) Rapid gene association search and discovery analysis of large genome-wide datasets; 2) Expanded visual display of gene associations for genome-wide variants (SNPs, indels, CNVs), including Manhattan plots, 2D and 3D snapshots of any gene region, and a dynamic genome browser illustrating gene association chromosomal regions; 3) Real-time validation/replication of candidate or putative genes suggested from other sources, limiting Bonferroni genome-wide association study (GWAS) penalties; 4) Open data release and sharing by eliminating privacy constraints (The National Human Genome Research Institute (NHGRI) Institutional Review Board (IRB), informed consent, The Health Insurance Portability and Accountability Act (HIPAA) of 1996 etc.) on unabridged results, which allows for open access comparative and meta-analysis. Conclusions GWATCH is suitable for both GWAS and whole genome sequence association datasets. We illustrate the utility of GWATCH with three large genome-wide association studies for HIV-AIDS resistance genes screened in large multicenter cohorts; however, association datasets from any study can be uploaded and analyzed by GWATCH. PMID:25374661

  13. QTL global meta-analysis: are trait determining genes clustered?

    Directory of Open Access Journals (Sweden)

    Adelson David L

    2009-04-01

    Full Text Available Abstract Background A key open question in biology is if genes are physically clustered with respect to their known functions or phenotypic effects. This is of particular interest for Quantitative Trait Loci (QTL where a QTL region could contain a number of genes that contribute to the trait being measured. Results We observed a significant increase in gene density within QTL regions compared to non-QTL regions and/or the entire bovine genome. By grouping QTL from the Bovine QTL Viewer database into 8 categories of non-redundant regions, we have been able to analyze gene density and gene function distribution, based on Gene Ontology (GO with relation to their location within QTL regions, outside of QTL regions and across the entire bovine genome. We identified a number of GO terms that were significantly over represented within particular QTL categories. Furthermore, select GO terms expected to be associated with the QTL category based on common biological knowledge have also proved to be significantly over represented in QTL regions. Conclusion Our analysis provides evidence of over represented GO terms in QTL regions. This increased GO term density indicates possible clustering of gene functions within QTL regions of the bovine genome. Genes with similar functions may be grouped in specific locales and could be contributing to QTL traits. Moreover, we have identified over-represented GO terminology that from a biological standpoint, makes sense with respect to QTL category type.

  14. Analysis of Misdiagnosis of Bipolar Disorder in An Outpatient Setting.

    Science.gov (United States)

    Shen, Hui; Zhang, Li; Xu, Chuchen; Zhu, Jinling; Chen, Meijuan; Fang, Yiru

    2018-04-25

    Bipolar disorder is a mental illness with a high misdiagnosis rate and commonly misdiagnosed as other mental disorders including depression, schizophrenia, anxiety disorders, obsessive-compulsive disorders, and personality disorders, resulting in the mistreatment of clinical symptoms and increasing of recurrent episodes. To understand the reasons for misdiagnosis of bipolar disorder in an outpatient setting in order to help clinicians more clearly identify the disease and avoid diagnostic errors. Data from an outpatient clinic included two groups: those with a confirmed diagnosis of bipolar disorder (CD group) and those who were misdiagnosed (i.e. those who did in fact have bipolar disorder but received a different diagnoses and those without bipolar disorder who received a bipolar diagnosis [MD group]). Information between these two groups was compared. There were a total of 177 cases that met the inclusion criteria for this study. Among them, 136 cases (76.8%) were in the MD group and 41 cases (23.2%) were in the CD group. Patents with depression had the most cases of misdiagnosis (70.6%). The first episode of the patients in the MD group was more likely to be a depressive episode (χ 2 =5.206, p =0.023) and these patients had a greater number of depressive episodes during the course of the disease ( Z =-2.268, p =0.023); the time from the onset of the disease to the first treatment was comparatively short ( Z =-2.612, p =0.009) in the group with misdiagnosis; the time from the onset of disease to a confirmed diagnosis was longer ( Z =-3.685, p bipolar and other related disorders in the misdiagnosis group than in the confirmed diagnosis group (11.0% v. 4.9%) and there were more patients in the MD group diagnosed with depressive episodes who had a recent episode (78.7% v. 65.9%). The rate of misdiagnosis of patients with bipolar receiving outpatient treatment was quite high and they often received a misdiagnosis of depression. In the misdiagnosis group the first

  15. Semi-supervised consensus clustering for gene expression data analysis

    OpenAIRE

    Wang, Yunli; Pan, Youlian

    2014-01-01

    Background Simple clustering methods such as hierarchical clustering and k-means are widely used for gene expression data analysis; but they are unable to deal with noise and high dimensionality associated with the microarray gene expression data. Consensus clustering appears to improve the robustness and quality of clustering results. Incorporating prior knowledge in clustering process (semi-supervised clustering) has been shown to improve the consistency between the data partitioning and do...

  16. Gene expression patterns combined with network analysis identify hub genes associated with bladder cancer.

    Science.gov (United States)

    Bi, Dongbin; Ning, Hao; Liu, Shuai; Que, Xinxiang; Ding, Kejia

    2015-06-01

    To explore molecular mechanisms of bladder cancer (BC), network strategy was used to find biomarkers for early detection and diagnosis. The differentially expressed genes (DEGs) between bladder carcinoma patients and normal subjects were screened using empirical Bayes method of the linear models for microarray data package. Co-expression networks were constructed by differentially co-expressed genes and links. Regulatory impact factors (RIF) metric was used to identify critical transcription factors (TFs). The protein-protein interaction (PPI) networks were constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and clusters were obtained through molecular complex detection (MCODE) algorithm. Centralities analyses for complex networks were performed based on degree, stress and betweenness. Enrichment analyses were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Co-expression networks and TFs (based on expression data of global DEGs and DEGs in different stages and grades) were identified. Hub genes of complex networks, such as UBE2C, ACTA2, FABP4, CKS2, FN1 and TOP2A, were also obtained according to analysis of degree. In gene enrichment analyses of global DEGs, cell adhesion, proteinaceous extracellular matrix and extracellular matrix structural constituent were top three GO terms. ECM-receptor interaction, focal adhesion, and cell cycle were significant pathways. Our results provide some potential underlying biomarkers of BC. However, further validation is required and deep studies are needed to elucidate the pathogenesis of BC. Copyright © 2015 Elsevier Ltd. All rights reserved.

  17. Gene Co-expression Analysis to Characterize Genes Related to Marbling Trait in Hanwoo (Korean) Cattle.

    Science.gov (United States)

    Lim, Dajeong; Lee, Seung-Hwan; Kim, Nam-Kuk; Cho, Yong-Min; Chai, Han-Ha; Seong, Hwan-Hoo; Kim, Heebal

    2013-01-01

    Marbling (intramuscular fat) is an important trait that affects meat quality and is a casual factor determining the price of beef in the Korean beef market. It is a complex trait and has many biological pathways related to muscle and fat. There is a need to identify functional modules or genes related to marbling traits and investigate their relationships through a weighted gene co-expression network analysis based on the system level. Therefore, we investigated the co-expression relationships of genes related to the 'marbling score' trait and systemically analyzed the network topology in Hanwoo (Korean cattle). As a result, we determined 3 modules (gene groups) that showed statistically significant results for marbling score. In particular, one module (denoted as red) has a statistically significant result for marbling score (p = 0.008) and intramuscular fat (p = 0.02) and water capacity (p = 0.006). From functional enrichment and relationship analysis of the red module, the pathway hub genes (IL6, CHRNE, RB1, INHBA and NPPA) have a direct interaction relationship and share the biological functions related to fat or muscle, such as adipogenesis or muscle growth. This is the first gene network study with m.logissimus in Hanwoo to observe co-expression patterns in divergent marbling phenotypes. It may provide insights into the functional mechanisms of the marbling trait.

  18. Gene Co-expression Analysis to Characterize Genes Related to Marbling Trait in Hanwoo (Korean Cattle

    Directory of Open Access Journals (Sweden)

    Dajeong Lim

    2013-01-01

    Full Text Available Marbling (intramuscular fat is an important trait that affects meat quality and is a casual factor determining the price of beef in the Korean beef market. It is a complex trait and has many biological pathways related to muscle and fat. There is a need to identify functional modules or genes related to marbling traits and investigate their relationships through a weighted gene co-expression network analysis based on the system level. Therefore, we investigated the co-expression relationships of genes related to the ‘marbling score’ trait and systemically analyzed the network topology in Hanwoo (Korean cattle. As a result, we determined 3 modules (gene groups that showed statistically significant results for marbling score. In particular, one module (denoted as red has a statistically significant result for marbling score (p = 0.008 and intramuscular fat (p = 0.02 and water capacity (p = 0.006. From functional enrichment and relationship analysis of the red module, the pathway hub genes (IL6, CHRNE, RB1, INHBA and NPPA have a direct interaction relationship and share the biological functions related to fat or muscle, such as adipogenesis or muscle growth. This is the first gene network study with m.logissimus in Hanwoo to observe co-expression patterns in divergent marbling phenotypes. It may provide insights into the functional mechanisms of the marbling trait.

  19. A multicolor panel of novel lentiviral "gene ontology" (LeGO) vectors for functional gene analysis.

    Science.gov (United States)

    Weber, Kristoffer; Bartsch, Udo; Stocking, Carol; Fehse, Boris

    2008-04-01

    Functional gene analysis requires the possibility of overexpression, as well as downregulation of one, or ideally several, potentially interacting genes. Lentiviral vectors are well suited for this purpose as they ensure stable expression of complementary DNAs (cDNAs), as well as short-hairpin RNAs (shRNAs), and can efficiently transduce a wide spectrum of cell targets when packaged within the coat proteins of other viruses. Here we introduce a multicolor panel of novel lentiviral "gene ontology" (LeGO) vectors designed according to the "building blocks" principle. Using a wide spectrum of different fluorescent markers, including drug-selectable enhanced green fluorescent protein (eGFP)- and dTomato-blasticidin-S resistance fusion proteins, LeGO vectors allow simultaneous analysis of multiple genes and shRNAs of interest within single, easily identifiable cells. Furthermore, each functional module is flanked by unique cloning sites, ensuring flexibility and individual optimization. The efficacy of these vectors for analyzing multiple genes in a single cell was demonstrated in several different cell types, including hematopoietic, endothelial, and neural stem and progenitor cells, as well as hepatocytes. LeGO vectors thus represent a valuable tool for investigating gene networks using conditional ectopic expression and knock-down approaches simultaneously.

  20. Candidate genes for chronic obstructive pulmonary disease in two large data sets

    DEFF Research Database (Denmark)

    Bakke, P S; Zhu, G; Gulsvik, A

    2011-01-01

    Lack of reproducibility of findings has been a criticism of genetic association studies in complex diseases like chronic obstructive pulmonary disease (COPD). We selected 257 polymorphisms of 16 genes with reported or potential relationshipsto COPD and genotyped these variants in a case......-control study which included 953 COPD cases and 956 control subjects. We explored the association of these polymorphisms to three COPD phenotypes: a COPD binary phenotype and two quantitative traits (post bronchodilator FEV1 in percent predicted and FEV1/FVC). The polymorphisms significantly associated...... to these phenotypes in this first study were tested in a second, family based, study that included 635 pedigrees with 1910 individuals. Significant associations to the binary COPD phenotype in both populations were seen for STAT1 (rs13010343) and NFKBIB/SIRT2 (rs2241704) (p

  1. Mining pathway associations for disease-related pathway activity analysis based on gene expression and methylation data.

    Science.gov (United States)

    Lee, Hyeonjeong; Shin, Miyoung

    2017-01-01

    The problem of discovering genetic markers as disease signatures is of great significance for the successful diagnosis, treatment, and prognosis of complex diseases. Even if many earlier studies worked on identifying disease markers from a variety of biological resources, they mostly focused on the markers of genes or gene-sets (i.e., pathways). However, these markers may not be enough to explain biological interactions between genetic variables that are related to diseases. Thus, in this study, our aim is to investigate distinctive associations among active pathways (i.e., pathway-sets) shown each in case and control samples which can be observed from gene expression and/or methylation data. The pathway-sets are obtained by identifying a set of associated pathways that are often active together over a significant number of class samples. For this purpose, gene expression or methylation profiles are first analyzed to identify significant (active) pathways via gene-set enrichment analysis. Then, regarding these active pathways, an association rule mining approach is applied to examine interesting pathway-sets in each class of samples (case or control). By doing so, the sets of associated pathways often working together in activity profiles are finally chosen as our distinctive signature of each class. The identified pathway-sets are aggregated into a pathway activity network (PAN), which facilitates the visualization of differential pathway associations between case and control samples. From our experiments with two publicly available datasets, we could find interesting PAN structures as the distinctive signatures of breast cancer and uterine leiomyoma cancer, respectively. Our pathway-set markers were shown to be superior or very comparable to other genetic markers (such as genes or gene-sets) in disease classification. Furthermore, the PAN structure, which can be constructed from the identified markers of pathway-sets, could provide deeper insights into

  2. Comparative gene expression analysis of Dtg, a novel target gene of Dpp signaling pathway in the early Drosophila melanogaster embryo.

    Science.gov (United States)

    Hodar, Christian; Zuñiga, Alejandro; Pulgar, Rodrigo; Travisany, Dante; Chacon, Carlos; Pino, Michael; Maass, Alejandro; Cambiazo, Verónica

    2014-02-10

    In the early Drosophila melanogaster embryo, Dpp, a secreted molecule that belongs to the TGF-β superfamily of growth factors, activates a set of downstream genes to subdivide the dorsal region into amnioserosa and dorsal epidermis. Here, we examined the expression pattern and transcriptional regulation of Dtg, a new target gene of Dpp signaling pathway that is required for proper amnioserosa differentiation. We showed that the expression of Dtg was controlled by Dpp and characterized a 524-bp enhancer that mediated expression in the dorsal midline, as well as, in the differentiated amnioserosa in transgenic reporter embryos. This enhancer contained a highly conserved region of 48-bp in which bioinformatic predictions and in vitro assays identified three Mad binding motifs. Mutational analysis revealed that these three motifs were necessary for proper expression of a reporter gene in transgenic embryos, suggesting that short and highly conserved genomic sequences may be indicative of functional regulatory regions in D. melanogaster genes. Dtg orthologs were not detected in basal lineages of Dipterans, which unlike D. melanogaster develop two extra-embryonic membranes, amnion and serosa, nevertheless Dtg orthologs were identified in the transcriptome of Musca domestica, in which dorsal ectoderm patterning leads to the formation of a single extra-embryonic membrane. These results suggest that Dtg was recruited as a new component of the network that controls dorsal ectoderm patterning in the lineage leading to higher Cyclorrhaphan flies, such as D. melanogaster and M. domestica. Copyright © 2013 Elsevier B.V. All rights reserved.

  3. Kidney gene expression analysis in a rat model of intrauterine growth restriction reveals massive alterations of coagulation genes.

    Science.gov (United States)

    Buffat, Christophe; Boubred, Farid; Mondon, Françoise; Chelbi, Sonia T; Feuerstein, Jean-Marc; Lelièvre-Pégorier, Martine; Vaiman, Daniel; Simeoni, Umberto

    2007-11-01

    In this study, low birth weight was induced in rats by feeding the dams with a low-protein diet during pregnancy. Kidneys from the fetuses at the end of gestation were collected and showed a reduction in overall and relative weight, in parallel with other tissues (heart and liver). This reduction was associated with a reduction in nephrons number. To better understand the molecular basis of this observation, a transcriptome analysis contrasting kidneys from control and protein-deprived rats was performed, using a platform based upon long isothermic oligonucleotides, strengthening the robustness of the results. We could identify over 1800 transcripts modified more than twice (772 induced and 1040 repressed). Genes of either category were automatically classified according to functional criteria, making it possible to bring to light a large cluster of genes involved in coagulation and complement cascades. The promoters of the most induced and most repressed genes were contrasted for their composition in putative transcription factor binding sites, suggesting an overrepresentation of the AP1R binding site, together with the transcription induction of factors actually binding to this site in the set of induced genes. The induction of coagulation cascades in the kidney of low-birth-weight rats provides a putative rationale for explaining thrombo-endothelial disorders also observed in intrauterine growth-restricted human newborns. These alterations in the kidneys have been reported as a probable cause for cardiovascular diseases in the adult.

  4. Integrated bioinformatics analysis reveals key candidate genes and pathways in breast cancer.

    Science.gov (United States)

    Wang, Yuzhi; Zhang, Yi; Huang, Qian; Li, Chengwen

    2018-04-19

    Breast cancer (BC) is the leading malignancy in women worldwide, yet relatively little is known about the genes and signaling pathways involved in BC tumorigenesis and progression. The present study aimed to elucidate potential key candidate genes and pathways in BC. Five gene expression profile data sets (GSE22035, GSE3744, GSE5764, GSE21422 and GSE26910) were downloaded from the Gene Expression Omnibus (GEO) database, which included data from 113 tumorous and 38 adjacent non‑tumorous tissue samples. Differentially expressed genes (DEGs) were identified using t‑tests in the limma R package. These DEGs were subsequently investigated by pathway enrichment analysis and a protein‑protein interaction (PPI) network was constructed. The most significant module from the PPI network was selected for pathway enrichment analysis. In total, 227 DEGs were identified, of which 82 were upregulated and 145 were downregulated. Pathway enrichment analysis results revealed that the upregulated DEGs were mainly enriched in 'cell division', the 'proteinaceous extracellular matrix (ECM)', 'ECM structural constituents' and 'ECM‑receptor interaction', whereas downregulated genes were mainly enriched in 'response to drugs', 'extracellular space', 'transcriptional activator activity' and the 'peroxisome proliferator‑activated receptor signaling pathway'. The PPI network contained 174 nodes and 1,257 edges. DNA topoisomerase 2‑a, baculoviral inhibitor of apoptosis repeat‑containing protein 5, cyclin‑dependent kinase 1, G2/mitotic‑specific cyclin‑B1 and kinetochore protein NDC80 homolog were identified as the top 5 hub genes. Furthermore, the genes in the most significant module were predominantly involved in 'mitotic nuclear division', 'mid‑body', 'protein binding' and 'cell cycle'. In conclusion, the DEGs, relative pathways and hub genes identified in the present study may aid in understanding of the molecular mechanisms underlying BC progression and provide

  5. Cross-study analysis of gene expression data for intermediate neuroblastoma identifies two biological subtypes

    International Nuclear Information System (INIS)

    Warnat, Patrick; Oberthuer, André; Fischer, Matthias; Westermann, Frank; Eils, Roland; Brors, Benedikt

    2007-01-01

    Neuroblastoma patients show heterogeneous clinical courses ranging from life-threatening progression to spontaneous regression. Recently, gene expression profiles of neuroblastoma tumours were associated with clinically different phenotypes. However, such data is still rare for important patient subgroups, such as patients with MYCN non-amplified advanced stage disease. Prediction of the individual course of disease and optimal therapy selection in this cohort is challenging. Additional research effort is needed to describe the patterns of gene expression in this cohort and to identify reliable prognostic markers for this subset of patients. We combined gene expression data from two studies in a meta-analysis in order to investigate differences in gene expression of advanced stage (3 or 4) tumours without MYCN amplification that show contrasting outcomes (alive or dead) at five years after initial diagnosis. In addition, a predictive model for outcome was generated. Gene expression profiles from 66 patients were included from two studies using different microarray platforms. In the combined data set, 72 genes were identified as differentially expressed by meta-analysis at a false discovery rate (FDR) of 8.33%. Meta-analysis detected 34 differentially expressed genes that were not found as significant in either single study. Outcome prediction based on data of both studies resulted in a predictive accuracy of 77%. Moreover, the genes that were differentially expressed in subgroups of advanced stage patients without MYCN amplification accurately separated MYCN amplified tumours from low stage tumours without MYCN amplification. Our findings support the hypothesis that neuroblastoma consists of two biologically distinct subgroups that differ by characteristic gene expression patterns, which are associated with divergent clinical outcome

  6. Integrative Analysis of Gene Expression Data Including an Assessment of Pathway Enrichment for Predicting Prostate Cancer

    Directory of Open Access Journals (Sweden)

    Pingzhao Hu

    2006-01-01

    Full Text Available Background: Microarray technology has been previously used to identify genes that are differentially expressed between tumour and normal samples in a single study, as well as in syntheses involving multiple studies. When integrating results from several Affymetrix microarray datasets, previous studies summarized probeset-level data, which may potentially lead to a loss of information available at the probe-level. In this paper, we present an approach for integrating results across studies while taking probe-level data into account. Additionally, we follow a new direction in the analysis of microarray expression data, namely to focus on the variation of expression phenotypes in predefined gene sets, such as pathways. This targeted approach can be helpful for revealing information that is not easily visible from the changes in the individual genes. Results: We used a recently developed method to integrate Affymetrix expression data across studies. The idea is based on a probe-level based test statistic developed for testing for differentially expressed genes in individual studies. We incorporated this test statistic into a classic random-effects model for integrating data across studies. Subsequently, we used a gene set enrichment test to evaluate the significance of enriched biological pathways in the differentially expressed genes identified from the integrative analysis. We compared statistical and biological significance of the prognostic gene expression signatures and pathways identified in the probe-level model (PLM with those in the probeset-level model (PSLM. Our integrative analysis of Affymetrix microarray data from 110 prostate cancer samples obtained from three studies reveals thousands of genes significantly correlated with tumour cell differentiation. The bioinformatics analysis, mapping these genes to the publicly available KEGG database, reveals evidence that tumour cell differentiation is significantly associated with many

  7. Serial analysis of gene expression (SAGE in bovine trypanotolerance: preliminary results

    Directory of Open Access Journals (Sweden)

    David Berthier

    2003-06-01

    Full Text Available Abstract In Africa, trypanosomosis is a tsetse-transmitted disease which represents the most important constraint to livestock production. Several indigenous West African taurine (Bos taurus breeds, such as the Longhorn (N'Dama cattle are well known to control trypanosome infections. This genetic ability named "trypanotolerance" results from various biological mechanisms under multigenic control. The methodologies used so far have not succeeded in identifying the complete pool of genes involved in trypanotolerance. New post genomic biotechnologies such as transcriptome analyses are efficient in characterising the pool of genes involved in the expression of specific biological functions. We used the serial analysis of gene expression (SAGE technique to construct, from Peripheral Blood Mononuclear Cells of an N'Dama cow, 2 total mRNA transcript libraries, at day 0 of a Trypanosoma congolense experimental infection and at day 10 post-infection, corresponding to the peak of parasitaemia. Bioinformatic comparisons in the bovine genomic databases allowed the identification of 187 up- and down- regulated genes, EST and unknown functional genes. Identification of the genes involved in trypanotolerance will allow to set up specific microarray sets for further metabolic and pharmacological studies and to design field marker-assisted selection by introgression programmes.

  8. GO-Bayes: Gene Ontology-based overrepresentation analysis using a Bayesian approach.

    Science.gov (United States)

    Zhang, Song; Cao, Jing; Kong, Y Megan; Scheuermann, Richard H

    2010-04-01

    A typical approach for the interpretation of high-throughput experiments, such as gene expression microarrays, is to produce groups of genes based on certain criteria (e.g. genes that are differentially expressed). To gain more mechanistic insights into the underlying biology, overrepresentation analysis (ORA) is often conducted to investigate whether gene sets associated with particular biological functions, for example, as represented by Gene Ontology (GO) annotations, are statistically overrepresented in the identified gene groups. However, the standard ORA, which is based on the hypergeometric test, analyzes each GO term in isolation and does not take into account the dependence structure of the GO-term hierarchy. We have developed a Bayesian approach (GO-Bayes) to measure overrepresentation of GO terms that incorporates the GO dependence structure by taking into account evidence not only from individual GO terms, but also from their related terms (i.e. parents, children, siblings, etc.). The Bayesian framework borrows information across related GO terms to strengthen the detection of overrepresentation signals. As a result, this method tends to identify sets of closely related GO terms rather than individual isolated GO terms. The advantage of the GO-Bayes approach is demonstrated with a simulation study and an application example.

  9. The Arabidopsis co-expression tool (act): a WWW-based tool and database for microarray-based gene expression analysis

    DEFF Research Database (Denmark)

    Jen, C. H.; Manfield, I. W.; Michalopoulos, D. W.

    2006-01-01

    be examined using the novel clique finder tool to determine the sets of genes most likely to be regulated in a similar manner. In combination, these tools offer three levels of analysis: creation of correlation lists of co-expressed genes, refinement of these lists using two-dimensional scatter plots......We present a new WWW-based tool for plant gene analysis, the Arabidopsis Co-Expression Tool (act) , based on a large Arabidopsis thaliana microarray data set obtained from the Nottingham Arabidopsis Stock Centre. The co-expression analysis tool allows users to identify genes whose expression...

  10. Exploring valid reference genes for quantitative real-time PCR analysis in Sesamia inferens (Lepidoptera: Noctuidae.

    Directory of Open Access Journals (Sweden)

    Meng Sun

    Full Text Available The pink stem borer, Sesamia inferens, which is endemic in China and other parts of Asia, is a major pest of rice and causes significant yield loss in this host plant. Very few studies have addressed gene expression in S. inferens. Quantitative real-time PCR (qRT-PCR is currently the most accurate and sensitive method for gene expression analysis. In qRT-PCR, data are normalized using reference genes, which help control for internal differences and reduce error between samples. In this study, seven candidate reference genes, 18S ribosomal RNA (18S rRNA, elongation factor 1 (EF1, glyceraldehyde-3-phosphate dehydrogenase (GAPDH, ribosomal protein S13 (RPS13, ribosomal protein S20 (RPS20, tubulin (TUB, and β-actin (ACTB were evaluated for their suitability in normalizing gene expression under different experimental conditions. The results indicated that three genes (RPS13, RPS20, and EF1 were optimal for normalizing gene expression in different insect tissues (head, epidermis, fat body, foregut, midgut, hindgut, Malpighian tubules, haemocytes, and salivary glands. 18S rRNA, EF1, and GAPDH were best for normalizing expression with respect to developmental stages and sex (egg masses; first, second, third, fourth, fifth, and sixth instar larvae; male and female pupae; and one-day-old male and female adults. 18S rRNA, RPS20, and TUB were optimal for fifth instars exposed to different temperatures (-8, -6, -4, -2, 0, and 27°C. To validate this recommendation, the expression profile of a target gene heat shock protein 83 gene (hsp83 was investigated, and results showed the selection was necessary and effective. In conclusion, this study describes reference gene sets that can be used to accurately measure gene expression in S. inferens.

  11. Exploring valid reference genes for quantitative real-time PCR analysis in Sesamia inferens (Lepidoptera: Noctuidae).

    Science.gov (United States)

    Sun, Meng; Lu, Ming-Xing; Tang, Xiao-Tian; Du, Yu-Zhou

    2015-01-01

    The pink stem borer, Sesamia inferens, which is endemic in China and other parts of Asia, is a major pest of rice and causes significant yield loss in this host plant. Very few studies have addressed gene expression in S. inferens. Quantitative real-time PCR (qRT-PCR) is currently the most accurate and sensitive method for gene expression analysis. In qRT-PCR, data are normalized using reference genes, which help control for internal differences and reduce error between samples. In this study, seven candidate reference genes, 18S ribosomal RNA (18S rRNA), elongation factor 1 (EF1), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), ribosomal protein S13 (RPS13), ribosomal protein S20 (RPS20), tubulin (TUB), and β-actin (ACTB) were evaluated for their suitability in normalizing gene expression under different experimental conditions. The results indicated that three genes (RPS13, RPS20, and EF1) were optimal for normalizing gene expression in different insect tissues (head, epidermis, fat body, foregut, midgut, hindgut, Malpighian tubules, haemocytes, and salivary glands). 18S rRNA, EF1, and GAPDH were best for normalizing expression with respect to developmental stages and sex (egg masses; first, second, third, fourth, fifth, and sixth instar larvae; male and female pupae; and one-day-old male and female adults). 18S rRNA, RPS20, and TUB were optimal for fifth instars exposed to different temperatures (-8, -6, -4, -2, 0, and 27°C). To validate this recommendation, the expression profile of a target gene heat shock protein 83 gene (hsp83) was investigated, and results showed the selection was necessary and effective. In conclusion, this study describes reference gene sets that can be used to accurately measure gene expression in S. inferens.

  12. A comprehensive analysis of gene expression changes provoked by bacterial and fungal infection in C. elegans.

    Directory of Open Access Journals (Sweden)

    Ilka Engelmann

    Full Text Available While Caenorhabditis elegans specifically responds to infection by the up-regulation of certain genes, distinct pathogens trigger the expression of a common set of genes. We applied new methods to conduct a comprehensive and comparative study of the transcriptional response of C. elegans to bacterial and fungal infection. Using tiling arrays and/or RNA-sequencing, we have characterized the genome-wide transcriptional changes that underlie the host's response to infection by three bacterial (Serratia marcescens, Enterococcus faecalis and otorhabdus luminescens and two fungal pathogens (Drechmeria coniospora and Harposporium sp.. We developed a flexible tool, the WormBase Converter (available at http://wormbasemanager.sourceforge.net/, to allow cross-study comparisons. The new data sets provided more extensive lists of differentially regulated genes than previous studies. Annotation analysis confirmed that genes commonly up-regulated by bacterial infections are related to stress responses. We found substantial overlaps between the genes regulated upon intestinal infection by the bacterial pathogens and Harposporium, and between those regulated by Harposporium and D. coniospora, which infects the epidermis. Among the fungus-regulated genes, there was a significant bias towards genes that are evolving rapidly and potentially encode small proteins. The results obtained using new methods reveal that the response to infection in C. elegans is determined by the nature of the pathogen, the site of infection and the physiological imbalance provoked by infection. They form the basis for future functional dissection of innate immune signaling. Finally, we also propose alternative methods to identify differentially regulated genes that take into account the greater variability in lowly expressed genes.

  13. Transcriptome profiling and digital gene expression analysis of genes associated with salinity resistance in peanut

    Directory of Open Access Journals (Sweden)

    Jiongming Sui

    2018-03-01

    Full Text Available Background: Soil salinity can significantly reduce crop production, but the molecular mechanism of salinity tolerance in peanut is poorly understood. A mutant (S1 with higher salinity resistance than its mutagenic parent HY22 (S3 was obtained. Transcriptome sequencing and digital gene expression (DGE analysis were performed with leaves of S1 and S3 before and after plants were irrigated with 250 mM NaCl. Results: A total of 107,725 comprehensive transcripts were assembled into 67,738 unigenes using TIGR Gene Indices clustering tools (TGICL. All unigenes were searched against the euKaryotic Ortholog Groups (KOG, gene ontology (GO and Kyoto Encyclopedia of Genes and Genomes (KEGG databases, and these unigenes were assigned to 26 functional KOG categories, 56 GO terms, 32 KEGG groups, respectively. In total 112 differentially expressed genes (DEGs between S1 and S3 after salinity stress were screened, among them, 86 were responsive to salinity stress in S1 and/or S3. These 86 DEGs included genes that encoded the following kinds of proteins that are known to be involved in resistance to salinity stress: late embryogenesis abundant proteins (LEAs, major intrinsic proteins (MIPs or aquaporins, metallothioneins (MTs, lipid transfer protein (LTP, calcineurin B-like protein-interacting protein kinases (CIPKs, 9-cis-epoxycarotenoid dioxygenase (NCED and oleosins, etc. Of these 86 DEGs, 18 could not be matched with known proteins. Conclusion: The results from this study will be useful for further research on the mechanism of salinity resistance and will provide a useful gene resource for the variety breeding of salinity resistance in peanut. Keywords: Digital gene expression, Gene, Mutant, NaCl, Peanut (Arachis hypogaea L., RNA-seq, Salinity stress, Salinity tolerance, Soil salinity, Transcripts, Unigenes

  14. Quantitative DNA methylation analysis of candidate genes in cervical cancer.

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    Erin M Siegel

    Full Text Available Aberrant DNA methylation has been observed in cervical cancer; however, most studies have used non-quantitative approaches to measure DNA methylation. The objective of this study was to quantify methylation within a select panel of genes previously identified as targets for epigenetic silencing in cervical cancer and to identify genes with elevated methylation that can distinguish cancer from normal cervical tissues. We identified 49 women with invasive squamous cell cancer of the cervix and 22 women with normal cytology specimens. Bisulfite-modified genomic DNA was amplified and quantitative pyrosequencing completed for 10 genes (APC, CCNA, CDH1, CDH13, WIF1, TIMP3, DAPK1, RARB, FHIT, and SLIT2. A Methylation Index was calculated as the mean percent methylation across all CpG sites analyzed per gene (~4-9 CpG site per sequence. A binary cut-point was defined at >15% methylation. Sensitivity, specificity and area under ROC curve (AUC of methylation in individual genes or a panel was examined. The median methylation index was significantly higher in cases compared to controls in 8 genes, whereas there was no difference in median methylation for 2 genes. Compared to HPV and age, the combination of DNA methylation level of DAPK1, SLIT2, WIF1 and RARB with HPV and age significantly improved the AUC from 0.79 to 0.99 (95% CI: 0.97-1.00, p-value = 0.003. Pyrosequencing analysis confirmed that several genes are common targets for aberrant methylation in cervical cancer and DNA methylation level of four genes appears to increase specificity to identify cancer compared to HPV detection alone. Alterations in DNA methylation of specific genes in cervical cancers, such as DAPK1, RARB, WIF1, and SLIT2, may also occur early in cervical carcinogenesis and should be evaluated.

  15. Phase analysis of circadian-related genes in two tissues

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    Li Leping

    2006-02-01

    Full Text Available Abstract Background Recent circadian clock studies using gene expression microarray in two different tissues of mouse have revealed not all circadian-related genes are synchronized in phase or peak expression times across tissues in vivo. Instead, some circadian-related genes may be delayed by 4–8 hrs in peak expression in one tissue relative to the other. These interesting biological observations prompt a statistical question regarding how to distinguish the synchronized genes from genes that are systematically lagged in phase/peak expression time across two tissues. Results We propose a set of techniques from circular statistics to analyze phase angles of circadian-related genes in two tissues. We first estimate the phases of a cycling gene separately in each tissue, which are then used to estimate the paired angular difference of the phase angles of the gene in the two tissues. These differences are modeled as a mixture of two von Mises distributions which enables us to cluster genes into two groups; one group having synchronized transcripts with the same phase in the two tissues, the other containing transcripts with a discrepancy in phase between the two tissues. For each cluster of genes we assess the association of phases across the tissue types using circular-circular regression. We also develop a bootstrap methodology based on a circular-circular regression model to evaluate the improvement in fit provided by allowing two components versus a one-component von-Mises model. Conclusion We applied our proposed methodologies to the circadian-related genes common to heart and liver tissues in Storch et al. 2, and found that an estimated 80% of circadian-related transcripts common to heart and liver tissues were synchronized in phase, and the other 20% of transcripts were lagged about 8 hours in liver relative to heart. The bootstrap p-value for being one cluster is 0.063, which suggests the possibility of two clusters. Our methodologies can

  16. Bayesian meta-analysis of genetic association studies with different sets of markers

    NARCIS (Netherlands)

    Verzilli, Claudio; Shah, Tina; Casas, Juan P.; Chapman, Juliet; Sandhu, Manjinder; Debenham, Sally L.; Boekholdt, Matthijs S.; Khaw, Kay Tee; Wareham, Nicholas J.; Judson, Richard; Benjamin, Emelia J.; Kathiresan, Sekar; Larson, Martin G.; Rong, Jian; Sofat, Reecha; Humphries, Steve E.; Smeeth, Liam; Cavalleri, Gianpiero; Whittaker, John C.; Hingorani, Aroon D.

    2008-01-01

    Robust assessment of genetic effects on quantitative traits or complex-disease risk requires synthesis of evidence from multiple studies. Frequently, studies have genotyped partially overlapping sets of SNPs within a gene or region of interest, hampering attempts to combine all the available data.

  17. Repression of Middle Sporulation Genes in Saccharomyces cerevisiae by the Sum1-Rfm1-Hst1 Complex Is Maintained by Set1 and H3K4 Methylation

    Science.gov (United States)

    Jaiswal, Deepika; Jezek, Meagan; Quijote, Jeremiah; Lum, Joanna; Choi, Grace; Kulkarni, Rushmie; Park, DoHwan; Green, Erin M.

    2017-01-01

    The conserved yeast histone methyltransferase Set1 targets H3 lysine 4 (H3K4) for mono, di, and trimethylation and is linked to active transcription due to the euchromatic distribution of these methyl marks and the recruitment of Set1 during transcription. However, loss of Set1 results in increased expression of multiple classes of genes, including genes adjacent to telomeres and middle sporulation genes, which are repressed under normal growth conditions because they function in meiotic progression and spore formation. The mechanisms underlying Set1-mediated gene repression are varied, and still unclear in some cases, although repression has been linked to both direct and indirect action of Set1, associated with noncoding transcription, and is often dependent on the H3K4me2 mark. We show that Set1, and particularly the H3K4me2 mark, are implicated in repression of a subset of middle sporulation genes during vegetative growth. In the absence of Set1, there is loss of the DNA-binding transcriptional regulator Sum1 and the associated histone deacetylase Hst1 from chromatin in a locus-specific manner. This is linked to increased H4K5ac at these loci and aberrant middle gene expression. These data indicate that, in addition to DNA sequence, histone modification status also contributes to proper localization of Sum1. Our results also show that the role for Set1 in middle gene expression control diverges as cells receive signals to undergo meiosis. Overall, this work dissects an unexplored role for Set1 in gene-specific repression, and provides important insights into a new mechanism associated with the control of gene expression linked to meiotic differentiation. PMID:29066473

  18. Incorporation of gene-specific variability improves expression analysis using high-density DNA microarrays

    Directory of Open Access Journals (Sweden)

    Spitznagel Edward

    2003-11-01

    Full Text Available Abstract Background The assessment of data reproducibility is essential for application of microarray technology to exploration of biological pathways and disease states. Technical variability in data analysis largely depends on signal intensity. Within that context, the reproducibility of individual probe sets has not been hitherto addressed. Results We used an extraordinarily large replicate data set derived from human placental trophoblast to analyze probe-specific contribution to variability of gene expression. We found that signal variability, in addition to being signal-intensity dependant, is probe set-specific. Importantly, we developed a novel method to quantify the contribution of this probe set-specific variability. Furthermore, we devised a formula that incorporates a priori-computed, replicate-based information on probe set- and intensity-specific variability in determination of expression changes even without technical replicates. Conclusion The strategy of incorporating probe set-specific variability is superior to analysis based on arbitrary fold-change thresholds. We recommend its incorporation to any computation of gene expression changes using high-density DNA microarrays. A Java application implementing our T-score is available at http://www.sadovsky.wustl.edu/tscore.html.

  19. A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse database

    Directory of Open Access Journals (Sweden)

    Tripputi Mark

    2006-10-01

    Full Text Available Abstract Background Many of the most popular pre-processing methods for Affymetrix expression arrays, such as RMA, gcRMA, and PLIER, simultaneously analyze data across a set of predetermined arrays to improve precision of the final measures of expression. One problem associated with these algorithms is that expression measurements for a particular sample are highly dependent on the set of samples used for normalization and results obtained by normalization with a different set may not be comparable. A related problem is that an organization producing and/or storing large amounts of data in a sequential fashion will need to either re-run the pre-processing algorithm every time an array is added or store them in batches that are pre-processed together. Furthermore, pre-processing of large numbers of arrays requires loading all the feature-level data into memory which is a difficult task even with modern computers. We utilize a scheme that produces all the information necessary for pre-processing using a very large training set that can be used for summarization of samples outside of the training set. All subsequent pre-processing tasks can be done on an individual array basis. We demonstrate the utility of this approach by defining a new version of the Robust Multi-chip Averaging (RMA algorithm which we refer to as refRMA. Results We assess performance based on multiple sets of samples processed over HG U133A Affymetrix GeneChip® arrays. We show that the refRMA workflow, when used in conjunction with a large, biologically diverse training set, results in the same general characteristics as that of RMA in its classic form when comparing overall data structure, sample-to-sample correlation, and variation. Further, we demonstrate that the refRMA workflow and reference set can be robustly applied to naïve organ types and to benchmark data where its performance indicates respectable results. Conclusion Our results indicate that a biologically diverse

  20. CANDIDATE GENE ANALYSIS IN ISRAELI SOLDIERS WITH STRESS FRACTURES

    Directory of Open Access Journals (Sweden)

    Ran Yanovich

    2012-03-01

    Full Text Available To investigate the association of polymorphisms within candidate genes which we hypothesized may contribute to stress fracture predisposition, a case-control, cross- sectional study design was employed. Genotyping 268 Single Nucleotide Polymorphisms- SNPs within 17 genes in 385 Israeli young male and female recruits (182 with and 203 without stress fractures. Twenty-five polymorphisms within 9 genes (NR3C1, ANKH, VDR, ROR2, CALCR, IL6, COL1A2, CBG, and LRP4 showed statistically significant differences (p < 0.05 in the distribution between stress fracture cases and non stress fracture controls. Seventeen genetic variants were associated with an increased stress fracture risk, and eight variants with a decreased stress fracture risk. None of the SNP associations remained significant after correcting for multiple comparisons (false discovery rate- FDR. Our findings suggest that genes may be involved in stress fracture pathogenesis. Specifically, the CALCR and the VDR genes are intriguing candidates. The putative involvement of these genes in stress fracture predisposition requires analysis of more cases and controls and sequencing the relevant genomic regions, in order to define the specific gene mutations

  1. Singular Perturbation Analysis and Gene Regulatory Networks with Delay

    Science.gov (United States)

    Shlykova, Irina; Ponosov, Arcady

    2009-09-01

    There are different ways of how to model gene regulatory networks. Differential equations allow for a detailed description of the network's dynamics and provide an explicit model of the gene concentration changes over time. Production and relative degradation rate functions used in such models depend on the vector of steeply sloped threshold functions which characterize the activity of genes. The most popular example of the threshold functions comes from the Boolean network approach, where the threshold functions are given by step functions. The system of differential equations becomes then piecewise linear. The dynamics of this system can be described very easily between the thresholds, but not in the switching domains. For instance this approach fails to analyze stationary points of the system and to define continuous solutions in the switching domains. These problems were studied in [2], [3], but the proposed model did not take into account a time delay in cellular systems. However, analysis of real gene expression data shows a considerable number of time-delayed interactions suggesting that time delay is essential in gene regulation. Therefore, delays may have a great effect on the dynamics of the system presenting one of the critical factors that should be considered in reconstruction of gene regulatory networks. The goal of this work is to apply the singular perturbation analysis to certain systems with delay and to obtain an analog of Tikhonov's theorem, which provides sufficient conditions for constracting the limit system in the delay case.

  2. Homogeneity analysis with k sets of variables: An alternating least squares method with optimal scaling features

    NARCIS (Netherlands)

    van der Burg, Eeke; de Leeuw, Jan; Verdegaal, Renée

    1988-01-01

    Homogeneity analysis, or multiple correspondence analysis, is usually applied tok separate variables. In this paper we apply it to sets of variables by using sums within sets. The resulting technique is called OVERALS. It uses the notion of optimal scaling, with transformations that can be multiple

  3. Detection and sequence analysis of accessory gene regulator genes of Staphylococcus pseudintermedius isolates

    Directory of Open Access Journals (Sweden)

    M. Ananda Chitra

    2015-07-01

    Full Text Available Background: Staphylococcus pseudintermedius (SP is the major pathogenic species of dogs involved in a wide variety of skin and soft tissue infections. The accessory gene regulator (agr locus of Staphylococcus aureus has been extensively studied, and it influences the expression of many virulence genes. It encodes a two-component signal transduction system that leads to down-regulation of surface proteins and up-regulation of secreted proteins during in vitro growth of S. aureus. The objective of this study was to detect and sequence analyzing the AgrA, B, and D of SP isolated from canine skin infections. Materials and Methods: In this study, we have isolated and identified SP from canine pyoderma and otitis cases by polymerase chain reaction (PCR and confirmed by PCR-restriction fragment length polymorphism. Primers for SP agrA and agrBD genes were designed using online primer designing software and BLAST searched for its specificity. Amplification of the agr genes was carried out for 53 isolates of SP by PCR and sequencing of agrA, B, and D were carried out for five isolates and analyzed using DNAstar and Mega5.2 software. Results: A total of 53 (59% SP isolates were obtained from 90 samples. 15 isolates (28% were confirmed to be methicillinresistant SP (MRSP with the detection of the mecA gene. Accessory gene regulator A, B, and D genes were detected in all the SP isolates. Complete nucleotide sequences of the above three genes for five isolates were submitted to GenBank, and their accession numbers are from KJ133557 to KJ133571. AgrA amino acid sequence analysis showed that it is mainly made of alpha-helices and is hydrophilic in nature. AgrB is a transmembrane protein, and AgrD encodes the precursor of the autoinducing peptide (AIP. Sequencing of the agrD gene revealed that the 5 canine SP strains tested could be divided into three Agr specificity groups (RIPTSTGFF, KIPTSTGFF, and RIPISTGFF based on the putative AIP produced by each strain

  4. RISK DISCLOSURE ANALYSIS IN THE CORPORATE GOVERNANCE ANNUAL REPORT USING FUZZY-SET QUALITATIVE COMPARATIVE ANALYSIS

    Directory of Open Access Journals (Sweden)

    Pedro Carmona

    2016-05-01

    Full Text Available This paper explores the necessary and sufficient conditions of good Corporate Governance practices for high risk disclosure by firms in their Corporate Governance Annual Report. Additionally, we explore whether those recipes have changed during the financial crisis. With a sample of 271 Spanish listed companies, we applied fuzzy-set qualitative comparative analysis to a database of financial and non-financial data. We report that Board of Directors independence, size, level of activity and gender diversity, CEO duality, Audit Committee independence, being audited by the Big Four auditing firms and the presence of institutional investors are associated with high risk disclosure. The conditions included in almost every combination are the presence of institutional investors and being audited by the Big Four. We found similar combinations for 2006 and 2012, while the analysis for 2009 showed the lowest number of causal configurations.

  5. Evolutionary Analysis of Minor Histocompatibility Genes In Hydra

    KAUST Repository

    Aalismail, Nojood

    2016-01-01

    In the present study we took initiative to study the self/nonself recognition in hydra and its relation to the immune response. Moreover, performing phylogenetic analysis to look for annotated immune genes in hydra gave us a potential to analyze the expression of minor histocompatibility genes that have been shown to play a major role in grafting and transplantation in mammals. Here we obtained the cDNA library that shows expression of minor histocompatibility genes and confirmed that the annotated sequences in databases are actually present. In addition, grafting experiments suggested, although still preliminary, that homograft showed less rejection response than in heterograft. Involvement of possible minor histocompatibility gene orthologous in immune response was examined by qPCR.

  6. Using logistic regression to improve the prognostic value of microarray gene expression data sets: application to early-stage squamous cell carcinoma of the lung and triple negative breast carcinoma.

    Science.gov (United States)

    Mount, David W; Putnam, Charles W; Centouri, Sara M; Manziello, Ann M; Pandey, Ritu; Garland, Linda L; Martinez, Jesse D

    2014-06-10

    Numerous microarray-based prognostic gene expression signatures of primary neoplasms have been published but often with little concurrence between studies, thus limiting their clinical utility. We describe a methodology using logistic regression, which circumvents limitations of conventional Kaplan Meier analysis. We applied this approach to a thrice-analyzed and published squamous cell carcinoma (SQCC) of the lung data set, with the objective of identifying gene expressions predictive of early death versus long survival in early-stage disease. A similar analysis was applied to a data set of triple negative breast carcinoma cases, which present similar clinical challenges. Important to our approach is the selection of homogenous patient groups for comparison. In the lung study, we selected two groups (including only stages I and II), equal in size, of earliest deaths and longest survivors. Genes varying at least four-fold were tested by logistic regression for accuracy of prediction (area under a ROC plot). The gene list was refined by applying two sliding-window analyses and by validations using a leave-one-out approach and model building with validation subsets. In the breast study, a similar logistic regression analysis was used after selecting appropriate cases for comparison. A total of 8594 variable genes were tested for accuracy in predicting earliest deaths versus longest survivors in SQCC. After applying the two sliding window and the leave-one-out analyses, 24 prognostic genes were identified; most of them were B-cell related. When the same data set of stage I and II cases was analyzed using a conventional Kaplan Meier (KM) approach, we identified fewer immune-related genes among the most statistically significant hits; when stage III cases were included, most of the prognostic genes were missed. Interestingly, logistic regression analysis of the breast cancer data set identified many immune-related genes predictive of clinical outcome. Stratification of

  7. A new sequence data set of SSU rRNA gene for Scleractinia and its phylogenetic and ecological applications

    KAUST Repository

    Arrigoni, Roberto; Vacherie, Benoî t; Benzoni, Francesca; Stefani, Fabrizio; Karsenti, Eric; Jaillon, Olivier; Not, Fabrice; Nunes, Flavia; Payri, Claude; Wincker, Patrick; Barbe, Valé rie

    2016-01-01

    Scleractinian corals (i.e. hard corals) play a fundamental role in building and maintaining coral reefs, one of the most diverse ecosystems on Earth. Nevertheless, their phylogenies remain largely unresolved and little is known about dispersal and survival of their planktonic larval phase. The small subunit ribosomal RNA (SSU rRNA) is a commonly used gene for DNA barcoding in several metazoans, and small variable regions of SSU rRNA are widely adopted as barcode marker to investigate marine plankton community structure worldwide. Here, we provide a large sequence data set of the complete SSU rRNA gene from 298 specimens, representing all known extant reef coral families and a total of 106 genera. The secondary structure was extremely conserved within the order with few exceptions due to insertions or deletions occurring in the variable regions. Remarkable differences in SSU rRNA length and base composition were detected between and within acroporids (Acropora, Montipora, Isopora and Alveopora) compared to other corals. The V4 and V9 regions seem to be promising barcode loci because variation at commonly used barcode primer binding sites was extremely low, while their levels of divergence allowed families and genera to be distinguished. A time-calibrated phylogeny of Scleractinia is provided, and mutation rate heterogeneity is demonstrated across main lineages. The use of this data set as a valuable reference for investigating aspects of ecology, biology, molecular taxonomy and evolution of scleractinian corals is discussed.

  8. Transcriptional differences between normal and glioma-derived glial progenitor cells identify a core set of dysregulated genes.

    Science.gov (United States)

    Auvergne, Romane M; Sim, Fraser J; Wang, Su; Chandler-Militello, Devin; Burch, Jaclyn; Al Fanek, Yazan; Davis, Danielle; Benraiss, Abdellatif; Walter, Kevin; Achanta, Pragathi; Johnson, Mahlon; Quinones-Hinojosa, Alfredo; Natesan, Sridaran; Ford, Heide L; Goldman, Steven A

    2013-06-27

    Glial progenitor cells (GPCs) are a potential source of malignant gliomas. We used A2B5-based sorting to extract tumorigenic GPCs from human gliomas spanning World Health Organization grades II-IV. Messenger RNA profiling identified a cohort of genes that distinguished A2B5+ glioma tumor progenitor cells (TPCs) from A2B5+ GPCs isolated from normal white matter. A core set of genes and pathways was substantially dysregulated in A2B5+ TPCs, which included the transcription factor SIX1 and its principal cofactors, EYA1 and DACH2. Small hairpin RNAi silencing of SIX1 inhibited the expansion of glioma TPCs in vitro and in vivo, suggesting a critical and unrecognized role of the SIX1-EYA1-DACH2 system in glioma genesis or progression. By comparing the expression patterns of glioma TPCs with those of normal GPCs, we have identified a discrete set of pathways by which glial tumorigenesis may be better understood and more specifically targeted. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

  9. A new sequence data set of SSU rRNA gene for Scleractinia and its phylogenetic and ecological applications

    KAUST Repository

    Arrigoni, Roberto

    2016-11-27

    Scleractinian corals (i.e. hard corals) play a fundamental role in building and maintaining coral reefs, one of the most diverse ecosystems on Earth. Nevertheless, their phylogenies remain largely unresolved and little is known about dispersal and survival of their planktonic larval phase. The small subunit ribosomal RNA (SSU rRNA) is a commonly used gene for DNA barcoding in several metazoans, and small variable regions of SSU rRNA are widely adopted as barcode marker to investigate marine plankton community structure worldwide. Here, we provide a large sequence data set of the complete SSU rRNA gene from 298 specimens, representing all known extant reef coral families and a total of 106 genera. The secondary structure was extremely conserved within the order with few exceptions due to insertions or deletions occurring in the variable regions. Remarkable differences in SSU rRNA length and base composition were detected between and within acroporids (Acropora, Montipora, Isopora and Alveopora) compared to other corals. The V4 and V9 regions seem to be promising barcode loci because variation at commonly used barcode primer binding sites was extremely low, while their levels of divergence allowed families and genera to be distinguished. A time-calibrated phylogeny of Scleractinia is provided, and mutation rate heterogeneity is demonstrated across main lineages. The use of this data set as a valuable reference for investigating aspects of ecology, biology, molecular taxonomy and evolution of scleractinian corals is discussed.

  10. Inferring Drosophila gap gene regulatory network: Pattern analysis of simulated gene expression profiles and stability analysis

    OpenAIRE

    Fomekong-Nanfack, Y.; Postma, M.; Kaandorp, J.A.

    2009-01-01

    Abstract Background Inference of gene regulatory networks (GRNs) requires accurate data, a method to simulate the expression patterns and an efficient optimization algorithm to estimate the unknown parameters. Using this approach it is possible to obtain alternative circuits without making any a priori assumptions about the interactions, which all simulate the observed patterns. It is important to analyze the properties of the circuits. Findings We have analyzed the simulated gene expression ...

  11. Network Graph Analysis of Gene-Gene Interactions in Genome-Wide Association Study Data

    Directory of Open Access Journals (Sweden)

    Sungyoung Lee

    2012-12-01

    Full Text Available Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs. For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR is one of the powerful and efficient methods for detecting high-order gene-gene (GxG interactions. However, the biological interpretation of GxG interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified GxG interactions. The proposed network graph analysis consists of three steps. The first step is for performing GxG interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified GxG interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform GxG interaction analysis of body mass index (BMI. Our network graph analysis successfully showed that many identified GxG interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of GxG interactions.

  12. Dysregulated Pathway Identification of Alzheimer's Disease Based on Internal Correlation Analysis of Genes and Pathways.

    Science.gov (United States)

    Kong, Wei; Mou, Xiaoyang; Di, Benteng; Deng, Jin; Zhong, Ruxing; Wang, Shuaiqun

    2017-11-20

    Dysregulated pathway identification is an important task which can gain insight into the underlying biological processes of disease. Current pathway-identification methods focus on a set of co-expression genes and single pathways and ignore the correlation between genes and pathways. The method proposed in this study, takes into account the internal correlations not only between genes but also pathways to identifying dysregulated pathways related to Alzheimer's disease (AD), the most common form of dementia. In order to find the significantly differential genes for AD, mutual information (MI) is used to measure interdependencies between genes other than expression valves. Then, by integrating the topology information from KEGG, the significant pathways involved in the feature genes are identified. Next, the distance correlation (DC) is applied to measure the pairwise pathway crosstalks since DC has the advantage of detecting nonlinear correlations when compared to Pearson correlation. Finally, the pathway pairs with significantly different correlations between normal and AD samples are known as dysregulated pathways. The molecular biology analysis demonstrated that many dysregulated pathways related to AD pathogenesis have been discovered successfully by the internal correlation detection. Furthermore, the insights of the dysregulated pathways in the development and deterioration of AD will help to find new effective target genes and provide important theoretical guidance for drug design. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  13. Genome-wide analysis of positively selected genes in seasonal and non-seasonal breeding species.

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    Yuhuan Meng

    Full Text Available Some mammals breed throughout the year, while others breed only at certain times of year. These differences in reproductive behavior can be explained by evolution. We identified positively-selected genes in two sets of species with different degrees of relatedness including seasonal and non-seasonal breeding species, using branch-site models. After stringent filtering by sum of pairs scoring, we revealed that more genes underwent positive selection in seasonal compared with non-seasonal breeding species. Positively-selected genes were verified by cDNA mapping of the positive sites with the corresponding cDNA sequences. The design of the evolutionary analysis can effectively lower the false-positive rate and thus identify valid positive genes. Validated, positively-selected genes, including CGA, DNAH1, INVS, and CD151, were related to reproductive behaviors such as spermatogenesis and cell proliferation in non-seasonal breeding species. Genes in seasonal breeding species, including THRAP3, TH1L, and CMTM6, may be related to the evolution of sperm and the circadian rhythm system. Identification of these positively-selected genes might help to identify the molecular mechanisms underlying seasonal and non-seasonal reproductive behaviors.

  14. Network Based Integrated Analysis of Phenotype-Genotype Data for Prioritization of Candidate Symptom Genes

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    Xing Li

    2014-01-01

    Full Text Available Background. Symptoms and signs (symptoms in brief are the essential clinical manifestations for individualized diagnosis and treatment in traditional Chinese medicine (TCM. To gain insights into the molecular mechanism of symptoms, we develop a computational approach to identify the candidate genes of symptoms. Methods. This paper presents a network-based approach for the integrated analysis of multiple phenotype-genotype data sources and the prediction of the prioritizing genes for the associated symptoms. The method first calculates the similarities between symptoms and diseases based on the symptom-disease relationships retrieved from the PubMed bibliographic database. Then the disease-gene associations and protein-protein interactions are utilized to construct a phenotype-genotype network. The PRINCE algorithm is finally used to rank the potential genes for the associated symptoms. Results. The proposed method gets reliable gene rank list with AUC (area under curve 0.616 in classification. Some novel genes like CALCA, ESR1, and MTHFR were predicted to be associated with headache symptoms, which are not recorded in the benchmark data set, but have been reported in recent published literatures. Conclusions. Our study demonstrated that by integrating phenotype-genotype relationships into a complex network framework it provides an effective approach to identify candidate genes of symptoms.

  15. The identification of functional motifs in temporal gene expression analysis

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    Michael G. Surette

    2005-01-01

    Full Text Available The identification of transcription factor binding sites is essential to the understanding of the regulation of gene expression and the reconstruction of genetic regulatory networks. The in silico identification of cis-regulatory motifs is challenging due to sequence variability and lack of sufficient data to generate consensus motifs that are of quantitative or even qualitative predictive value. To determine functional motifs in gene expression, we propose a strategy to adopt false discovery rate (FDR and estimate motif effects to evaluate combinatorial analysis of motif candidates and temporal gene expression data. The method decreases the number of predicted motifs, which can then be confirmed by genetic analysis. To assess the method we used simulated motif/expression data to evaluate parameters. We applied this approach to experimental data for a group of iron responsive genes in Salmonella typhimurium 14028S. The method identified known and potentially new ferric-uptake regulator (Fur binding sites. In addition, we identified uncharacterized functional motif candidates that correlated with specific patterns of expression. A SAS code for the simulation and analysis gene expression data is available from the first author upon request.

  16. Analysis of the clonal repertoire of gene-corrected cells in gene therapy.

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    Paruzynski, Anna; Glimm, Hanno; Schmidt, Manfred; Kalle, Christof von

    2012-01-01

    Gene therapy-based clinical phase I/II studies using integrating retroviral vectors could successfully treat different monogenetic inherited diseases. However, with increased efficiency of this therapy, severe side effects occurred in various gene therapy trials. In all cases, integration of the vector close to or within a proto-oncogene contributed substantially to the development of the malignancies. Thus, the in-depth analysis of integration site patterns is of high importance to uncover potential clonal outgrowth and to assess the safety of gene transfer vectors and gene therapy protocols. The standard and nonrestrictive linear amplification-mediated PCR (nrLAM-PCR) in combination with high-throughput sequencing exhibits technologies that allow to comprehensively analyze the clonal repertoire of gene-corrected cells and to assess the safety of the used vector system at an early stage on the molecular level. It enables clarifying the biological consequences of the vector system on the fate of the transduced cell. Furthermore, the downstream performance of real-time PCR allows a quantitative estimation of the clonality of individual cells and their clonal progeny. Here, we present a guideline that should allow researchers to perform comprehensive integration site analysis in preclinical and clinical studies. Copyright © 2012 Elsevier Inc. All rights reserved.

  17. Identification of conserved drought-adaptive genes using a cross-species meta-analysis approach.

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    Shaar-Moshe, Lidor; Hübner, Sariel; Peleg, Zvi

    2015-05-03

    Drought is the major environmental stress threatening crop-plant productivity worldwide. Identification of new genes and metabolic pathways involved in plant adaptation to progressive drought stress at the reproductive stage is of great interest for agricultural research. We developed a novel Cross-Species meta-Analysis of progressive Drought stress at the reproductive stage (CSA:Drought) to identify key drought adaptive genes and mechanisms and to test their evolutionary conservation. Empirically defined filtering criteria were used to facilitate a robust integration of 17 deposited microarray experiments (148 arrays) of Arabidopsis, rice, wheat and barley. By prioritizing consistency over intensity, our approach was able to identify 225 differentially expressed genes shared across studies and taxa. Gene ontology enrichment and pathway analyses classified the shared genes into functional categories involved predominantly in metabolic processes (e.g. amino acid and carbohydrate metabolism), regulatory function (e.g. protein degradation and transcription) and response to stimulus. We further investigated drought related cis-acting elements in the shared gene promoters, and the evolutionary conservation of shared genes. The universal nature of the identified drought-adaptive genes was further validated in a fifth species, Brachypodium distachyon that was not included in the meta-analysis. qPCR analysis of 27, randomly selected, shared orthologs showed similar expression pattern as was found by the CSA:Drought.In accordance, morpho-physiological characterization of progressive drought stress, in B. distachyon, highlighted the key role of osmotic adjustment as evolutionary conserved drought-adaptive mechanism. Our CSA:Drought strategy highlights major drought-adaptive genes and metabolic pathways that were only partially, if at all, reported in the original studies included in the meta-analysis. These genes include a group of unclassified genes that could be involved

  18. A meta-analysis of gene expression signatures of blood pressure and hypertension.

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    Tianxiao Huan

    2015-03-01

    Full Text Available Genome-wide association studies (GWAS have uncovered numerous genetic variants (SNPs that are associated with blood pressure (BP. Genetic variants may lead to BP changes by acting on intermediate molecular phenotypes such as coded protein sequence or gene expression, which in turn affect BP variability. Therefore, characterizing genes whose expression is associated with BP may reveal cellular processes involved in BP regulation and uncover how transcripts mediate genetic and environmental effects on BP variability. A meta-analysis of results from six studies of global gene expression profiles of BP and hypertension in whole blood was performed in 7017 individuals who were not receiving antihypertensive drug treatment. We identified 34 genes that were differentially expressed in relation to BP (Bonferroni-corrected p<0.05. Among these genes, FOS and PTGS2 have been previously reported to be involved in BP-related processes; the others are novel. The top BP signature genes in aggregate explain 5%-9% of inter-individual variance in BP. Of note, rs3184504 in SH2B3, which was also reported in GWAS to be associated with BP, was found to be a trans regulator of the expression of 6 of the transcripts we found to be associated with BP (FOS, MYADM, PP1R15A, TAGAP, S100A10, and FGBP2. Gene set enrichment analysis suggested that the BP-related global gene expression changes include genes involved in inflammatory response and apoptosis pathways. Our study provides new insights into molecular mechanisms underlying BP regulation, and suggests novel transcriptomic markers for the treatment and prevention of hypertension.

  19. Integrative analysis of RUNX1 downstream pathways and target genes

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    Liu Marjorie

    2008-07-01

    Full Text Available Abstract Background The RUNX1 transcription factor gene is frequently mutated in sporadic myeloid and lymphoid leukemia through translocation, point mutation or amplification. It is also responsible for a familial platelet disorder with predisposition to acute myeloid leukemia (FPD-AML. The disruption of the largely unknown biological pathways controlled by RUNX1 is likely to be responsible for the development of leukemia. We have used multiple microarray platforms and bioinformatic techniques to help identify these biological pathways to aid in the understanding of why RUNX1 mutations lead to leukemia. Results Here we report genes regulated either directly or indirectly by RUNX1 based on the study of gene expression profiles generated from 3 different human and mouse platforms. The platforms used were global gene expression profiling of: 1 cell lines with RUNX1 mutations from FPD-AML patients, 2 over-expression of RUNX1 and CBFβ, and 3 Runx1 knockout mouse embryos using either cDNA or Affymetrix microarrays. We observe that our datasets (lists of differentially expressed genes significantly correlate with published microarray data from sporadic AML patients with mutations in either RUNX1 or its cofactor, CBFβ. A number of biological processes were identified among the differentially expressed genes and functional assays suggest that heterozygous RUNX1 point mutations in patients with FPD-AML impair cell proliferation, microtubule dynamics and possibly genetic stability. In addition, analysis of the regulatory regions of the differentially expressed genes has for the first time systematically identified numerous potential novel RUNX1 target genes. Conclusion This work is the first large-scale study attempting to identify the genetic networks regulated by RUNX1, a master regulator in the development of the hematopoietic system and leukemia. The biological pathways and target genes controlled by RUNX1 will have considerable importance in disease

  20. Spectral map-analysis: a method to analyze gene expression data

    OpenAIRE

    Bijnens, Luc J.M.; Lewi, Paul J.; Göhlmann, Hinrich W.; Molenberghs, Geert; Wouters, Luc

    2004-01-01

    bioinformatics; biplot; correspondence factor analysis; data mining; data visualization; gene expression data; microarray data; multivariate exploratory data analysis; principal component analysis; Spectral map analysis

  1. Genetic variations in the CLNK gene and ZNF518B gene are associated with gout in case-control sample sets.

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    Jin, Tian-Bo; Ren, Yongchao; Shi, Xugang; Jiri, Mutu; He, Na; Feng, Tian; Yuan, Dongya; Kang, Longli

    2015-07-01

    A genome-wide association study of gout in European populations identified 12 genetic variants strongly associated with risk of gout, but it is unknown whether these variants are also associated with gout risk in Chinese populations. A total of 145 patients with gout and 310 healthy control patients were recruited for a case-control association study. Twelve SNPs of CLNK and ZNF518B gene were genotyped, and association analysis was performed. Odds ratios (ORs) with 95 % confidence intervals (CIs) were used to assess the association. Overall, we found four risk alleles for gout in patients: the allele "G" of rs2041215 and rs1686947 in the CLNK gene by dominant model (OR 1.66; 95 % CI 1.04-2.63; p = 0.031) (OR 2.19; 95 % CI 1.38-3.46; p = 0.001) and additive model (OR 1.39; 95 % CI 1.00-1.93; p = 0.049) (OR 1.67; 95 % CI 1.19-2.32; p = 0.003), respectively, and the allele "A" of rs10938799 and rs10016022 in ZNF518B gene by recessive model (OR 4.66; 95 % CI 1.44-15.09; p = 0.008) (OR 4.54; 95 % CI 1.23-16.76; p = 0.020). Further haplotype analysis showed that the TCATTCTGA haplotype of CLNK was more frequent among patients with gout (adjusted OR 0.48; 95 % CI 0.24-0.95; p = 0.036). Additionally, polymorphisms of rs2041215, rs10938799, and rs17467273 were also correlated with clinical pathological parameters. This study provides evidence for gout susceptibility genes, CLNK and ZNF518B, in a Chinese population, which may have potential as diagnostic and prognostic marker for gout patients.

  2. Comprehensive analysis of gene expression patterns of hedgehog-related genes

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    Baillie David

    2006-10-01

    Full Text Available Abstract Background The Caenorhabditis elegans genome encodes ten proteins that share sequence similarity with the Hedgehog signaling molecule through their C-terminal autoprocessing Hint/Hog domain. These proteins contain novel N-terminal domains, and C. elegans encodes dozens of additional proteins containing only these N-terminal domains. These gene families are called warthog, groundhog, ground-like and quahog, collectively called hedgehog (hh-related genes. Previously, the expression pattern of seventeen genes was examined, which showed that they are primarily expressed in the ectoderm. Results With the completion of the C. elegans genome sequence in November 2002, we reexamined and identified 61 hh-related ORFs. Further, we identified 49 hh-related ORFs in C. briggsae. ORF analysis revealed that 30% of the genes still had errors in their predictions and we improved these predictions here. We performed a comprehensive expression analysis using GFP fusions of the putative intergenic regulatory sequence with one or two transgenic lines for most genes. The hh-related genes are expressed in one or a few of the following tissues: hypodermis, seam cells, excretory duct and pore cells, vulval epithelial cells, rectal epithelial cells, pharyngeal muscle or marginal cells, arcade cells, support cells of sensory organs, and neuronal cells. Using time-lapse recordings, we discovered that some hh-related genes are expressed in a cyclical fashion in phase with molting during larval development. We also generated several translational GFP fusions, but they did not show any subcellular localization. In addition, we also studied the expression patterns of two genes with similarity to Drosophila frizzled, T23D8.1 and F27E11.3A, and the ortholog of the Drosophila gene dally-like, gpn-1, which is a heparan sulfate proteoglycan. The two frizzled homologs are expressed in a few neurons in the head, and gpn-1 is expressed in the pharynx. Finally, we compare the

  3. Heart morphogenesis gene regulatory networks revealed by temporal expression analysis.

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    Hill, Jonathon T; Demarest, Bradley; Gorsi, Bushra; Smith, Megan; Yost, H Joseph

    2017-10-01

    During embryogenesis the heart forms as a linear tube that then undergoes multiple simultaneous morphogenetic events to obtain its mature shape. To understand the gene regulatory networks (GRNs) driving this phase of heart development, during which many congenital heart disease malformations likely arise, we conducted an RNA-seq timecourse in zebrafish from 30 hpf to 72 hpf and identified 5861 genes with altered expression. We clustered the genes by temporal expression pattern, identified transcription factor binding motifs enriched in each cluster, and generated a model GRN for the major gene batteries in heart morphogenesis. This approach predicted hundreds of regulatory interactions and found batteries enriched in specific cell and tissue types, indicating that the approach can be used to narrow the search for novel genetic markers and regulatory interactions. Subsequent analyses confirmed the GRN using two mutants, Tbx5 and nkx2-5 , and identified sets of duplicated zebrafish genes that do not show temporal subfunctionalization. This dataset provides an essential resource for future studies on the genetic/epigenetic pathways implicated in congenital heart defects and the mechanisms of cardiac transcriptional regulation. © 2017. Published by The Company of Biologists Ltd.

  4. Sexually Dimorphic Gene Expression Associated with Growth and Reproduction of Tongue Sole (Cynoglossus semilaevis) Revealed by Brain Transcriptome Analysis.

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    Wang, Pingping; Zheng, Min; Liu, Jian; Liu, Yongzhuang; Lu, Jianguo; Sun, Xiaowen

    2016-08-26

    In this study, we performed a comprehensive analysis of the transcriptome of one- and two-year-old male and female brains of Cynoglossus semilaevis by high-throughput Illumina sequencing. A total of 77,066 transcripts, corresponding to 21,475 unigenes, were obtained with a N50 value of 4349 bp. Of these unigenes, 33 genes were found to have significant differential expression and potentially associated with growth, from which 18 genes were down-regulated and 12 genes were up-regulated in two-year-old males, most of these genes had no significant differences in expression among one-year-old males and females and two-year-old females. A similar analysis was conducted to look for genes associated with reproduction; 25 genes were identified, among them, five genes were found to be down regulated and 20 genes up regulated in two-year-old males, again, most of the genes had no significant expression differences among the other three. The performance of up regulated genes in Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was significantly different between two-year-old males and females. Males had a high gene expression in genetic information processing, while female's highly expressed genes were mainly enriched on organismal systems. Our work identified a set of sex-biased genes potentially associated with growth and re