Multiple imputation by chained equations for systematically and sporadically missing multilevel data.

Science.gov (United States)

Resche-Rigon, Matthieu; White, Ian R

2018-06-01

In multilevel settings such as individual participant data meta-analysis, a variable is 'systematically missing' if it is wholly missing in some clusters and 'sporadically missing' if it is partly missing in some clusters. Previously proposed methods to impute incomplete multilevel data handle either systematically or sporadically missing data, but frequently both patterns are observed. We describe a new multiple imputation by chained equations (MICE) algorithm for multilevel data with arbitrary patterns of systematically and sporadically missing variables. The algorithm is described for multilevel normal data but can easily be extended for other variable types. We first propose two methods for imputing a single incomplete variable: an extension of an existing method and a new two-stage method which conveniently allows for heteroscedastic data. We then discuss the difficulties of imputing missing values in several variables in multilevel data using MICE, and show that even the simplest joint multilevel model implies conditional models which involve cluster means and heteroscedasticity. However, a simulation study finds that the proposed methods can be successfully combined in a multilevel MICE procedure, even when cluster means are not included in the imputation models.

Multiple imputation to account for missing data in a survey: estimating the prevalence of osteoporosis.

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Kmetic, Andrew; Joseph, Lawrence; Berger, Claudie; Tenenhouse, Alan

2002-07-01

Nonresponse bias is a concern in any epidemiologic survey in which a subset of selected individuals declines to participate. We reviewed multiple imputation, a widely applicable and easy to implement Bayesian methodology to adjust for nonresponse bias. To illustrate the method, we used data from the Canadian Multicentre Osteoporosis Study, a large cohort study of 9423 randomly selected Canadians, designed in part to estimate the prevalence of osteoporosis. Although subjects were randomly selected, only 42% of individuals who were contacted agreed to participate fully in the study. The study design included a brief questionnaire for those invitees who declined further participation in order to collect information on the major risk factors for osteoporosis. These risk factors (which included age, sex, previous fractures, family history of osteoporosis, and current smoking status) were then used to estimate the missing osteoporosis status for nonparticipants using multiple imputation. Both ignorable and nonignorable imputation models are considered. Our results suggest that selection bias in the study is of concern, but only slightly, in very elderly (age 80+ years), both women and men. Epidemiologists should consider using multiple imputation more often than is current practice.

Methods for significance testing of categorical covariates in logistic regression models after multiple imputation: power and applicability analysis

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Eekhout, I.; Wiel, M.A. van de; Heymans, M.W.

2017-01-01

Background. Multiple imputation is a recommended method to handle missing data. For significance testing after multiple imputation, Rubin’s Rules (RR) are easily applied to pool parameter estimates. In a logistic regression model, to consider whether a categorical covariate with more than two levels

Dealing with missing data in a multi-question depression scale: a comparison of imputation methods

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Stuart Heather

2006-12-01

Full Text Available Abstract Background Missing data present a challenge to many research projects. The problem is often pronounced in studies utilizing self-report scales, and literature addressing different strategies for dealing with missing data in such circumstances is scarce. The objective of this study was to compare six different imputation techniques for dealing with missing data in the Zung Self-reported Depression scale (SDS. Methods 1580 participants from a surgical outcomes study completed the SDS. The SDS is a 20 question scale that respondents complete by circling a value of 1 to 4 for each question. The sum of the responses is calculated and respondents are classified as exhibiting depressive symptoms when their total score is over 40. Missing values were simulated by randomly selecting questions whose values were then deleted (a missing completely at random simulation. Additionally, a missing at random and missing not at random simulation were completed. Six imputation methods were then considered; 1 multiple imputation, 2 single regression, 3 individual mean, 4 overall mean, 5 participant's preceding response, and 6 random selection of a value from 1 to 4. For each method, the imputed mean SDS score and standard deviation were compared to the population statistics. The Spearman correlation coefficient, percent misclassified and the Kappa statistic were also calculated. Results When 10% of values are missing, all the imputation methods except random selection produce Kappa statistics greater than 0.80 indicating 'near perfect' agreement. MI produces the most valid imputed values with a high Kappa statistic (0.89, although both single regression and individual mean imputation also produced favorable results. As the percent of missing information increased to 30%, or when unbalanced missing data were introduced, MI maintained a high Kappa statistic. The individual mean and single regression method produced Kappas in the 'substantial agreement' range

Missing data treatments matter: an analysis of multiple imputation for anterior cervical discectomy and fusion procedures.

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Ondeck, Nathaniel T; Fu, Michael C; Skrip, Laura A; McLynn, Ryan P; Cui, Jonathan J; Basques, Bryce A; Albert, Todd J; Grauer, Jonathan N

2018-04-09

The presence of missing data is a limitation of large datasets, including the National Surgical Quality Improvement Program (NSQIP). In addressing this issue, most studies use complete case analysis, which excludes cases with missing data, thus potentially introducing selection bias. Multiple imputation, a statistically rigorous approach that approximates missing data and preserves sample size, may be an improvement over complete case analysis. The present study aims to evaluate the impact of using multiple imputation in comparison with complete case analysis for assessing the associations between preoperative laboratory values and adverse outcomes following anterior cervical discectomy and fusion (ACDF) procedures. This is a retrospective review of prospectively collected data. Patients undergoing one-level ACDF were identified in NSQIP 2012-2015. Perioperative adverse outcome variables assessed included the occurrence of any adverse event, severe adverse events, and hospital readmission. Missing preoperative albumin and hematocrit values were handled using complete case analysis and multiple imputation. These preoperative laboratory levels were then tested for associations with 30-day postoperative outcomes using logistic regression. A total of 11,999 patients were included. Of this cohort, 63.5% of patients had missing preoperative albumin and 9.9% had missing preoperative hematocrit. When using complete case analysis, only 4,311 patients were studied. The removed patients were significantly younger, healthier, of a common body mass index, and male. Logistic regression analysis failed to identify either preoperative hypoalbuminemia or preoperative anemia as significantly associated with adverse outcomes. When employing multiple imputation, all 11,999 patients were included. Preoperative hypoalbuminemia was significantly associated with the occurrence of any adverse event and severe adverse events. Preoperative anemia was significantly associated with the

Practical considerations for sensitivity analysis after multiple imputation applied to epidemiological studies with incomplete data

Science.gov (United States)

2012-01-01

Background Multiple Imputation as usually implemented assumes that data are Missing At Random (MAR), meaning that the underlying missing data mechanism, given the observed data, is independent of the unobserved data. To explore the sensitivity of the inferences to departures from the MAR assumption, we applied the method proposed by Carpenter et al. (2007). This approach aims to approximate inferences under a Missing Not At random (MNAR) mechanism by reweighting estimates obtained after multiple imputation where the weights depend on the assumed degree of departure from the MAR assumption. Methods The method is illustrated with epidemiological data from a surveillance system of hepatitis C virus (HCV) infection in France during the 2001–2007 period. The subpopulation studied included 4343 HCV infected patients who reported drug use. Risk factors for severe liver disease were assessed. After performing complete-case and multiple imputation analyses, we applied the sensitivity analysis to 3 risk factors of severe liver disease: past excessive alcohol consumption, HIV co-infection and infection with HCV genotype 3. Results In these data, the association between severe liver disease and HIV was underestimated, if given the observed data the chance of observing HIV status is high when this is positive. Inference for two other risk factors were robust to plausible local departures from the MAR assumption. Conclusions We have demonstrated the practical utility of, and advocate, a pragmatic widely applicable approach to exploring plausible departures from the MAR assumption post multiple imputation. We have developed guidelines for applying this approach to epidemiological studies. PMID:22681630

Multiply-Imputed Synthetic Data: Advice to the Imputer

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Loong Bronwyn

2017-12-01

Full Text Available Several statistical agencies have started to use multiply-imputed synthetic microdata to create public-use data in major surveys. The purpose of doing this is to protect the confidentiality of respondents’ identities and sensitive attributes, while allowing standard complete-data analyses of microdata. A key challenge, faced by advocates of synthetic data, is demonstrating that valid statistical inferences can be obtained from such synthetic data for non-confidential questions. Large discrepancies between observed-data and synthetic-data analytic results for such questions may arise because of uncongeniality; that is, differences in the types of inputs available to the imputer, who has access to the actual data, and to the analyst, who has access only to the synthetic data. Here, we discuss a simple, but possibly canonical, example of uncongeniality when using multiple imputation to create synthetic data, which specifically addresses the choices made by the imputer. An initial, unanticipated but not surprising, conclusion is that non-confidential design information used to impute synthetic data should be released with the confidential synthetic data to allow users of synthetic data to avoid possible grossly conservative inferences.

Trend in BMI z-score among Private Schools’ Students in Delhi using Multiple Imputation for Growth Curve Model

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Vinay K Gupta

2016-06-01

Full Text Available Objective: The aim of the study is to assess the trend in mean BMI z-score among private schools’ students from their anthropometric records when there were missing values in the outcome. Methodology: The anthropometric measurements of student from class 1 to 12 were taken from the records of two private schools in Delhi, India from 2005 to 2010. These records comprise of an unbalanced longitudinal data that is not all the students had measurements recorded at each year. The trend in mean BMI z-score was estimated through growth curve model. Prior to that, missing values of BMI z-score were imputed through multiple imputation using the same model. A complete case analysis was also performed after excluding missing values to compare the results with those obtained from analysis of multiply imputed data. Results: The mean BMI z-score among school student significantly decreased over time in imputed data (β= -0.2030, se=0.0889, p=0.0232 after adjusting age, gender, class and school. Complete case analysis also shows a decrease in mean BMI z-score though it was not statistically significant (β= -0.2861, se=0.0987, p=0.065. Conclusions: The estimates obtained from multiple imputation analysis were better than those of complete data after excluding missing values in terms of lower standard errors. We showed that anthropometric measurements from schools records can be used to monitor the weight status of children and adolescents and multiple imputation using growth curve model can be useful while analyzing such data

Auxiliary variables in multiple imputation in regression with missing X: a warning against including too many in small sample research

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Hardt Jochen

2012-12-01

Full Text Available Abstract Background Multiple imputation is becoming increasingly popular. Theoretical considerations as well as simulation studies have shown that the inclusion of auxiliary variables is generally of benefit. Methods A simulation study of a linear regression with a response Y and two predictors X1 and X2 was performed on data with n = 50, 100 and 200 using complete cases or multiple imputation with 0, 10, 20, 40 and 80 auxiliary variables. Mechanisms of missingness were either 100% MCAR or 50% MAR + 50% MCAR. Auxiliary variables had low (r=.10 vs. moderate correlations (r=.50 with X’s and Y. Results The inclusion of auxiliary variables can improve a multiple imputation model. However, inclusion of too many variables leads to downward bias of regression coefficients and decreases precision. When the correlations are low, inclusion of auxiliary variables is not useful. Conclusion More research on auxiliary variables in multiple imputation should be performed. A preliminary rule of thumb could be that the ratio of variables to cases with complete data should not go below 1 : 3.

Factors associated with low birth weight in Nepal using multiple imputation

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Usha Singh

2017-02-01

Full Text Available Abstract Background Survey data from low income countries on birth weight usually pose a persistent problem. The studies conducted on birth weight have acknowledged missing data on birth weight, but they are not included in the analysis. Furthermore, other missing data presented on determinants of birth weight are not addressed. Thus, this study tries to identify determinants that are associated with low birth weight (LBW using multiple imputation to handle missing data on birth weight and its determinants. Methods The child dataset from Nepal Demographic and Health Survey (NDHS, 2011 was utilized in this study. A total of 5,240 children were born between 2006 and 2011, out of which 87% had at least one measured variable missing and 21% had no recorded birth weight. All the analyses were carried out in R version 3.1.3. Transform-then impute method was applied to check for interaction between explanatory variables and imputed missing data. Survey package was applied to each imputed dataset to account for survey design and sampling method. Survey logistic regression was applied to identify the determinants associated with LBW. Results The prevalence of LBW was 15.4% after imputation. Women with the highest autonomy on their own health compared to those with health decisions involving husband or others (adjusted odds ratio (OR 1.87, 95% confidence interval (95% CI = 1.31, 2.67, and husband and women together (adjusted OR 1.57, 95% CI = 1.05, 2.35 were less likely to give birth to LBW infants. Mothers using highly polluting cooking fuels (adjusted OR 1.49, 95% CI = 1.03, 2.22 were more likely to give birth to LBW infants than mothers using non-polluting cooking fuels. Conclusion The findings of this study suggested that obtaining the prevalence of LBW from only the sample of measured birth weight and ignoring missing data results in underestimation.

Multiple imputation using linked proxy outcome data resulted in important bias reduction and efficiency gains: a simulation study.

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Cornish, R P; Macleod, J; Carpenter, J R; Tilling, K

2017-01-01

When an outcome variable is missing not at random (MNAR: probability of missingness depends on outcome values), estimates of the effect of an exposure on this outcome are often biased. We investigated the extent of this bias and examined whether the bias can be reduced through incorporating proxy outcomes obtained through linkage to administrative data as auxiliary variables in multiple imputation (MI). Using data from the Avon Longitudinal Study of Parents and Children (ALSPAC) we estimated the association between breastfeeding and IQ (continuous outcome), incorporating linked attainment data (proxies for IQ) as auxiliary variables in MI models. Simulation studies explored the impact of varying the proportion of missing data (from 20 to 80%), the correlation between the outcome and its proxy (0.1-0.9), the strength of the missing data mechanism, and having a proxy variable that was incomplete. Incorporating a linked proxy for the missing outcome as an auxiliary variable reduced bias and increased efficiency in all scenarios, even when 80% of the outcome was missing. Using an incomplete proxy was similarly beneficial. High correlations (> 0.5) between the outcome and its proxy substantially reduced the missing information. Consistent with this, ALSPAC analysis showed inclusion of a proxy reduced bias and improved efficiency. Gains with additional proxies were modest. In longitudinal studies with loss to follow-up, incorporating proxies for this study outcome obtained via linkage to external sources of data as auxiliary variables in MI models can give practically important bias reduction and efficiency gains when the study outcome is MNAR.

Taking a multiple intelligences (MI) perspective.

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Gardner, Howard

2017-01-01

The theory of multiple intelligences (MI) seeks to describe and encompass the range of human cognitive capacities. In challenging the concept of general intelligence, we can apply an MI perspective that may provide a more useful approach to cognitive differences within and across species.

Treatments of Missing Values in Large National Data Affect Conclusions: The Impact of Multiple Imputation on Arthroplasty Research.

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Ondeck, Nathaniel T; Fu, Michael C; Skrip, Laura A; McLynn, Ryan P; Su, Edwin P; Grauer, Jonathan N

2018-03-01

Despite the advantages of large, national datasets, one continuing concern is missing data values. Complete case analysis, where only cases with complete data are analyzed, is commonly used rather than more statistically rigorous approaches such as multiple imputation. This study characterizes the potential selection bias introduced using complete case analysis and compares the results of common regressions using both techniques following unicompartmental knee arthroplasty. Patients undergoing unicompartmental knee arthroplasty were extracted from the 2005 to 2015 National Surgical Quality Improvement Program. As examples, the demographics of patients with and without missing preoperative albumin and hematocrit values were compared. Missing data were then treated with both complete case analysis and multiple imputation (an approach that reproduces the variation and associations that would have been present in a full dataset) and the conclusions of common regressions for adverse outcomes were compared. A total of 6117 patients were included, of which 56.7% were missing at least one value. Younger, female, and healthier patients were more likely to have missing preoperative albumin and hematocrit values. The use of complete case analysis removed 3467 patients from the study in comparison with multiple imputation which included all 6117 patients. The 2 methods of handling missing values led to differing associations of low preoperative laboratory values with commonly studied adverse outcomes. The use of complete case analysis can introduce selection bias and may lead to different conclusions in comparison with the statistically rigorous multiple imputation approach. Joint surgeons should consider the methods of handling missing values when interpreting arthroplasty research. Copyright © 2017 Elsevier Inc. All rights reserved.

Multiple imputation to account for measurement error in marginal structural models

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Edwards, Jessie K.; Cole, Stephen R.; Westreich, Daniel; Crane, Heidi; Eron, Joseph J.; Mathews, W. Christopher; Moore, Richard; Boswell, Stephen L.; Lesko, Catherine R.; Mugavero, Michael J.

2015-01-01

Background Marginal structural models are an important tool for observational studies. These models typically assume that variables are measured without error. We describe a method to account for differential and non-differential measurement error in a marginal structural model. Methods We illustrate the method estimating the joint effects of antiretroviral therapy initiation and current smoking on all-cause mortality in a United States cohort of 12,290 patients with HIV followed for up to 5 years between 1998 and 2011. Smoking status was likely measured with error, but a subset of 3686 patients who reported smoking status on separate questionnaires composed an internal validation subgroup. We compared a standard joint marginal structural model fit using inverse probability weights to a model that also accounted for misclassification of smoking status using multiple imputation. Results In the standard analysis, current smoking was not associated with increased risk of mortality. After accounting for misclassification, current smoking without therapy was associated with increased mortality [hazard ratio (HR): 1.2 (95% CI: 0.6, 2.3)]. The HR for current smoking and therapy (0.4 (95% CI: 0.2, 0.7)) was similar to the HR for no smoking and therapy (0.4; 95% CI: 0.2, 0.6). Conclusions Multiple imputation can be used to account for measurement error in concert with methods for causal inference to strengthen results from observational studies. PMID:26214338

Multiple Imputation to Account for Measurement Error in Marginal Structural Models.

Science.gov (United States)

Edwards, Jessie K; Cole, Stephen R; Westreich, Daniel; Crane, Heidi; Eron, Joseph J; Mathews, W Christopher; Moore, Richard; Boswell, Stephen L; Lesko, Catherine R; Mugavero, Michael J

2015-09-01

Marginal structural models are an important tool for observational studies. These models typically assume that variables are measured without error. We describe a method to account for differential and nondifferential measurement error in a marginal structural model. We illustrate the method estimating the joint effects of antiretroviral therapy initiation and current smoking on all-cause mortality in a United States cohort of 12,290 patients with HIV followed for up to 5 years between 1998 and 2011. Smoking status was likely measured with error, but a subset of 3,686 patients who reported smoking status on separate questionnaires composed an internal validation subgroup. We compared a standard joint marginal structural model fit using inverse probability weights to a model that also accounted for misclassification of smoking status using multiple imputation. In the standard analysis, current smoking was not associated with increased risk of mortality. After accounting for misclassification, current smoking without therapy was associated with increased mortality (hazard ratio [HR]: 1.2 [95% confidence interval [CI] = 0.6, 2.3]). The HR for current smoking and therapy [0.4 (95% CI = 0.2, 0.7)] was similar to the HR for no smoking and therapy (0.4; 95% CI = 0.2, 0.6). Multiple imputation can be used to account for measurement error in concert with methods for causal inference to strengthen results from observational studies.

Multiple imputation strategies for zero-inflated cost data in economic evaluations : which method works best?

NARCIS (Netherlands)

MacNeil Vroomen, Janet; Eekhout, Iris; Dijkgraaf, Marcel G; van Hout, Hein; de Rooij, Sophia E; Heymans, Martijn W; Bosmans, Judith E

2016-01-01

Cost and effect data often have missing data because economic evaluations are frequently added onto clinical studies where cost data are rarely the primary outcome. The objective of this article was to investigate which multiple imputation strategy is most appropriate to use for missing

Multiple imputation for multivariate data with missing and below-threshold measurements: time-series concentrations of pollutants in the Arctic.

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Hopke, P K; Liu, C; Rubin, D B

2001-03-01

Many chemical and environmental data sets are complicated by the existence of fully missing values or censored values known to lie below detection thresholds. For example, week-long samples of airborne particulate matter were obtained at Alert, NWT, Canada, between 1980 and 1991, where some of the concentrations of 24 particulate constituents were coarsened in the sense of being either fully missing or below detection limits. To facilitate scientific analysis, it is appealing to create complete data by filling in missing values so that standard complete-data methods can be applied. We briefly review commonly used strategies for handling missing values and focus on the multiple-imputation approach, which generally leads to valid inferences when faced with missing data. Three statistical models are developed for multiply imputing the missing values of airborne particulate matter. We expect that these models are useful for creating multiple imputations in a variety of incomplete multivariate time series data sets.

Comparison of results from different imputation techniques for missing data from an anti-obesity drug trial

DEFF Research Database (Denmark)

Jørgensen, Anders W.; Lundstrøm, Lars H; Wetterslev, Jørn

2014-01-01

BACKGROUND: In randomised trials of medical interventions, the most reliable analysis follows the intention-to-treat (ITT) principle. However, the ITT analysis requires that missing outcome data have to be imputed. Different imputation techniques may give different results and some may lead to bias...... of handling missing data in a 60-week placebo controlled anti-obesity drug trial on topiramate. METHODS: We compared an analysis of complete cases with datasets where missing body weight measurements had been replaced using three different imputation methods: LOCF, baseline carried forward (BOCF) and MI...

Missing data imputation: focusing on single imputation.

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Zhang, Zhongheng

2016-01-01

Complete case analysis is widely used for handling missing data, and it is the default method in many statistical packages. However, this method may introduce bias and some useful information will be omitted from analysis. Therefore, many imputation methods are developed to make gap end. The present article focuses on single imputation. Imputations with mean, median and mode are simple but, like complete case analysis, can introduce bias on mean and deviation. Furthermore, they ignore relationship with other variables. Regression imputation can preserve relationship between missing values and other variables. There are many sophisticated methods exist to handle missing values in longitudinal data. This article focuses primarily on how to implement R code to perform single imputation, while avoiding complex mathematical calculations.

A Note on the Effect of Data Clustering on the Multiple-Imputation Variance Estimator: A Theoretical Addendum to the Lewis et al. article in JOS 2014

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He Yulei

2016-03-01

Full Text Available Multiple imputation is a popular approach to handling missing data. Although it was originally motivated by survey nonresponse problems, it has been readily applied to other data settings. However, its general behavior still remains unclear when applied to survey data with complex sample designs, including clustering. Recently, Lewis et al. (2014 compared single- and multiple-imputation analyses for certain incomplete variables in the 2008 National Ambulatory Medicare Care Survey, which has a nationally representative, multistage, and clustered sampling design. Their study results suggested that the increase of the variance estimate due to multiple imputation compared with single imputation largely disappears for estimates with large design effects. We complement their empirical research by providing some theoretical reasoning. We consider data sampled from an equally weighted, single-stage cluster design and characterize the process using a balanced, one-way normal random-effects model. Assuming that the missingness is completely at random, we derive analytic expressions for the within- and between-multiple-imputation variance estimators for the mean estimator, and thus conveniently reveal the impact of design effects on these variance estimators. We propose approximations for the fraction of missing information in clustered samples, extending previous results for simple random samples. We discuss some generalizations of this research and its practical implications for data release by statistical agencies.

Statistical Analysis of a Class: Monte Carlo and Multiple Imputation Spreadsheet Methods for Estimation and Extrapolation

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Fish, Laurel J.; Halcoussis, Dennis; Phillips, G. Michael

2017-01-01

The Monte Carlo method and related multiple imputation methods are traditionally used in math, physics and science to estimate and analyze data and are now becoming standard tools in analyzing business and financial problems. However, few sources explain the application of the Monte Carlo method for individuals and business professionals who are…

Flexible Imputation of Missing Data

CERN Document Server

van Buuren, Stef

2012-01-01

Missing data form a problem in every scientific discipline, yet the techniques required to handle them are complicated and often lacking. One of the great ideas in statistical science--multiple imputation--fills gaps in the data with plausible values, the uncertainty of which is coded in the data itself. It also solves other problems, many of which are missing data problems in disguise. Flexible Imputation of Missing Data is supported by many examples using real data taken from the author's vast experience of collaborative research, and presents a practical guide for handling missing data unde

Simultaneous inhibition of multiple oncogenic miRNAs by a multi-potent microRNA sponge.

Science.gov (United States)

Jung, Jaeyun; Yeom, Chanjoo; Choi, Yeon-Sook; Kim, Sinae; Lee, EunJi; Park, Min Ji; Kang, Sang Wook; Kim, Sung Bae; Chang, Suhwan

2015-08-21

The roles of oncogenic miRNAs are widely recognized in many cancers. Inhibition of single miRNA using antagomiR can efficiently knock-down a specific miRNA. However, the effect is transient and often results in subtle phenotype, as there are other miRNAs contribute to tumorigenesis. Here we report a multi-potent miRNA sponge inhibiting multiple miRNAs simultaneously. As a model system, we targeted miR-21, miR-155 and miR-221/222, known as oncogenic miRNAs in multiple tumors including breast and pancreatic cancers. To achieve efficient knockdown, we generated perfect and bulged-matched miRNA binding sites (MBS) and introduced multiple copies of MBS, ranging from one to five, in the multi-potent miRNA sponge. Luciferase reporter assay showed the multi-potent miRNA sponge efficiently inhibited 4 miRNAs in breast and pancreatic cancer cells. Furthermore, a stable and inducible version of the multi-potent miRNA sponge cell line showed the miRNA sponge efficiently reduces the level of 4 target miRNAs and increase target protein level of these oncogenic miRNAs. Finally, we showed the miRNA sponge sensitize cells to cancer drug and attenuate cell migratory activity. Altogether, our study demonstrates the multi-potent miRNA sponge is a useful tool to examine the functional impact of simultaneous inhibition of multiple miRNAs and proposes a therapeutic potential.

A Comparison of Joint Model and Fully Conditional Specification Imputation for Multilevel Missing Data

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Mistler, Stephen A.; Enders, Craig K.

2017-01-01

Multiple imputation methods can generally be divided into two broad frameworks: joint model (JM) imputation and fully conditional specification (FCS) imputation. JM draws missing values simultaneously for all incomplete variables using a multivariate distribution, whereas FCS imputes variables one at a time from a series of univariate conditional…

R package imputeTestbench to compare imputations methods for univariate time series

OpenAIRE

Bokde, Neeraj; Kulat, Kishore; Beck, Marcus W; Asencio-Cortés, Gualberto

2016-01-01

This paper describes the R package imputeTestbench that provides a testbench for comparing imputation methods for missing data in univariate time series. The imputeTestbench package can be used to simulate the amount and type of missing data in a complete dataset and compare filled data using different imputation methods. The user has the option to simulate missing data by removing observations completely at random or in blocks of different sizes. Several default imputation methods are includ...

Missing Value Imputation Improves Mortality Risk Prediction Following Cardiac Surgery: An Investigation of an Australian Patient Cohort.

Science.gov (United States)

Karim, Md Nazmul; Reid, Christopher M; Tran, Lavinia; Cochrane, Andrew; Billah, Baki

2017-03-01

The aim of this study was to evaluate the impact of missing values on the prediction performance of the model predicting 30-day mortality following cardiac surgery as an example. Information from 83,309 eligible patients, who underwent cardiac surgery, recorded in the Australia and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) database registry between 2001 and 2014, was used. An existing 30-day mortality risk prediction model developed from ANZSCTS database was re-estimated using the complete cases (CC) analysis and using multiple imputation (MI) analysis. Agreement between the risks generated by the CC and MI analysis approaches was assessed by the Bland-Altman method. Performances of the two models were compared. One or more missing predictor variables were present in 15.8% of the patients in the dataset. The Bland-Altman plot demonstrated significant disagreement between the risk scores (prisk of mortality. Compared to CC analysis, MI analysis resulted in an average of 8.5% decrease in standard error, a measure of uncertainty. The MI model provided better prediction of mortality risk (observed: 2.69%; MI: 2.63% versus CC: 2.37%, Pvalues improved the 30-day mortality risk prediction following cardiac surgery. Copyright © 2016 Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) and the Cardiac Society of Australia and New Zealand (CSANZ). Published by Elsevier B.V. All rights reserved.

Multiple imputation of missing passenger boarding data in the national census of ferry operators

Science.gov (United States)

2008-08-01

This report presents findings from the 2006 National Census of Ferry Operators (NCFO) augmented with imputed values for passengers and passenger miles. Due to the imputation procedures used to calculate missing data, totals in Table 1 may not corresp...

Imputing data that are missing at high rates using a boosting algorithm

Energy Technology Data Exchange (ETDEWEB)

Cauthen, Katherine Regina [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Lambert, Gregory [Apple Inc., Cupertino, CA (United States); Ray, Jaideep [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Lefantzi, Sophia [Sandia National Lab. (SNL-CA), Livermore, CA (United States)

2016-09-01

Traditional multiple imputation approaches may perform poorly for datasets with high rates of missingness unless many m imputations are used. This paper implements an alternative machine learning-based approach to imputing data that are missing at high rates. Here, we use boosting to create a strong learner from a weak learner fitted to a dataset missing many observations. This approach may be applied to a variety of types of learners (models). The approach is demonstrated by application to a spatiotemporal dataset for predicting dengue outbreaks in India from meteorological covariates. A Bayesian spatiotemporal CAR model is boosted to produce imputations, and the overall RMSE from a k-fold cross-validation is used to assess imputation accuracy.

Missing value imputation for epistatic MAPs

LENUS (Irish Health Repository)

Ryan, Colm

2010-04-20

expands the number of mapped epistatic interactions. In addition we make implementations of our algorithms available for use by other researchers. Conclusions We address the problem of missing value imputation for E-MAPs, and suggest the use of symmetric nearest neighbor based approaches as they offer consistently accurate imputations across multiple datasets in a tractable manner.

Multiple imputation of rainfall missing data in the Iberian Mediterranean context

Science.gov (United States)

Miró, Juan Javier; Caselles, Vicente; Estrela, María José

2017-11-01

Given the increasing need for complete rainfall data networks, in recent years have been proposed diverse methods for filling gaps in observed precipitation series, progressively more advanced that traditional approaches to overcome the problem. The present study has consisted in validate 10 methods (6 linear, 2 non-linear and 2 hybrid) that allow multiple imputation, i.e., fill at the same time missing data of multiple incomplete series in a dense network of neighboring stations. These were applied for daily and monthly rainfall in two sectors in the Júcar River Basin Authority (east Iberian Peninsula), which is characterized by a high spatial irregularity and difficulty of rainfall estimation. A classification of precipitation according to their genetic origin was applied as pre-processing, and a quantile-mapping adjusting as post-processing technique. The results showed in general a better performance for the non-linear and hybrid methods, highlighting that the non-linear PCA (NLPCA) method outperforms considerably the Self Organizing Maps (SOM) method within non-linear approaches. On linear methods, the Regularized Expectation Maximization method (RegEM) was the best, but far from NLPCA. Applying EOF filtering as post-processing of NLPCA (hybrid approach) yielded the best results.

Genotype Imputation for Latinos Using the HapMap and 1000 Genomes Project Reference Panels

Directory of Open Access Journals (Sweden)

Xiaoyi eGao

2012-06-01

Full Text Available Genotype imputation is a vital tool in genome-wide association studies (GWAS and meta-analyses of multiple GWAS results. Imputation enables researchers to increase genomic coverage and to pool data generated using different genotyping platforms. HapMap samples are often employed as the reference panel. More recently, the 1000 Genomes Project resource is becoming the primary source for reference panels. Multiple GWAS and meta-analyses are targeting Latinos, the most populous and fastest growing minority group in the US. However, genotype imputation resources for Latinos are rather limited compared to individuals of European ancestry at present, largely because of the lack of good reference data. One choice of reference panel for Latinos is one derived from the population of Mexican individuals in Los Angeles contained in the HapMap Phase 3 project and the 1000 Genomes Project. However, a detailed evaluation of the quality of the imputed genotypes derived from the public reference panels has not yet been reported. Using simulation studies, the Illumina OmniExpress GWAS data from the Los Angles Latino Eye Study and the MACH software package, we evaluated the accuracy of genotype imputation in Latinos. Our results show that the 1000 Genomes Project AMR+CEU+YRI reference panel provides the highest imputation accuracy for Latinos, and that also including Asian samples in the panel can reduce imputation accuracy. We also provide the imputation accuracy for each autosomal chromosome using the 1000 Genomes Project panel for Latinos. Our results serve as a guide to future imputation-based analysis in Latinos.

3D-MICE: integration of cross-sectional and longitudinal imputation for multi-analyte longitudinal clinical data.

Science.gov (United States)

Luo, Yuan; Szolovits, Peter; Dighe, Anand S; Baron, Jason M

2018-06-01

A key challenge in clinical data mining is that most clinical datasets contain missing data. Since many commonly used machine learning algorithms require complete datasets (no missing data), clinical analytic approaches often entail an imputation procedure to "fill in" missing data. However, although most clinical datasets contain a temporal component, most commonly used imputation methods do not adequately accommodate longitudinal time-based data. We sought to develop a new imputation algorithm, 3-dimensional multiple imputation with chained equations (3D-MICE), that can perform accurate imputation of missing clinical time series data. We extracted clinical laboratory test results for 13 commonly measured analytes (clinical laboratory tests). We imputed missing test results for the 13 analytes using 3 imputation methods: multiple imputation with chained equations (MICE), Gaussian process (GP), and 3D-MICE. 3D-MICE utilizes both MICE and GP imputation to integrate cross-sectional and longitudinal information. To evaluate imputation method performance, we randomly masked selected test results and imputed these masked results alongside results missing from our original data. We compared predicted results to measured results for masked data points. 3D-MICE performed significantly better than MICE and GP-based imputation in a composite of all 13 analytes, predicting missing results with a normalized root-mean-square error of 0.342, compared to 0.373 for MICE alone and 0.358 for GP alone. 3D-MICE offers a novel and practical approach to imputing clinical laboratory time series data. 3D-MICE may provide an additional tool for use as a foundation in clinical predictive analytics and intelligent clinical decision support.

Improving accuracy of rare variant imputation with a two-step imputation approach

DEFF Research Database (Denmark)

Kreiner-Møller, Eskil; Medina-Gomez, Carolina; Uitterlinden, André G

2015-01-01

not being comprehensively scrutinized. Next-generation arrays ensuring sufficient coverage together with new reference panels, as the 1000 Genomes panel, are emerging to facilitate imputation of low frequent single-nucleotide polymorphisms (minor allele frequency (MAF) ... reference sample genotyped on a dense array and hereafter to the 1000 Genomes reference panel. We show that mean imputation quality, measured by the r(2) using this approach, increases by 28% for variants with a MAF between 1 and 5% as compared with direct imputation to 1000 Genomes reference. Similarly......Genotype imputation has been the pillar of the success of genome-wide association studies (GWAS) for identifying common variants associated with common diseases. However, most GWAS have been run using only 60 HapMap samples as reference for imputation, meaning less frequent and rare variants...

On multivariate imputation and forecasting of decadal wind speed missing data.

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Wesonga, Ronald

2015-01-01

This paper demonstrates the application of multiple imputations by chained equations and time series forecasting of wind speed data. The study was motivated by the high prevalence of missing wind speed historic data. Findings based on the fully conditional specification under multiple imputations by chained equations, provided reliable wind speed missing data imputations. Further, the forecasting model shows, the smoothing parameter, alpha (0.014) close to zero, confirming that recent past observations are more suitable for use to forecast wind speeds. The maximum decadal wind speed for Entebbe International Airport was estimated to be 17.6 metres per second at a 0.05 level of significance with a bound on the error of estimation of 10.8 metres per second. The large bound on the error of estimations confirms the dynamic tendencies of wind speed at the airport under study.

Post-transcriptional generation of miRNA variants by multiple nucleotidyl transferases contributes to miRNA transcriptome complexity.

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Wyman, Stacia K; Knouf, Emily C; Parkin, Rachael K; Fritz, Brian R; Lin, Daniel W; Dennis, Lucas M; Krouse, Michael A; Webster, Philippa J; Tewari, Muneesh

2011-09-01

Modification of microRNA sequences by the 3' addition of nucleotides to generate so-called "isomiRs" adds to the complexity of miRNA function, with recent reports showing that 3' modifications can influence miRNA stability and efficiency of target repression. Here, we show that the 3' modification of miRNAs is a physiological and common post-transcriptional event that shows selectivity for specific miRNAs and is observed across species ranging from C. elegans to human. The modifications result predominantly from adenylation and uridylation and are seen across tissue types, disease states, and developmental stages. To quantitatively profile 3' nucleotide additions, we developed and validated a novel assay based on NanoString Technologies' nCounter platform. For certain miRNAs, the frequency of modification was altered by processes such as cell differentiation, indicating that 3' modification is a biologically regulated process. To investigate the mechanism of 3' nucleotide additions, we used RNA interference to screen a panel of eight candidate miRNA nucleotidyl transferases for 3' miRNA modification activity in human cells. Multiple enzymes, including MTPAP, PAPD4, PAPD5, ZCCHC6, ZCCHC11, and TUT1, were found to govern 3' nucleotide addition to miRNAs in a miRNA-specific manner. Three of these enzymes-MTPAP, ZCCHC6, and TUT1-have not previously been known to modify miRNAs. Collectively, our results indicate that 3' modification observed in next-generation small RNA sequencing data is a biologically relevant process, and identify enzymatic mechanisms that may lead to new approaches for modulating miRNA activity in vivo.

Arabidopsis ARGONAUTE7 selects miR390 through multiple checkpoints during RISC assembly.

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Endo, Yayoi; Iwakawa, Hiro-oki; Tomari, Yukihide

2013-07-01

Plant ARGONAUTE7 (AGO7) assembles RNA-induced silencing complex (RISC) specifically with miR390 and regulates the auxin-signalling pathway via production of TAS3 trans-acting siRNAs (tasiRNAs). However, how AGO7 discerns miR390 among other miRNAs remains unclear. Here, we show that the 5' adenosine of miR390 and the central region of miR390/miR390* duplex are critical for the specific interaction with AGO7. Furthermore, despite the existence of mismatches in the seed and central regions of the duplex, cleavage of the miR390* strand is required for maturation of AGO7-RISC. These findings suggest that AGO7 uses multiple checkpoints to select miR390, thereby circumventing promiscuous tasiRNA production.

[Imputing missing data in public health: general concepts and application to dichotomous variables].

Science.gov (United States)

Hernández, Gilma; Moriña, David; Navarro, Albert

The presence of missing data in collected variables is common in health surveys, but the subsequent imputation thereof at the time of analysis is not. Working with imputed data may have certain benefits regarding the precision of the estimators and the unbiased identification of associations between variables. The imputation process is probably still little understood by many non-statisticians, who view this process as highly complex and with an uncertain goal. To clarify these questions, this note aims to provide a straightforward, non-exhaustive overview of the imputation process to enable public health researchers ascertain its strengths. All this in the context of dichotomous variables which are commonplace in public health. To illustrate these concepts, an example in which missing data is handled by means of simple and multiple imputation is introduced. Copyright © 2017 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

Data imputation analysis for Cosmic Rays time series

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Fernandes, R. C.; Lucio, P. S.; Fernandez, J. H.

2017-05-01

The occurrence of missing data concerning Galactic Cosmic Rays time series (GCR) is inevitable since loss of data is due to mechanical and human failure or technical problems and different periods of operation of GCR stations. The aim of this study was to perform multiple dataset imputation in order to depict the observational dataset. The study has used the monthly time series of GCR Climax (CLMX) and Roma (ROME) from 1960 to 2004 to simulate scenarios of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% and 90% of missing data compared to observed ROME series, with 50 replicates. Then, the CLMX station as a proxy for allocation of these scenarios was used. Three different methods for monthly dataset imputation were selected: AMÉLIA II - runs the bootstrap Expectation Maximization algorithm, MICE - runs an algorithm via Multivariate Imputation by Chained Equations and MTSDI - an Expectation Maximization algorithm-based method for imputation of missing values in multivariate normal time series. The synthetic time series compared with the observed ROME series has also been evaluated using several skill measures as such as RMSE, NRMSE, Agreement Index, R, R2, F-test and t-test. The results showed that for CLMX and ROME, the R2 and R statistics were equal to 0.98 and 0.96, respectively. It was observed that increases in the number of gaps generate loss of quality of the time series. Data imputation was more efficient with MTSDI method, with negligible errors and best skill coefficients. The results suggest a limit of about 60% of missing data for imputation, for monthly averages, no more than this. It is noteworthy that CLMX, ROME and KIEL stations present no missing data in the target period. This methodology allowed reconstructing 43 time series.

LinkImputeR: user-guided genotype calling and imputation for non-model organisms.

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Money, Daniel; Migicovsky, Zoë; Gardner, Kyle; Myles, Sean

2017-07-10

Genomic studies such as genome-wide association and genomic selection require genome-wide genotype data. All existing technologies used to create these data result in missing genotypes, which are often then inferred using genotype imputation software. However, existing imputation methods most often make use only of genotypes that are successfully inferred after having passed a certain read depth threshold. Because of this, any read information for genotypes that did not pass the threshold, and were thus set to missing, is ignored. Most genomic studies also choose read depth thresholds and quality filters without investigating their effects on the size and quality of the resulting genotype data. Moreover, almost all genotype imputation methods require ordered markers and are therefore of limited utility in non-model organisms. Here we introduce LinkImputeR, a software program that exploits the read count information that is normally ignored, and makes use of all available DNA sequence information for the purposes of genotype calling and imputation. It is specifically designed for non-model organisms since it requires neither ordered markers nor a reference panel of genotypes. Using next-generation DNA sequence (NGS) data from apple, cannabis and grape, we quantify the effect of varying read count and missingness thresholds on the quantity and quality of genotypes generated from LinkImputeR. We demonstrate that LinkImputeR can increase the number of genotype calls by more than an order of magnitude, can improve genotyping accuracy by several percent and can thus improve the power of downstream analyses. Moreover, we show that the effects of quality and read depth filters can differ substantially between data sets and should therefore be investigated on a per-study basis. By exploiting DNA sequence data that is normally ignored during genotype calling and imputation, LinkImputeR can significantly improve both the quantity and quality of genotype data generated from

A Nonparametric, Multiple Imputation-Based Method for the Retrospective Integration of Data Sets

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Carrig, Madeline M.; Manrique-Vallier, Daniel; Ranby, Krista W.; Reiter, Jerome P.; Hoyle, Rick H.

2015-01-01

Complex research questions often cannot be addressed adequately with a single data set. One sensible alternative to the high cost and effort associated with the creation of large new data sets is to combine existing data sets containing variables related to the constructs of interest. The goal of the present research was to develop a flexible, broadly applicable approach to the integration of disparate data sets that is based on nonparametric multiple imputation and the collection of data from a convenient, de novo calibration sample. We demonstrate proof of concept for the approach by integrating three existing data sets containing items related to the extent of problematic alcohol use and associations with deviant peers. We discuss both necessary conditions for the approach to work well and potential strengths and weaknesses of the method compared to other data set integration approaches. PMID:26257437

Using imputation to provide location information for nongeocoded addresses.

Directory of Open Access Journals (Sweden)

Frank C Curriero

2010-02-01

Full Text Available The importance of geography as a source of variation in health research continues to receive sustained attention in the literature. The inclusion of geographic information in such research often begins by adding data to a map which is predicated by some knowledge of location. A precise level of spatial information is conventionally achieved through geocoding, the geographic information system (GIS process of translating mailing address information to coordinates on a map. The geocoding process is not without its limitations, though, since there is always a percentage of addresses which cannot be converted successfully (nongeocodable. This raises concerns regarding bias since traditionally the practice has been to exclude nongeocoded data records from analysis.In this manuscript we develop and evaluate a set of imputation strategies for dealing with missing spatial information from nongeocoded addresses. The strategies are developed assuming a known zip code with increasing use of collateral information, namely the spatial distribution of the population at risk. Strategies are evaluated using prostate cancer data obtained from the Maryland Cancer Registry. We consider total case enumerations at the Census county, tract, and block group level as the outcome of interest when applying and evaluating the methods. Multiple imputation is used to provide estimated total case counts based on complete data (geocodes plus imputed nongeocodes with a measure of uncertainty. Results indicate that the imputation strategy based on using available population-based age, gender, and race information performed the best overall at the county, tract, and block group levels.The procedure allows for the potentially biased and likely under reported outcome, case enumerations based on only the geocoded records, to be presented with a statistically adjusted count (imputed count with a measure of uncertainty that are based on all the case data, the geocodes and imputed

miR-186 inhibits cell proliferation in multiple myeloma by repressing Jagged1

International Nuclear Information System (INIS)

Liu, Zengyan; Zhang, Guoqiang; Yu, Wenzheng; Gao, Na; Peng, Jun

2016-01-01

MicroRNAs (miRNAs) are small, noncoding ribonucleic acids that regulate gene expression by targeting mRNAs for translational repression and degradation. Accumulating experimental evidence supports a causal role of miRNAs in hematology tumorigenesis. However, the specific functions of miRNAs in the pathogenesis of multiple myeloma (MM) remain to be established. In this study, we demonstrated that miR-186 is commonly downregulated in MM cell lines and patient MM cells. Ectopic expression of miR-186 significantly inhibited cell growth, both in vitro and in vivo, and induced cell cycle G_0/G_1 arrest. Furthermore, miR-186 induced downregulation of Jagged1 protein expression by directly targeting its 3′-untranslated region (3′-UTR). Conversely, overexpression of Jagged1 rescued cells from miR-186-induced growth inhibition. Our collective results clearly indicate that miR-186 functions as a tumor suppressor in MM, supporting its potential as a therapeutic target for the disease. - Highlights: • miR-186 expression is decreased in MM. • miR-186 inhibits MM cell proliferation in vitro and in vivo. • Jagged1 is regulated by miR-186. • Overexpression of Jagged1 reverses the effects of miR-186.

miR-186 inhibits cell proliferation in multiple myeloma by repressing Jagged1

Energy Technology Data Exchange (ETDEWEB)

Liu, Zengyan [Department of Hematology, Qilu Hospital, Shandong University, 107 Wenhuaxi Road, Jinan, Shandong 250012 (China); Department of Hematology, Hospital Affiliated to Binzhou Medical University, 661 Second Huanghe Street, Binzhou 256603 (China); Zhang, Guoqiang [Department of Thyroid and Breast Surgery, Hospital Affiliated to Binzhou Medical University, 661 Second Huanghe Street, Binzhou 256603 (China); Yu, Wenzheng; Gao, Na [Department of Hematology, Hospital Affiliated to Binzhou Medical University, 661 Second Huanghe Street, Binzhou 256603 (China); Peng, Jun, E-mail: junpeng885@sina.com [Department of Hematology, Qilu Hospital, Shandong University, 107 Wenhuaxi Road, Jinan, Shandong 250012 (China)

2016-01-15

MicroRNAs (miRNAs) are small, noncoding ribonucleic acids that regulate gene expression by targeting mRNAs for translational repression and degradation. Accumulating experimental evidence supports a causal role of miRNAs in hematology tumorigenesis. However, the specific functions of miRNAs in the pathogenesis of multiple myeloma (MM) remain to be established. In this study, we demonstrated that miR-186 is commonly downregulated in MM cell lines and patient MM cells. Ectopic expression of miR-186 significantly inhibited cell growth, both in vitro and in vivo, and induced cell cycle G{sub 0}/G{sub 1} arrest. Furthermore, miR-186 induced downregulation of Jagged1 protein expression by directly targeting its 3′-untranslated region (3′-UTR). Conversely, overexpression of Jagged1 rescued cells from miR-186-induced growth inhibition. Our collective results clearly indicate that miR-186 functions as a tumor suppressor in MM, supporting its potential as a therapeutic target for the disease. - Highlights: • miR-186 expression is decreased in MM. • miR-186 inhibits MM cell proliferation in vitro and in vivo. • Jagged1 is regulated by miR-186. • Overexpression of Jagged1 reverses the effects of miR-186.

Effects of Different Missing Data Imputation Techniques on the Performance of Undiagnosed Diabetes Risk Prediction Models in a Mixed-Ancestry Population of South Africa.

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Katya L Masconi

Full Text Available Imputation techniques used to handle missing data are based on the principle of replacement. It is widely advocated that multiple imputation is superior to other imputation methods, however studies have suggested that simple methods for filling missing data can be just as accurate as complex methods. The objective of this study was to implement a number of simple and more complex imputation methods, and assess the effect of these techniques on the performance of undiagnosed diabetes risk prediction models during external validation.Data from the Cape Town Bellville-South cohort served as the basis for this study. Imputation methods and models were identified via recent systematic reviews. Models' discrimination was assessed and compared using C-statistic and non-parametric methods, before and after recalibration through simple intercept adjustment.The study sample consisted of 1256 individuals, of whom 173 were excluded due to previously diagnosed diabetes. Of the final 1083 individuals, 329 (30.4% had missing data. Family history had the highest proportion of missing data (25%. Imputation of the outcome, undiagnosed diabetes, was highest in stochastic regression imputation (163 individuals. Overall, deletion resulted in the lowest model performances while simple imputation yielded the highest C-statistic for the Cambridge Diabetes Risk model, Kuwaiti Risk model, Omani Diabetes Risk model and Rotterdam Predictive model. Multiple imputation only yielded the highest C-statistic for the Rotterdam Predictive model, which were matched by simpler imputation methods.Deletion was confirmed as a poor technique for handling missing data. However, despite the emphasized disadvantages of simpler imputation methods, this study showed that implementing these methods results in similar predictive utility for undiagnosed diabetes when compared to multiple imputation.

Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes

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Lotz Meredith J

2008-01-01

Full Text Available Abstract Background Gene expression data frequently contain missing values, however, most down-stream analyses for microarray experiments require complete data. In the literature many methods have been proposed to estimate missing values via information of the correlation patterns within the gene expression matrix. Each method has its own advantages, but the specific conditions for which each method is preferred remains largely unclear. In this report we describe an extensive evaluation of eight current imputation methods on multiple types of microarray experiments, including time series, multiple exposures, and multiple exposures × time series data. We then introduce two complementary selection schemes for determining the most appropriate imputation method for any given data set. Results We found that the optimal imputation algorithms (LSA, LLS, and BPCA are all highly competitive with each other, and that no method is uniformly superior in all the data sets we examined. The success of each method can also depend on the underlying "complexity" of the expression data, where we take complexity to indicate the difficulty in mapping the gene expression matrix to a lower-dimensional subspace. We developed an entropy measure to quantify the complexity of expression matrixes and found that, by incorporating this information, the entropy-based selection (EBS scheme is useful for selecting an appropriate imputation algorithm. We further propose a simulation-based self-training selection (STS scheme. This technique has been used previously for microarray data imputation, but for different purposes. The scheme selects the optimal or near-optimal method with high accuracy but at an increased computational cost. Conclusion Our findings provide insight into the problem of which imputation method is optimal for a given data set. Three top-performing methods (LSA, LLS and BPCA are competitive with each other. Global-based imputation methods (PLS, SVD, BPCA

Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes.

Science.gov (United States)

Brock, Guy N; Shaffer, John R; Blakesley, Richard E; Lotz, Meredith J; Tseng, George C

2008-01-10

Gene expression data frequently contain missing values, however, most down-stream analyses for microarray experiments require complete data. In the literature many methods have been proposed to estimate missing values via information of the correlation patterns within the gene expression matrix. Each method has its own advantages, but the specific conditions for which each method is preferred remains largely unclear. In this report we describe an extensive evaluation of eight current imputation methods on multiple types of microarray experiments, including time series, multiple exposures, and multiple exposures x time series data. We then introduce two complementary selection schemes for determining the most appropriate imputation method for any given data set. We found that the optimal imputation algorithms (LSA, LLS, and BPCA) are all highly competitive with each other, and that no method is uniformly superior in all the data sets we examined. The success of each method can also depend on the underlying "complexity" of the expression data, where we take complexity to indicate the difficulty in mapping the gene expression matrix to a lower-dimensional subspace. We developed an entropy measure to quantify the complexity of expression matrixes and found that, by incorporating this information, the entropy-based selection (EBS) scheme is useful for selecting an appropriate imputation algorithm. We further propose a simulation-based self-training selection (STS) scheme. This technique has been used previously for microarray data imputation, but for different purposes. The scheme selects the optimal or near-optimal method with high accuracy but at an increased computational cost. Our findings provide insight into the problem of which imputation method is optimal for a given data set. Three top-performing methods (LSA, LLS and BPCA) are competitive with each other. Global-based imputation methods (PLS, SVD, BPCA) performed better on mcroarray data with lower complexity

miR-137 inhibits the invasion of melanoma cells through downregulation of multiple oncogenic target genes.

Science.gov (United States)

Luo, Chonglin; Tetteh, Paul W; Merz, Patrick R; Dickes, Elke; Abukiwan, Alia; Hotz-Wagenblatt, Agnes; Holland-Cunz, Stefan; Sinnberg, Tobias; Schittek, Birgit; Schadendorf, Dirk; Diederichs, Sven; Eichmüller, Stefan B

2013-03-01

MicroRNAs are small noncoding RNAs that regulate gene expression and have important roles in various types of cancer. Previously, miR-137 was reported to act as a tumor suppressor in different cancers, including malignant melanoma. In this study, we show that low miR-137 expression is correlated with poor survival in stage IV melanoma patients. We identified and validated two genes (c-Met and YB1) as direct targets of miR-137 and confirmed two previously known targets, namely enhancer of zeste homolog 2 (EZH2) and microphthalmia-associated transcription factor (MITF). Functional studies showed that miR-137 suppressed melanoma cell invasion through the downregulation of multiple target genes. The decreased invasion caused by miR-137 overexpression could be phenocopied by small interfering RNA knockdown of EZH2, c-Met, or Y box-binding protein 1 (YB1). Furthermore, miR-137 inhibited melanoma cell migration and proliferation. Finally, miR-137 induced apoptosis in melanoma cell lines and decreased BCL2 levels. In summary, our study confirms that miR-137 acts as a tumor suppressor in malignant melanoma and reveals that miR-137 regulates multiple targets including c-Met, YB1, EZH2, and MITF.

Multiple Myeloma-Derived Exosomes Regulate the Functions of Mesenchymal Stem Cells Partially via Modulating miR-21 and miR-146a

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Qian Cheng

2017-01-01

Full Text Available Exosomes derived from cancer cells can affect various functions of mesenchymal stem cells (MSCs via conveying microRNAs (miRs. miR-21 and miR-146a have been demonstrated to regulate MSC proliferation and transformation. Interleukin-6 (IL-6 secreted from transformed MSCs in turn favors the survival of multiple myeloma (MM cells. However, the effects of MM exosomes on MSC functions remain largely unclear. In this study, we investigated the effects of OPM2 (a MM cell line exosomes (OPM2-exo on regulating the proliferation, cancer-associated fibroblast (CAF transformation, and IL-6 secretion of MSCs and determined the role of miR-21 and miR-146a in these effects. We found that OPM2-exo harbored high levels of miR-21 and miR-146a and that OPM2-exo coculture significantly increased MSC proliferation with upregulation of miR-21 and miR-146a. Moreover, OPM2-exo induced CAF transformation of MSCs, which was evidenced by increased fibroblast-activated protein (FAP, α-smooth muscle actin (α-SMA, and stromal-derived factor 1 (SDF-1 expressions and IL-6 secretion. Inhibition of miR-21 or miR-146a reduced these effects of OPM2-exo on MSCs. In conclusion, MM could promote the proliferation, CAF transformation, and IL-6 secretion of MSCs partially through regulating miR21 and miR146a.

Imputation methods for filling missing data in urban air pollution data for Malaysia

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Nur Afiqah Zakaria

2018-06-01

Full Text Available The air quality measurement data obtained from the continuous ambient air quality monitoring (CAAQM station usually contained missing data. The missing observations of the data usually occurred due to machine failure, routine maintenance and human error. In this study, the hourly monitoring data of CO, O3, PM10, SO2, NOx, NO2, ambient temperature and humidity were used to evaluate four imputation methods (Mean Top Bottom, Linear Regression, Multiple Imputation and Nearest Neighbour. The air pollutants observations were simulated into four percentages of simulated missing data i.e. 5%, 10%, 15% and 20%. Performance measures namely the Mean Absolute Error, Root Mean Squared Error, Coefficient of Determination and Index of Agreement were used to describe the goodness of fit of the imputation methods. From the results of the performance measures, Mean Top Bottom method was selected as the most appropriate imputation method for filling in the missing values in air pollutants data.

Cost reduction for web-based data imputation

KAUST Repository

Li, Zhixu

2014-01-01

Web-based Data Imputation enables the completion of incomplete data sets by retrieving absent field values from the Web. In particular, complete fields can be used as keywords in imputation queries for absent fields. However, due to the ambiguity of these keywords and the data complexity on the Web, different queries may retrieve different answers to the same absent field value. To decide the most probable right answer to each absent filed value, existing method issues quite a few available imputation queries for each absent value, and then vote on deciding the most probable right answer. As a result, we have to issue a large number of imputation queries for filling all absent values in an incomplete data set, which brings a large overhead. In this paper, we work on reducing the cost of Web-based Data Imputation in two aspects: First, we propose a query execution scheme which can secure the most probable right answer to an absent field value by issuing as few imputation queries as possible. Second, we recognize and prune queries that probably will fail to return any answers a priori. Our extensive experimental evaluation shows that our proposed techniques substantially reduce the cost of Web-based Imputation without hurting its high imputation accuracy. © 2014 Springer International Publishing Switzerland.

An integrative genomic approach reveals coordinated expression of intronic miR-335, miR-342, and miR-561 with deregulated host genes in multiple myeloma

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Agnelli Luca

2008-08-01

Full Text Available Abstract Background The role of microRNAs (miRNAs in multiple myeloma (MM has yet to be fully elucidated. To identify miRNAs that are potentially deregulated in MM, we investigated those mapping within transcription units, based on evidence that intronic miRNAs are frequently coexpressed with their host genes. To this end, we monitored host transcript expression values in a panel of 20 human MM cell lines (HMCLs and focused on transcripts whose expression varied significantly across the dataset. Methods miRNA expression was quantified by Quantitative Real-Time PCR. Gene expression and genome profiling data were generated on Affymetrix oligonucleotide microarrays. Significant Analysis of Microarrays algorithm was used to investigate differentially expressed transcripts. Conventional statistics were used to test correlations for significance. Public libraries were queried to predict putative miRNA targets. Results We identified transcripts specific to six miRNA host genes (CCPG1, GULP1, EVL, TACSTD1, MEST, and TNIK whose average changes in expression varied at least 2-fold from the mean of the examined dataset. We evaluated the expression levels of the corresponding intronic miRNAs and identified a significant correlation between the expression levels of MEST, EVL, and GULP1 and those of the corresponding miRNAs miR-335, miR-342-3p, and miR-561, respectively. Genome-wide profiling of the 20 HMCLs indicated that the increased expression of the three host genes and their corresponding intronic miRNAs was not correlated with local copy number variations. Notably, miRNAs and their host genes were overexpressed in a fraction of primary tumors with respect to normal plasma cells; however, this finding was not correlated with known molecular myeloma groups. The predicted putative miRNA targets and the transcriptional profiles associated with the primary tumors suggest that MEST/miR-335 and EVL/miR-342-3p may play a role in plasma cell homing and

Improved imputation accuracy of rare and low-frequency variants using population-specific high-coverage WGS-based imputation reference panel.

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Mitt, Mario; Kals, Mart; Pärn, Kalle; Gabriel, Stacey B; Lander, Eric S; Palotie, Aarno; Ripatti, Samuli; Morris, Andrew P; Metspalu, Andres; Esko, Tõnu; Mägi, Reedik; Palta, Priit

2017-06-01

Genetic imputation is a cost-efficient way to improve the power and resolution of genome-wide association (GWA) studies. Current publicly accessible imputation reference panels accurately predict genotypes for common variants with minor allele frequency (MAF)≥5% and low-frequency variants (0.5≤MAF<5%) across diverse populations, but the imputation of rare variation (MAF<0.5%) is still rather limited. In the current study, we evaluate imputation accuracy achieved with reference panels from diverse populations with a population-specific high-coverage (30 ×) whole-genome sequencing (WGS) based reference panel, comprising of 2244 Estonian individuals (0.25% of adult Estonians). Although the Estonian-specific panel contains fewer haplotypes and variants, the imputation confidence and accuracy of imputed low-frequency and rare variants was significantly higher. The results indicate the utility of population-specific reference panels for human genetic studies.

Data driven estimation of imputation error-a strategy for imputation with a reject option

DEFF Research Database (Denmark)

Bak, Nikolaj; Hansen, Lars Kai

2016-01-01

Missing data is a common problem in many research fields and is a challenge that always needs careful considerations. One approach is to impute the missing values, i.e., replace missing values with estimates. When imputation is applied, it is typically applied to all records with missing values i...

Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies.

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Lazar, Cosmin; Gatto, Laurent; Ferro, Myriam; Bruley, Christophe; Burger, Thomas

2016-04-01

Missing values are a genuine issue in label-free quantitative proteomics. Recent works have surveyed the different statistical methods to conduct imputation and have compared them on real or simulated data sets and recommended a list of missing value imputation methods for proteomics application. Although insightful, these comparisons do not account for two important facts: (i) depending on the proteomics data set, the missingness mechanism may be of different natures and (ii) each imputation method is devoted to a specific type of missingness mechanism. As a result, we believe that the question at stake is not to find the most accurate imputation method in general but instead the most appropriate one. We describe a series of comparisons that support our views: For instance, we show that a supposedly "under-performing" method (i.e., giving baseline average results), if applied at the "appropriate" time in the data-processing pipeline (before or after peptide aggregation) on a data set with the "appropriate" nature of missing values, can outperform a blindly applied, supposedly "better-performing" method (i.e., the reference method from the state-of-the-art). This leads us to formulate few practical guidelines regarding the choice and the application of an imputation method in a proteomics context.

iVAR: a program for imputing missing data in multivariate time series using vector autoregressive models.

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Liu, Siwei; Molenaar, Peter C M

2014-12-01

This article introduces iVAR, an R program for imputing missing data in multivariate time series on the basis of vector autoregressive (VAR) models. We conducted a simulation study to compare iVAR with three methods for handling missing data: listwise deletion, imputation with sample means and variances, and multiple imputation ignoring time dependency. The results showed that iVAR produces better estimates for the cross-lagged coefficients than do the other three methods. We demonstrate the use of iVAR with an empirical example of time series electrodermal activity data and discuss the advantages and limitations of the program.

Identifying TF-MiRNA Regulatory Relationships Using Multiple Features.

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Mingyu Shao

Full Text Available MicroRNAs are known to play important roles in the transcriptional and post-transcriptional regulation of gene expression. While intensive research has been conducted to identify miRNAs and their target genes in various genomes, there is only limited knowledge about how microRNAs are regulated. In this study, we construct a pipeline that can infer the regulatory relationships between transcription factors and microRNAs from ChIP-Seq data with high confidence. In particular, after identifying candidate peaks from ChIP-Seq data, we formulate the inference as a PU learning (learning from only positive and unlabeled examples problem. Multiple features including the statistical significance of the peaks, the location of the peaks, the transcription factor binding site motifs, and the evolutionary conservation are derived from peaks for training and prediction. To further improve the accuracy of our inference, we also apply a mean reciprocal rank (MRR-based method to the candidate peaks. We apply our pipeline to infer TF-miRNA regulatory relationships in mouse embryonic stem cells. The experimental results show that our approach provides very specific findings of TF-miRNA regulatory relationships.

Multiple Regression Analysis of mRNA-miRNA Associations in Colorectal Cancer Pathway

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Wang, Fengfeng; Wong, S. C. Cesar; Chan, Lawrence W. C.; Cho, William C. S.; Yip, S. P.; Yung, Benjamin Y. M.

2014-01-01

Background. MicroRNA (miRNA) is a short and endogenous RNA molecule that regulates posttranscriptional gene expression. It is an important factor for tumorigenesis of colorectal cancer (CRC), and a potential biomarker for diagnosis, prognosis, and therapy of CRC. Our objective is to identify the related miRNAs and their associations with genes frequently involved in CRC microsatellite instability (MSI) and chromosomal instability (CIN) signaling pathways. Results. A regression model was adopted to identify the significantly associated miRNAs targeting a set of candidate genes frequently involved in colorectal cancer MSI and CIN pathways. Multiple linear regression analysis was used to construct the model and find the significant mRNA-miRNA associations. We identified three significantly associated mRNA-miRNA pairs: BCL2 was positively associated with miR-16 and SMAD4 was positively associated with miR-567 in the CRC tissue, while MSH6 was positively associated with miR-142-5p in the normal tissue. As for the whole model, BCL2 and SMAD4 models were not significant, and MSH6 model was significant. The significant associations were different in the normal and the CRC tissues. Conclusion. Our results have laid down a solid foundation in exploration of novel CRC mechanisms, and identification of miRNA roles as oncomirs or tumor suppressor mirs in CRC. PMID:24895601

Missing data imputation using statistical and machine learning methods in a real breast cancer problem.

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Jerez, José M; Molina, Ignacio; García-Laencina, Pedro J; Alba, Emilio; Ribelles, Nuria; Martín, Miguel; Franco, Leonardo

2010-10-01

Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. This work evaluates the performance of several statistical and machine learning imputation methods that were used to predict recurrence in patients in an extensive real breast cancer data set. Imputation methods based on statistical techniques, e.g., mean, hot-deck and multiple imputation, and machine learning techniques, e.g., multi-layer perceptron (MLP), self-organisation maps (SOM) and k-nearest neighbour (KNN), were applied to data collected through the "El Álamo-I" project, and the results were then compared to those obtained from the listwise deletion (LD) imputation method. The database includes demographic, therapeutic and recurrence-survival information from 3679 women with operable invasive breast cancer diagnosed in 32 different hospitals belonging to the Spanish Breast Cancer Research Group (GEICAM). The accuracies of predictions on early cancer relapse were measured using artificial neural networks (ANNs), in which different ANNs were estimated using the data sets with imputed missing values. The imputation methods based on machine learning algorithms outperformed imputation statistical methods in the prediction of patient outcome. Friedman's test revealed a significant difference (p=0.0091) in the observed area under the ROC curve (AUC) values, and the pairwise comparison test showed that the AUCs for MLP, KNN and SOM were significantly higher (p=0.0053, p=0.0048 and p=0.0071, respectively) than the AUC from the LD-based prognosis model. The methods based on machine learning techniques were the most suited for the imputation of missing values and led to a significant enhancement of prognosis accuracy compared to imputation methods based on statistical procedures. Copyright © 2010 Elsevier B.V. All rights reserved.

MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA-disease association prediction.

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Chen, Xing; Niu, Ya-Wei; Wang, Guang-Hui; Yan, Gui-Ying

2017-12-12

Recently, as the research of microRNA (miRNA) continues, there are plenty of experimental evidences indicating that miRNA could be associated with various human complex diseases development and progression. Hence, it is necessary and urgent to pay more attentions to the relevant study of predicting diseases associated miRNAs, which may be helpful for effective prevention, diagnosis and treatment of human diseases. Especially, constructing computational methods to predict potential miRNA-disease associations is worthy of more studies because of the feasibility and effectivity. In this work, we developed a novel computational model of multiple kernels learning-based Kronecker regularized least squares for MiRNA-disease association prediction (MKRMDA), which could reveal potential miRNA-disease associations by automatically optimizing the combination of multiple kernels for disease and miRNA. MKRMDA obtained AUCs of 0.9040 and 0.8446 in global and local leave-one-out cross validation, respectively. Meanwhile, MKRMDA achieved average AUCs of 0.8894 ± 0.0015 in fivefold cross validation. Furthermore, we conducted three different kinds of case studies on some important human cancers for further performance evaluation. In the case studies of colonic cancer, esophageal cancer and lymphoma based on known miRNA-disease associations in HMDDv2.0 database, 76, 94 and 88% of the corresponding top 50 predicted miRNAs were confirmed by experimental reports, respectively. In another two kinds of case studies for new diseases without any known associated miRNAs and diseases only with known associations in HMDDv1.0 database, the verified ratios of two different cancers were 88 and 94%, respectively. All the results mentioned above adequately showed the reliable prediction ability of MKRMDA. We anticipated that MKRMDA could serve to facilitate further developments in the field and the follow-up investigations by biomedical researchers.

Analyzing the changing gender wage gap based on multiply imputed right censored wages

OpenAIRE

Gartner, Hermann; Rässler, Susanne

2005-01-01

"In order to analyze the gender wage gap with the German IAB-employment register we have to solve the problem of censored wages at the upper limit of the social security system. We treat this problem as a missing data problem. We regard the missingness mechanism as not missing at random (NMAR, according to Little and Rubin, 1987, 2002) as well as missing by design. The censored wages are multiply imputed by draws of a random variable from a truncated distribution. The multiple imputation is b...

Combining Fourier and lagged k-nearest neighbor imputation for biomedical time series data.

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Rahman, Shah Atiqur; Huang, Yuxiao; Claassen, Jan; Heintzman, Nathaniel; Kleinberg, Samantha

2015-12-01

Most clinical and biomedical data contain missing values. A patient's record may be split across multiple institutions, devices may fail, and sensors may not be worn at all times. While these missing values are often ignored, this can lead to bias and error when the data are mined. Further, the data are not simply missing at random. Instead the measurement of a variable such as blood glucose may depend on its prior values as well as that of other variables. These dependencies exist across time as well, but current methods have yet to incorporate these temporal relationships as well as multiple types of missingness. To address this, we propose an imputation method (FLk-NN) that incorporates time lagged correlations both within and across variables by combining two imputation methods, based on an extension to k-NN and the Fourier transform. This enables imputation of missing values even when all data at a time point is missing and when there are different types of missingness both within and across variables. In comparison to other approaches on three biological datasets (simulated and actual Type 1 diabetes datasets, and multi-modality neurological ICU monitoring) the proposed method has the highest imputation accuracy. This was true for up to half the data being missing and when consecutive missing values are a significant fraction of the overall time series length. Copyright © 2015 Elsevier Inc. All rights reserved.

Identification of Subtype Specific miRNA-mRNA Functional Regulatory Modules in Matched miRNA-mRNA Expression Data: Multiple Myeloma as a Case

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Yunpeng Zhang

2015-01-01

Full Text Available Identification of miRNA-mRNA modules is an important step to elucidate their combinatorial effect on the pathogenesis and mechanisms underlying complex diseases. Current identification methods primarily are based upon miRNA-target information and matched miRNA and mRNA expression profiles. However, for heterogeneous diseases, the miRNA-mRNA regulatory mechanisms may differ between subtypes, leading to differences in clinical behavior. In order to explore the pathogenesis of each subtype, it is important to identify subtype specific miRNA-mRNA modules. In this study, we integrated the Ping-Pong algorithm and multiobjective genetic algorithm to identify subtype specific miRNA-mRNA functional regulatory modules (MFRMs through integrative analysis of three biological data sets: GO biological processes, miRNA target information, and matched miRNA and mRNA expression data. We applied our method on a heterogeneous disease, multiple myeloma (MM, to identify MM subtype specific MFRMs. The constructed miRNA-mRNA regulatory networks provide modular outlook at subtype specific miRNA-mRNA interactions. Furthermore, clustering analysis demonstrated that heterogeneous MFRMs were able to separate corresponding MM subtypes. These subtype specific MFRMs may aid in the further elucidation of the pathogenesis of each subtype and may serve to guide MM subtype diagnosis and treatment.

BRITS: Bidirectional Recurrent Imputation for Time Series

OpenAIRE

Cao, Wei; Wang, Dong; Li, Jian; Zhou, Hao; Li, Lei; Li, Yitan

2018-01-01

Time series are widely used as signals in many classification/regression tasks. It is ubiquitous that time series contains many missing values. Given multiple correlated time series data, how to fill in missing values and to predict their class labels? Existing imputation methods often impose strong assumptions of the underlying data generating process, such as linear dynamics in the state space. In this paper, we propose BRITS, a novel method based on recurrent neural networks for missing va...

MiRroring the Multiple Potentials of MicroRNAs in Acute Myocardial Infarction

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Solenne Paiva

2017-11-01

as AMI diagnostic or prognostic biomarkers. Furthermore, a precise combo was shown to be powerful enough to transdifferentiate human fibroblasts into CMs opening doors in the therapeutics. Following these discoveries, they also emerged as optional tools to transfect in order to mature CMs derived from pluripotent stem cells. Ultimately, the multiple potentials carried by the myomiRs miR-1; miR-133; miR-208a/b; and miR-499a still remain to be fully unveiled.

Gaussian mixture clustering and imputation of microarray data.

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Ouyang, Ming; Welsh, William J; Georgopoulos, Panos

2004-04-12

In microarray experiments, missing entries arise from blemishes on the chips. In large-scale studies, virtually every chip contains some missing entries and more than 90% of the genes are affected. Many analysis methods require a full set of data. Either those genes with missing entries are excluded, or the missing entries are filled with estimates prior to the analyses. This study compares methods of missing value estimation. Two evaluation metrics of imputation accuracy are employed. First, the root mean squared error measures the difference between the true values and the imputed values. Second, the number of mis-clustered genes measures the difference between clustering with true values and that with imputed values; it examines the bias introduced by imputation to clustering. The Gaussian mixture clustering with model averaging imputation is superior to all other imputation methods, according to both evaluation metrics, on both time-series (correlated) and non-time series (uncorrelated) data sets.

A web-based approach to data imputation

KAUST Repository

Li, Zhixu

2013-10-24

In this paper, we present WebPut, a prototype system that adopts a novel web-based approach to the data imputation problem. Towards this, Webput utilizes the available information in an incomplete database in conjunction with the data consistency principle. Moreover, WebPut extends effective Information Extraction (IE) methods for the purpose of formulating web search queries that are capable of effectively retrieving missing values with high accuracy. WebPut employs a confidence-based scheme that efficiently leverages our suite of data imputation queries to automatically select the most effective imputation query for each missing value. A greedy iterative algorithm is proposed to schedule the imputation order of the different missing values in a database, and in turn the issuing of their corresponding imputation queries, for improving the accuracy and efficiency of WebPut. Moreover, several optimization techniques are also proposed to reduce the cost of estimating the confidence of imputation queries at both the tuple-level and the database-level. Experiments based on several real-world data collections demonstrate not only the effectiveness of WebPut compared to existing approaches, but also the efficiency of our proposed algorithms and optimization techniques. © 2013 Springer Science+Business Media New York.

Comparison of missing value imputation methods in time series: the case of Turkish meteorological data

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Yozgatligil, Ceylan; Aslan, Sipan; Iyigun, Cem; Batmaz, Inci

2013-04-01

This study aims to compare several imputation methods to complete the missing values of spatio-temporal meteorological time series. To this end, six imputation methods are assessed with respect to various criteria including accuracy, robustness, precision, and efficiency for artificially created missing data in monthly total precipitation and mean temperature series obtained from the Turkish State Meteorological Service. Of these methods, simple arithmetic average, normal ratio (NR), and NR weighted with correlations comprise the simple ones, whereas multilayer perceptron type neural network and multiple imputation strategy adopted by Monte Carlo Markov Chain based on expectation-maximization (EM-MCMC) are computationally intensive ones. In addition, we propose a modification on the EM-MCMC method. Besides using a conventional accuracy measure based on squared errors, we also suggest the correlation dimension (CD) technique of nonlinear dynamic time series analysis which takes spatio-temporal dependencies into account for evaluating imputation performances. Depending on the detailed graphical and quantitative analysis, it can be said that although computational methods, particularly EM-MCMC method, are computationally inefficient, they seem favorable for imputation of meteorological time series with respect to different missingness periods considering both measures and both series studied. To conclude, using the EM-MCMC algorithm for imputing missing values before conducting any statistical analyses of meteorological data will definitely decrease the amount of uncertainty and give more robust results. Moreover, the CD measure can be suggested for the performance evaluation of missing data imputation particularly with computational methods since it gives more precise results in meteorological time series.

Multiple imputation for estimating the risk of developing dementia and its impact on survival.

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Yu, Binbing; Saczynski, Jane S; Launer, Lenore

2010-10-01

Dementia, Alzheimer's disease in particular, is one of the major causes of disability and decreased quality of life among the elderly and a leading obstacle to successful aging. Given the profound impact on public health, much research has focused on the age-specific risk of developing dementia and the impact on survival. Early work has discussed various methods of estimating age-specific incidence of dementia, among which the illness-death model is popular for modeling disease progression. In this article we use multiple imputation to fit multi-state models for survival data with interval censoring and left truncation. This approach allows semi-Markov models in which survival after dementia depends on onset age. Such models can be used to estimate the cumulative risk of developing dementia in the presence of the competing risk of dementia-free death. Simulations are carried out to examine the performance of the proposed method. Data from the Honolulu Asia Aging Study are analyzed to estimate the age-specific and cumulative risks of dementia and to examine the effect of major risk factors on dementia onset and death.

An efficient method to transcription factor binding sites imputation via simultaneous completion of multiple matrices with positional consistency.

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Guo, Wei-Li; Huang, De-Shuang

2017-08-22

Transcription factors (TFs) are DNA-binding proteins that have a central role in regulating gene expression. Identification of DNA-binding sites of TFs is a key task in understanding transcriptional regulation, cellular processes and disease. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) enables genome-wide identification of in vivo TF binding sites. However, it is still difficult to map every TF in every cell line owing to cost and biological material availability, which poses an enormous obstacle for integrated analysis of gene regulation. To address this problem, we propose a novel computational approach, TFBSImpute, for predicting additional TF binding profiles by leveraging information from available ChIP-seq TF binding data. TFBSImpute fuses the dataset to a 3-mode tensor and imputes missing TF binding signals via simultaneous completion of multiple TF binding matrices with positional consistency. We show that signals predicted by our method achieve overall similarity with experimental data and that TFBSImpute significantly outperforms baseline approaches, by assessing the performance of imputation methods against observed ChIP-seq TF binding profiles. Besides, motif analysis shows that TFBSImpute preforms better in capturing binding motifs enriched in observed data compared with baselines, indicating that the higher performance of TFBSImpute is not simply due to averaging related samples. We anticipate that our approach will constitute a useful complement to experimental mapping of TF binding, which is beneficial for further study of regulation mechanisms and disease.

Integration of Multiple Genomic and Phenotype Data to Infer Novel miRNA-Disease Associations.

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Shi, Hongbo; Zhang, Guangde; Zhou, Meng; Cheng, Liang; Yang, Haixiu; Wang, Jing; Sun, Jie; Wang, Zhenzhen

2016-01-01

MicroRNAs (miRNAs) play an important role in the development and progression of human diseases. The identification of disease-associated miRNAs will be helpful for understanding the molecular mechanisms of diseases at the post-transcriptional level. Based on different types of genomic data sources, computational methods for miRNA-disease association prediction have been proposed. However, individual source of genomic data tends to be incomplete and noisy; therefore, the integration of various types of genomic data for inferring reliable miRNA-disease associations is urgently needed. In this study, we present a computational framework, CHNmiRD, for identifying miRNA-disease associations by integrating multiple genomic and phenotype data, including protein-protein interaction data, gene ontology data, experimentally verified miRNA-target relationships, disease phenotype information and known miRNA-disease connections. The performance of CHNmiRD was evaluated by experimentally verified miRNA-disease associations, which achieved an area under the ROC curve (AUC) of 0.834 for 5-fold cross-validation. In particular, CHNmiRD displayed excellent performance for diseases without any known related miRNAs. The results of case studies for three human diseases (glioblastoma, myocardial infarction and type 1 diabetes) showed that all of the top 10 ranked miRNAs having no known associations with these three diseases in existing miRNA-disease databases were directly or indirectly confirmed by our latest literature mining. All these results demonstrated the reliability and efficiency of CHNmiRD, and it is anticipated that CHNmiRD will serve as a powerful bioinformatics method for mining novel disease-related miRNAs and providing a new perspective into molecular mechanisms underlying human diseases at the post-transcriptional level. CHNmiRD is freely available at http://www.bio-bigdata.com/CHNmiRD.

Integration of Multiple Genomic and Phenotype Data to Infer Novel miRNA-Disease Associations.

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Hongbo Shi

Full Text Available MicroRNAs (miRNAs play an important role in the development and progression of human diseases. The identification of disease-associated miRNAs will be helpful for understanding the molecular mechanisms of diseases at the post-transcriptional level. Based on different types of genomic data sources, computational methods for miRNA-disease association prediction have been proposed. However, individual source of genomic data tends to be incomplete and noisy; therefore, the integration of various types of genomic data for inferring reliable miRNA-disease associations is urgently needed. In this study, we present a computational framework, CHNmiRD, for identifying miRNA-disease associations by integrating multiple genomic and phenotype data, including protein-protein interaction data, gene ontology data, experimentally verified miRNA-target relationships, disease phenotype information and known miRNA-disease connections. The performance of CHNmiRD was evaluated by experimentally verified miRNA-disease associations, which achieved an area under the ROC curve (AUC of 0.834 for 5-fold cross-validation. In particular, CHNmiRD displayed excellent performance for diseases without any known related miRNAs. The results of case studies for three human diseases (glioblastoma, myocardial infarction and type 1 diabetes showed that all of the top 10 ranked miRNAs having no known associations with these three diseases in existing miRNA-disease databases were directly or indirectly confirmed by our latest literature mining. All these results demonstrated the reliability and efficiency of CHNmiRD, and it is anticipated that CHNmiRD will serve as a powerful bioinformatics method for mining novel disease-related miRNAs and providing a new perspective into molecular mechanisms underlying human diseases at the post-transcriptional level. CHNmiRD is freely available at http://www.bio-bigdata.com/CHNmiRD.

Comparação de métodos de imputação única e múltipla usando como exemplo um modelo de risco para mortalidade cirúrgica Comparison of simple and multiple imputation methods using a risk model for surgical mortality as example

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Luciana Neves Nunes

2010-12-01

sample size was 450 patients. The imputation methods applied were: two single imputations and one multiple imputation and the assumption was MAR (Missing at Random. RESULTS: The variable with missing data was serum albumin with 27.1% of missing rate. The logistic models adjusted by simple imputation were similar, but differed from models obtained by multiple imputation in relation to the inclusion of variables. CONCLUSIONS: The results indicate that it is important to take into account the relationship of albumin to other variables observed, because different models were obtained in single and multiple imputations. Single imputation underestimates the variability generating narrower confidence intervals. It is important to consider the use of imputation methods when there is missing data, especially multiple imputation that takes into account the variability between imputations for estimates of the model.

Combining item response theory with multiple imputation to equate health assessment questionnaires.

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Gu, Chenyang; Gutman, Roee

2017-09-01

The assessment of patients' functional status across the continuum of care requires a common patient assessment tool. However, assessment tools that are used in various health care settings differ and cannot be easily contrasted. For example, the Functional Independence Measure (FIM) is used to evaluate the functional status of patients who stay in inpatient rehabilitation facilities, the Minimum Data Set (MDS) is collected for all patients who stay in skilled nursing facilities, and the Outcome and Assessment Information Set (OASIS) is collected if they choose home health care provided by home health agencies. All three instruments or questionnaires include functional status items, but the specific items, rating scales, and instructions for scoring different activities vary between the different settings. We consider equating different health assessment questionnaires as a missing data problem, and propose a variant of predictive mean matching method that relies on Item Response Theory (IRT) models to impute unmeasured item responses. Using real data sets, we simulated missing measurements and compared our proposed approach to existing methods for missing data imputation. We show that, for all of the estimands considered, and in most of the experimental conditions that were examined, the proposed approach provides valid inferences, and generally has better coverages, relatively smaller biases, and shorter interval estimates. The proposed method is further illustrated using a real data set. © 2016, The International Biometric Society.

Estimating the accuracy of geographical imputation

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Boscoe Francis P

2008-01-01

Full Text Available Abstract Background To reduce the number of non-geocoded cases researchers and organizations sometimes include cases geocoded to postal code centroids along with cases geocoded with the greater precision of a full street address. Some analysts then use the postal code to assign information to the cases from finer-level geographies such as a census tract. Assignment is commonly completed using either a postal centroid or by a geographical imputation method which assigns a location by using both the demographic characteristics of the case and the population characteristics of the postal delivery area. To date no systematic evaluation of geographical imputation methods ("geo-imputation" has been completed. The objective of this study was to determine the accuracy of census tract assignment using geo-imputation. Methods Using a large dataset of breast, prostate and colorectal cancer cases reported to the New Jersey Cancer Registry, we determined how often cases were assigned to the correct census tract using alternate strategies of demographic based geo-imputation, and using assignments obtained from postal code centroids. Assignment accuracy was measured by comparing the tract assigned with the tract originally identified from the full street address. Results Assigning cases to census tracts using the race/ethnicity population distribution within a postal code resulted in more correctly assigned cases than when using postal code centroids. The addition of age characteristics increased the match rates even further. Match rates were highly dependent on both the geographic distribution of race/ethnicity groups and population density. Conclusion Geo-imputation appears to offer some advantages and no serious drawbacks as compared with the alternative of assigning cases to census tracts based on postal code centroids. For a specific analysis, researchers will still need to consider the potential impact of geocoding quality on their results and evaluate

Design of a bovine low-density SNP array optimized for imputation.

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Didier Boichard

Full Text Available The Illumina BovineLD BeadChip was designed to support imputation to higher density genotypes in dairy and beef breeds by including single-nucleotide polymorphisms (SNPs that had a high minor allele frequency as well as uniform spacing across the genome except at the ends of the chromosome where densities were increased. The chip also includes SNPs on the Y chromosome and mitochondrial DNA loci that are useful for determining subspecies classification and certain paternal and maternal breed lineages. The total number of SNPs was 6,909. Accuracy of imputation to Illumina BovineSNP50 genotypes using the BovineLD chip was over 97% for most dairy and beef populations. The BovineLD imputations were about 3 percentage points more accurate than those from the Illumina GoldenGate Bovine3K BeadChip across multiple populations. The improvement was greatest when neither parent was genotyped. The minor allele frequencies were similar across taurine beef and dairy breeds as was the proportion of SNPs that were polymorphic. The new BovineLD chip should facilitate low-cost genomic selection in taurine beef and dairy cattle.

Cost reduction for web-based data imputation

KAUST Repository

Li, Zhixu; Shang, Shuo; Xie, Qing; Zhang, Xiangliang

2014-01-01

Web-based Data Imputation enables the completion of incomplete data sets by retrieving absent field values from the Web. In particular, complete fields can be used as keywords in imputation queries for absent fields. However, due to the ambiguity

Assessment of imputation methods using varying ecological information to fill the gaps in a tree functional trait database

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Poyatos, Rafael; Sus, Oliver; Vilà-Cabrera, Albert; Vayreda, Jordi; Badiella, Llorenç; Mencuccini, Maurizio; Martínez-Vilalta, Jordi

2016-04-01

Plant functional traits are increasingly being used in ecosystem ecology thanks to the growing availability of large ecological databases. However, these databases usually contain a large fraction of missing data because measuring plant functional traits systematically is labour-intensive and because most databases are compilations of datasets with different sampling designs. As a result, within a given database, there is an inevitable variability in the number of traits available for each data entry and/or the species coverage in a given geographical area. The presence of missing data may severely bias trait-based analyses, such as the quantification of trait covariation or trait-environment relationships and may hamper efforts towards trait-based modelling of ecosystem biogeochemical cycles. Several data imputation (i.e. gap-filling) methods have been recently tested on compiled functional trait databases, but the performance of imputation methods applied to a functional trait database with a regular spatial sampling has not been thoroughly studied. Here, we assess the effects of data imputation on five tree functional traits (leaf biomass to sapwood area ratio, foliar nitrogen, maximum height, specific leaf area and wood density) in the Ecological and Forest Inventory of Catalonia, an extensive spatial database (covering 31900 km2). We tested the performance of species mean imputation, single imputation by the k-nearest neighbors algorithm (kNN) and a multiple imputation method, Multivariate Imputation with Chained Equations (MICE) at different levels of missing data (10%, 30%, 50%, and 80%). We also assessed the changes in imputation performance when additional predictors (species identity, climate, forest structure, spatial structure) were added in kNN and MICE imputations. We evaluated the imputed datasets using a battery of indexes describing departure from the complete dataset in trait distribution, in the mean prediction error, in the correlation matrix

Education and health and well-being: direct and indirect effects with multiple mediators and interactions with multiple imputed data in Stata.

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Sheikh, Mashhood Ahmed; Abelsen, Birgit; Olsen, Jan Abel

2017-11-01

Previous methods for assessing mediation assume no multiplicative interactions. The inverse odds weighting (IOW) approach has been presented as a method that can be used even when interactions exist. The substantive aim of this study was to assess the indirect effect of education on health and well-being via four indicators of adult socioeconomic status (SES): income, management position, occupational hierarchy position and subjective social status. 8516 men and women from the Tromsø Study (Norway) were followed for 17 years. Education was measured at age 25-74 years, while SES and health and well-being were measured at age 42-91 years. Natural direct and indirect effects (NIE) were estimated using weighted Poisson regression models with IOW. Stata code is provided that makes it easy to assess mediation in any multiple imputed dataset with multiple mediators and interactions. Low education was associated with lower SES. Consequently, low SES was associated with being unhealthy and having a low level of well-being. The effect (NIE) of education on health and well-being is mediated by income, management position, occupational hierarchy position and subjective social status. This study contributes to the literature on mediation analysis, as well as the literature on the importance of education for health-related quality of life and subjective well-being. The influence of education on health and well-being had different pathways in this Norwegian sample. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Accuracy of genome-wide imputation of untyped markers and impacts on statistical power for association studies

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McElwee Joshua

2009-06-01

-eQTL discoveries detected by various methods can be interpreted as their relative statistical power in the GWAS. In this study, we find that imputation offer modest additional power (by 4% on top of either Ilmn317K or Ilmn650Y, much less than the power gain from Ilmn317K to Ilmn650Y (13%. Conclusion Current algorithms can accurately impute genotypes for untyped markers, which enables researchers to pool data between studies conducted using different SNP sets. While genotyping itself results in a small error rate (e.g. 0.5%, imputing genotypes is surprisingly accurate. We found that dense marker sets (e.g. Ilmn650Y outperform sparser ones (e.g. Ilmn317K in terms of imputation yield and accuracy. We also noticed it was harder to impute genotypes for African American samples, partially due to population admixture, although using a pooled reference boosts performance. Interestingly, GWAS carried out using imputed genotypes only slightly increased power on top of assayed SNPs. The reason is likely due to adding more markers via imputation only results in modest gain in genetic coverage, but worsens the multiple testing penalties. Furthermore, cis-eQTL mapping using dense SNP set derived from imputation achieves great resolution, and locate associate peak closer to causal variants than conventional approach.

Comparison of different Methods for Univariate Time Series Imputation in R

OpenAIRE

Moritz, Steffen; Sardá, Alexis; Bartz-Beielstein, Thomas; Zaefferer, Martin; Stork, Jörg

2015-01-01

Missing values in datasets are a well-known problem and there are quite a lot of R packages offering imputation functions. But while imputation in general is well covered within R, it is hard to find functions for imputation of univariate time series. The problem is, most standard imputation techniques can not be applied directly. Most algorithms rely on inter-attribute correlations, while univariate time series imputation needs to employ time dependencies. This paper provides an overview of ...

In vivo activity of miR-34a mimics delivered by stable nucleic acid lipid particles (SNALPs against multiple myeloma.

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Maria Teresa Di Martino

Full Text Available Multiple myeloma (MM is a disease with an adverse outcome and new therapeutic strategies are urgently awaited. A rising body of evidence supports the notion that microRNAs (miRNAs, master regulators of eukaryotic gene expression, may exert anti-MM activity. Here, we evaluated the activity of synthetic miR-34a in MM cells. We found that transfection of miR-34a mimics in MM cells induces a significant change of gene expression with relevant effects on multiple signal transduction pathways. We detected early inactivation of pro-survival and proliferative kinases Erk-2 and Akt followed at later time points by caspase-6 and -3 activation and apoptosis induction. To improve the in vivo delivery, we encapsulated miR-34a mimics in stable nucleic acid lipid particles (SNALPs. We found that SNALPs miR-34a were highly efficient in vitro in inhibiting growth of MM cells. Then, we investigated the activity of the SNALPs miR-34a against MM xenografts in SCID mice. We observed significant tumor growth inhibition (p<0.05 which translated in mice survival benefits (p=0.0047. Analysis of miR-34a and NOTCH1 expression in tumor retrieved from animal demonstrated efficient delivery and gene modulation induced by SNALPs miR-34a in the absence of systemic toxicity. We here therefore provide evidence that SNALPs miR-34a may represent a promising tool for miRNA-therapeutics in MM.

Randomly and Non-Randomly Missing Renal Function Data in the Strong Heart Study: A Comparison of Imputation Methods.

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Nawar Shara

Full Text Available Kidney and cardiovascular disease are widespread among populations with high prevalence of diabetes, such as American Indians participating in the Strong Heart Study (SHS. Studying these conditions simultaneously in longitudinal studies is challenging, because the morbidity and mortality associated with these diseases result in missing data, and these data are likely not missing at random. When such data are merely excluded, study findings may be compromised. In this article, a subset of 2264 participants with complete renal function data from Strong Heart Exams 1 (1989-1991, 2 (1993-1995, and 3 (1998-1999 was used to examine the performance of five methods used to impute missing data: listwise deletion, mean of serial measures, adjacent value, multiple imputation, and pattern-mixture. Three missing at random models and one non-missing at random model were used to compare the performance of the imputation techniques on randomly and non-randomly missing data. The pattern-mixture method was found to perform best for imputing renal function data that were not missing at random. Determining whether data are missing at random or not can help in choosing the imputation method that will provide the most accurate results.

Missing Data Imputation of Solar Radiation Data under Different Atmospheric Conditions

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Turrado, Concepción Crespo; López, María del Carmen Meizoso; Lasheras, Fernando Sánchez; Gómez, Benigno Antonio Rodríguez; Rollé, José Luis Calvo; de Cos Juez, Francisco Javier

2014-01-01

Global solar broadband irradiance on a planar surface is measured at weather stations by pyranometers. In the case of the present research, solar radiation values from nine meteorological stations of the MeteoGalicia real-time observational network, captured and stored every ten minutes, are considered. In this kind of record, the lack of data and/or the presence of wrong values adversely affects any time series study. Consequently, when this occurs, a data imputation process must be performed in order to replace missing data with estimated values. This paper aims to evaluate the multivariate imputation of ten-minute scale data by means of the chained equations method (MICE). This method allows the network itself to impute the missing or wrong data of a solar radiation sensor, by using either all or just a group of the measurements of the remaining sensors. Very good results have been obtained with the MICE method in comparison with other methods employed in this field such as Inverse Distance Weighting (IDW) and Multiple Linear Regression (MLR). The average RMSE value of the predictions for the MICE algorithm was 13.37% while that for the MLR it was 28.19%, and 31.68% for the IDW. PMID:25356644

Missing Data Imputation of Solar Radiation Data under Different Atmospheric Conditions

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Concepción Crespo Turrado

2014-10-01

Full Text Available Global solar broadband irradiance on a planar surface is measured at weather stations by pyranometers. In the case of the present research, solar radiation values from nine meteorological stations of the MeteoGalicia real-time observational network, captured and stored every ten minutes, are considered. In this kind of record, the lack of data and/or the presence of wrong values adversely affects any time series study. Consequently, when this occurs, a data imputation process must be performed in order to replace missing data with estimated values. This paper aims to evaluate the multivariate imputation of ten-minute scale data by means of the chained equations method (MICE. This method allows the network itself to impute the missing or wrong data of a solar radiation sensor, by using either all or just a group of the measurements of the remaining sensors. Very good results have been obtained with the MICE method in comparison with other methods employed in this field such as Inverse Distance Weighting (IDW and Multiple Linear Regression (MLR. The average RMSE value of the predictions for the MICE algorithm was 13.37% while that for the MLR it was 28.19%, and 31.68% for the IDW.

Missing data imputation of solar radiation data under different atmospheric conditions.

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Turrado, Concepción Crespo; López, María Del Carmen Meizoso; Lasheras, Fernando Sánchez; Gómez, Benigno Antonio Rodríguez; Rollé, José Luis Calvo; Juez, Francisco Javier de Cos

2014-10-29

Global solar broadband irradiance on a planar surface is measured at weather stations by pyranometers. In the case of the present research, solar radiation values from nine meteorological stations of the MeteoGalicia real-time observational network, captured and stored every ten minutes, are considered. In this kind of record, the lack of data and/or the presence of wrong values adversely affects any time series study. Consequently, when this occurs, a data imputation process must be performed in order to replace missing data with estimated values. This paper aims to evaluate the multivariate imputation of ten-minute scale data by means of the chained equations method (MICE). This method allows the network itself to impute the missing or wrong data of a solar radiation sensor, by using either all or just a group of the measurements of the remaining sensors. Very good results have been obtained with the MICE method in comparison with other methods employed in this field such as Inverse Distance Weighting (IDW) and Multiple Linear Regression (MLR). The average RMSE value of the predictions for the MICE algorithm was 13.37% while that for the MLR it was 28.19%, and 31.68% for the IDW.

MiMC: Efficient encryption and cryptographic hashing with minimal multiplicative complexity

DEFF Research Database (Denmark)

Albrecht, Martin; Grassi, Lorenzo; Rechberger, Christian

2016-01-01

and cryptographic hash functions is to reconsider and simplify the round function of the Knudsen-Nyberg cipher from 1995. The mapping F(x) := x3 is used as the main component there and is also the main component of our family of proposals called “MiMC”. We study various attack vectors for this construction and give...... a new attack vector that outperforms others in relevant settings. Due to its very low number of multiplications, the design lends itself well to a large class of applications, especially when the depth does not matter but the total number of multiplications in the circuit dominates all aspects...

VIGAN: Missing View Imputation with Generative Adversarial Networks.

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Shang, Chao; Palmer, Aaron; Sun, Jiangwen; Chen, Ko-Shin; Lu, Jin; Bi, Jinbo

2017-01-01

In an era when big data are becoming the norm, there is less concern with the quantity but more with the quality and completeness of the data. In many disciplines, data are collected from heterogeneous sources, resulting in multi-view or multi-modal datasets. The missing data problem has been challenging to address in multi-view data analysis. Especially, when certain samples miss an entire view of data, it creates the missing view problem. Classic multiple imputations or matrix completion methods are hardly effective here when no information can be based on in the specific view to impute data for such samples. The commonly-used simple method of removing samples with a missing view can dramatically reduce sample size, thus diminishing the statistical power of a subsequent analysis. In this paper, we propose a novel approach for view imputation via generative adversarial networks (GANs), which we name by VIGAN. This approach first treats each view as a separate domain and identifies domain-to-domain mappings via a GAN using randomly-sampled data from each view, and then employs a multi-modal denoising autoencoder (DAE) to reconstruct the missing view from the GAN outputs based on paired data across the views. Then, by optimizing the GAN and DAE jointly, our model enables the knowledge integration for domain mappings and view correspondences to effectively recover the missing view. Empirical results on benchmark datasets validate the VIGAN approach by comparing against the state of the art. The evaluation of VIGAN in a genetic study of substance use disorders further proves the effectiveness and usability of this approach in life science.

Evaluating Imputation Algorithms for Low-Depth Genotyping-By-Sequencing (GBS Data.

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Ariel W Chan

Full Text Available Well-powered genomic studies require genome-wide marker coverage across many individuals. For non-model species with few genomic resources, high-throughput sequencing (HTS methods, such as Genotyping-By-Sequencing (GBS, offer an inexpensive alternative to array-based genotyping. Although affordable, datasets derived from HTS methods suffer from sequencing error, alignment errors, and missing data, all of which introduce noise and uncertainty to variant discovery and genotype calling. Under such circumstances, meaningful analysis of the data is difficult. Our primary interest lies in the issue of how one can accurately infer or impute missing genotypes in HTS-derived datasets. Many of the existing genotype imputation algorithms and software packages were primarily developed by and optimized for the human genetics community, a field where a complete and accurate reference genome has been constructed and SNP arrays have, in large part, been the common genotyping platform. We set out to answer two questions: 1 can we use existing imputation methods developed by the human genetics community to impute missing genotypes in datasets derived from non-human species and 2 are these methods, which were developed and optimized to impute ascertained variants, amenable for imputation of missing genotypes at HTS-derived variants? We selected Beagle v.4, a widely used algorithm within the human genetics community with reportedly high accuracy, to serve as our imputation contender. We performed a series of cross-validation experiments, using GBS data collected from the species Manihot esculenta by the Next Generation (NEXTGEN Cassava Breeding Project. NEXTGEN currently imputes missing genotypes in their datasets using a LASSO-penalized, linear regression method (denoted 'glmnet'. We selected glmnet to serve as a benchmark imputation method for this reason. We obtained estimates of imputation accuracy by masking a subset of observed genotypes, imputing, and

Evaluating Imputation Algorithms for Low-Depth Genotyping-By-Sequencing (GBS) Data.

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Chan, Ariel W; Hamblin, Martha T; Jannink, Jean-Luc

2016-01-01

Well-powered genomic studies require genome-wide marker coverage across many individuals. For non-model species with few genomic resources, high-throughput sequencing (HTS) methods, such as Genotyping-By-Sequencing (GBS), offer an inexpensive alternative to array-based genotyping. Although affordable, datasets derived from HTS methods suffer from sequencing error, alignment errors, and missing data, all of which introduce noise and uncertainty to variant discovery and genotype calling. Under such circumstances, meaningful analysis of the data is difficult. Our primary interest lies in the issue of how one can accurately infer or impute missing genotypes in HTS-derived datasets. Many of the existing genotype imputation algorithms and software packages were primarily developed by and optimized for the human genetics community, a field where a complete and accurate reference genome has been constructed and SNP arrays have, in large part, been the common genotyping platform. We set out to answer two questions: 1) can we use existing imputation methods developed by the human genetics community to impute missing genotypes in datasets derived from non-human species and 2) are these methods, which were developed and optimized to impute ascertained variants, amenable for imputation of missing genotypes at HTS-derived variants? We selected Beagle v.4, a widely used algorithm within the human genetics community with reportedly high accuracy, to serve as our imputation contender. We performed a series of cross-validation experiments, using GBS data collected from the species Manihot esculenta by the Next Generation (NEXTGEN) Cassava Breeding Project. NEXTGEN currently imputes missing genotypes in their datasets using a LASSO-penalized, linear regression method (denoted 'glmnet'). We selected glmnet to serve as a benchmark imputation method for this reason. We obtained estimates of imputation accuracy by masking a subset of observed genotypes, imputing, and calculating the

Partial F-tests with multiply imputed data in the linear regression framework via coefficient of determination.

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Chaurasia, Ashok; Harel, Ofer

2015-02-10

Tests for regression coefficients such as global, local, and partial F-tests are common in applied research. In the framework of multiple imputation, there are several papers addressing tests for regression coefficients. However, for simultaneous hypothesis testing, the existing methods are computationally intensive because they involve calculation with vectors and (inversion of) matrices. In this paper, we propose a simple method based on the scalar entity, coefficient of determination, to perform (global, local, and partial) F-tests with multiply imputed data. The proposed method is evaluated using simulated data and applied to suicide prevention data. Copyright © 2014 John Wiley & Sons, Ltd.

Outlier Removal in Model-Based Missing Value Imputation for Medical Datasets

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Min-Wei Huang

2018-01-01

Full Text Available Many real-world medical datasets contain some proportion of missing (attribute values. In general, missing value imputation can be performed to solve this problem, which is to provide estimations for the missing values by a reasoning process based on the (complete observed data. However, if the observed data contain some noisy information or outliers, the estimations of the missing values may not be reliable or may even be quite different from the real values. The aim of this paper is to examine whether a combination of instance selection from the observed data and missing value imputation offers better performance than performing missing value imputation alone. In particular, three instance selection algorithms, DROP3, GA, and IB3, and three imputation algorithms, KNNI, MLP, and SVM, are used in order to find out the best combination. The experimental results show that that performing instance selection can have a positive impact on missing value imputation over the numerical data type of medical datasets, and specific combinations of instance selection and imputation methods can improve the imputation results over the mixed data type of medical datasets. However, instance selection does not have a definitely positive impact on the imputation result for categorical medical datasets.

Two-pass imputation algorithm for missing value estimation in gene expression time series.

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Tsiporkova, Elena; Boeva, Veselka

2007-10-01

Gene expression microarray experiments frequently generate datasets with multiple values missing. However, most of the analysis, mining, and classification methods for gene expression data require a complete matrix of gene array values. Therefore, the accurate estimation of missing values in such datasets has been recognized as an important issue, and several imputation algorithms have already been proposed to the biological community. Most of these approaches, however, are not particularly suitable for time series expression profiles. In view of this, we propose a novel imputation algorithm, which is specially suited for the estimation of missing values in gene expression time series data. The algorithm utilizes Dynamic Time Warping (DTW) distance in order to measure the similarity between time expression profiles, and subsequently selects for each gene expression profile with missing values a dedicated set of candidate profiles for estimation. Three different DTW-based imputation (DTWimpute) algorithms have been considered: position-wise, neighborhood-wise, and two-pass imputation. These have initially been prototyped in Perl, and their accuracy has been evaluated on yeast expression time series data using several different parameter settings. The experiments have shown that the two-pass algorithm consistently outperforms, in particular for datasets with a higher level of missing entries, the neighborhood-wise and the position-wise algorithms. The performance of the two-pass DTWimpute algorithm has further been benchmarked against the weighted K-Nearest Neighbors algorithm, which is widely used in the biological community; the former algorithm has appeared superior to the latter one. Motivated by these findings, indicating clearly the added value of the DTW techniques for missing value estimation in time series data, we have built an optimized C++ implementation of the two-pass DTWimpute algorithm. The software also provides for a choice between three different

Assessing accuracy of genotype imputation in American Indians.

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Alka Malhotra

Full Text Available Genotype imputation is commonly used in genetic association studies to test untyped variants using information on linkage disequilibrium (LD with typed markers. Imputing genotypes requires a suitable reference population in which the LD pattern is known, most often one selected from HapMap. However, some populations, such as American Indians, are not represented in HapMap. In the present study, we assessed accuracy of imputation using HapMap reference populations in a genome-wide association study in Pima Indians.Data from six randomly selected chromosomes were used. Genotypes in the study population were masked (either 1% or 20% of SNPs available for a given chromosome. The masked genotypes were then imputed using the software Markov Chain Haplotyping Algorithm. Using four HapMap reference populations, average genotype error rates ranged from 7.86% for Mexican Americans to 22.30% for Yoruba. In contrast, use of the original Pima Indian data as a reference resulted in an average error rate of 1.73%.Our results suggest that the use of HapMap reference populations results in substantial inaccuracy in the imputation of genotypes in American Indians. A possible solution would be to densely genotype or sequence a reference American Indian population.

Highly accurate sequence imputation enables precise QTL mapping in Brown Swiss cattle.

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Frischknecht, Mirjam; Pausch, Hubert; Bapst, Beat; Signer-Hasler, Heidi; Flury, Christine; Garrick, Dorian; Stricker, Christian; Fries, Ruedi; Gredler-Grandl, Birgit

2017-12-29

Within the last few years a large amount of genomic information has become available in cattle. Densities of genomic information vary from a few thousand variants up to whole genome sequence information. In order to combine genomic information from different sources and infer genotypes for a common set of variants, genotype imputation is required. In this study we evaluated the accuracy of imputation from high density chips to whole genome sequence data in Brown Swiss cattle. Using four popular imputation programs (Beagle, FImpute, Impute2, Minimac) and various compositions of reference panels, the accuracy of the imputed sequence variant genotypes was high and differences between the programs and scenarios were small. We imputed sequence variant genotypes for more than 1600 Brown Swiss bulls and performed genome-wide association studies for milk fat percentage at two stages of lactation. We found one and three quantitative trait loci for early and late lactation fat content, respectively. Known causal variants that were imputed from the sequenced reference panel were among the most significantly associated variants of the genome-wide association study. Our study demonstrates that whole-genome sequence information can be imputed at high accuracy in cattle populations. Using imputed sequence variant genotypes in genome-wide association studies may facilitate causal variant detection.

PSMB4 promotes multiple myeloma cell growth by activating NF-κB-miR-21 signaling

Energy Technology Data Exchange (ETDEWEB)

Zheng, Peihao; Guo, Honggang [Department of Hematology, Navy General Hospital, Beijing 100048 (China); Li, Guangchao [School of Bioscience and Bioengineering, South China University of Technology, Guangzhou 510006 (China); Han, Siqi [Department of Medical Oncology, Jinling Hospital, Nanjing 210002 (China); Luo, Fei [Department of Stomatology, Jinling Hospital, Nanjing 210002 (China); Liu, Yi, E-mail: liuyi2033@163.com [Department of Hematology, Navy General Hospital, Beijing 100048 (China)

2015-03-06

Proteasomal subunit PSMB4, was recently identified as potential cancer driver genes in several tumors. However, the regulatory mechanism of PSMB4 on carcinogenesis process remains unclear. In this study, we investigated the expression and roles of PSMB4 in multiple myeloma (MM). We found a significant up-regulation of PSMB4 in MM plasma and cell lines. Ectopic overexpression of PSMB4 promoted cell growth and colony forming ability of MM cells, whereas inhibition of PSMB4 led to a decrease of such events. Furthermore, our results demonstrated the up-regulation of miR-21 and a positive correlation between the levels of miR-21 and PSMB4 in MM. Re-expression of miR-21 markedly rescued PSMB4 knockdown-mediated suppression of cell proliferation and clone-formation. Additionally, while enforced expression of PSMB4 profoundly increased NF-κB activity and the level of miR-21, PSMB4 knockdown or NF-κB inhibition suppressed miR-21 expression in MM cells. Taken together, our results demonstrated that PSMB4 regulated MM cell growth in part by activating NF-κB-miR-21 signaling, which may represent promising targets for novel specific therapies. - Highlights: • First reported upregulation of PSMB4 in MM plasma and cell lines. • PSMB4 promoted MM cell growth and colony forming ability. • Further found miR-21 was up-regulated by PSMB4 in MM plasma and cell lines. • PSMB4-induced miR-21 expression was modulated by NF-κB. • PSMB4-NF-κB-miR-21 axis may be potential therapeutic targets of MM.

Collateral missing value imputation: a new robust missing value estimation algorithm for microarray data.

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Sehgal, Muhammad Shoaib B; Gondal, Iqbal; Dooley, Laurence S

2005-05-15

Microarray data are used in a range of application areas in biology, although often it contains considerable numbers of missing values. These missing values can significantly affect subsequent statistical analysis and machine learning algorithms so there is a strong motivation to estimate these values as accurately as possible before using these algorithms. While many imputation algorithms have been proposed, more robust techniques need to be developed so that further analysis of biological data can be accurately undertaken. In this paper, an innovative missing value imputation algorithm called collateral missing value estimation (CMVE) is presented which uses multiple covariance-based imputation matrices for the final prediction of missing values. The matrices are computed and optimized using least square regression and linear programming methods. The new CMVE algorithm has been compared with existing estimation techniques including Bayesian principal component analysis imputation (BPCA), least square impute (LSImpute) and K-nearest neighbour (KNN). All these methods were rigorously tested to estimate missing values in three separate non-time series (ovarian cancer based) and one time series (yeast sporulation) dataset. Each method was quantitatively analyzed using the normalized root mean square (NRMS) error measure, covering a wide range of randomly introduced missing value probabilities from 0.01 to 0.2. Experiments were also undertaken on the yeast dataset, which comprised 1.7% actual missing values, to test the hypothesis that CMVE performed better not only for randomly occurring but also for a real distribution of missing values. The results confirmed that CMVE consistently demonstrated superior and robust estimation capability of missing values compared with other methods for both series types of data, for the same order of computational complexity. A concise theoretical framework has also been formulated to validate the improved performance of the CMVE

Differential network analysis with multiply imputed lipidomic data.

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Maiju Kujala

Full Text Available The importance of lipids for cell function and health has been widely recognized, e.g., a disorder in the lipid composition of cells has been related to atherosclerosis caused cardiovascular disease (CVD. Lipidomics analyses are characterized by large yet not a huge number of mutually correlated variables measured and their associations to outcomes are potentially of a complex nature. Differential network analysis provides a formal statistical method capable of inferential analysis to examine differences in network structures of the lipids under two biological conditions. It also guides us to identify potential relationships requiring further biological investigation. We provide a recipe to conduct permutation test on association scores resulted from partial least square regression with multiple imputed lipidomic data from the LUdwigshafen RIsk and Cardiovascular Health (LURIC study, particularly paying attention to the left-censored missing values typical for a wide range of data sets in life sciences. Left-censored missing values are low-level concentrations that are known to exist somewhere between zero and a lower limit of quantification. To make full use of the LURIC data with the missing values, we utilize state of the art multiple imputation techniques and propose solutions to the challenges that incomplete data sets bring to differential network analysis. The customized network analysis helps us to understand the complexities of the underlying biological processes by identifying lipids and lipid classes that interact with each other, and by recognizing the most important differentially expressed lipids between two subgroups of coronary artery disease (CAD patients, the patients that had a fatal CVD event and the ones who remained stable during two year follow-up.

Imputation of variants from the 1000 Genomes Project modestly improves known associations and can identify low-frequency variant-phenotype associations undetected by HapMap based imputation.

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Wood, Andrew R; Perry, John R B; Tanaka, Toshiko; Hernandez, Dena G; Zheng, Hou-Feng; Melzer, David; Gibbs, J Raphael; Nalls, Michael A; Weedon, Michael N; Spector, Tim D; Richards, J Brent; Bandinelli, Stefania; Ferrucci, Luigi; Singleton, Andrew B; Frayling, Timothy M

2013-01-01

Genome-wide association (GWA) studies have been limited by the reliance on common variants present on microarrays or imputable from the HapMap Project data. More recently, the completion of the 1000 Genomes Project has provided variant and haplotype information for several million variants derived from sequencing over 1,000 individuals. To help understand the extent to which more variants (including low frequency (1% ≤ MAF 1000 Genomes imputation, respectively, and 9 and 11 that reached a stricter, likely conservative, threshold of P1000 Genomes genotype data modestly improved the strength of known associations. Of 20 associations detected at P1000 Genomes imputed data and one was nominally more strongly associated in HapMap imputed data. We also detected an association between a low frequency variant and phenotype that was previously missed by HapMap based imputation approaches. An association between rs112635299 and alpha-1 globulin near the SERPINA gene represented the known association between rs28929474 (MAF = 0.007) and alpha1-antitrypsin that predisposes to emphysema (P = 2.5×10(-12)). Our data provide important proof of principle that 1000 Genomes imputation will detect novel, low frequency-large effect associations.

Post-transcriptional generation of miRNA variants by multiple nucleotidyl transferases contributes to miRNA transcriptome complexity

OpenAIRE

Wyman, Stacia K.; Knouf, Emily C.; Parkin, Rachael K.; Fritz, Brian R.; Lin, Daniel W.; Dennis, Lucas M.; Krouse, Michael A.; Webster, Philippa J.; Tewari, Muneesh

2011-01-01

Modification of microRNA sequences by the 3′ addition of nucleotides to generate so-called “isomiRs” adds to the complexity of miRNA function, with recent reports showing that 3′ modifications can influence miRNA stability and efficiency of target repression. Here, we show that the 3′ modification of miRNAs is a physiological and common post-transcriptional event that shows selectivity for specific miRNAs and is observed across species ranging from C. elegans to human. The modifications resul...

Performance of genotype imputation for low frequency and rare variants from the 1000 genomes.

Science.gov (United States)

Zheng, Hou-Feng; Rong, Jing-Jing; Liu, Ming; Han, Fang; Zhang, Xing-Wei; Richards, J Brent; Wang, Li

2015-01-01

Genotype imputation is now routinely applied in genome-wide association studies (GWAS) and meta-analyses. However, most of the imputations have been run using HapMap samples as reference, imputation of low frequency and rare variants (minor allele frequency (MAF) 1000 Genomes panel) are available to facilitate imputation of these variants. Therefore, in order to estimate the performance of low frequency and rare variants imputation, we imputed 153 individuals, each of whom had 3 different genotype array data including 317k, 610k and 1 million SNPs, to three different reference panels: the 1000 Genomes pilot March 2010 release (1KGpilot), the 1000 Genomes interim August 2010 release (1KGinterim), and the 1000 Genomes phase1 November 2010 and May 2011 release (1KGphase1) by using IMPUTE version 2. The differences between these three releases of the 1000 Genomes data are the sample size, ancestry diversity, number of variants and their frequency spectrum. We found that both reference panel and GWAS chip density affect the imputation of low frequency and rare variants. 1KGphase1 outperformed the other 2 panels, at higher concordance rate, higher proportion of well-imputed variants (info>0.4) and higher mean info score in each MAF bin. Similarly, 1M chip array outperformed 610K and 317K. However for very rare variants (MAF ≤ 0.3%), only 0-1% of the variants were well imputed. We conclude that the imputation of low frequency and rare variants improves with larger reference panels and higher density of genome-wide genotyping arrays. Yet, despite a large reference panel size and dense genotyping density, very rare variants remain difficult to impute.

Public Undertakings and Imputability

DEFF Research Database (Denmark)

Ølykke, Grith Skovgaard

2013-01-01

In this article, the issue of impuability to the State of public undertakings’ decision-making is analysed and discussed in the context of the DSBFirst case. DSBFirst is owned by the independent public undertaking DSB and the private undertaking FirstGroup plc and won the contracts in the 2008...... Oeresund tender for the provision of passenger transport by railway. From the start, the services were provided at a loss, and in the end a part of DSBFirst was wound up. In order to frame the problems illustrated by this case, the jurisprudence-based imputability requirement in the definition of State aid...... in Article 107(1) TFEU is analysed. It is concluded that where the public undertaking transgresses the control system put in place by the State, conditions for imputability are not fulfilled, and it is argued that in the current state of law, there is no conditional link between the level of control...

Reference miRNAs for miRNAome analysis of urothelial carcinomas.

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Nadine Ratert

Full Text Available BACKGROUND/OBJECTIVE: Reverse transcription quantitative real-time PCR (RT-qPCR is widely used in microRNA (miRNA expression studies on cancer. To compensate for the analytical variability produced by the multiple steps of the method, relative quantification of the measured miRNAs is required, which is based on normalization to endogenous reference genes. No study has been performed so far on reference miRNAs for normalization of miRNA expression in urothelial carcinoma. The aim of this study was to identify suitable reference miRNAs for miRNA expression studies by RT-qPCR in urothelial carcinoma. METHODS: Candidate reference miRNAs were selected from 24 urothelial carcinoma and normal bladder tissue samples by miRNA microarrays. The usefulness of these candidate reference miRNAs together with the commonly for normalization purposes used small nuclear RNAs RNU6B, RNU48, and Z30 were thereafter validated by RT-qPCR in 58 tissue samples and analyzed by the algorithms geNorm, NormFinder, and BestKeeper. PRINCIPAL FINDINGS: Based on the miRNA microarray data, a total of 16 miRNAs were identified as putative reference genes. After validation by RT-qPCR, miR-101, miR-125a-5p, miR-148b, miR-151-5p, miR-181a, miR-181b, miR-29c, miR-324-3p, miR-424, miR-874, RNU6B, RNU48, and Z30 were used for geNorm, NormFinder, and BestKeeper analyses that gave different combinations of recommended reference genes for normalization. CONCLUSIONS: The present study provided the first systematic analysis for identifying suitable reference miRNAs for miRNA expression studies of urothelial carcinoma by RT-qPCR. Different combinations of reference genes resulted in reliable expression data for both strongly and less strongly altered miRNAs. Notably, RNU6B, which is the most frequently used reference gene for miRNA studies, gave inaccurate normalization. The combination of four (miR-101, miR-125a-5p, miR-148b, and miR-151-5p or three (miR-148b, miR-181b, and miR-874

Accounting for one-channel depletion improves missing value imputation in 2-dye microarray data.

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Ritz, Cecilia; Edén, Patrik

2008-01-19

For 2-dye microarray platforms, some missing values may arise from an un-measurably low RNA expression in one channel only. Information of such "one-channel depletion" is so far not included in algorithms for imputation of missing values. Calculating the mean deviation between imputed values and duplicate controls in five datasets, we show that KNN-based imputation gives a systematic bias of the imputed expression values of one-channel depleted spots. Evaluating the correction of this bias by cross-validation showed that the mean square deviation between imputed values and duplicates were reduced up to 51%, depending on dataset. By including more information in the imputation step, we more accurately estimate missing expression values.

Missing value imputation for microarray gene expression data using histone acetylation information

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Feng Jihua

2008-05-01

Full Text Available Abstract Background It is an important pre-processing step to accurately estimate missing values in microarray data, because complete datasets are required in numerous expression profile analysis in bioinformatics. Although several methods have been suggested, their performances are not satisfactory for datasets with high missing percentages. Results The paper explores the feasibility of doing missing value imputation with the help of gene regulatory mechanism. An imputation framework called histone acetylation information aided imputation method (HAIimpute method is presented. It incorporates the histone acetylation information into the conventional KNN(k-nearest neighbor and LLS(local least square imputation algorithms for final prediction of the missing values. The experimental results indicated that the use of acetylation information can provide significant improvements in microarray imputation accuracy. The HAIimpute methods consistently improve the widely used methods such as KNN and LLS in terms of normalized root mean squared error (NRMSE. Meanwhile, the genes imputed by HAIimpute methods are more correlated with the original complete genes in terms of Pearson correlation coefficients. Furthermore, the proposed methods also outperform GOimpute, which is one of the existing related methods that use the functional similarity as the external information. Conclusion We demonstrated that the using of histone acetylation information could greatly improve the performance of the imputation especially at high missing percentages. This idea can be generalized to various imputation methods to facilitate the performance. Moreover, with more knowledge accumulated on gene regulatory mechanism in addition to histone acetylation, the performance of our approach can be further improved and verified.

Evaluation and application of summary statistic imputation to discover new height-associated loci.

Science.gov (United States)

Rüeger, Sina; McDaid, Aaron; Kutalik, Zoltán

2018-05-01

As most of the heritability of complex traits is attributed to common and low frequency genetic variants, imputing them by combining genotyping chips and large sequenced reference panels is the most cost-effective approach to discover the genetic basis of these traits. Association summary statistics from genome-wide meta-analyses are available for hundreds of traits. Updating these to ever-increasing reference panels is very cumbersome as it requires reimputation of the genetic data, rerunning the association scan, and meta-analysing the results. A much more efficient method is to directly impute the summary statistics, termed as summary statistics imputation, which we improved to accommodate variable sample size across SNVs. Its performance relative to genotype imputation and practical utility has not yet been fully investigated. To this end, we compared the two approaches on real (genotyped and imputed) data from 120K samples from the UK Biobank and show that, genotype imputation boasts a 3- to 5-fold lower root-mean-square error, and better distinguishes true associations from null ones: We observed the largest differences in power for variants with low minor allele frequency and low imputation quality. For fixed false positive rates of 0.001, 0.01, 0.05, using summary statistics imputation yielded a decrease in statistical power by 9, 43 and 35%, respectively. To test its capacity to discover novel associations, we applied summary statistics imputation to the GIANT height meta-analysis summary statistics covering HapMap variants, and identified 34 novel loci, 19 of which replicated using data in the UK Biobank. Additionally, we successfully replicated 55 out of the 111 variants published in an exome chip study. Our study demonstrates that summary statistics imputation is a very efficient and cost-effective way to identify and fine-map trait-associated loci. Moreover, the ability to impute summary statistics is important for follow-up analyses, such as Mendelian

Missing value imputation: with application to handwriting data

Science.gov (United States)

Xu, Zhen; Srihari, Sargur N.

2015-01-01

Missing values make pattern analysis difficult, particularly with limited available data. In longitudinal research, missing values accumulate, thereby aggravating the problem. Here we consider how to deal with temporal data with missing values in handwriting analysis. In the task of studying development of individuality of handwriting, we encountered the fact that feature values are missing for several individuals at several time instances. Six algorithms, i.e., random imputation, mean imputation, most likely independent value imputation, and three methods based on Bayesian network (static Bayesian network, parameter EM, and structural EM), are compared with children's handwriting data. We evaluate the accuracy and robustness of the algorithms under different ratios of missing data and missing values, and useful conclusions are given. Specifically, static Bayesian network is used for our data which contain around 5% missing data to provide adequate accuracy and low computational cost.

Imputed prices of greenhouse gases and land forests

International Nuclear Information System (INIS)

Uzawa, Hirofumi

1993-01-01

The theory of dynamic optimum formulated by Maeler gives us the basic theoretical framework within which it is possible to analyse the economic and, possibly, political circumstances under which the phenomenon of global warming occurs, and to search for the policy and institutional arrangements whereby it would be effectively arrested. The analysis developed here is an application of Maeler's theory to atmospheric quality. In the analysis a central role is played by the concept of imputed price in the dynamic context. Our determination of imputed prices of atmospheric carbon dioxide and land forests takes into account the difference in the stages of economic development. Indeed, the ratios of the imputed prices of atmospheric carbon dioxide and land forests over the per capita level of real national income are identical for all countries involved. (3 figures, 2 tables) (Author)

Identification of miRSNPs associated with the risk of multiple myeloma

DEFF Research Database (Denmark)

Macauda, Angelica; Calvetti, Diego; Maccari, Giuseppe

2017-01-01

to be associated with risk of various types of cancer, but they have never been investigated in MM. We performed an in silico genome-wide search for miRSNPs predicted to alter binding of miRNAs to their target sequences. We selected 12 miRSNPs and tested their association with MM risk. Our study population...... TCF19). Results from IMMEnSE were meta-analyzed with data from a previously published genome-wide association study (GWAS). The SNPs rs13409 (located in the 3'UTR of the POU5F1 gene), rs1419881 (TCF19), rs1049633, rs1049623 (both in DDR1) showed significant associations with MM risk. In conclusion, we...... pathogenesis, and several studies have identified single nucleotide polymorphisms (SNPs) associated with the susceptibility to the disease. SNPs within miRNA-binding sites in target genes (miRSNPs) may alter the strength of miRNA-mRNA interactions, thus deregulating protein expression. MiRSNPs are known...

Handling missing data for the identification of charged particles in a multilayer detector: A comparison between different imputation methods

Energy Technology Data Exchange (ETDEWEB)

Riggi, S., E-mail: sriggi@oact.inaf.it [INAF - Osservatorio Astrofisico di Catania (Italy); Riggi, D. [Keras Strategy - Milano (Italy); Riggi, F. [Dipartimento di Fisica e Astronomia - Università di Catania (Italy); INFN, Sezione di Catania (Italy)

2015-04-21

Identification of charged particles in a multilayer detector by the energy loss technique may also be achieved by the use of a neural network. The performance of the network becomes worse when a large fraction of information is missing, for instance due to detector inefficiencies. Algorithms which provide a way to impute missing information have been developed over the past years. Among the various approaches, we focused on normal mixtures’ models in comparison with standard mean imputation and multiple imputation methods. Further, to account for the intrinsic asymmetry of the energy loss data, we considered skew-normal mixture models and provided a closed form implementation in the Expectation-Maximization (EM) algorithm framework to handle missing patterns. The method has been applied to a test case where the energy losses of pions, kaons and protons in a six-layers’ Silicon detector are considered as input neurons to a neural network. Results are given in terms of reconstruction efficiency and purity of the various species in different momentum bins.

miR-320a regulates cell proliferation and apoptosis in multiple myeloma by targeting pre-B-cell leukemia transcription factor 3

Energy Technology Data Exchange (ETDEWEB)

Lu, Yinghao [Jiangsu Institute of Hematology, First Affiliated Hospital of Soochow University, Key Laboratory of Thrombosis and Hemostasis Under Ministry of Health, Collaborative Innovation Center of Hematology, Suzhou, 215006 (China); Department of Hematology, Affiliated Hospital of Guizhou Medical University, The Hematopoietic Stem Cell Transplant Center of Guizhou Province, Blood Diseases Diagnosis and Treatment Center of Guizhou Province, Guiyang, 550004, Guizhou Province (China); Wu, Depei, E-mail: wudepei@medmail.com.cn [Jiangsu Institute of Hematology, First Affiliated Hospital of Soochow University, Key Laboratory of Thrombosis and Hemostasis Under Ministry of Health, Collaborative Innovation Center of Hematology, Suzhou, 215006 (China); Wang, Jishi, E-mail: lgylhlyh@aliyun.com [Department of Hematology, Affiliated Hospital of Guizhou Medical University, The Hematopoietic Stem Cell Transplant Center of Guizhou Province, Blood Diseases Diagnosis and Treatment Center of Guizhou Province, Guiyang, 550004, Guizhou Province (China); Li, Yan; Chai, Xiao; Kang, Qian [Department of Hematology, Affiliated Hospital of Guizhou Medical University, The Hematopoietic Stem Cell Transplant Center of Guizhou Province, Blood Diseases Diagnosis and Treatment Center of Guizhou Province, Guiyang, 550004, Guizhou Province (China)

2016-05-13

Aberrant expression of microRNAs (miRNAs) is implicated in cancer development and progression. While miR-320a is reported to be deregulated in many malignancy types, its biological role in multiple myeloma (MM) remains unclear. Here, we observed reduced expression of miR-320a in MM samples and cell lines. Ectopic expression of miR-320a dramatically suppressed cell viability and clonogenicity and induced apoptosis in vitro. Mechanistic investigation led to the identification of Pre-B-cellleukemia transcription factor 3 (PBX3) as a novel and direct downstream target of miR-320a. Interestingly, reintroduction of PBX3 abrogated miR-320a-induced MM cell growth inhibition and apoptosis. In a mouse xenograft model, miR-320a overexpression inhibited tumorigenicity and promoted apoptosis. Our findings collectively indicate that miR-320a inhibits cell proliferation and induces apoptosis in MM cells by directly targeting PBX3, supporting its utility as a novel and potential therapeutic agent for miRNA-based MM therapy. -- Highlights: •Expression of miR-320a in MM cell induces apoptosis in vitro. •miR-320a represses PBX3 via targeting specific sequences in the 3′UTR region. •Exogenous expression of PBX3 reverses the effects of miR-320a in inhibiting MM cell growth and promoting apoptosis. •Overexpression of miR-320a inhibits tumor growth and increases apoptosis in vivo.

Synthetic miR-34a mimics as a novel therapeutic agent for multiple myeloma: in vitro and in vivo evidence.

Science.gov (United States)

Di Martino, Maria T; Leone, Emanuela; Amodio, Nicola; Foresta, Umberto; Lionetti, Marta; Pitari, Maria R; Cantafio, Maria E Gallo; Gullà, Annamaria; Conforti, Francesco; Morelli, Eugenio; Tomaino, Vera; Rossi, Marco; Negrini, Massimo; Ferrarini, Manlio; Caraglia, Michele; Shammas, Masood A; Munshi, Nikhil C; Anderson, Kenneth C; Neri, Antonino; Tagliaferri, Pierosandro; Tassone, Pierfrancesco

2012-11-15

Deregulated expression of miRNAs has been shown in multiple myeloma (MM). A promising strategy to achieve a therapeutic effect by targeting the miRNA regulatory network is to enforce the expression of miRNAs that act as tumor suppressor genes, such as miR-34a. Here, we investigated the therapeutic potential of synthetic miR-34a against human MM cells in vitro and in vivo. Either transient expression of miR-34a synthetic mimics or lentivirus-based miR-34a-stable enforced expression triggered growth inhibition and apoptosis in MM cells in vitro. Synthetic miR-34a downregulated canonic targets BCL2, CDK6, and NOTCH1 at both the mRNA and protein level. Lentiviral vector-transduced MM xenografts with constitutive miR-34a expression showed high growth inhibition in severe combined immunodeficient (SCID) mice. The anti-MM activity of lipidic-formulated miR-34a was further shown in vivo in two different experimental settings: (i) SCID mice bearing nontransduced MM xenografts; and (ii) SCID-synth-hu mice implanted with synthetic 3-dimensional scaffolds reconstituted with human bone marrow stromal cells and then engrafted with human MM cells. Relevant tumor growth inhibition and survival improvement were observed in mice bearing TP53-mutated MM xenografts treated with miR-34a mimics in the absence of systemic toxicity. Our findings provide a proof-of-principle that formulated synthetic miR-34a has therapeutic activity in preclinical models and support a framework for development of miR-34a-based treatment strategies in MM patients. ©2012 AACR.

48 CFR 1830.7002-4 - Determining imputed cost of money.

Science.gov (United States)

2010-10-01

... money. 1830.7002-4 Section 1830.7002-4 Federal Acquisition Regulations System NATIONAL AERONAUTICS AND... Determining imputed cost of money. (a) Determine the imputed cost of money for an asset under construction, fabrication, or development by applying a cost of money rate (see 1830.7002-2) to the representative...

Assessing and comparison of different machine learning methods in parent-offspring trios for genotype imputation.

Science.gov (United States)

Mikhchi, Abbas; Honarvar, Mahmood; Kashan, Nasser Emam Jomeh; Aminafshar, Mehdi

2016-06-21

Genotype imputation is an important tool for prediction of unknown genotypes for both unrelated individuals and parent-offspring trios. Several imputation methods are available and can either employ universal machine learning methods, or deploy algorithms dedicated to infer missing genotypes. In this research the performance of eight machine learning methods: Support Vector Machine, K-Nearest Neighbors, Extreme Learning Machine, Radial Basis Function, Random Forest, AdaBoost, LogitBoost, and TotalBoost compared in terms of the imputation accuracy, computation time and the factors affecting imputation accuracy. The methods employed using real and simulated datasets to impute the un-typed SNPs in parent-offspring trios. The tested methods show that imputation of parent-offspring trios can be accurate. The Random Forest and Support Vector Machine were more accurate than the other machine learning methods. The TotalBoost performed slightly worse than the other methods.The running times were different between methods. The ELM was always most fast algorithm. In case of increasing the sample size, the RBF requires long imputation time.The tested methods in this research can be an alternative for imputation of un-typed SNPs in low missing rate of data. However, it is recommended that other machine learning methods to be used for imputation. Copyright © 2016 Elsevier Ltd. All rights reserved.

TRIP: An interactive retrieving-inferring data imputation approach

KAUST Repository

Li, Zhixu

2016-06-25

Data imputation aims at filling in missing attribute values in databases. Existing imputation approaches to nonquantitive string data can be roughly put into two categories: (1) inferring-based approaches [2], and (2) retrieving-based approaches [1]. Specifically, the inferring-based approaches find substitutes or estimations for the missing ones from the complete part of the data set. However, they typically fall short in filling in unique missing attribute values which do not exist in the complete part of the data set [1]. The retrieving-based approaches resort to external resources for help by formulating proper web search queries to retrieve web pages containing the missing values from the Web, and then extracting the missing values from the retrieved web pages [1]. This webbased retrieving approach reaches a high imputation precision and recall, but on the other hand, issues a large number of web search queries, which brings a large overhead [1]. © 2016 IEEE.

TRIP: An interactive retrieving-inferring data imputation approach

KAUST Repository

Li, Zhixu; Qin, Lu; Cheng, Hong; Zhang, Xiangliang; Zhou, Xiaofang

2016-01-01

Data imputation aims at filling in missing attribute values in databases. Existing imputation approaches to nonquantitive string data can be roughly put into two categories: (1) inferring-based approaches [2], and (2) retrieving-based approaches [1]. Specifically, the inferring-based approaches find substitutes or estimations for the missing ones from the complete part of the data set. However, they typically fall short in filling in unique missing attribute values which do not exist in the complete part of the data set [1]. The retrieving-based approaches resort to external resources for help by formulating proper web search queries to retrieve web pages containing the missing values from the Web, and then extracting the missing values from the retrieved web pages [1]. This webbased retrieving approach reaches a high imputation precision and recall, but on the other hand, issues a large number of web search queries, which brings a large overhead [1]. © 2016 IEEE.

Imputation of genotypes in Danish two-way crossbred pigs using low density panels

DEFF Research Database (Denmark)

Xiang, Tao; Christensen, Ole Fredslund; Legarra, Andres

Genotype imputation is commonly used as an initial step of genomic selection. Studies on humans, plants and ruminants suggested many factors would affect the performance of imputation. However, studies rarely investigated pigs, especially crossbred pigs. In this study, different scenarios...... of imputation from 5K SNPs to 7K SNPs on Danish Landrace, Yorkshire, and crossbred Landrace-Yorkshire were compared. In conclusion, genotype imputation on crossbreds performs equally well as in purebreds, when parental breeds are used as the reference panel. When the size of reference is considerably large...... SNPs. This dataset will be analyzed for genomic selection in a future study...

An Overview and Evaluation of Recent Machine Learning Imputation Methods Using Cardiac Imaging Data.

Science.gov (United States)

Liu, Yuzhe; Gopalakrishnan, Vanathi

2017-03-01

Many clinical research datasets have a large percentage of missing values that directly impacts their usefulness in yielding high accuracy classifiers when used for training in supervised machine learning. While missing value imputation methods have been shown to work well with smaller percentages of missing values, their ability to impute sparse clinical research data can be problem specific. We previously attempted to learn quantitative guidelines for ordering cardiac magnetic resonance imaging during the evaluation for pediatric cardiomyopathy, but missing data significantly reduced our usable sample size. In this work, we sought to determine if increasing the usable sample size through imputation would allow us to learn better guidelines. We first review several machine learning methods for estimating missing data. Then, we apply four popular methods (mean imputation, decision tree, k-nearest neighbors, and self-organizing maps) to a clinical research dataset of pediatric patients undergoing evaluation for cardiomyopathy. Using Bayesian Rule Learning (BRL) to learn ruleset models, we compared the performance of imputation-augmented models versus unaugmented models. We found that all four imputation-augmented models performed similarly to unaugmented models. While imputation did not improve performance, it did provide evidence for the robustness of our learned models.

The Ability of Different Imputation Methods to Preserve the Significant Genes and Pathways in Cancer

Directory of Open Access Journals (Sweden)

Rosa Aghdam

2017-12-01

Full Text Available Deciphering important genes and pathways from incomplete gene expression data could facilitate a better understanding of cancer. Different imputation methods can be applied to estimate the missing values. In our study, we evaluated various imputation methods for their performance in preserving significant genes and pathways. In the first step, 5% genes are considered in random for two types of ignorable and non-ignorable missingness mechanisms with various missing rates. Next, 10 well-known imputation methods were applied to the complete datasets. The significance analysis of microarrays (SAM method was applied to detect the significant genes in rectal and lung cancers to showcase the utility of imputation approaches in preserving significant genes. To determine the impact of different imputation methods on the identification of important genes, the chi-squared test was used to compare the proportions of overlaps between significant genes detected from original data and those detected from the imputed datasets. Additionally, the significant genes are tested for their enrichment in important pathways, using the ConsensusPathDB. Our results showed that almost all the significant genes and pathways of the original dataset can be detected in all imputed datasets, indicating that there is no significant difference in the performance of various imputation methods tested. The source code and selected datasets are available on http://profiles.bs.ipm.ir/softwares/imputation_methods/.

Mi-DISCOVERER: A bioinformatics tool for the detection of mi-RNA in human genome.

Science.gov (United States)

Arshad, Saadia; Mumtaz, Asia; Ahmad, Freed; Liaquat, Sadia; Nadeem, Shahid; Mehboob, Shahid; Afzal, Muhammad

2010-11-27

MicroRNAs (miRNAs) are 22 nucleotides non-coding RNAs that play pivotal regulatory roles in diverse organisms including the humans and are difficult to be identified due to lack of either sequence features or robust algorithms to efficiently identify. Therefore, we made a tool that is Mi-Discoverer for the detection of miRNAs in human genome. The tools used for the development of software are Microsoft Office Access 2003, the JDK version 1.6.0, BioJava version 1.0, and the NetBeans IDE version 6.0. All already made miRNAs softwares were web based; so the advantage of our project was to make a desktop facility to the user for sequence alignment search with already identified miRNAs of human genome present in the database. The user can also insert and update the newly discovered human miRNA in the database. Mi-Discoverer, a bioinformatics tool successfully identifies human miRNAs based on multiple sequence alignment searches. It's a non redundant database containing a large collection of publicly available human miRNAs.

Imputing amino acid polymorphisms in human leukocyte antigens.

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Xiaoming Jia

Full Text Available DNA sequence variation within human leukocyte antigen (HLA genes mediate susceptibility to a wide range of human diseases. The complex genetic structure of the major histocompatibility complex (MHC makes it difficult, however, to collect genotyping data in large cohorts. Long-range linkage disequilibrium between HLA loci and SNP markers across the major histocompatibility complex (MHC region offers an alternative approach through imputation to interrogate HLA variation in existing GWAS data sets. Here we describe a computational strategy, SNP2HLA, to impute classical alleles and amino acid polymorphisms at class I (HLA-A, -B, -C and class II (-DPA1, -DPB1, -DQA1, -DQB1, and -DRB1 loci. To characterize performance of SNP2HLA, we constructed two European ancestry reference panels, one based on data collected in HapMap-CEPH pedigrees (90 individuals and another based on data collected by the Type 1 Diabetes Genetics Consortium (T1DGC, 5,225 individuals. We imputed HLA alleles in an independent data set from the British 1958 Birth Cohort (N = 918 with gold standard four-digit HLA types and SNPs genotyped using the Affymetrix GeneChip 500 K and Illumina Immunochip microarrays. We demonstrate that the sample size of the reference panel, rather than SNP density of the genotyping platform, is critical to achieve high imputation accuracy. Using the larger T1DGC reference panel, the average accuracy at four-digit resolution is 94.7% using the low-density Affymetrix GeneChip 500 K, and 96.7% using the high-density Illumina Immunochip. For amino acid polymorphisms within HLA genes, we achieve 98.6% and 99.3% accuracy using the Affymetrix GeneChip 500 K and Illumina Immunochip, respectively. Finally, we demonstrate how imputation and association testing at amino acid resolution can facilitate fine-mapping of primary MHC association signals, giving a specific example from type 1 diabetes.

The Ability of Different Imputation Methods to Preserve the Significant Genes and Pathways in Cancer.

Science.gov (United States)

Aghdam, Rosa; Baghfalaki, Taban; Khosravi, Pegah; Saberi Ansari, Elnaz

2017-12-01

Deciphering important genes and pathways from incomplete gene expression data could facilitate a better understanding of cancer. Different imputation methods can be applied to estimate the missing values. In our study, we evaluated various imputation methods for their performance in preserving significant genes and pathways. In the first step, 5% genes are considered in random for two types of ignorable and non-ignorable missingness mechanisms with various missing rates. Next, 10 well-known imputation methods were applied to the complete datasets. The significance analysis of microarrays (SAM) method was applied to detect the significant genes in rectal and lung cancers to showcase the utility of imputation approaches in preserving significant genes. To determine the impact of different imputation methods on the identification of important genes, the chi-squared test was used to compare the proportions of overlaps between significant genes detected from original data and those detected from the imputed datasets. Additionally, the significant genes are tested for their enrichment in important pathways, using the ConsensusPathDB. Our results showed that almost all the significant genes and pathways of the original dataset can be detected in all imputed datasets, indicating that there is no significant difference in the performance of various imputation methods tested. The source code and selected datasets are available on http://profiles.bs.ipm.ir/softwares/imputation_methods/. Copyright © 2017. Production and hosting by Elsevier B.V.

A note on the relationships between multiple imputation, maximum likelihood and fully Bayesian methods for missing responses in linear regression models.

Science.gov (United States)

Chen, Qingxia; Ibrahim, Joseph G

2014-07-01

Multiple Imputation, Maximum Likelihood and Fully Bayesian methods are the three most commonly used model-based approaches in missing data problems. Although it is easy to show that when the responses are missing at random (MAR), the complete case analysis is unbiased and efficient, the aforementioned methods are still commonly used in practice for this setting. To examine the performance of and relationships between these three methods in this setting, we derive and investigate small sample and asymptotic expressions of the estimates and standard errors, and fully examine how these estimates are related for the three approaches in the linear regression model when the responses are MAR. We show that when the responses are MAR in the linear model, the estimates of the regression coefficients using these three methods are asymptotically equivalent to the complete case estimates under general conditions. One simulation and a real data set from a liver cancer clinical trial are given to compare the properties of these methods when the responses are MAR.

miR-192, miR-194 and miR-215: a convergent microRNA network suppressing tumor progression in renal cell carcinoma.

Science.gov (United States)

Khella, H W Z; Bakhet, M; Allo, G; Jewett, M A S; Girgis, A H; Latif, A; Girgis, H; Von Both, I; Bjarnason, G A; Yousef, G M

2013-10-01

MicroRNAs (miRNAs) play a crucial role in tumor progression and metastasis. We, and others, recently identified a number of miRNAs that are dysregulated in metastatic renal cell carcinoma compared with primary renal cell carcinoma. Here, we investigated three miRNAs that are significantly downregulated in metastatic tumors: miR-192, miR-194 and miR-215. Gain-of-function analyses showed that restoration of their expression decreases cell migration and invasion in renal cell carcinoma cell line models, whereas knockdown of these miRNAs resulted in enhancing cellular migration and invasion abilities. We identified three targets of these miRNAs with potential role in tumor aggressiveness: murine double minute 2, thymidylate synthase, and Smad Interacting protein 1/zinc finger E-box binding homeobox 2. We observed a convergent effect (the same molecule can be targeted by all three miRNAs) and a divergent effect (the same miRNA can control multiple targets) for these miRNAs. We experimentally validated these miRNA-target interactions using three independent approaches. First, we observed that miRNA overexpression significantly reduces the mRNA and protein levels of their targets. In the second, we observed significant reduction of the luciferase signal of a vector containing the 3'UTR of the target upon miRNA overexpression. Finally, we show the presence of inverse correlation between miRNA changes and the expression levels of their targets in patient specimens. We also examined the prognostic significance of miR-215 in renal cell carcinoma. Lower expression of miR-215 is associated with significantly reduced disease-free survival time. These findings were validated on an independent data set from The Cancer Genome Atlas. These results can pave the way to the clinical use of miRNAs as prognostic markers and therapeutic targets.

Imputation across genotyping arrays for genome-wide association studies: assessment of bias and a correction strategy.

Science.gov (United States)

Johnson, Eric O; Hancock, Dana B; Levy, Joshua L; Gaddis, Nathan C; Saccone, Nancy L; Bierut, Laura J; Page, Grier P

2013-05-01

A great promise of publicly sharing genome-wide association data is the potential to create composite sets of controls. However, studies often use different genotyping arrays, and imputation to a common set of SNPs has shown substantial bias: a problem which has no broadly applicable solution. Based on the idea that using differing genotyped SNP sets as inputs creates differential imputation errors and thus bias in the composite set of controls, we examined the degree to which each of the following occurs: (1) imputation based on the union of genotyped SNPs (i.e., SNPs available on one or more arrays) results in bias, as evidenced by spurious associations (type 1 error) between imputed genotypes and arbitrarily assigned case/control status; (2) imputation based on the intersection of genotyped SNPs (i.e., SNPs available on all arrays) does not evidence such bias; and (3) imputation quality varies by the size of the intersection of genotyped SNP sets. Imputations were conducted in European Americans and African Americans with reference to HapMap phase II and III data. Imputation based on the union of genotyped SNPs across the Illumina 1M and 550v3 arrays showed spurious associations for 0.2 % of SNPs: ~2,000 false positives per million SNPs imputed. Biases remained problematic for very similar arrays (550v1 vs. 550v3) and were substantial for dissimilar arrays (Illumina 1M vs. Affymetrix 6.0). In all instances, imputing based on the intersection of genotyped SNPs (as few as 30 % of the total SNPs genotyped) eliminated such bias while still achieving good imputation quality.

Characterization of novel precursor miRNAs using next generation sequencing and prediction of miRNA targets in Atlantic halibut.

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Teshome Tilahun Bizuayehu

Full Text Available BACKGROUND: microRNAs (miRNAs are implicated in regulation of many cellular processes. miRNAs are processed to their mature functional form in a step-wise manner by multiple proteins and cofactors in the nucleus and cytoplasm. Many miRNAs are conserved across vertebrates. Mature miRNAs have recently been characterized in Atlantic halibut (Hippoglossus hippoglossus L.. The aim of this study was to identify and characterize precursor miRNA (pre-miRNAs and miRNA targets in this non-model flatfish. Discovery of miRNA precursor forms and targets in non-model organisms is difficult because of limited source information available. Therefore, we have developed a methodology to overcome this limitation. METHODS: Genomic DNA and small transcriptome of Atlantic halibut were sequenced using Roche 454 pyrosequencing and SOLiD next generation sequencing (NGS, respectively. Identified pre- miRNAs were further validated with reverse-transcription PCR. miRNA targets were identified using miRanda and RNAhybrid target prediction tools using sequences from public databases. Some of miRNA targets were also identified using RACE-PCR. miRNA binding sites were validated with luciferase assay using the RTS34st cell line. RESULTS: We obtained more than 1.3 M and 92 M sequence reads from 454 genomic DNA sequencing and SOLiD small RNA sequencing, respectively. We identified 34 known and 9 novel pre-miRNAs. We predicted a number of miRNA target genes involved in various biological pathways. miR-24 binding to kisspeptin 1 receptor-2 (kiss1-r2 was confirmed using luciferase assay. CONCLUSION: This study demonstrates that identification of conserved and novel pre-miRNAs in a non-model vertebrate lacking substantial genomic resources can be performed by combining different next generation sequencing technologies. Our results indicate a wide conservation of miRNA precursors and involvement of miRNA in multiple regulatory pathways, and provide resources for further research on mi

miR-29b, miR-205 and miR-221 enhance chemosensitivity to gemcitabine in HuH28 human cholangiocarcinoma cells.

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Kinya Okamoto

Full Text Available BACKGROUND AND AIMS: Cholangiocarcinoma (CCA is highly resistant to chemotherapy, including gemcitabine (Gem treatment. MicroRNAs (miRNAs are endogenous, non-coding, short RNAs that can regulate multiple genes expression. Some miRNAs play important roles in the chemosensitivity of tumors. Here, we examined the relationship between miRNA expression and the sensitivity of CCA cells to Gem. METHODS: Microarray analysis was used to determine the miRNA expression profiles of two CCA cell lines, HuH28 and HuCCT1. To determine the effect of candidate miRNAs on Gem sensitivity, expression of each candidate miRNA was modified via either transfection of a miRNA mimic or transfection of an anti-oligonucleotide. Ontology-based programs were used to identify potential target genes of candidate miRNAs that were confirmed to affect the Gem sensitivity of CCA cells. RESULTS: HuCCT1 cells were more sensitive to Gem than were HuH28 cells, and 18 miRNAs were differentially expressed whose ratios over ± 2log2 between HuH28 and HuCCT1. Among these 18 miRNAs, ectopic overexpression of each of three downregulated miRNAs in HuH28 (miR-29b, miR-205, miR-221 restored Gem sensitivity to HuH28. Suppression of one upregulated miRNA in HuH28, miR-125a-5p, inhibited HuH28 cell proliferation independently to Gem treatment. Selective siRNA-mediated downregulation of either of two software-predicted targets, PIK3R1 (target of miR-29b and miR-221 or MMP-2 (target of miR-29b, also conferred Gem sensitivity to HuH28. CONCLUSIONS: miRNA expression profiling was used to identify key miRNAs that regulate Gem sensitivity in CCA cells, and software that predicts miRNA targets was used to identify promising target genes for anti-tumor therapies.

Missing Value Imputation Based on Gaussian Mixture Model for the Internet of Things

Directory of Open Access Journals (Sweden)

Xiaobo Yan

2015-01-01

Full Text Available This paper addresses missing value imputation for the Internet of Things (IoT. Nowadays, the IoT has been used widely and commonly by a variety of domains, such as transportation and logistics domain and healthcare domain. However, missing values are very common in the IoT for a variety of reasons, which results in the fact that the experimental data are incomplete. As a result of this, some work, which is related to the data of the IoT, can’t be carried out normally. And it leads to the reduction in the accuracy and reliability of the data analysis results. This paper, for the characteristics of the data itself and the features of missing data in IoT, divides the missing data into three types and defines three corresponding missing value imputation problems. Then, we propose three new models to solve the corresponding problems, and they are model of missing value imputation based on context and linear mean (MCL, model of missing value imputation based on binary search (MBS, and model of missing value imputation based on Gaussian mixture model (MGI. Experimental results showed that the three models can improve the accuracy, reliability, and stability of missing value imputation greatly and effectively.

Expression patterns of miR-146a and miR-146b in mastitis infected dairy cattle.

Science.gov (United States)

Wang, Xing Ping; Luoreng, Zhuo Ma; Zan, Lin Sen; Raza, Sayed Haidar Abbas; Li, Feng; Li, Na; Liu, Shuan

2016-10-01

This study reports a significant up-regulation of bta-miR-146a and bta-miR-146b expression levels in bovine mammary tissues infected with subclinical, clinical and experimental mastitis. Potential target genes are involved in multiple immunological pathways. These results suggest a regulatory function of both miRNAs for the bovine inflammatory response in mammary tissue. Copyright © 2016 Elsevier Ltd. All rights reserved.

MiR-218 Mediates tumorigenesis and metastasis: Perspectives and implications

Energy Technology Data Exchange (ETDEWEB)

Lu, Ying-fei [Institute Guangzhou of Advanced Technology, Chinese Academy of Sciences, Guangzhou (China); Department of Orthopaedics & Traumatology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong (China); Zhang, Li [School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong (China); Department of Anatomical and Cellular Pathology, State Key Laboratory of Oncology in South China, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong (China); Waye, Mary Miu Yee [School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong (China); Fu, Wei-ming, E-mail: wm.fu@giat.ac.cn [Institute Guangzhou of Advanced Technology, Chinese Academy of Sciences, Guangzhou (China); School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong (China); Zhang, Jin-fang, E-mail: zhangjf06@cuhk.edu.hk [Department of Orthopaedics & Traumatology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong (China); School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong (China); Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen (China)

2015-05-15

MicroRNAs (miRNAs) are a class of small non-coding RNAs that negatively regulate gene expression at the post-transcriptional level. As a highly conserved miRNA across a variety of species, microRNA-218 (miR-218) was found to play pivotal roles in tumorigenesis and progression. A group of evidence has demonstrated that miR-218 acts as a tumor suppressor by targeting many oncogenes related to proliferation, apoptosis and invasion. In this review, we provide a complex overview of miR-218, including its regulatory mechanisms, known functions in cancer and future challenges as a potential therapeutic target in human cancers. - Highlights: • miR-218 is frequently down regulated in multiple cancers. • miR-218 plays pivotal roles in carcinogenesis. • miR-218 mediates proliferation, apoptosis, metastasis, invasion, etc. • miR-218 mediates tumorigenesis and metastasis via multiple pathways.

MiR-218 Mediates tumorigenesis and metastasis: Perspectives and implications

International Nuclear Information System (INIS)

Lu, Ying-fei; Zhang, Li; Waye, Mary Miu Yee; Fu, Wei-ming; Zhang, Jin-fang

2015-01-01

MicroRNAs (miRNAs) are a class of small non-coding RNAs that negatively regulate gene expression at the post-transcriptional level. As a highly conserved miRNA across a variety of species, microRNA-218 (miR-218) was found to play pivotal roles in tumorigenesis and progression. A group of evidence has demonstrated that miR-218 acts as a tumor suppressor by targeting many oncogenes related to proliferation, apoptosis and invasion. In this review, we provide a complex overview of miR-218, including its regulatory mechanisms, known functions in cancer and future challenges as a potential therapeutic target in human cancers. - Highlights: • miR-218 is frequently down regulated in multiple cancers. • miR-218 plays pivotal roles in carcinogenesis. • miR-218 mediates proliferation, apoptosis, metastasis, invasion, etc. • miR-218 mediates tumorigenesis and metastasis via multiple pathways

Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits

NARCIS (Netherlands)

I. Tachmazidou (Ioanna); Süveges, D. (Dániel); J. Min (Josine); G.R.S. Ritchie (Graham R.S.); Steinberg, J. (Julia); K. Walter (Klaudia); V. Iotchkova (Valentina); J.A. Schwartzentruber (Jeremy); J. Huang (Jian); Y. Memari (Yasin); McCarthy, S. (Shane); Crawford, A.A. (Andrew A.); C. Bombieri (Cristina); M. Cocca (Massimiliano); A.-E. Farmaki (Aliki-Eleni); T.R. Gaunt (Tom); P. Jousilahti (Pekka); M.N. Kooijman (Marjolein ); Lehne, B. (Benjamin); G. Malerba (Giovanni); S. Männistö (Satu); A. Matchan (Angela); M.C. Medina-Gomez (Carolina); S. Metrustry (Sarah); A. Nag (Abhishek); I. Ntalla (Ioanna); L. Paternoster (Lavinia); N.W. Rayner (Nigel William); C. Sala (Cinzia); W.R. Scott (William R.); H.A. Shihab (Hashem A.); L. Southam (Lorraine); B. St Pourcain (Beate); M. Traglia (Michela); K. Trajanoska (Katerina); Zaza, G. (Gialuigi); W. Zhang (Weihua); M.S. Artigas; Bansal, N. (Narinder); M. Benn (Marianne); Chen, Z. (Zhongsheng); P. Danecek (Petr); Lin, W.-Y. (Wei-Yu); A. Locke (Adam); J. Luan (Jian'An); A.K. Manning (Alisa); Mulas, A. (Antonella); C. Sidore (Carlo); A. Tybjaerg-Hansen; A. Varbo (Anette); M. Zoledziewska (Magdalena); C. Finan (Chris); Hatzikotoulas, K. (Konstantinos); A.E. Hendricks (Audrey E.); J.P. Kemp (John); A. Moayyeri (Alireza); Panoutsopoulou, K. (Kalliope); Szpak, M. (Michal); S.G. Wilson (Scott); M. Boehnke (Michael); F. Cucca (Francesco); Di Angelantonio, E. (Emanuele); C. Langenberg (Claudia); C.M. Lindgren (Cecilia M.); McCarthy, M.I. (Mark I.); A.P. Morris (Andrew); B.G. Nordestgaard (Børge); R.A. Scott (Robert); M.D. Tobin (Martin); N.J. Wareham (Nick); P.R. Burton (Paul); J.C. Chambers (John); Smith, G.D. (George Davey); G.V. Dedoussis (George); J.F. Felix (Janine); O.H. Franco (Oscar); Gambaro, G. (Giovanni); P. Gasparini (Paolo); C.J. Hammond (Christopher J.); A. Hofman (Albert); V.W.V. Jaddoe (Vincent); M.E. Kleber (Marcus); J.S. Kooner (Jaspal S.); M. Perola (Markus); C.L. Relton (Caroline); S.M. Ring (Susan); F. Rivadeneira Ramirez (Fernando); V. Salomaa (Veikko); T.D. Spector (Timothy); O. Stegle (Oliver); D. Toniolo (Daniela); A.G. Uitterlinden (André); I.E. Barroso (Inês); C.M.T. Greenwood (Celia); Perry, J.R.B. (John R.B.); Walker, B.R. (Brian R.); A.S. Butterworth (Adam); Y. Xue (Yali); R. Durbin (Richard); K.S. Small (Kerrin); N. Soranzo (Nicole); N.J. Timpson (Nicholas); E. Zeggini (Eleftheria)

2016-01-01

textabstractDeep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the

Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits

DEFF Research Database (Denmark)

Tachmazidou, Ioanna; Süveges, Dániel; Min, Josine L

2017-01-01

Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader alleli...

Missing value imputation in DNA microarrays based on conjugate gradient method.

Science.gov (United States)

Dorri, Fatemeh; Azmi, Paeiz; Dorri, Faezeh

2012-02-01

Analysis of gene expression profiles needs a complete matrix of gene array values; consequently, imputation methods have been suggested. In this paper, an algorithm that is based on conjugate gradient (CG) method is proposed to estimate missing values. k-nearest neighbors of the missed entry are first selected based on absolute values of their Pearson correlation coefficient. Then a subset of genes among the k-nearest neighbors is labeled as the best similar ones. CG algorithm with this subset as its input is then used to estimate the missing values. Our proposed CG based algorithm (CGimpute) is evaluated on different data sets. The results are compared with sequential local least squares (SLLSimpute), Bayesian principle component analysis (BPCAimpute), local least squares imputation (LLSimpute), iterated local least squares imputation (ILLSimpute) and adaptive k-nearest neighbors imputation (KNNKimpute) methods. The average of normalized root mean squares error (NRMSE) and relative NRMSE in different data sets with various missing rates shows CGimpute outperforms other methods. Copyright © 2011 Elsevier Ltd. All rights reserved.

Clustering with Missing Values: No Imputation Required

Science.gov (United States)

Wagstaff, Kiri

2004-01-01

Clustering algorithms can identify groups in large data sets, such as star catalogs and hyperspectral images. In general, clustering methods cannot analyze items that have missing data values. Common solutions either fill in the missing values (imputation) or ignore the missing data (marginalization). Imputed values are treated as just as reliable as the truly observed data, but they are only as good as the assumptions used to create them. In contrast, we present a method for encoding partially observed features as a set of supplemental soft constraints and introduce the KSC algorithm, which incorporates constraints into the clustering process. In experiments on artificial data and data from the Sloan Digital Sky Survey, we show that soft constraints are an effective way to enable clustering with missing values.

miR-34a: Multiple Opposing Targets and One Destiny in Hepatocellular Carcinoma.

Science.gov (United States)

Yacoub, Radwa Alaa; Fawzy, Injie Omar; Assal, Reem Amr; Hosny, Karim Adel; Zekri, Abdel-Rahman Nabawy; Esmat, Gamal; El Tayebi, Hend Mohamed; Abdelaziz, Ahmed Ihab

2016-12-28

Background and Aims: The role of miR-34a in hepatocellular carcinoma (HCC) is controversial and several unresolved issues remain, including its expression pattern and relevance to tumor etiology, tumor stage and prognosis, and finally, its impact on apoptosis. Methods: miR-34a expression was assessed in hepatitis C virus (HCV)-induced non-metastatic HCC tissues by RT-Q-PCR. Huh-7 cells were transfected with miR-34a mimics and the impact of miR-34a was examined on 84 pro-apoptotic/anti-apoptotic genes using PCR array; its net effect was tested on cell viability via MTT assay. Results: miR-34a expression was up-regulated in HCC tissues. Moreover, miR-34a induced a large set of pro-apoptotic/anti-apoptotic genes, with a net result of triggering apoptosis and repressing cell viability. Conclusions: HCC-related differential expression of miR-34a could be etiology-based or stage-specific, and low expression of miR-34a may predict poor prognosis. This study's findings also emphasize the role of miR-34a in apoptosis.

Time Series Imputation via L1 Norm-Based Singular Spectrum Analysis

Science.gov (United States)

Kalantari, Mahdi; Yarmohammadi, Masoud; Hassani, Hossein; Silva, Emmanuel Sirimal

Missing values in time series data is a well-known and important problem which many researchers have studied extensively in various fields. In this paper, a new nonparametric approach for missing value imputation in time series is proposed. The main novelty of this research is applying the L1 norm-based version of Singular Spectrum Analysis (SSA), namely L1-SSA which is robust against outliers. The performance of the new imputation method has been compared with many other established methods. The comparison is done by applying them to various real and simulated time series. The obtained results confirm that the SSA-based methods, especially L1-SSA can provide better imputation in comparison to other methods.

GACT: a Genome build and Allele definition Conversion Tool for SNP imputation and meta-analysis in genetic association studies.

Science.gov (United States)

Sulovari, Arvis; Li, Dawei

2014-07-19

Genome-wide association studies (GWAS) have successfully identified genes associated with complex human diseases. Although much of the heritability remains unexplained, combining single nucleotide polymorphism (SNP) genotypes from multiple studies for meta-analysis will increase the statistical power to identify new disease-associated variants. Meta-analysis requires same allele definition (nomenclature) and genome build among individual studies. Similarly, imputation, commonly-used prior to meta-analysis, requires the same consistency. However, the genotypes from various GWAS are generated using different genotyping platforms, arrays or SNP-calling approaches, resulting in use of different genome builds and allele definitions. Incorrect assumptions of identical allele definition among combined GWAS lead to a large portion of discarded genotypes or incorrect association findings. There is no published tool that predicts and converts among all major allele definitions. In this study, we have developed a tool, GACT, which stands for Genome build and Allele definition Conversion Tool, that predicts and inter-converts between any of the common SNP allele definitions and between the major genome builds. In addition, we assessed several factors that may affect imputation quality, and our results indicated that inclusion of singletons in the reference had detrimental effects while ambiguous SNPs had no measurable effect. Unexpectedly, exclusion of genotypes with missing rate > 0.001 (40% of study SNPs) showed no significant decrease of imputation quality (even significantly higher when compared to the imputation with singletons in the reference), especially for rare SNPs. GACT is a new, powerful, and user-friendly tool with both command-line and interactive online versions that can accurately predict, and convert between any of the common allele definitions and between genome builds for genome-wide meta-analysis and imputation of genotypes from SNP-arrays or deep

Randomization-Based Inference about Latent Variables from Complex Samples: The Case of Two-Stage Sampling

Science.gov (United States)

Li, Tiandong

2012-01-01

In large-scale assessments, such as the National Assessment of Educational Progress (NAEP), plausible values based on Multiple Imputations (MI) have been used to estimate population characteristics for latent constructs under complex sample designs. Mislevy (1991) derived a closed-form analytic solution for a fixed-effect model in creating…

Search for mi>CP> Violation and Measurement of the Branching Fraction in the Decay mi>D>0→<mi>KS>0<mi>KS>0

Energy Technology Data Exchange (ETDEWEB)

Dash, N.; Bahinipati, S.; Bhardwaj, V.; Trabelsi, K.; Adachi, I.; Aihara, H.; Al Said, S.; Asner, D. M.; Aulchenko, V.; Aushev, T.; Ayad, R.; Babu, V.; Badhrees, I.; Bakich, A. M.; Bansal, V.; Barberio, E.; Bhuyan, B.; Biswal, J.; Bobrov, A.; Bondar, A.; Bonvicini, G.; Bozek, A.; Bračko, M.; Breibeck, F.; Browder, T. E.; Červenkov, D.; Chang, M. -C.; Chekelian, V.; Chen, A.; Cheon, B. G.; Chilikin, K.; Cho, K.; Choi, Y.; Cinabro, D.; Di Carlo, S.; Doležal, Z.; Drásal, Z.; Dutta, D.; Eidelman, S.; Epifanov, D.; Farhat, H.; Fast, J. E.; Ferber, T.; Fulsom, B. G.; Gaur, V.; Gabyshev, N.; Garmash, A.; Gillard, R.; Goldenzweig, P.; Haba, J.; Hara, T.; Hayasaka, K.; Hayashii, H.; Hedges, M. T.; Hou, W. -S.; Iijima, T.; Inami, K.; Ishikawa, A.; Itoh, R.; Iwasaki, Y.; Jacobs, W. W.; Jaegle, I.; Jeon, H. B.; Jin, Y.; Joffe, D.; Joo, K. K.; Julius, T.; Kahn, J.; Kaliyar, A. B.; Karyan, G.; Katrenko, P.; Kawasaki, T.; Kiesling, C.; Kim, D. Y.; Kim, H. J.; Kim, J. B.; Kim, K. T.; Kim, M. J.; Kim, S. H.; Kim, Y. J.; Kinoshita, K.; Kodyš, P.; Korpar, S.; Kotchetkov, D.; Križan, P.; Krokovny, P.; Kuhr, T.; Kulasiri, R.; Kumar, R.; Kumita, T.; Kuzmin, A.; Kwon, Y. -J.; Lange, J. S.; Lee, I. S.; Li, C. H.; Li, L.; Li, Y.; Li Gioi, L.; Libby, J.; Liventsev, D.; Lubej, M.; Luo, T.; Masuda, M.; Matvienko, D.; Merola, M.; Miyabayashi, K.; Miyata, H.; Mizuk, R.; Mohanty, G. B.; Mohanty, S.; Moon, H. K.; Mori, T.; Mussa, R.; Nakano, E.; Nakao, M.; Nanut, T.; Nath, K. J.; Natkaniec, Z.; Nayak, M.; Niiyama, M.; Nisar, N. K.; Nishida, S.; Ogawa, S.; Okuno, S.; Ono, H.; Pakhlov, P.; Pakhlova, G.; Pal, B.; Pardi, S.; Park, C. -S.; Park, H.; Paul, S.; Pedlar, T. K.; Pesántez, L.; Pestotnik, R.; Piilonen, L. E.; Prasanth, K.; Ritter, M.; Rostomyan, A.; Sahoo, H.; Sakai, Y.; Sandilya, S.; Santelj, L.; Sanuki, T.; Sato, Y.; Savinov, V.; Schneider, O.; Schnell, G.; Schwanda, C.; Schwartz, A. J.; Seino, Y.; Senyo, K.; Sevior, M. E.; Shebalin, V.; Shen, C. P.; Shibata, T. -A.; Shiu, J. -G.; Shwartz, B.; Simon, F.; Sokolov, A.; Solovieva, E.; Starič, M.; Strube, J. F.; Stypula, J.; Sumisawa, K.; Sumiyoshi, T.; Takizawa, M.; Tamponi, U.; Tanida, K.; Tenchini, F.; Uchida, M.; Uglov, T.; Unno, Y.; Uno, S.; Urquijo, P.; Usov, Y.; Van Hulse, C.; Varner, G.; Vorobyev, V.; Vossen, A.; Waheed, E.; Wang, C. H.; Wang, M. -Z.; Wang, P.; Watanabe, M.; Watanabe, Y.; Widmann, E.; Williams, K. M.; Won, E.; Yamashita, Y.; Ye, H.; Yelton, J.; Yook, Y.; Yuan, C. Z.; Yusa, Y.; Zhang, Z. P.; Zhilich, V.; Zhukova, V.; Zhulanov, V.; Zupanc, A.

2017-10-01

We report a study of the decay mi>D>0→<mi>KS>0<mi>KS>0 using 921 fb^-1 of data collected at or near the Υ(4S) and Υ(5S) resonances with the Belle detector at the KEKB asymmetric energy e⁺e^- collider. The measured time-integrated CP asymmetry is A_CP(mi>D>0→<mi>KS>0<mi>KS>0) = (-0.02 ± 1.53 ± 0.02 ± 0.17)%, and the branching fraction is B(mi>D>0→<mi>KS>0<mi>KS>0) = (1.321 ± 0.023 ± 0.036 ± 0.044) × 10^-4, where the first uncertainty is statistical, the second is systematic, and the third is due to the normalization mode (mi>D>0→<mi>KS>0π⁰). These results are significantly more precise than previous measurements available for this mode. The A_CP measurement is consistent with the standard model expectation.

The Epstein-Barr Virus BART miRNA Cluster of the M81 Strain Modulates Multiple Functions in Primary B Cells

Science.gov (United States)

Lin, Xiaochen; Tsai, Ming-Han; Shumilov, Anatoliy; Poirey, Remy; Bannert, Helmut; Middeldorp, Jaap M.; Feederle, Regina; Delecluse, Henri-Jacques

2015-01-01

The Epstein-Barr virus (EBV) is a B lymphotropic virus that infects the majority of the human population. All EBV strains transform B lymphocytes, but some strains, such as M81, also induce spontaneous virus replication. EBV encodes 22 microRNAs (miRNAs) that form a cluster within the BART region of the virus and have been previously been found to stimulate tumor cell growth. Here we describe their functions in B cells infected by M81. We found that the BART miRNAs are downregulated in replicating cells, and that exposure of B cells in vitro or in vivo in humanized mice to a BART miRNA knockout virus resulted in an increased proportion of spontaneously replicating cells, relative to wild type virus. The BART miRNAs subcluster 1, and to a lesser extent subcluster 2, prevented expression of BZLF1, the key protein for initiation of lytic replication. Thus, multiple BART miRNAs cooperate to repress lytic replication. The BART miRNAs also downregulated pro- and anti-apoptotic mediators such as caspase 3 and LMP1, and their deletion did not sensitize B-cells to apoptosis. To the contrary, the majority of humanized mice infected with the BART miRNA knockout mutant developed tumors more rapidly, probably due to enhanced LMP1 expression, although deletion of the BART miRNAs did not modify the virus transforming abilities in vitro. This ability to slow cell growth could be confirmed in non-humanized immunocompromized mice. Injection of resting B cells exposed to a virus that lacks the BART miRNAs resulted in accelerated tumor growth, relative to wild type controls. Therefore, we found that the M81 BART miRNAs do not enhance B-cell tumorigenesis but rather repress it. The repressive effects of the BART miRNAs on potentially pathogenic viral functions in infected B cells are likely to facilitate long-term persistence of the virus in the infected host. PMID:26694854

Fully conditional specification in multivariate imputation

NARCIS (Netherlands)

van Buuren, S.; Brand, J. P.L.; Groothuis-Oudshoorn, C. G.M.; Rubin, D. B.

2006-01-01

The use of the Gibbs sampler with fully conditionally specified models, where the distribution of each variable given the other variables is the starting point, has become a popular method to create imputations in incomplete multivariate data. The theoretical weakness of this approach is that the

Gap-filling a spatially explicit plant trait database: comparing imputation methods and different levels of environmental information

Science.gov (United States)

Poyatos, Rafael; Sus, Oliver; Badiella, Llorenç; Mencuccini, Maurizio; Martínez-Vilalta, Jordi

2018-05-01

The ubiquity of missing data in plant trait databases may hinder trait-based analyses of ecological patterns and processes. Spatially explicit datasets with information on intraspecific trait variability are rare but offer great promise in improving our understanding of functional biogeography. At the same time, they offer specific challenges in terms of data imputation. Here we compare statistical imputation approaches, using varying levels of environmental information, for five plant traits (leaf biomass to sapwood area ratio, leaf nitrogen content, maximum tree height, leaf mass per area and wood density) in a spatially explicit plant trait dataset of temperate and Mediterranean tree species (Ecological and Forest Inventory of Catalonia, IEFC, dataset for Catalonia, north-east Iberian Peninsula, 31 900 km2). We simulated gaps at different missingness levels (10-80 %) in a complete trait matrix, and we used overall trait means, species means, k nearest neighbours (kNN), ordinary and regression kriging, and multivariate imputation using chained equations (MICE) to impute missing trait values. We assessed these methods in terms of their accuracy and of their ability to preserve trait distributions, multi-trait correlation structure and bivariate trait relationships. The relatively good performance of mean and species mean imputations in terms of accuracy masked a poor representation of trait distributions and multivariate trait structure. Species identity improved MICE imputations for all traits, whereas forest structure and topography improved imputations for some traits. No method performed best consistently for the five studied traits, but, considering all traits and performance metrics, MICE informed by relevant ecological variables gave the best results. However, at higher missingness (> 30 %), species mean imputations and regression kriging tended to outperform MICE for some traits. MICE informed by relevant ecological variables allowed us to fill the gaps in

Multi-generational imputation of single nucleotide polymorphism marker genotypes and accuracy of genomic selection.

Science.gov (United States)

Toghiani, S; Aggrey, S E; Rekaya, R

2016-07-01

Availability of high-density single nucleotide polymorphism (SNP) genotyping platforms provided unprecedented opportunities to enhance breeding programmes in livestock, poultry and plant species, and to better understand the genetic basis of complex traits. Using this genomic information, genomic breeding values (GEBVs), which are more accurate than conventional breeding values. The superiority of genomic selection is possible only when high-density SNP panels are used to track genes and QTLs affecting the trait. Unfortunately, even with the continuous decrease in genotyping costs, only a small fraction of the population has been genotyped with these high-density panels. It is often the case that a larger portion of the population is genotyped with low-density and low-cost SNP panels and then imputed to a higher density. Accuracy of SNP genotype imputation tends to be high when minimum requirements are met. Nevertheless, a certain rate of genotype imputation errors is unavoidable. Thus, it is reasonable to assume that the accuracy of GEBVs will be affected by imputation errors; especially, their cumulative effects over time. To evaluate the impact of multi-generational selection on the accuracy of SNP genotypes imputation and the reliability of resulting GEBVs, a simulation was carried out under varying updating of the reference population, distance between the reference and testing sets, and the approach used for the estimation of GEBVs. Using fixed reference populations, imputation accuracy decayed by about 0.5% per generation. In fact, after 25 generations, the accuracy was only 7% lower than the first generation. When the reference population was updated by either 1% or 5% of the top animals in the previous generations, decay of imputation accuracy was substantially reduced. These results indicate that low-density panels are useful, especially when the generational interval between reference and testing population is small. As the generational interval

miR-181a and miR-630 regulate cisplatin-induced cancer cell death.

Science.gov (United States)

Galluzzi, Lorenzo; Morselli, Eugenia; Vitale, Ilio; Kepp, Oliver; Senovilla, Laura; Criollo, Alfredo; Servant, Nicolas; Paccard, Caroline; Hupé, Philippe; Robert, Thomas; Ripoche, Hugues; Lazar, Vladimir; Harel-Bellan, Annick; Dessen, Philippe; Barillot, Emmanuel; Kroemer, Guido

2010-03-01

MicroRNAs (miRNA) are noncoding RNAs that regulate multiple cellular processes, including proliferation and apoptosis. We used microarray technology to identify miRNAs that were upregulated by non-small cell lung cancer (NSCLC) A549 cells in response to cisplatin (CDDP). The corresponding synthetic miRNA precursors (pre-miRNAs) per se were not lethal when transfected into A549 cells yet affected cell death induction by CDDP, C2-ceramide, cadmium, etoposide, and mitoxantrone in an inducer-specific fashion. Whereas synthetic miRNA inhibitors (anti-miRNAs) targeting miR-181a and miR-630 failed to modulate the response of A549 to CDDP, pre-miR-181a and pre-miR-630 enhanced and reduced CDDP-triggered cell death, respectively. Pre-miR-181a and pre-miR-630 consistently modulated mitochondrial/postmitochondrial steps of the intrinsic pathway of apoptosis, including Bax oligomerization, mitochondrial transmembrane potential dissipation, and the proteolytic maturation of caspase-9 and caspase-3. In addition, pre-miR-630 blocked early manifestations of the DNA damage response, including the phosphorylation of the ataxia-telangiectasia mutated (ATM) kinase and of two ATM substrates, histone H2AX and p53. Pharmacologic and genetic inhibition of p53 corroborated the hypothesis that pre-miR-630 (but not pre-miR-181a) blocks the upstream signaling pathways that are ignited by DNA damage and converge on p53 activation. Pre-miR-630 arrested A549 cells in the G0-G1 phase of the cell cycle, correlating with increased levels of the cell cycle inhibitor p27(Kip1) as well as with reduced proliferation rates and resulting in greatly diminished sensitivity of A549 cells to the late S-G2-M cell cycle arrest mediated by CDDP. Altogether, these results identify miR-181a and miR-630 as novel modulators of the CDDP response in NSCLC.

Comparison of three boosting methods in parent-offspring trios for genotype imputation using simulation study

Directory of Open Access Journals (Sweden)

Abbas Mikhchi

2016-01-01

Full Text Available Abstract Background Genotype imputation is an important process of predicting unknown genotypes, which uses reference population with dense genotypes to predict missing genotypes for both human and animal genetic variations at a low cost. Machine learning methods specially boosting methods have been used in genetic studies to explore the underlying genetic profile of disease and build models capable of predicting missing values of a marker. Methods In this study strategies and factors affecting the imputation accuracy of parent-offspring trios compared from lower-density SNP panels (5 K to high density (10 K SNP panel using three different Boosting methods namely TotalBoost (TB, LogitBoost (LB and AdaBoost (AB. The methods employed using simulated data to impute the un-typed SNPs in parent-offspring trios. Four different datasets of G1 (100 trios with 5 k SNPs, G2 (100 trios with 10 k SNPs, G3 (500 trios with 5 k SNPs, and G4 (500 trio with 10 k SNPs were simulated. In four datasets all parents were genotyped completely, and offspring genotyped with a lower density panel. Results Comparison of the three methods for imputation showed that the LB outperformed AB and TB for imputation accuracy. The time of computation were different between methods. The AB was the fastest algorithm. The higher SNP densities resulted the increase of the accuracy of imputation. Larger trios (i.e. 500 was better for performance of LB and TB. Conclusions The conclusion is that the three methods do well in terms of imputation accuracy also the dense chip is recommended for imputation of parent-offspring trios.

Handling Missing Data in Structural Equation Models in R: A Replication Study for Applied Researchers

Science.gov (United States)

Wolgast, Anett; Schwinger, Malte; Hahnel, Carolin; Stiensmeier-Pelster, Joachim

2017-01-01

Introduction: Multiple imputation (MI) is one of the most highly recommended methods for replacing missing values in research data. The scope of this paper is to demonstrate missing data handling in SEM by analyzing two modified data examples from educational psychology, and to give practical recommendations for applied researchers. Method: We…

Methods for Mediation Analysis with Missing Data

Science.gov (United States)

Zhang, Zhiyong; Wang, Lijuan

2013-01-01

Despite wide applications of both mediation models and missing data techniques, formal discussion of mediation analysis with missing data is still rare. We introduce and compare four approaches to dealing with missing data in mediation analysis including list wise deletion, pairwise deletion, multiple imputation (MI), and a two-stage maximum…

A New Missing Data Imputation Algorithm Applied to Electrical Data Loggers

Directory of Open Access Journals (Sweden)

Concepción Crespo Turrado

2015-12-01

Full Text Available Nowadays, data collection is a key process in the study of electrical power networks when searching for harmonics and a lack of balance among phases. In this context, the lack of data of any of the main electrical variables (phase-to-neutral voltage, phase-to-phase voltage, and current in each phase and power factor adversely affects any time series study performed. When this occurs, a data imputation process must be accomplished in order to substitute the data that is missing for estimated values. This paper presents a novel missing data imputation method based on multivariate adaptive regression splines (MARS and compares it with the well-known technique called multivariate imputation by chained equations (MICE. The results obtained demonstrate how the proposed method outperforms the MICE algorithm.

Imputation of missing data in time series for air pollutants

Science.gov (United States)

Junger, W. L.; Ponce de Leon, A.

2015-02-01

Missing data are major concerns in epidemiological studies of the health effects of environmental air pollutants. This article presents an imputation-based method that is suitable for multivariate time series data, which uses the EM algorithm under the assumption of normal distribution. Different approaches are considered for filtering the temporal component. A simulation study was performed to assess validity and performance of proposed method in comparison with some frequently used methods. Simulations showed that when the amount of missing data was as low as 5%, the complete data analysis yielded satisfactory results regardless of the generating mechanism of the missing data, whereas the validity began to degenerate when the proportion of missing values exceeded 10%. The proposed imputation method exhibited good accuracy and precision in different settings with respect to the patterns of missing observations. Most of the imputations obtained valid results, even under missing not at random. The methods proposed in this study are implemented as a package called mtsdi for the statistical software system R.

MiR-155 Enhances Insulin Sensitivity by Coordinated Regulation of Multiple Genes in Mice

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Lin, Taoyan; Lin, Xia; Chen, Li; Zeng, Hui; Han, Yanjiang; Wu, Lihong; Huang, Shun; Wang, Meng; Huang, Shenhao; Xie, Raoying; Liang, Liqi; Liu, Yu; Liu, Ruiyu; Zhang, Tingting; Li, Jing; Wang, Shengchun; Sun, Penghui; Huang, Wenhua; Yao, Kaitai; Xu, Kang; Du, Tao; Xiao, Dong

2016-01-01

miR-155 plays critical roles in numerous physiological and pathological processes, however, its function in the regulation of blood glucose homeostasis and insulin sensitivity and underlying mechanisms remain unknown. Here, we reveal that miR-155 levels are downregulated in serum from type 2 diabetes (T2D) patients, suggesting that miR-155 might be involved in blood glucose control and diabetes. Gain-of-function and loss-of-function studies in mice demonstrate that miR-155 has no effects on the pancreatic β-cell proliferation and function. Global transgenic overexpression of miR-155 in mice leads to hypoglycaemia, improved glucose tolerance and insulin sensitivity. Conversely, miR-155 deficiency in mice causes hyperglycemia, impaired glucose tolerance and insulin resistance. In addition, consistent with a positive regulatory role of miR-155 in glucose metabolism, miR-155 positively modulates glucose uptake in all cell types examined, while mice overexpressing miR-155 transgene show enhanced glycolysis, and insulin-stimulated AKT and IRS-1 phosphorylation in liver, adipose tissue or skeletal muscle. Furthermore, we reveal these aforementioned phenomena occur, at least partially, through miR-155-mediated repression of important negative regulators (i.e. C/EBPβ, HDAC4 and SOCS1) of insulin signaling. Taken together, these findings demonstrate, for the first time, that miR-155 is a positive regulator of insulin sensitivity with potential applications for diabetes treatment. PMID:27711113

Imputation of the rare HOXB13 G84E mutation and cancer risk in a large population-based cohort.

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Thomas J Hoffmann

2015-01-01

Full Text Available An efficient approach to characterizing the disease burden of rare genetic variants is to impute them into large well-phenotyped cohorts with existing genome-wide genotype data using large sequenced referenced panels. The success of this approach hinges on the accuracy of rare variant imputation, which remains controversial. For example, a recent study suggested that one cannot adequately impute the HOXB13 G84E mutation associated with prostate cancer risk (carrier frequency of 0.0034 in European ancestry participants in the 1000 Genomes Project. We show that by utilizing the 1000 Genomes Project data plus an enriched reference panel of mutation carriers we were able to accurately impute the G84E mutation into a large cohort of 83,285 non-Hispanic White participants from the Kaiser Permanente Research Program on Genes, Environment and Health Genetic Epidemiology Research on Adult Health and Aging cohort. Imputation authenticity was confirmed via a novel classification and regression tree method, and then empirically validated analyzing a subset of these subjects plus an additional 1,789 men from Kaiser specifically genotyped for the G84E mutation (r2 = 0.57, 95% CI = 0.37–0.77. We then show the value of this approach by using the imputed data to investigate the impact of the G84E mutation on age-specific prostate cancer risk and on risk of fourteen other cancers in the cohort. The age-specific risk of prostate cancer among G84E mutation carriers was higher than among non-carriers. Risk estimates from Kaplan-Meier curves were 36.7% versus 13.6% by age 72, and 64.2% versus 24.2% by age 80, for G84E mutation carriers and non-carriers, respectively (p = 3.4x10-12. The G84E mutation was also associated with an increase in risk for the fourteen other most common cancers considered collectively (p = 5.8x10-4 and more so in cases diagnosed with multiple cancer types, both those including and not including prostate cancer, strongly suggesting

PRIMAL: Fast and accurate pedigree-based imputation from sequence data in a founder population.

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Oren E Livne

2015-03-01

Full Text Available Founder populations and large pedigrees offer many well-known advantages for genetic mapping studies, including cost-efficient study designs. Here, we describe PRIMAL (PedigRee IMputation ALgorithm, a fast and accurate pedigree-based phasing and imputation algorithm for founder populations. PRIMAL incorporates both existing and original ideas, such as a novel indexing strategy of Identity-By-Descent (IBD segments based on clique graphs. We were able to impute the genomes of 1,317 South Dakota Hutterites, who had genome-wide genotypes for ~300,000 common single nucleotide variants (SNVs, from 98 whole genome sequences. Using a combination of pedigree-based and LD-based imputation, we were able to assign 87% of genotypes with >99% accuracy over the full range of allele frequencies. Using the IBD cliques we were also able to infer the parental origin of 83% of alleles, and genotypes of deceased recent ancestors for whom no genotype information was available. This imputed data set will enable us to better study the relative contribution of rare and common variants on human phenotypes, as well as parental origin effect of disease risk alleles in >1,000 individuals at minimal cost.

Sequence imputation of HPV16 genomes for genetic association studies.

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Benjamin Smith

Full Text Available Human Papillomavirus type 16 (HPV16 causes over half of all cervical cancer and some HPV16 variants are more oncogenic than others. The genetic basis for the extraordinary oncogenic properties of HPV16 compared to other HPVs is unknown. In addition, we neither know which nucleotides vary across and within HPV types and lineages, nor which of the single nucleotide polymorphisms (SNPs determine oncogenicity.A reference set of 62 HPV16 complete genome sequences was established and used to examine patterns of evolutionary relatedness amongst variants using a pairwise identity heatmap and HPV16 phylogeny. A BLAST-based algorithm was developed to impute complete genome data from partial sequence information using the reference database. To interrogate the oncogenic risk of determined and imputed HPV16 SNPs, odds-ratios for each SNP were calculated in a case-control viral genome-wide association study (VWAS using biopsy confirmed high-grade cervix neoplasia and self-limited HPV16 infections from Guanacaste, Costa Rica.HPV16 variants display evolutionarily stable lineages that contain conserved diagnostic SNPs. The imputation algorithm indicated that an average of 97.5±1.03% of SNPs could be accurately imputed. The VWAS revealed specific HPV16 viral SNPs associated with variant lineages and elevated odds ratios; however, individual causal SNPs could not be distinguished with certainty due to the nature of HPV evolution.Conserved and lineage-specific SNPs can be imputed with a high degree of accuracy from limited viral polymorphic data due to the lack of recombination and the stochastic mechanism of variation accumulation in the HPV genome. However, to determine the role of novel variants or non-lineage-specific SNPs by VWAS will require direct sequence analysis. The investigation of patterns of genetic variation and the identification of diagnostic SNPs for lineages of HPV16 variants provides a valuable resource for future studies of HPV16

MiRNA-directed regulation of VEGF and other angiogenic factors under hypoxia.

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Zhong Hua

Full Text Available MicroRNAs (miRNAs are a class of 20-24 nt non-coding RNAs that regulate gene expression primarily through post-transcriptional repression or mRNA degradation in a sequence-specific manner. The roles of miRNAs are just beginning to be understood, but the study of miRNA function has been limited by poor understanding of the general principles of gene regulation by miRNAs. Here we used CNE cells from a human nasopharyngeal carcinoma cell line as a cellular system to investigate miRNA-directed regulation of VEGF and other angiogenic factors under hypoxia, and to explore the principles of gene regulation by miRNAs. Through computational analysis, 96 miRNAs were predicted as putative regulators of VEGF. But when we analyzed the miRNA expression profile of CNE and four other VEGF-expressing cell lines, we found that only some of these miRNAs could be involved in VEGF regulation, and that VEGF may be regulated by different miRNAs that were differentially chosen from 96 putative regulatory miRNAs of VEGF in different cells. Some of these miRNAs also co-regulate other angiogenic factors (differential regulation and co-regulation principle. We also found that VEGF was regulated by multiple miRNAs using different combinations, including both coordinate and competitive interactions. The coordinate principle states that miRNAs with independent binding sites in a gene can produce coordinate action to increase the repressive effect of miRNAs on this gene. By contrast, the competitive principle states when multiple miRNAs compete with each other for a common binding site, or when a functional miRNA competes with a false positive miRNA for the same binding site, the repressive effects of miRNAs may be decreased. Through the competitive principle, false positive miRNAs, which cannot directly repress gene expression, can sometimes play a role in miRNA-mediated gene regulation. The competitive principle, differential regulation, multi-miRNA binding sites, and false

MiR-7 triggers cell cycle arrest at the G1/S transition by targeting multiple genes including Skp2 and Psme3.

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Noelia Sanchez

Full Text Available MiR-7 acts as a tumour suppressor in many cancers and abrogates proliferation of CHO cells in culture. In this study we demonstrate that miR-7 targets key regulators of the G1 to S phase transition, including Skp2 and Psme3, to promote increased levels of p27(KIP and temporary growth arrest of CHO cells in the G1 phase. Simultaneously, the down-regulation of DNA repair-specific proteins via miR-7 including Rad54L, and pro-apoptotic regulators such as p53, combined with the up-regulation of anti-apoptotic factors like p-Akt, promoted cell survival while arrested in G1. Thus miR-7 can co-ordinate the levels of multiple genes and proteins to influence G1 to S phase transition and the apoptotic response in order to maintain cellular homeostasis. This work provides further mechanistic insight into the role of miR-7 as a regulator of cell growth in times of cellular stress.

Gap-filling a spatially explicit plant trait database: comparing imputation methods and different levels of environmental information

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R. Poyatos

2018-05-01

Full Text Available The ubiquity of missing data in plant trait databases may hinder trait-based analyses of ecological patterns and processes. Spatially explicit datasets with information on intraspecific trait variability are rare but offer great promise in improving our understanding of functional biogeography. At the same time, they offer specific challenges in terms of data imputation. Here we compare statistical imputation approaches, using varying levels of environmental information, for five plant traits (leaf biomass to sapwood area ratio, leaf nitrogen content, maximum tree height, leaf mass per area and wood density in a spatially explicit plant trait dataset of temperate and Mediterranean tree species (Ecological and Forest Inventory of Catalonia, IEFC, dataset for Catalonia, north-east Iberian Peninsula, 31 900 km2. We simulated gaps at different missingness levels (10–80 % in a complete trait matrix, and we used overall trait means, species means, k nearest neighbours (kNN, ordinary and regression kriging, and multivariate imputation using chained equations (MICE to impute missing trait values. We assessed these methods in terms of their accuracy and of their ability to preserve trait distributions, multi-trait correlation structure and bivariate trait relationships. The relatively good performance of mean and species mean imputations in terms of accuracy masked a poor representation of trait distributions and multivariate trait structure. Species identity improved MICE imputations for all traits, whereas forest structure and topography improved imputations for some traits. No method performed best consistently for the five studied traits, but, considering all traits and performance metrics, MICE informed by relevant ecological variables gave the best results. However, at higher missingness (> 30 %, species mean imputations and regression kriging tended to outperform MICE for some traits. MICE informed by relevant ecological variables

"Seed-Milarity" confers to hsa-miR-210 and hsa-miR-147b similar functional activity.

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Thomas Bertero

Full Text Available Specificity of interaction between a microRNA (miRNA and its targets crucially depends on the seed region located in its 5'-end. It is often implicitly considered that two miRNAs sharing the same biological activity should display similarity beyond the strict six nucleotide region that forms the seed, in order to form specific complexes with the same mRNA targets. We have found that expression of hsa-miR-147b and hsa-miR-210, though triggered by different stimuli (i.e. lipopolysaccharides and hypoxia, respectively, induce very similar cellular effects in term of proliferation, migration and apoptosis. Hsa-miR-147b only shares a "minimal" 6-nucleotides seed sequence with hsa-miR-210, but is identical with hsa-miR-147a over 20 nucleotides, except for one base located in the seed region. Phenotypic changes induced after heterologous expression of miR-147a strikingly differ from those induced by miR-147b or miR-210. In particular, miR-147a behaves as a potent inhibitor of cell proliferation and migration. These data fit well with the gene expression profiles observed for miR-147b and miR-210, which are very similar, and the gene expression profile of miR-147a, which is distinct from the two others. Bioinformatics analysis of all human miRNA sequences indicates multiple cases of miRNAs from distinct families exhibiting the same kind of similarity that would need to be further characterized in terms of putative functional redundancy. Besides, it implies that functional impact of some miRNAs can be masked by robust expression of miRNAs belonging to distinct families.

Mobile Motion Capture--MiMiC.

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Harbert, Simeon D; Jaiswal, Tushar; Harley, Linda R; Vaughn, Tyler W; Baranak, Andrew S

2013-01-01

The low cost, simple, robust, mobile, and easy to use Mobile Motion Capture (MiMiC) system is presented and the constraints which guided the design of MiMiC are discussed. The MiMiC Android application allows motion data to be captured from kinematic modules such as Shimmer 2r sensors over Bluetooth. MiMiC is cost effective and can be used for an entire day in a person's daily routine without being intrusive. MiMiC is a flexible motion capture system which can be used for many applications including fall detection, detection of fatigue in industry workers, and analysis of individuals' work patterns in various environments.

Normal Theory Two-Stage ML Estimator When Data Are Missing at the Item Level.

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Savalei, Victoria; Rhemtulla, Mijke

2017-08-01

In many modeling contexts, the variables in the model are linear composites of the raw items measured for each participant; for instance, regression and path analysis models rely on scale scores, and structural equation models often use parcels as indicators of latent constructs. Currently, no analytic estimation method exists to appropriately handle missing data at the item level. Item-level multiple imputation (MI), however, can handle such missing data straightforwardly. In this article, we develop an analytic approach for dealing with item-level missing data-that is, one that obtains a unique set of parameter estimates directly from the incomplete data set and does not require imputations. The proposed approach is a variant of the two-stage maximum likelihood (TSML) methodology, and it is the analytic equivalent of item-level MI. We compare the new TSML approach to three existing alternatives for handling item-level missing data: scale-level full information maximum likelihood, available-case maximum likelihood, and item-level MI. We find that the TSML approach is the best analytic approach, and its performance is similar to item-level MI. We recommend its implementation in popular software and its further study.

Imputation Accuracy from Low to Moderate Density Single Nucleotide Polymorphism Chips in a Thai Multibreed Dairy Cattle Population

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Danai Jattawa

2016-04-01

Full Text Available The objective of this study was to investigate the accuracy of imputation from low density (LDC to moderate density SNP chips (MDC in a Thai Holstein-Other multibreed dairy cattle population. Dairy cattle with complete pedigree information (n = 1,244 from 145 dairy farms were genotyped with GeneSeek GGP20K (n = 570, GGP26K (n = 540 and GGP80K (n = 134 chips. After checking for single nucleotide polymorphism (SNP quality, 17,779 SNP markers in common between the GGP20K, GGP26K, and GGP80K were used to represent MDC. Animals were divided into two groups, a reference group (n = 912 and a test group (n = 332. The SNP markers chosen for the test group were those located in positions corresponding to GeneSeek GGP9K (n = 7,652. The LDC to MDC genotype imputation was carried out using three different software packages, namely Beagle 3.3 (population-based algorithm, FImpute 2.2 (combined family- and population-based algorithms and Findhap 4 (combined family- and population-based algorithms. Imputation accuracies within and across chromosomes were calculated as ratios of correctly imputed SNP markers to overall imputed SNP markers. Imputation accuracy for the three software packages ranged from 76.79% to 93.94%. FImpute had higher imputation accuracy (93.94% than Findhap (84.64% and Beagle (76.79%. Imputation accuracies were similar and consistent across chromosomes for FImpute, but not for Findhap and Beagle. Most chromosomes that showed either high (73% or low (80% imputation accuracies were the same chromosomes that had above and below average linkage disequilibrium (LD; defined here as the correlation between pairs of adjacent SNP within chromosomes less than or equal to 1 Mb apart. Results indicated that FImpute was more suitable than Findhap and Beagle for genotype imputation in this Thai multibreed population. Perhaps additional increments in imputation accuracy could be achieved by increasing the completeness of pedigree information.

The utility of imputed matched sets. Analyzing probabilistically linked databases in a low information setting.

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Thomas, A M; Cook, L J; Dean, J M; Olson, L M

2014-01-01

To compare results from high probability matched sets versus imputed matched sets across differing levels of linkage information. A series of linkages with varying amounts of available information were performed on two simulated datasets derived from multiyear motor vehicle crash (MVC) and hospital databases, where true matches were known. Distributions of high probability and imputed matched sets were compared against the true match population for occupant age, MVC county, and MVC hour. Regression models were fit to simulated log hospital charges and hospitalization status. High probability and imputed matched sets were not significantly different from occupant age, MVC county, and MVC hour in high information settings (p > 0.999). In low information settings, high probability matched sets were significantly different from occupant age and MVC county (p sets were not (p > 0.493). High information settings saw no significant differences in inference of simulated log hospital charges and hospitalization status between the two methods. High probability and imputed matched sets were significantly different from the outcomes in low information settings; however, imputed matched sets were more robust. The level of information available to a linkage is an important consideration. High probability matched sets are suitable for high to moderate information settings and for situations involving case-specific analysis. Conversely, imputed matched sets are preferable for low information settings when conducting population-based analyses.

An Imputation Model for Dropouts in Unemployment Data

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Nilsson Petra

2016-09-01

Full Text Available Incomplete unemployment data is a fundamental problem when evaluating labour market policies in several countries. Many unemployment spells end for unknown reasons; in the Swedish Public Employment Service’s register as many as 20 percent. This leads to an ambiguity regarding destination states (employment, unemployment, retired, etc.. According to complete combined administrative data, the employment rate among dropouts was close to 50 for the years 1992 to 2006, but from 2007 the employment rate has dropped to 40 or less. This article explores an imputation approach. We investigate imputation models estimated both on survey data from 2005/2006 and on complete combined administrative data from 2005/2006 and 2011/2012. The models are evaluated in terms of their ability to make correct predictions. The models have relatively high predictive power.

Diagnostic and prognostic potential of serum miR-7, miR-16, miR-25, miR-93, miR-182, miR-376a and miR-429 in ovarian cancer patients.

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Meng, Xiaodan; Joosse, Simon A; Müller, Volkmar; Trillsch, Fabian; Milde-Langosch, Karin; Mahner, Sven; Geffken, Maria; Pantel, Klaus; Schwarzenbach, Heidi

2015-11-03

Owing to late diagnosis in advanced disease stages, prognosis of patients with epithelial ovarian cancer (EOC) is poor. The quantification of deregulated levels of microRNAs could facilitate earlier diagnosis and improve prognosis of EOC. Seven microRNAs (miR-7, miR-16, miR-25, miR-93, miR-182, miR-376a and miR-429) were quantified in the serum of 180 EOC patients and 66 healthy women by TaqMan PCR microRNA assays. Median follow-up time was 21 months. The effects of miR-7 and miR-429 on apoptosis, cell proliferation, migration and invasion were investigated in two (EOC) cell lines. Serum levels of miR-25 (P=0.0001) and miR-93 (P=0.0001) were downregulated, whereas those of miR-7 (P=0.001) and miR-429 (P=0.0001) were upregulated in EOC patients compared with healthy women. The four microRNAs discriminated EOC patients from healthy women with a sensitivity of 93% and a specificity of 92%. The levels of miR-429 positively correlated with CA125 values (P=0.0001) and differed between FIGO I-II and III-IV stages (P=0.001). MiR-429 was an independent predictor of overall survival (P=0.011). Overexpressed miR-429 in SKOV3 cells led to suppression of cell migration (P=0.037) and invasion (P=0.011). Increased levels of miR-7 were associated with lymph node metastases (P=0.0001) and FIGO stages III-IV (P=0.0001). Overexpressed miR-7 in SKOV3 cells resulted in increased cell migration (P=0.001) and invasion (P=0.011). Additionally, the increased levels of miR-376a correlated with FIGO stages III-IV (P=0.02). Our data indicate the diagnostic potential of miR-7, miR-25, miR-93 and miR-429 in EOC and the prognostic potential of miR-429. This microRNA panel may be promising molecules to be targeted in the treatment of EOC.

A suggested approach for imputation of missing dietary data for young children in daycare.

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Stevens, June; Ou, Fang-Shu; Truesdale, Kimberly P; Zeng, Donglin; Vaughn, Amber E; Pratt, Charlotte; Ward, Dianne S

2015-01-01

Parent-reported 24-h diet recalls are an accepted method of estimating intake in young children. However, many children eat while at childcare making accurate proxy reports by parents difficult. The goal of this study was to demonstrate a method to impute missing weekday lunch and daytime snack nutrient data for daycare children and to explore the concurrent predictive and criterion validity of the method. Data were from children aged 2-5 years in the My Parenting SOS project (n=308; 870 24-h diet recalls). Mixed models were used to simultaneously predict breakfast, dinner, and evening snacks (B+D+ES); lunch; and daytime snacks for all children after adjusting for age, sex, and body mass index (BMI). From these models, we imputed the missing weekday daycare lunches by interpolation using the mean lunch to B+D+ES [L/(B+D+ES)] ratio among non-daycare children on weekdays and the L/(B+D+ES) ratio for all children on weekends. Daytime snack data were used to impute snacks. The reported mean (± standard deviation) weekday intake was lower for daycare children [725 (±324) kcal] compared to non-daycare children [1,048 (±463) kcal]. Weekend intake for all children was 1,173 (±427) kcal. After imputation, weekday caloric intake for daycare children was 1,230 (±409) kcal. Daily intakes that included imputed data were associated with age and sex but not with BMI. This work indicates that imputation is a promising method for improving the precision of daily nutrient data from young children.

UniFIeD Univariate Frequency-based Imputation for Time Series Data

OpenAIRE

Friese, Martina; Stork, Jörg; Ramos Guerra, Ricardo; Bartz-Beielstein, Thomas; Thaker, Soham; Flasch, Oliver; Zaefferer, Martin

2013-01-01

This paper introduces UniFIeD, a new data preprocessing method for time series. UniFIeD can cope with large intervals of missing data. A scalable test function generator, which allows the simulation of time series with different gap sizes, is presented additionally. An experimental study demonstrates that (i) UniFIeD shows a significant better performance than simple imputation methods and (ii) UniFIeD is able to handle situations, where advanced imputation methods fail. The results are indep...

A 4-miRNA signature to predict survival in glioblastomas

DEFF Research Database (Denmark)

Hermansen, Simon K; Sørensen, Mia D; Hansen, Anker

2017-01-01

multiple genes representing an additional level of gene regulation possibly more prognostically powerful than a single gene. The aim of the study was to identify a novel miRNA signature with the ability to separate patients into prognostic subgroups. Samples from 40 glioblastoma patients were included...... association to survival in univariate (HR 8.50; 95% CI 3.06-23.62; psignature of miR-107 and miR-331 (miR sum score), which were the only miRNAs available...

Interactions of miR-323/miR-326/miR-329 and miR-130a/miR-155/miR-210 as prognostic indicators for clinical outcome of glioblastoma patients

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Qiu Shuwei

2013-01-01

Full Text Available Abstract Background Glioblastoma multiforme (GBM is the most common and aggressive brain tumor with poor clinical outcome. Identification and development of new markers could be beneficial for the diagnosis and prognosis of GBM patients. Deregulation of microRNAs (miRNAs or miRs is involved in GBM. Therefore, we attempted to identify and develop specific miRNAs as prognostic and predictive markers for GBM patient survival. Methods Expression profiles of miRNAs and genes and the corresponding clinical information of 480 GBM samples from The Cancer Genome Atlas (TCGA dataset were downloaded and interested miRNAs were identified. Patients’ overall survival (OS and progression-free survival (PFS associated with interested miRNAs and miRNA-interactions were performed by Kaplan-Meier survival analysis. The impacts of miRNA expressions and miRNA-interactions on survival were evaluated by Cox proportional hazard regression model. Biological processes and network of putative and validated targets of miRNAs were analyzed by bioinformatics. Results In this study, 6 interested miRNAs were identified. Survival analysis showed that high levels of miR-326/miR-130a and low levels of miR-323/miR-329/miR-155/miR-210 were significantly associated with long OS of GBM patients, and also showed that high miR-326/miR-130a and low miR-155/miR-210 were related with extended PFS. Moreover, miRNA-323 and miRNA-329 were found to be increased in patients with no-recurrence or long time to progression (TTP. More notably, our analysis revealed miRNA-interactions were more specific and accurate to discriminate and predict OS and PFS. This interaction stratified OS and PFS related with different miRNA levels more detailed, and could obtain longer span of mean survival in comparison to that of one single miRNA. Moreover, miR-326, miR-130a, miR-155, miR-210 and 4 miRNA-interactions were confirmed for the first time as independent predictors for survival by Cox regression model

Candidate gene analysis using imputed genotypes: cell cycle single-nucleotide polymorphisms and ovarian cancer risk

DEFF Research Database (Denmark)

Goode, Ellen L; Fridley, Brooke L; Vierkant, Robert A

2009-01-01

Polymorphisms in genes critical to cell cycle control are outstanding candidates for association with ovarian cancer risk; numerous genes have been interrogated by multiple research groups using differing tagging single-nucleotide polymorphism (SNP) sets. To maximize information gleaned from......, and rs3212891; CDK2 rs2069391, rs2069414, and rs17528736; and CCNE1 rs3218036. These results exemplify the utility of imputation in candidate gene studies and lend evidence to a role of cell cycle genes in ovarian cancer etiology, suggest a reduced set of SNPs to target in additional cases and controls....

TargetCompare: A web interface to compare simultaneous miRNAs targets.

Science.gov (United States)

Moreira, Fabiano Cordeiro; Dustan, Bruno; Hamoy, Igor G; Ribeiro-Dos-Santos, André M; Dos Santos, Andrea Ribeiro

2014-01-01

MicroRNAs (miRNAs) are small non-coding nucleotide sequences between 17 and 25 nucleotides in length that primarily function in the regulation of gene expression. A since miRNA has thousand of predict targets in a complex, regulatory cell signaling network. Therefore, it is of interest to study multiple target genes simultaneously. Hence, we describe a web tool (developed using Java programming language and MySQL database server) to analyse multiple targets of pre-selected miRNAs. We cross validated the tool in eight most highly expressed miRNAs in the antrum region of stomach. This helped to identify 43 potential genes that are target of at least six of the referred miRNAs. The developed tool aims to reduce the randomness and increase the chance of selecting strong candidate target genes and miRNAs responsible for playing important roles in the studied tissue. http://lghm.ufpa.br/targetcompare.

Quick, “Imputation-free” meta-analysis with proxy-SNPs

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Meesters Christian

2012-09-01

Full Text Available Abstract Background Meta-analysis (MA is widely used to pool genome-wide association studies (GWASes in order to a increase the power to detect strong or weak genotype effects or b as a result verification method. As a consequence of differing SNP panels among genotyping chips, imputation is the method of choice within GWAS consortia to avoid losing too many SNPs in a MA. YAMAS (Yet Another Meta Analysis Software, however, enables cross-GWAS conclusions prior to finished and polished imputation runs, which eventually are time-consuming. Results Here we present a fast method to avoid forfeiting SNPs present in only a subset of studies, without relying on imputation. This is accomplished by using reference linkage disequilibrium data from 1,000 Genomes/HapMap projects to find proxy-SNPs together with in-phase alleles for SNPs missing in at least one study. MA is conducted by combining association effect estimates of a SNP and those of its proxy-SNPs. Our algorithm is implemented in the MA software YAMAS. Association results from GWAS analysis applications can be used as input files for MA, tremendously speeding up MA compared to the conventional imputation approach. We show that our proxy algorithm is well-powered and yields valuable ad hoc results, possibly providing an incentive for follow-up studies. We propose our method as a quick screening step prior to imputation-based MA, as well as an additional main approach for studies without available reference data matching the ethnicities of study participants. As a proof of principle, we analyzed six dbGaP Type II Diabetes GWAS and found that the proxy algorithm clearly outperforms naïve MA on the p-value level: for 17 out of 23 we observe an improvement on the p-value level by a factor of more than two, and a maximum improvement by a factor of 2127. Conclusions YAMAS is an efficient and fast meta-analysis program which offers various methods, including conventional MA as well as inserting proxy

Genetic versus Non-Genetic Regulation of miR-103, miR-143 and miR-483-3p Expression in Adipose Tissue and Their Metabolic Implications—A Twin Study

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Jette Bork-Jensen

2014-07-01

Full Text Available Murine models suggest that the microRNAs miR-103 and miR-143 may play central roles in the regulation of subcutaneous adipose tissue (SAT and development of type 2 diabetes (T2D. The microRNA miR-483-3p may reduce adipose tissue expandability and cause ectopic lipid accumulation, insulin resistance and T2D. We aimed to explore the genetic and non-genetic factors that regulate these microRNAs in human SAT, and to investigate their impact on metabolism in humans. Levels of miR-103, miR-143 and miR-483-3p were measured in SAT biopsies from 244 elderly monozygotic and dizygotic twins using real-time PCR. Heritability estimates were calculated and multiple regression analyses were performed to study associations between these microRNAs and measures of metabolism, as well as between these microRNAs and possible regulating factors. We found that increased BMI was associated with increased miR-103 expression levels. In addition, the miR-103 levels were positively associated with 2 h plasma glucose levels and hemoglobin A1c independently of BMI. Heritability estimates for all three microRNAs were low. In conclusion, the expression levels of miR-103, miR-143 and miR-483-3p in adipose tissue are primarily influenced by non-genetic factors, and miR-103 may be involved in the development of adiposity and control of glucose metabolism in humans.

Learning-Based Adaptive Imputation Methodwith kNN Algorithm for Missing Power Data

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Minkyung Kim

2017-10-01

Full Text Available This paper proposes a learning-based adaptive imputation method (LAI for imputing missing power data in an energy system. This method estimates the missing power data by using the pattern that appears in the collected data. Here, in order to capture the patterns from past power data, we newly model a feature vector by using past data and its variations. The proposed LAI then learns the optimal length of the feature vector and the optimal historical length, which are significant hyper parameters of the proposed method, by utilizing intentional missing data. Based on a weighted distance between feature vectors representing a missing situation and past situation, missing power data are estimated by referring to the k most similar past situations in the optimal historical length. We further extend the proposed LAI to alleviate the effect of unexpected variation in power data and refer to this new approach as the extended LAI method (eLAI. The eLAI selects a method between linear interpolation (LI and the proposed LAI to improve accuracy under unexpected variations. Finally, from a simulation under various energy consumption profiles, we verify that the proposed eLAI achieves about a 74% reduction of the average imputation error in an energy system, compared to the existing imputation methods.

MiR-17-5p impairs trafficking of H-ERG K+ channel protein by targeting multiple er stress-related chaperones during chronic oxidative stress.

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Qi Wang

Full Text Available BACKGROUND: To investigate if microRNAs (miRNAs play a role in regulating h-ERG trafficking in the setting of chronic oxidative stress as a common deleterious factor for many cardiac disorders. METHODS: We treated neonatal rat ventricular myocytes and HEK293 cells with stable expression of h-ERG with H2O2 for 12 h and 48 h. Expression of miR-17-5p seed miRNAs was quantified by real-time RT-PCR. Protein levels of chaperones and h-ERG trafficking were measured by Western blot analysis. Luciferase reporter gene assay was used to study miRNA and target interactions. Whole-cell patch-clamp techniques were employed to record h-ERG K(+ current. RESULTS: H-ERG trafficking was impaired by H2O2 after 48 h treatment, accompanied by reciprocal changes of expression between miR-17-5p seed miRNAs and several chaperones (Hsp70, Hsc70, CANX, and Golga2, with the former upregulated and the latter downregulated. We established these chaperones as targets for miR-17-5p. Application miR-17-5p inhibitor rescued H2O2-induced impairment of h-ERG trafficking. Upregulation of endogenous by H2O2 or forced miR-17-5p expression either reduced h-ERG current. Sequestration of AP1 by its decoy molecule eliminated the upregulation of miR-17-5p, and ameliorated impairment of h-ERG trafficking. CONCLUSIONS: Collectively, deregulation of the miR-17-5p seed family miRNAs can cause severe impairment of h-ERG trafficking through targeting multiple ER stress-related chaperones, and activation of AP1 likely accounts for the deleterious upregulation of these miRNAs, in the setting of prolonged duration of oxidative stress. These findings revealed the role of miRNAs in h-ERG trafficking, which may contribute to the cardiac electrical disturbances associated with oxidative stress.

MiR-17-5p impairs trafficking of H-ERG K+ channel protein by targeting multiple er stress-related chaperones during chronic oxidative stress.

Science.gov (United States)

Wang, Qi; Hu, Weina; Lei, Mingming; Wang, Yong; Yan, Bing; Liu, Jun; Zhang, Ren; Jin, Yuanzhe

2013-01-01

To investigate if microRNAs (miRNAs) play a role in regulating h-ERG trafficking in the setting of chronic oxidative stress as a common deleterious factor for many cardiac disorders. We treated neonatal rat ventricular myocytes and HEK293 cells with stable expression of h-ERG with H2O2 for 12 h and 48 h. Expression of miR-17-5p seed miRNAs was quantified by real-time RT-PCR. Protein levels of chaperones and h-ERG trafficking were measured by Western blot analysis. Luciferase reporter gene assay was used to study miRNA and target interactions. Whole-cell patch-clamp techniques were employed to record h-ERG K(+) current. H-ERG trafficking was impaired by H2O2 after 48 h treatment, accompanied by reciprocal changes of expression between miR-17-5p seed miRNAs and several chaperones (Hsp70, Hsc70, CANX, and Golga2), with the former upregulated and the latter downregulated. We established these chaperones as targets for miR-17-5p. Application miR-17-5p inhibitor rescued H2O2-induced impairment of h-ERG trafficking. Upregulation of endogenous by H2O2 or forced miR-17-5p expression either reduced h-ERG current. Sequestration of AP1 by its decoy molecule eliminated the upregulation of miR-17-5p, and ameliorated impairment of h-ERG trafficking. Collectively, deregulation of the miR-17-5p seed family miRNAs can cause severe impairment of h-ERG trafficking through targeting multiple ER stress-related chaperones, and activation of AP1 likely accounts for the deleterious upregulation of these miRNAs, in the setting of prolonged duration of oxidative stress. These findings revealed the role of miRNAs in h-ERG trafficking, which may contribute to the cardiac electrical disturbances associated with oxidative stress.

Down-regulation of 5S rRNA by miR-150 and miR-383 enhances c-Myc-rpL11 interaction and inhibits proliferation of esophageal squamous carcinoma cells.

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Wang, Xinyu; Ren, Yanli; Wang, Zhiqiong; Xiong, Xiangyu; Han, Sichong; Pan, Wenting; Chen, Hongwei; Zhou, Liqing; Zhou, Changchun; Yuan, Qipeng; Yang, Ming

2015-12-21

5S rRNA plays an important part in ribosome biology and is over-expression in multiple cancers. In this study, we found that 5S rRNA is a direct target of miR-150 and miR-383 in esophageal squamous cell carcinoma (ESCC). Overexpression of miR-150 and miR-383 inhibited ESCC cell proliferation in vitro and in vivo. Moreover, 5S rRNA silencing by miR-150 and miR-383 might intensify rpL11-c-Myc interaction, which attenuated role of c-Myc as an oncogenic transcriptional factor and dysregulation of multiple c-Myc target genes. Taken together, our results highlight the involvement of miRNAs in ribosomal regulation during tumorigenesis. Copyright © 2015 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

A suggested approach for imputation of missing dietary data for young children in daycare

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June Stevens

2015-12-01

Full Text Available Background: Parent-reported 24-h diet recalls are an accepted method of estimating intake in young children. However, many children eat while at childcare making accurate proxy reports by parents difficult. Objective: The goal of this study was to demonstrate a method to impute missing weekday lunch and daytime snack nutrient data for daycare children and to explore the concurrent predictive and criterion validity of the method. Design: Data were from children aged 2-5 years in the My Parenting SOS project (n=308; 870 24-h diet recalls. Mixed models were used to simultaneously predict breakfast, dinner, and evening snacks (B+D+ES; lunch; and daytime snacks for all children after adjusting for age, sex, and body mass index (BMI. From these models, we imputed the missing weekday daycare lunches by interpolation using the mean lunch to B+D+ES [L/(B+D+ES] ratio among non-daycare children on weekdays and the L/(B+D+ES ratio for all children on weekends. Daytime snack data were used to impute snacks. Results: The reported mean (± standard deviation weekday intake was lower for daycare children [725 (±324 kcal] compared to non-daycare children [1,048 (±463 kcal]. Weekend intake for all children was 1,173 (±427 kcal. After imputation, weekday caloric intake for daycare children was 1,230 (±409 kcal. Daily intakes that included imputed data were associated with age and sex but not with BMI. Conclusion: This work indicates that imputation is a promising method for improving the precision of daily nutrient data from young children.

Inclusion of Population-specific Reference Panel from India to the 1000 Genomes Phase 3 Panel Improves Imputation Accuracy.

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Ahmad, Meraj; Sinha, Anubhav; Ghosh, Sreya; Kumar, Vikrant; Davila, Sonia; Yajnik, Chittaranjan S; Chandak, Giriraj R

2017-07-27

Imputation is a computational method based on the principle of haplotype sharing allowing enrichment of genome-wide association study datasets. It depends on the haplotype structure of the population and density of the genotype data. The 1000 Genomes Project led to the generation of imputation reference panels which have been used globally. However, recent studies have shown that population-specific panels provide better enrichment of genome-wide variants. We compared the imputation accuracy using 1000 Genomes phase 3 reference panel and a panel generated from genome-wide data on 407 individuals from Western India (WIP). The concordance of imputed variants was cross-checked with next-generation re-sequencing data on a subset of genomic regions. Further, using the genome-wide data from 1880 individuals, we demonstrate that WIP works better than the 1000 Genomes phase 3 panel and when merged with it, significantly improves the imputation accuracy throughout the minor allele frequency range. We also show that imputation using only South Asian component of the 1000 Genomes phase 3 panel works as good as the merged panel, making it computationally less intensive job. Thus, our study stresses that imputation accuracy using 1000 Genomes phase 3 panel can be further improved by including population-specific reference panels from South Asia.

Increasing imputation and prediction accuracy for Chinese Holsteins using joint Chinese-Nordic reference population

DEFF Research Database (Denmark)

Ma, Peipei; Lund, Mogens Sandø; Ding, X

2015-01-01

This study investigated the effect of including Nordic Holsteins in the reference population on the imputation accuracy and prediction accuracy for Chinese Holsteins. The data used in this study include 85 Chinese Holstein bulls genotyped with both 54K chip and 777K (HD) chip, 2862 Chinese cows...... was improved slightly when using the marker data imputed based on the combined HD reference data, compared with using the marker data imputed based on the Chinese HD reference data only. On the other hand, when using the combined reference population including 4398 Nordic Holstein bulls, the accuracy...... to increase reference population rather than increasing marker density...

Simultaneous Treatment of Missing Data and Measurement Error in HIV Research Using Multiple Overimputation.

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Schomaker, Michael; Hogger, Sara; Johnson, Leigh F; Hoffmann, Christopher J; Bärnighausen, Till; Heumann, Christian

2015-09-01

Both CD4 count and viral load in HIV-infected persons are measured with error. There is no clear guidance on how to deal with this measurement error in the presence of missing data. We used multiple overimputation, a method recently developed in the political sciences, to account for both measurement error and missing data in CD4 count and viral load measurements from four South African cohorts of a Southern African HIV cohort collaboration. Our knowledge about the measurement error of ln CD4 and log10 viral load is part of an imputation model that imputes both missing and mismeasured data. In an illustrative example, we estimate the association of CD4 count and viral load with the hazard of death among patients on highly active antiretroviral therapy by means of a Cox model. Simulation studies evaluate the extent to which multiple overimputation is able to reduce bias in survival analyses. Multiple overimputation emphasizes more strongly the influence of having high baseline CD4 counts compared to both a complete case analysis and multiple imputation (hazard ratio for >200 cells/mm vs. <25 cells/mm: 0.21 [95% confidence interval: 0.18, 0.24] vs. 0.38 [0.29, 0.48], and 0.29 [0.25, 0.34], respectively). Similar results are obtained when varying assumptions about measurement error, when using p-splines, and when evaluating time-updated CD4 count in a longitudinal analysis. The estimates of the association with viral load are slightly more attenuated when using multiple imputation instead of multiple overimputation. Our simulation studies suggest that multiple overimputation is able to reduce bias and mean squared error in survival analyses. Multiple overimputation, which can be used with existing software, offers a convenient approach to account for both missing and mismeasured data in HIV research.

The expression of miR-181a-5p and miR-371b-5p in chondrosarcoma.

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Mutlu, S; Mutlu, H; Kirkbes, S; Eroglu, S; Kabukcuoglu, Y S; Kabukcuoglu, F; Duymus, T M; ISık, M; Ulasli, M

2015-07-01

Chondrosarcomas are malignant tumors of chondrocytes that affect bones and joints, and it represents the third most common type of primary bone tumors. Chondrosarcoma is difficult to treat because it is relatively resistant to both chemotherapy and radiation. Thus, surgery remains the best available treatment. It is important to find new diagnostic markers and improve treatment options. miRNAs are small non-coding transcripts (19-25 nucleotides) that regulate gene expression via targeting complementary sequences within messenger RNAs (mRNAs). miRNAs have been shown to be involved in regulation of many biochemical pathways. Dysregulated expression of many miRNAs has also been associated with multiple human diseases, such as cancer. 18 surgical chondrosarcoma specimens were obtained from patients. RNA extractions were performed from decalcified paraffin embedded tissues. The aim of this study was to investigate the expression levels of miR-181a and miR-371b in patients with chondrosarcoma by using RT-PCR and to evaluate the relationship between these miRNAs and chondrosarcoma. miR-181a was found to be upregulated in chondrosarcoma specimens whereas no significant alteration was found for miR-371b expression. It has been proposed that miRNA expression studies might be used as diagnostic, prognostic marker in cancer. miRNA expression data produced in our study may contribute future chondrosarcoma diagnosis and therapy.

Multiple Imputation of Groundwater Data to Evaluate Spatial and Temporal Anthropogenic Influences on Subsurface Water Fluxes in Los Angeles, CA

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Manago, K. F.; Hogue, T. S.; Hering, A. S.

2014-12-01

In the City of Los Angeles, groundwater accounts for 11% of the total water supply on average, and 30% during drought years. Due to ongoing drought in California, increased reliance on local water supply highlights the need for better understanding of regional groundwater dynamics and estimating sustainable groundwater supply. However, in an urban setting, such as Los Angeles, understanding or modeling groundwater levels is extremely complicated due to various anthropogenic influences such as groundwater pumping, artificial recharge, landscape irrigation, leaking infrastructure, seawater intrusion, and extensive impervious surfaces. This study analyzes anthropogenic effects on groundwater levels using groundwater monitoring well data from the County of Los Angeles Department of Public Works. The groundwater data is irregularly sampled with large gaps between samples, resulting in a sparsely populated dataset. A multiple imputation method is used to fill the missing data, allowing for multiple ensembles and improved error estimates. The filled data is interpolated to create spatial groundwater maps utilizing information from all wells. The groundwater data is evaluated at a monthly time step over the last several decades to analyze the effect of land cover and identify other influencing factors on groundwater levels spatially and temporally. Preliminary results show irrigated parks have the largest influence on groundwater fluctuations, resulting in large seasonal changes, exceeding changes in spreading grounds. It is assumed that these fluctuations are caused by watering practices required to sustain non-native vegetation. Conversely, high intensity urbanized areas resulted in muted groundwater fluctuations and behavior decoupling from climate patterns. Results provides improved understanding of anthropogenic effects on groundwater levels in addition to providing high quality datasets for validation of regional groundwater models.

Three novel serum biomarkers, miR-1, miR-133a, and miR-206 for Limb-girdle muscular dystrophy, Facioscapulohumeral muscular dystrophy, and Becker muscular dystrophy.

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Matsuzaka, Yasunari; Kishi, Soichiro; Aoki, Yoshitsugu; Komaki, Hirofumi; Oya, Yasushi; Takeda, Shin-Ichi; Hashido, Kazuo

2014-11-01

Muscular dystrophies are a clinically and genetically heterogeneous group of inherited myogenic disorders. In clinical tests for these diseases, creatine kinase (CK) is generally used as diagnostic blood-based biomarker. However, because CK levels can be altered by various other factors, such as vigorous exercise, etc., false positive is observed. Therefore, three microRNAs (miRNAs), miR-1, miR-133a, and miR-206, were previously reported as alternative biomarkers for duchenne muscular dystrophy (DMD). However, no alternative biomarkers have been established for the other muscular dystrophies. We, therefore, evaluated whether these miR-1, miR-133a, and miR-206 can be used as powerful biomarkers using the serum from muscular dystrophy patients including DMD, myotonic dystrophy 1 (DM1), limb-girdle muscular dystrophy (LGMD), facioscapulohumeral muscular dystrophy (FSHD), becker muscular dystrophy (BMD), and distal myopathy with rimmed vacuoles (DMRV) by qualitative polymerase chain reaction (PCR) amplification assay. Statistical analysis indicated that all these miRNA levels in serum represented no significant differences between all muscle disorders examined in this study and controls by Bonferroni correction. However, some of these indicated significant differences without correction for testing multiple diseases (P < 0.05). The median values of miR-1 levels in the serum of patients with LGMD, FSHD, and BMD were approximately 5.5, 3.3 and 1.7 compared to that in controls, 0.68, respectively. Similarly, those of miR-133a and miR-206 levels in the serum of BMD patients were about 2.5 and 2.1 compared to those in controls, 1.03 and 1.32, respectively. Taken together, our data demonstrate that levels of miR-1, miR-133a, and miR-206 in serum of BMD and miR-1 in sera of LGMD and FSHD patients showed no significant differences compared with those of controls by Bonferroni correction. However, the results might need increase in sample sizes to evaluate these three miRNAs as

Clinical relevance of microRNA miR-21, miR-31, miR-92a, miR-101, miR-106a and miR-145 in colorectal cancer

International Nuclear Information System (INIS)

Schee, Kristina; Boye, Kjetil; Abrahamsen, Torveig Weum; Fodstad, Øystein; Flatmark, Kjersti

2012-01-01

MicroRNAs (miRNAs) regulate gene expression by binding to mRNA, and can function as oncogenes or tumor suppressors depending on the target. In this study, using qRT-PCR, we examined the expression of six miRNAs (miR-21, miR-31, miR-92a, miR-101, miR-106a and miR-145) in tumors from 193 prospectively recruited patients with colorectal cancer, and associations with clinicopathological parameters and patient outcome were analyzed. The miRNAs were chosen based on previous studies for their biomarker potential and suggested biological relevance in colorectal cancer. The miRNA expression was examined by qRT-PCR. Associations between miRNA expression and clinicopathological variables were explored using Mann–Whitney U and Kruskal-Wallis test while survival was estimated using the Kaplan-Meier method and compared using the log-rank test. MiR-101 was hardly expressed in the tumor samples, while for the other miRNAs, variable expression levels and expression ranges were observed, with miR-21 being most abundantly expressed relative to the reference (RNU44). In our study cohort, major clinical significance was demonstrated only for miR-31, as high expression was associated with advanced tumor stage and poor differentiation. No significant associations were found between expression of the investigated miRNAs and metastasis-free or overall survival. Investigating the expression of six miRNAs previously identified as candidate biomarkers in colorectal cancer, few clinically relevant associations were detected in our patient cohort. Our results emphasize the importance of validating potential tumor markers in independent patient cohorts, and indicate that the role of miRNAs as colorectal cancer biomarkers is still undetermined

Identification of Subtype Specific miRNA-mRNA Functional Regulatory Modules in Matched miRNA-mRNA Expression Data: Multiple Myeloma as a Case

OpenAIRE

Zhang, Yunpeng; Liu, Wei; Xu, Yanjun; Li, Chunquan; Wang, Yingying; Yang, Haixiu; Zhang, Chunlong; Su, Fei; Li, Yixue; Li, Xia

2015-01-01

Identification of miRNA-mRNA modules is an important step to elucidate their combinatorial effect on the pathogenesis and mechanisms underlying complex diseases. Current identification methods primarily are based upon miRNA-target information and matched miRNA and mRNA expression profiles. However, for heterogeneous diseases, the miRNA-mRNA regulatory mechanisms may differ between subtypes, leading to differences in clinical behavior. In order to explore the pathogenesis of each subtype, it i...

Comparison of different methods for imputing genome-wide marker genotypes in Swedish and Finnish Red Cattle

DEFF Research Database (Denmark)

Ma, Peipei; Brøndum, Rasmus Froberg; Qin, Zahng

2013-01-01

This study investigated the imputation accuracy of different methods, considering both the minor allele frequency and relatedness between individuals in the reference and test data sets. Two data sets from the combined population of Swedish and Finnish Red Cattle were used to test the influence...... coefficient was lower when the minor allele frequency was lower. The results indicate that Beagle and IMPUTE2 provide the most robust and accurate imputation accuracies, but considering computing time and memory usage, FImpute is another alternative method....

Imputing forest carbon stock estimates from inventory plots to a nationally continuous coverage

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Wilson Barry Tyler

2013-01-01

Full Text Available Abstract The U.S. has been providing national-scale estimates of forest carbon (C stocks and stock change to meet United Nations Framework Convention on Climate Change (UNFCCC reporting requirements for years. Although these currently are provided as national estimates by pool and year to meet greenhouse gas monitoring requirements, there is growing need to disaggregate these estimates to finer scales to enable strategic forest management and monitoring activities focused on various ecosystem services such as C storage enhancement. Through application of a nearest-neighbor imputation approach, spatially extant estimates of forest C density were developed for the conterminous U.S. using the U.S.’s annual forest inventory. Results suggest that an existing forest inventory plot imputation approach can be readily modified to provide raster maps of C density across a range of pools (e.g., live tree to soil organic carbon and spatial scales (e.g., sub-county to biome. Comparisons among imputed maps indicate strong regional differences across C pools. The C density of pools closely related to detrital input (e.g., dead wood is often highest in forests suffering from recent mortality events such as those in the northern Rocky Mountains (e.g., beetle infestations. In contrast, live tree carbon density is often highest on the highest quality forest sites such as those found in the Pacific Northwest. Validation results suggest strong agreement between the estimates produced from the forest inventory plots and those from the imputed maps, particularly when the C pool is closely associated with the imputation model (e.g., aboveground live biomass and live tree basal area, with weaker agreement for detrital pools (e.g., standing dead trees. Forest inventory imputed plot maps provide an efficient and flexible approach to monitoring diverse C pools at national (e.g., UNFCCC and regional scales (e.g., Reducing Emissions from Deforestation and Forest

TRANSPOSABLE REGULARIZED COVARIANCE MODELS WITH AN APPLICATION TO MISSING DATA IMPUTATION.

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Allen, Genevera I; Tibshirani, Robert

2010-06-01

Missing data estimation is an important challenge with high-dimensional data arranged in the form of a matrix. Typically this data matrix is transposable , meaning that either the rows, columns or both can be treated as features. To model transposable data, we present a modification of the matrix-variate normal, the mean-restricted matrix-variate normal , in which the rows and columns each have a separate mean vector and covariance matrix. By placing additive penalties on the inverse covariance matrices of the rows and columns, these so called transposable regularized covariance models allow for maximum likelihood estimation of the mean and non-singular covariance matrices. Using these models, we formulate EM-type algorithms for missing data imputation in both the multivariate and transposable frameworks. We present theoretical results exploiting the structure of our transposable models that allow these models and imputation methods to be applied to high-dimensional data. Simulations and results on microarray data and the Netflix data show that these imputation techniques often outperform existing methods and offer a greater degree of flexibility.

Imputation of missing genotypes within LD-blocks relying on the basic coalescent and beyond: consideration of population growth and structure.

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Kabisch, Maria; Hamann, Ute; Lorenzo Bermejo, Justo

2017-10-17

Genotypes not directly measured in genetic studies are often imputed to improve statistical power and to increase mapping resolution. The accuracy of standard imputation techniques strongly depends on the similarity of linkage disequilibrium (LD) patterns in the study and reference populations. Here we develop a novel approach for genotype imputation in low-recombination regions that relies on the coalescent and permits to explicitly account for population demographic factors. To test the new method, study and reference haplotypes were simulated and gene trees were inferred under the basic coalescent and also considering population growth and structure. The reference haplotypes that first coalesced with study haplotypes were used as templates for genotype imputation. Computer simulations were complemented with the analysis of real data. Genotype concordance rates were used to compare the accuracies of coalescent-based and standard (IMPUTE2) imputation. Simulations revealed that, in LD-blocks, imputation accuracy relying on the basic coalescent was higher and less variable than with IMPUTE2. Explicit consideration of population growth and structure, even if present, did not practically improve accuracy. The advantage of coalescent-based over standard imputation increased with the minor allele frequency and it decreased with population stratification. Results based on real data indicated that, even in low-recombination regions, further research is needed to incorporate recombination in coalescence inference, in particular for studies with genetically diverse and admixed individuals. To exploit the full potential of coalescent-based methods for the imputation of missing genotypes in genetic studies, further methodological research is needed to reduce computer time, to take into account recombination, and to implement these methods in user-friendly computer programs. Here we provide reproducible code which takes advantage of publicly available software to facilitate

A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation.

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Välikangas, Tommi; Suomi, Tomi; Elo, Laura L

2017-05-31

Label-free mass spectrometry (MS) has developed into an important tool applied in various fields of biological and life sciences. Several software exist to process the raw MS data into quantified protein abundances, including open source and commercial solutions. Each software includes a set of unique algorithms for different tasks of the MS data processing workflow. While many of these algorithms have been compared separately, a thorough and systematic evaluation of their overall performance is missing. Moreover, systematic information is lacking about the amount of missing values produced by the different proteomics software and the capabilities of different data imputation methods to account for them.In this study, we evaluated the performance of five popular quantitative label-free proteomics software workflows using four different spike-in data sets. Our extensive testing included the number of proteins quantified and the number of missing values produced by each workflow, the accuracy of detecting differential expression and logarithmic fold change and the effect of different imputation and filtering methods on the differential expression results. We found that the Progenesis software performed consistently well in the differential expression analysis and produced few missing values. The missing values produced by the other software decreased their performance, but this difference could be mitigated using proper data filtering or imputation methods. Among the imputation methods, we found that the local least squares (lls) regression imputation consistently increased the performance of the software in the differential expression analysis, and a combination of both data filtering and local least squares imputation increased performance the most in the tested data sets. © The Author 2017. Published by Oxford University Press.

Accuracy of hemoglobin A1c imputation using fasting plasma glucose in diabetes research using electronic health records data

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Stanley Xu

2014-05-01

Full Text Available In studies that use electronic health record data, imputation of important data elements such as Glycated hemoglobin (A1c has become common. However, few studies have systematically examined the validity of various imputation strategies for missing A1c values. We derived a complete dataset using an incident diabetes population that has no missing values in A1c, fasting and random plasma glucose (FPG and RPG, age, and gender. We then created missing A1c values under two assumptions: missing completely at random (MCAR and missing at random (MAR. We then imputed A1c values, compared the imputed values to the true A1c values, and used these data to assess the impact of A1c on initiation of antihyperglycemic therapy. Under MCAR, imputation of A1c based on FPG 1 estimated a continuous A1c within ± 1.88% of the true A1c 68.3% of the time; 2 estimated a categorical A1c within ± one category from the true A1c about 50% of the time. Including RPG in imputation slightly improved the precision but did not improve the accuracy. Under MAR, including gender and age in addition to FPG improved the accuracy of imputed continuous A1c but not categorical A1c. Moreover, imputation of up to 33% of missing A1c values did not change the accuracy and precision and did not alter the impact of A1c on initiation of antihyperglycemic therapy. When using A1c values as a predictor variable, a simple imputation algorithm based only on age, sex, and fasting plasma glucose gave acceptable results.

What are the appropriate methods for analyzing patient-reported outcomes in randomized trials when data are missing?

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Hamel, J F; Sebille, V; Le Neel, T; Kubis, G; Boyer, F C; Hardouin, J B

2017-12-01

Subjective health measurements using Patient Reported Outcomes (PRO) are increasingly used in randomized trials, particularly for patient groups comparisons. Two main types of analytical strategies can be used for such data: Classical Test Theory (CTT) and Item Response Theory models (IRT). These two strategies display very similar characteristics when data are complete, but in the common case when data are missing, whether IRT or CTT would be the most appropriate remains unknown and was investigated using simulations. We simulated PRO data such as quality of life data. Missing responses to items were simulated as being completely random, depending on an observable covariate or on an unobserved latent trait. The considered CTT-based methods allowed comparing scores using complete-case analysis, personal mean imputations or multiple-imputations based on a two-way procedure. The IRT-based method was the Wald test on a Rasch model including a group covariate. The IRT-based method and the multiple-imputations-based method for CTT displayed the highest observed power and were the only unbiased method whatever the kind of missing data. Online software and Stata® modules compatibles with the innate mi impute suite are provided for performing such analyses. Traditional procedures (listwise deletion and personal mean imputations) should be avoided, due to inevitable problems of biases and lack of power.

Comparing strategies for selection of low-density SNPs for imputation-mediated genomic prediction in U. S. Holsteins.

Science.gov (United States)

He, Jun; Xu, Jiaqi; Wu, Xiao-Lin; Bauck, Stewart; Lee, Jungjae; Morota, Gota; Kachman, Stephen D; Spangler, Matthew L

2018-04-01

SNP chips are commonly used for genotyping animals in genomic selection but strategies for selecting low-density (LD) SNPs for imputation-mediated genomic selection have not been addressed adequately. The main purpose of the present study was to compare the performance of eight LD (6K) SNP panels, each selected by a different strategy exploiting a combination of three major factors: evenly-spaced SNPs, increased minor allele frequencies, and SNP-trait associations either for single traits independently or for all the three traits jointly. The imputation accuracies from 6K to 80K SNP genotypes were between 96.2 and 98.2%. Genomic prediction accuracies obtained using imputed 80K genotypes were between 0.817 and 0.821 for daughter pregnancy rate, between 0.838 and 0.844 for fat yield, and between 0.850 and 0.863 for milk yield. The two SNP panels optimized on the three major factors had the highest genomic prediction accuracy (0.821-0.863), and these accuracies were very close to those obtained using observed 80K genotypes (0.825-0.868). Further exploration of the underlying relationships showed that genomic prediction accuracies did not respond linearly to imputation accuracies, but were significantly affected by genotype (imputation) errors of SNPs in association with the traits to be predicted. SNPs optimal for map coverage and MAF were favorable for obtaining accurate imputation of genotypes whereas trait-associated SNPs improved genomic prediction accuracies. Thus, optimal LD SNP panels were the ones that combined both strengths. The present results have practical implications on the design of LD SNP chips for imputation-enabled genomic prediction.

Estimating the role of casual contact from the community in transmission of Bordetella pertussis to young infants

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Poole Charles

2007-10-01

Full Text Available Abstract The proportion of infant pertussis cases due to transmission from casual contact in the community has not been estimated since before the introduction of pertussis vaccines in the 1950s. This study aimed to estimate the proportion of pertussis transmission due to casual contact using demographic and clinical data from a study of 95 infant pertussis cases and their close contacts enrolled at 14 hospitals in France, Germany, Canada, and the U.S. between February 2003 and September 2004. A complete case analysis was conducted as well as multiple imputation (MI to account for missing data for participants and close contacts who did not participate. By considering all possible close contacts, the MI analysis estimated 66% of source cases were close contacts, implying the minimum attributable proportion of infant cases due to transmission from casual contact with community members was 34% (95% CI = 24%, 44%. Estimates from the complete case analysis were comparable but less precise. Results were sensitive to changes in the operational definition of a source case, which broadened the range of MI point estimates of transmission from casual community contact to 20%–47%. We conclude that casual contact appears to be responsible for a substantial proportion of pertussis transmission to young infants. Medical subject headings (MeSH: multiple imputation, pertussis, transmission, casual contact, sensitivity analysis, missing data, community.

Missing in space: an evaluation of imputation methods for missing data in spatial analysis of risk factors for type II diabetes.

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Baker, Jannah; White, Nicole; Mengersen, Kerrie

2014-11-20

Spatial analysis is increasingly important for identifying modifiable geographic risk factors for disease. However, spatial health data from surveys are often incomplete, ranging from missing data for only a few variables, to missing data for many variables. For spatial analyses of health outcomes, selection of an appropriate imputation method is critical in order to produce the most accurate inferences. We present a cross-validation approach to select between three imputation methods for health survey data with correlated lifestyle covariates, using as a case study, type II diabetes mellitus (DM II) risk across 71 Queensland Local Government Areas (LGAs). We compare the accuracy of mean imputation to imputation using multivariate normal and conditional autoregressive prior distributions. Choice of imputation method depends upon the application and is not necessarily the most complex method. Mean imputation was selected as the most accurate method in this application. Selecting an appropriate imputation method for health survey data, after accounting for spatial correlation and correlation between covariates, allows more complete analysis of geographic risk factors for disease with more confidence in the results to inform public policy decision-making.

A multiplexed miRNA and transgene expression platform for simultaneous repression and expression of protein coding sequences.

Science.gov (United States)

Seyhan, Attila A

2016-01-01

Knockdown of single or multiple gene targets by RNA interference (RNAi) is necessary to overcome escape mutants or isoform redundancy. It is also necessary to use multiple RNAi reagents to knockdown multiple targets. It is also desirable to express a transgene or positive regulatory elements and inhibit a target gene in a coordinated fashion. This study reports a flexible multiplexed RNAi and transgene platform using endogenous intronic primary microRNAs (pri-miRNAs) as a scaffold located in the green fluorescent protein (GFP) as a model for any functional transgene. The multiplexed intronic miRNA - GFP transgene platform was designed to co-express multiple small RNAs within the polycistronic cluster from a Pol II promoter at more moderate levels to reduce potential vector toxicity. The native intronic miRNAs are co-transcribed with a precursor GFP mRNA as a single transcript and presumably cleaved out of the precursor-(pre) mRNA by the RNA splicing machinery, spliceosome. The spliced intron with miRNA hairpins will be further processed into mature miRNAs or small interfering RNAs (siRNAs) capable of triggering RNAi effects, while the ligated exons become a mature messenger RNA for the translation of the functional GFP protein. Data show that this approach led to robust RNAi-mediated silencing of multiple Renilla Luciferase (R-Luc)-tagged target genes and coordinated expression of functional GFP from a single transcript in transiently transfected HeLa cells. The results demonstrated that this design facilitates the coordinated expression of all mature miRNAs either as individual miRNAs or as multiple miRNAs and the associated protein. The data suggest that, it is possible to simultaneously deliver multiple negative (miRNA or shRNA) and positive (transgene) regulatory elements. Because many cellular processes require simultaneous repression and activation of downstream pathways, this approach offers a platform technology to achieve that dual manipulation efficiently

miR-181a regulates multiple pathways in hypopharyngeal ...

African Journals Online (AJOL)

Expression of four pathway reporters were significantly increased (p53/DNA damage, TGFβ, MAPK/ERK and MAPK/JNK), while expression of two pathway reporters were decreased (Wnt and NFkB) upon miR-181a down-regulation. Notch, Myc/Max, hypoxia and cell cycle/pRB-E2F pathways were not significantly affected ...

miR-200b mediates post-transcriptional repression of ZFHX1B

DEFF Research Database (Denmark)

Christoffersen, Nanna Rønbjerg; Silahtaroglu, Asli; Ørom, Ulf Lupo Andersson

2007-01-01

of E-cadherin. We show that Zfhx1b and miR-200b are regionally coexpressed in the adult mouse brain and that miR-200b represses the expression of Zfhx1b via multiple sequence elements present in the 3'-untranslated region. Overexpression of miR-200b leads to repression of endogenous ZFHX1B...

miR-19, a component of the oncogenic miR-17∼92 cluster, targets the DNA-end resection factor CtIP

DEFF Research Database (Denmark)

Hühn, D; Kousholt, A N; Sørensen, Claus Storgaard

2014-01-01

MicroRNA-19 (miR-19) was recently identified as the key oncogenic component of the polycistronic miR-17∼92 cluster, also known as oncomiR-1, which is frequently upregulated or amplified in multiple tumor types. However, the gene targets and the pathways underlying the tumor-promoting activity of mi......R-19 still remain largely elusive. CtIP/RBBP8 promotes DNA-end resection, a critical step in the repair of DNA double-strand breaks (DSBs) by homologous recombination (HR), and is considered to function as a tumor suppressor. In this study, we show that miR-19 downregulates CtIP expression by binding...... to two highly conserved sequences located in the 3'-untranslated region of CtIP mRNA. We further demonstrate that CtIP expression is repressed by miR-19 during continuous genotoxic stress in a p53-dependent manner. Finally, we report that miR-19 impairs CtIP-mediated DNA-end resection, which results...

Stress-activated miR-21/miR-21* in hepatocytes promotes lipid and glucose metabolic disorders associated with high-fat diet consumption.

Science.gov (United States)

Calo, Nicolas; Ramadori, Pierluigi; Sobolewski, Cyril; Romero, Yannick; Maeder, Christine; Fournier, Margot; Rantakari, Pia; Zhang, Fu-Ping; Poutanen, Matti; Dufour, Jean-François; Humar, Bostjan; Nef, Serge; Foti, Michelangelo

2016-11-01

miR-21 is an oncomir highly upregulated in hepatocellular carcinoma and in early stages of liver diseases characterised by the presence of steatosis. Whether upregulation of miR-21 contributes to hepatic metabolic disorders and their progression towards cancer is unknown. This study aims at investigating the role of miR-21/miR-21* in early stages of metabolic liver disorders associated with diet-induced obesity (DIO). Constitutive miR-21/miR-21* knockout (miR21KO) and liver-specific miR-21/miR-21* knockout (LImiR21KO) mice were generated. Mice were then fed with high-fat diet (HFD) and alterations of the lipid and glucose metabolism were investigated. Serum and ex vivo explanted liver tissue were analysed. Under normal breeding conditions and standard diet, miR-21/miR-21* deletion in mice was not associated with any detectable phenotypic alterations. However, when mice were challenged with an obesogenic diet, glucose intolerance, steatosis and adiposity were improved in mice lacking miR-21/miR-21* . Deletion of miR-21/miR-21* specifically in hepatocytes led to similar improvements in mice fed an HFD, indicating a crucial role for hepatic miR-21/miR-21* in metabolic disorders associated with DIO. Further molecular analyses demonstrated that miR-21/miR-21* deletion in hepatocytes increases insulin sensitivity and modulates the expression of multiple key metabolic transcription factors involved in fatty acid uptake, de novo lipogenesis, gluconeogenesis and glucose output. Hepatic miR-21/miR-21* deficiency prevents glucose intolerance and steatosis in mice fed an obesogenic diet by altering the expression of several master metabolic regulators. This study points out miR-21/miR-21 * as a potential therapeutic target for non-alcoholic fatty liver disease and the metabolic syndrome. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Imputation of microsatellite alleles from dense SNP genotypes for parental verification

Directory of Open Access Journals (Sweden)

Matthew eMcclure

2012-08-01

Full Text Available Microsatellite (MS markers have recently been used for parental verification and are still the international standard despite higher cost, error rate, and turnaround time compared with Single Nucleotide Polymorphisms (SNP-based assays. Despite domestic and international interest from producers and research communities, no viable means currently exist to verify parentage for an individual unless all familial connections were analyzed using the same DNA marker type (MS or SNP. A simple and cost-effective method was devised to impute MS alleles from SNP haplotypes within breeds. For some MS, imputation results may allow inference across breeds. A total of 347 dairy cattle representing 4 dairy breeds (Brown Swiss, Guernsey, Holstein, and Jersey were used to generate reference haplotypes. This approach has been verified (>98% accurate for imputing the International Society of Animal Genetics (ISAG recommended panel of 12 MS for cattle parentage verification across a validation set of 1,307 dairy animals.. Implementation of this method will allow producers and breed associations to transition to SNP-based parentage verification utilizing MS genotypes from historical data on parents where SNP genotypes are missing. This approach may be applicable to additional cattle breeds and other species that wish to migrate from MS- to SNP- based parental verification.

ParaHaplo 3.0: A program package for imputation and a haplotype-based whole-genome association study using hybrid parallel computing

Directory of Open Access Journals (Sweden)

Kamatani Naoyuki

2011-05-01

Full Text Available Abstract Background Use of missing genotype imputations and haplotype reconstructions are valuable in genome-wide association studies (GWASs. By modeling the patterns of linkage disequilibrium in a reference panel, genotypes not directly measured in the study samples can be imputed and used for GWASs. Since millions of single nucleotide polymorphisms need to be imputed in a GWAS, faster methods for genotype imputation and haplotype reconstruction are required. Results We developed a program package for parallel computation of genotype imputation and haplotype reconstruction. Our program package, ParaHaplo 3.0, is intended for use in workstation clusters using the Intel Message Passing Interface. We compared the performance of ParaHaplo 3.0 on the Japanese in Tokyo, Japan and Han Chinese in Beijing, and Chinese in the HapMap dataset. A parallel version of ParaHaplo 3.0 can conduct genotype imputation 20 times faster than a non-parallel version of ParaHaplo. Conclusions ParaHaplo 3.0 is an invaluable tool for conducting haplotype-based GWASs. The need for faster genotype imputation and haplotype reconstruction using parallel computing will become increasingly important as the data sizes of such projects continue to increase. ParaHaplo executable binaries and program sources are available at http://en.sourceforge.jp/projects/parallelgwas/releases/.

Saturated linkage map construction in Rubus idaeus using genotyping by sequencing and genome-independent imputation

Directory of Open Access Journals (Sweden)

Ward Judson A

2013-01-01

Full Text Available Abstract Background Rapid development of highly saturated genetic maps aids molecular breeding, which can accelerate gain per breeding cycle in woody perennial plants such as Rubus idaeus (red raspberry. Recently, robust genotyping methods based on high-throughput sequencing were developed, which provide high marker density, but result in some genotype errors and a large number of missing genotype values. Imputation can reduce the number of missing values and can correct genotyping errors, but current methods of imputation require a reference genome and thus are not an option for most species. Results Genotyping by Sequencing (GBS was used to produce highly saturated maps for a R. idaeus pseudo-testcross progeny. While low coverage and high variance in sequencing resulted in a large number of missing values for some individuals, a novel method of imputation based on maximum likelihood marker ordering from initial marker segregation overcame the challenge of missing values, and made map construction computationally tractable. The two resulting parental maps contained 4521 and 2391 molecular markers spanning 462.7 and 376.6 cM respectively over seven linkage groups. Detection of precise genomic regions with segregation distortion was possible because of map saturation. Microsatellites (SSRs linked these results to published maps for cross-validation and map comparison. Conclusions GBS together with genome-independent imputation provides a rapid method for genetic map construction in any pseudo-testcross progeny. Our method of imputation estimates the correct genotype call of missing values and corrects genotyping errors that lead to inflated map size and reduced precision in marker placement. Comparison of SSRs to published R. idaeus maps showed that the linkage maps constructed with GBS and our method of imputation were robust, and marker positioning reliable. The high marker density allowed identification of genomic regions with segregation

Simple nuclear norm based algorithms for imputing missing data and forecasting in time series

OpenAIRE

Butcher, Holly Louise; Gillard, Jonathan William

2017-01-01

There has been much recent progress on the use of the nuclear norm for the so-called matrix completion problem (the problem of imputing missing values of a matrix). In this paper we investigate the use of the nuclear norm for modelling time series, with particular attention to imputing missing data and forecasting. We introduce a simple alternating projections type algorithm based on the nuclear norm for these tasks, and consider a number of practical examples.

A spatial haplotype copying model with applications to genotype imputation.

Science.gov (United States)

Yang, Wen-Yun; Hormozdiari, Farhad; Eskin, Eleazar; Pasaniuc, Bogdan

2015-05-01

Ever since its introduction, the haplotype copy model has proven to be one of the most successful approaches for modeling genetic variation in human populations, with applications ranging from ancestry inference to genotype phasing and imputation. Motivated by coalescent theory, this approach assumes that any chromosome (haplotype) can be modeled as a mosaic of segments copied from a set of chromosomes sampled from the same population. At the core of the model is the assumption that any chromosome from the sample is equally likely to contribute a priori to the copying process. Motivated by recent works that model genetic variation in a geographic continuum, we propose a new spatial-aware haplotype copy model that jointly models geography and the haplotype copying process. We extend hidden Markov models of haplotype diversity such that at any given location, haplotypes that are closest in the genetic-geographic continuum map are a priori more likely to contribute to the copying process than distant ones. Through simulations starting from the 1000 Genomes data, we show that our model achieves superior accuracy in genotype imputation over the standard spatial-unaware haplotype copy model. In addition, we show the utility of our model in selecting a small personalized reference panel for imputation that leads to both improved accuracy as well as to a lower computational runtime than the standard approach. Finally, we show our proposed model can be used to localize individuals on the genetic-geographical map on the basis of their genotype data.

Data Editing and Imputation in Business Surveys Using “R”

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Elena Romascanu

2014-06-01

Full Text Available Purpose – Missing data are a recurring problem that can cause bias or lead to inefficient analyses. The objective of this paper is a direct comparison between the two statistical software features R and SPSS, in order to take full advantage of the existing automated methods for data editing process and imputation in business surveys (with a proper design of consistency rules as a partial alternative to the manual editing of data. Approach – The comparison of different methods on editing surveys data, in R with the ‘editrules’ and ‘survey’ packages because inside those, exist commonly used transformations in official statistics, as visualization of missing values pattern using ‘Amelia’ and ‘VIM’ packages, imputation approaches for longitudinal data using ‘VIMGUI’ and a comparison of another statistical software performance on the same features, such as SPSS. Findings – Data on business statistics received by NIS’s (National Institute of Statistics are not ready to be used for direct analysis due to in-record inconsistencies, errors and missing values from the collected data sets. The appropriate automatic methods from R packages, offers the ability to set the erroneous fields in edit-violating records, to verify the results after the imputation of missing values providing for users a flexible, less time consuming approach and easy to perform automation in R than in SPSS Macros syntax situations, when macros are very handy.

Conservation and diversification of the miR166 family in soybean and potential roles of newly identified miR166s.

Science.gov (United States)

Li, Xuyan; Xie, Xin; Li, Ji; Cui, Yuhai; Hou, Yanming; Zhai, Lulu; Wang, Xiao; Fu, Yanli; Liu, Ranran; Bian, Shaomin

2017-02-01

microRNA166 (miR166) is a highly conserved family of miRNAs implicated in a wide range of cellular and physiological processes in plants. miR166 family generally comprises multiple miR166 members in plants, which might exhibit functional redundancy and specificity. The soybean miR166 family consists of 21 members according to the miRBase database. However, the evolutionary conservation and functional diversification of miR166 family members in soybean remain poorly understood. We identified five novel miR166s in soybean by data mining approach, thus enlarging the size of miR166 family from 21 to 26 members. Phylogenetic analyses of the 26 miR166s and their precursors indicated that soybean miR166 family exhibited both evolutionary conservation and diversification, and ten pairs of miR166 precursors with high sequence identity were individually grouped into a discrete clade in the phylogenetic tree. The analysis of genomic organization and evolution of MIR166 gene family revealed that eight segmental duplications and four tandem duplications might occur during evolution of the miR166 family in soybean. The cis-elements in promoters of MIR166 family genes and their putative targets pointed to their possible contributions to the functional conservation and diversification. The targets of soybean miR166s were predicted, and the cleavage of ATHB14-LIKE transcript was experimentally validated by RACE PCR. Further, the expression patterns of the five newly identified MIR166s and 12 target genes were examined during seed development and in response to abiotic stresses, which provided important clues for dissecting their functions and isoform specificity. This study enlarged the size of soybean miR166 family from 21 to 26 members, and the 26 soybean miR166s exhibited evolutionary conservation and diversification. These findings have laid a foundation for elucidating functional conservation and diversification of miR166 family members, especially during seed development or

Diet and lifestyle factors associated with miRNA expression in colorectal tissue

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Slattery ML

2016-12-01

Full Text Available Martha L Slattery,1 Jennifer S Herrick,1 Lila E Mullany,1 John R Stevens,2 Roger K Wolff1 1Department of Internal Medicine, The University of Utah, Salt Lake City, 2Department of Mathematics and Statistics, Utah State University, Logan, UT, USA Abstract: MicroRNAs (miRNAs are small non-protein-coding RNA molecules that regulate gene expression. Diet and lifestyle factors have been hypothesized to be involved in the regulation of miRNA expression. In this study it was hypothesized that diet and lifestyle factors are associated with miRNA expression. Data from 1,447 cases of colorectal cancer to evaluate 34 diet and lifestyle variables using miRNA expression in normal colorectal mucosa as well as for differential expression between paired carcinoma and normal tissue were used. miRNA data were obtained using an Agilent platform. Multiple comparisons were adjusted for using the false discovery rate q-value. There were 250 miRNAs differentially expressed between carcinoma and normal colonic tissue by level of carbohydrate intake and 198 miRNAs differentially expressed by the level of sucrose intake. Of these miRNAs, 166 miRNAs were differentially expressed for both carbohydrate intake and sucrose intake. Ninety-nine miRNAs were differentially expressed by the level of whole grain intake in normal colonic mucosa. Level of oxidative balance score was associated with 137 differentially expressed miRNAs between carcinoma and paired normal rectal mucosa. Additionally, 135 miRNAs were differentially expressed in colon tissue based on recent NSAID use. Other dietary factors, body mass index, waist and hip circumference, and long-term physical activity levels did not alter miRNA expression after adjustment for multiple comparisons. These results suggest that diet and lifestyle factors regulate miRNA level. They provide additional support for the influence of carbohydrate, sucrose, whole grains, NSAIDs, and oxidative balance score on colorectal cancer risk

Reimagine Procrastination: Music Preference and Health Habits as Factors on Self-Perceived Procrastination of Young People

OpenAIRE

Wei, Shu

2018-01-01

As the buzzword phenomenon, procrastination holds a continued need for a comprehensive examination of its nature and the associated factors. The presented study explores the potential relationship between music taste, life style and the youngsters' procrastination through quantitative modelling. To handle the big set of survey statistics and the uncertainty caused by the data missingness, the combined methods of factor analysis, multiple imputation (MI) and Ordered logit regression are employ...

miRConnect: Identifying Effector Genes of miRNAs and miRNA Families in Cancer Cells

DEFF Research Database (Denmark)

Hua, Youjia; Duan, Shiwei; Murmann, Andrea E

2011-01-01

have generated custom data sets containing expression information of 54 miRNA families sharing the same seed match. We have developed a novel strategy for correlating miRNAs with individual genes based on a summed Pearson Correlation Coefficient (sPCC) that mimics an in silico titration experiment......micro(mi)RNAs are small non-coding RNAs that negatively regulate expression of most mRNAs. They are powerful regulators of various differentiation stages, and the expression of genes that either negatively or positively correlate with expressed miRNAs is expected to hold information....... By focusing on the genes that correlate with the expression of miRNAs without necessarily being direct targets of miRNAs, we have clustered miRNAs into different functional groups. This has resulted in the identification of three novel miRNAs that are linked to the epithelial-to-mesenchymal transition (EMT...

miR-92a family and their target genes in tumorigenesis and metastasis

Energy Technology Data Exchange (ETDEWEB)

Li, Molin, E-mail: molin_li@hotmail.com [Department of Pathophysiology, Basic Medical Science of Dalian Medical University, Dalian 116044 (China); Institute of Cancer Stem Cell, Dalian Medical University Cancer Center, Dalian 116044 (China); Guan, Xingfang; Sun, Yuqiang [Department of Pathophysiology, Basic Medical Science of Dalian Medical University, Dalian 116044 (China); Mi, Jun [Institute of Cancer Stem Cell, Dalian Medical University Cancer Center, Dalian 116044 (China); Shu, Xiaohong [College of Pharmacy, Dalian Medical University Cancer Center, Dalian 116044 (China); Liu, Fang [Department of Surgery, The Second Affiliated Hospital of Dalian Medical University, Dalian 116027 (China); Li, Chuangang, E-mail: li_chuangang@sina.com [Department of Surgery, The Second Affiliated Hospital of Dalian Medical University, Dalian 116027 (China)

2014-04-15

The miR-92a family, including miR-25, miR-92a-1, miR-92a-2 and miR-363, arises from three different paralog clusters miR-17-92, miR-106a-363, and miR-106b-25 that are highly conservative in the process of evolution, and it was thought as a group of microRNAs (miRNAs) correlated with endothelial cells. Aberrant expression of miR-92a family was detected in multiple cancers, and the disturbance of miR-92a family was related with tumorigenesis and tumor development. In this review, the progress on the relationship between miR-92a family and their target genes and malignant tumors will be summarized. - Highlights: • Aberrant expression of miR-92a, miR-25 and miR-363 can be observed in many kinds of malignant tumors. • The expression of miR-92a family is regulated by LOH, epigenetic alteration, transcriptional factors such as SP1, MYC, E2F, wild-type p53 etc. • Roles of miR-92a family in tumorigenesis and development: promoting cell proliferation, invasion and metastasis, inhibiting cell apoptosis.

miR-92a family and their target genes in tumorigenesis and metastasis

International Nuclear Information System (INIS)

Li, Molin; Guan, Xingfang; Sun, Yuqiang; Mi, Jun; Shu, Xiaohong; Liu, Fang; Li, Chuangang

2014-01-01

The miR-92a family, including miR-25, miR-92a-1, miR-92a-2 and miR-363, arises from three different paralog clusters miR-17-92, miR-106a-363, and miR-106b-25 that are highly conservative in the process of evolution, and it was thought as a group of microRNAs (miRNAs) correlated with endothelial cells. Aberrant expression of miR-92a family was detected in multiple cancers, and the disturbance of miR-92a family was related with tumorigenesis and tumor development. In this review, the progress on the relationship between miR-92a family and their target genes and malignant tumors will be summarized. - Highlights: • Aberrant expression of miR-92a, miR-25 and miR-363 can be observed in many kinds of malignant tumors. • The expression of miR-92a family is regulated by LOH, epigenetic alteration, transcriptional factors such as SP1, MYC, E2F, wild-type p53 etc. • Roles of miR-92a family in tumorigenesis and development: promoting cell proliferation, invasion and metastasis, inhibiting cell apoptosis

Estimating Classification Errors Under Edit Restrictions in Composite Survey-Register Data Using Multiple Imputation Latent Class Modelling (MILC

Directory of Open Access Journals (Sweden)

Boeschoten Laura

2017-12-01

Full Text Available Both registers and surveys can contain classification errors. These errors can be estimated by making use of a composite data set. We propose a new method based on latent class modelling to estimate the number of classification errors across several sources while taking into account impossible combinations with scores on other variables. Furthermore, the latent class model, by multiply imputing a new variable, enhances the quality of statistics based on the composite data set. The performance of this method is investigated by a simulation study, which shows that whether or not the method can be applied depends on the entropy R2 of the latent class model and the type of analysis a researcher is planning to do. Finally, the method is applied to public data from Statistics Netherlands.

Expression profiling of miR-96, miR-584 and miR-422a in colon ...

African Journals Online (AJOL)

Purpose: To determine the correlation between miRNAs; miR-96, miR-422a and miR584, and colon cancer, and also to test whether any of these miRNAs can act as non-invasive biomarkers in colon cancer. Methods: The tumor samples and the corresponding normal mucosa used in this study were collected from 60 ...

Identification of miR-93 as a suitable miR for normalizing miRNA in plasma of tuberculosis patients.

Science.gov (United States)

Barry, Simone E; Chan, Brian; Ellis, Magda; Yang, YuRong; Plit, Marshall L; Guan, Guangyu; Wang, Xiaolin; Britton, Warwick J; Saunders, Bernadette M

2015-07-01

Tuberculosis (TB) remains a major public health issue. New tests to aid diagnoses and monitor the response to therapy are urgently required. There is growing interest in the use of microRNA (miRNA) profiles as diagnostic, prognostic or predictive markers in a range of clinical and infectious diseases, including Mycobacterium tuberculosis infection, however, challenges exist to accurately normalise miRNA levels in cohorts. This study examined the appropriateness of 12 miRs and RNU6B to normalise circulating plasma miRNA levels in individuals with active TB from 2 different geographical and ethnic regions. Twelve miRs (let-7, miR-16, miR-22, miR-26, miR-93, miR-103, miR-191, miR-192, miR-221, miR-423, miR-425 and miR-451) and RNU6B were selected based on their reported production by lung cells, expression in blood and previous use as a reference miRNA. Expression levels were analysed in the plasma of newly diagnosed TB patients from Australia and China compared with individuals with latent TB infection and healthy volunteers. Analysis with both geNorm and NormFinder software identified miR-93 as the most suitable reference miR in both cohorts, either when analysed separately or collectively. Interestingly, there were large variations in the expression levels of some miRs, in particular miR-192 and let-7, between the two cohorts, independent of disease status. These data identify miR-93 is a suitable reference miR for normalizing miRNA levels in TB patients, and highlight how environmental, and possibly ethnic, factors influence miRNA expression levels, demonstrating the necessity of assessing the suitability of reference miRs within the study population. © 2015 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine.

RIDDLE: Race and ethnicity Imputation from Disease history with Deep LEarning

KAUST Repository

Kim, Ji-Sung; Gao, Xin; Rzhetsky, Andrey

2018-01-01

are predictive of race and ethnicity. We used these characterizations of informative features to perform a systematic comparison of differential disease patterns by race and ethnicity. The fact that clinical histories are informative for imputing race

In vivo delivery of miRNAs for cancer therapy: Challenges and strategies⋆

Science.gov (United States)

Chen, Yunching; Gao, Dong-Yu; Huang, Leaf

2016-01-01

MicroRNAs (miRNAs), small non-coding RNAs, can regulate post-transcriptional gene expressions and silence a broad set of target genes. miRNAs, aberrantly expressed in cancer cells, play an important role in modulating gene expressions, thereby regulating downstream signaling pathways and affecting cancer formation and progression. Oncogenes or tumor suppressor genes regulated by miRNAs mediate cell cycle progression, metabolism, cell death, angiogenesis, metastasis and immunosuppression in cancer. Recently, miRNAs have emerged as therapeutic targets or tools and biomarkers for diagnosis and therapy monitoring in cancer. Since miRNAs can regulate multiple cancer-related genes simultaneously, using miRNAs as a therapeutic approach plays an important role in cancer therapy. However, one of the major challenges of miRNA-based cancer therapy is to achieve specific, efficient and safe systemic delivery of therapeutic miRNAs In vivo. This review discusses the key challenges to the development of the carriers for miRNA-based therapy and explores current strategies to systemically deliver miRNAs to cancer without induction of toxicity. PMID:24859533

Effect of imputing markers from a low-density chip on the reliability of genomic breeding values in Holstein populations

DEFF Research Database (Denmark)

Dassonneville, R; Brøndum, Rasmus Froberg; Druet, T

2011-01-01

The purpose of this study was to investigate the imputation error and loss of reliability of direct genomic values (DGV) or genomically enhanced breeding values (GEBV) when using genotypes imputed from a 3,000-marker single nucleotide polymorphism (SNP) panel to a 50,000-marker SNP panel. Data...... of missing markers and prediction of breeding values were performed using 2 different reference populations in each country: either a national reference population or a combined EuroGenomics reference population. Validation for accuracy of imputation and genomic prediction was done based on national test...... with a national reference data set gave an absolute loss of 0.05 in mean reliability of GEBV in the French study, whereas a loss of 0.03 was obtained for reliability of DGV in the Nordic study. When genotypes were imputed using the EuroGenomics reference, a loss of 0.02 in mean reliability of GEBV was detected...

Imputation by the mean score should be avoided when validating a Patient Reported Outcomes questionnaire by a Rasch model in presence of informative missing data

LENUS (Irish Health Repository)

Hardouin, Jean-Benoit

2011-07-14

Abstract Background Nowadays, more and more clinical scales consisting in responses given by the patients to some items (Patient Reported Outcomes - PRO), are validated with models based on Item Response Theory, and more specifically, with a Rasch model. In the validation sample, presence of missing data is frequent. The aim of this paper is to compare sixteen methods for handling the missing data (mainly based on simple imputation) in the context of psychometric validation of PRO by a Rasch model. The main indexes used for validation by a Rasch model are compared. Methods A simulation study was performed allowing to consider several cases, notably the possibility for the missing values to be informative or not and the rate of missing data. Results Several imputations methods produce bias on psychometrical indexes (generally, the imputation methods artificially improve the psychometric qualities of the scale). In particular, this is the case with the method based on the Personal Mean Score (PMS) which is the most commonly used imputation method in practice. Conclusions Several imputation methods should be avoided, in particular PMS imputation. From a general point of view, it is important to use an imputation method that considers both the ability of the patient (measured for example by his\\/her score), and the difficulty of the item (measured for example by its rate of favourable responses). Another recommendation is to always consider the addition of a random process in the imputation method, because such a process allows reducing the bias. Last, the analysis realized without imputation of the missing data (available case analyses) is an interesting alternative to the simple imputation in this context.

Large scale association analysis identifies three susceptibility loci for coronary artery disease.

Directory of Open Access Journals (Sweden)

Stephanie Saade

Full Text Available Genome wide association studies (GWAS and their replications that have associated DNA variants with myocardial infarction (MI and/or coronary artery disease (CAD are predominantly based on populations of European or Eastern Asian descent. Replication of the most significantly associated polymorphisms in multiple populations with distinctive genetic backgrounds and lifestyles is crucial to the understanding of the pathophysiology of a multifactorial disease like CAD. We have used our Lebanese cohort to perform a replication study of nine previously identified CAD/MI susceptibility loci (LTA, CDKN2A-CDKN2B, CELSR2-PSRC1-SORT1, CXCL12, MTHFD1L, WDR12, PCSK9, SH2B3, and SLC22A3, and 88 genes in related phenotypes. The study was conducted on 2,002 patients with detailed demographic, clinical characteristics, and cardiac catheterization results. One marker, rs6922269, in MTHFD1L was significantly protective against MI (OR=0.68, p=0.0035, while the variant rs4977574 in CDKN2A-CDKN2B was significantly associated with MI (OR=1.33, p=0.0086. Associations were detected after adjustment for family history of CAD, gender, hypertension, hyperlipidemia, diabetes, and smoking. The parallel study of 88 previously published genes in related phenotypes encompassed 20,225 markers, three quarters of which with imputed genotypes The study was based on our genome-wide genotype data set, with imputation across the whole genome to HapMap II release 22 using HapMap CEU population as a reference. Analysis was conducted on both the genotyped and imputed variants in the 88 regions covering selected genes. This approach replicated HNRNPA3P1-CXCL12 association with CAD and identified new significant associations of CDKAL1, ST6GAL1, and PTPRD with CAD. Our study provides evidence for the importance of the multifactorial aspect of CAD/MI and describes genes predisposing to their etiology.

Measurement of target and double-spin asymmetries for the mi>e>→<mi>p→→eπ+(n>) reaction in the nucleon resonance region at low mi>Q>2

Energy Technology Data Exchange (ETDEWEB)

Zheng, X.; Adhikari, K. P.; Bosted, P.; Deur, A.; Drozdov, V.; El Fassi, L.; Kang, Hyekoo; Kovacs, K.; Kuhn, S.; Long, E.; Phillips, S. K.; Ripani, M.; Slifer, K.; Smith, L. C.; Adikaram, D.; Akbar, Z.; Amaryan, M. J.; Anefalos Pereira, S.; Asryan, G.; Avakian, H.; Badui, R. A.; Ball, J.; Baltzell, N. A.; Battaglieri, M.; Batourine, V.; Bedlinskiy, I.; Biselli, A. S.; Briscoe, W. J.; Bültmann, S.; Burkert, V. D.; Carman, D. S.; Celentano, A.; Chandavar, S.; Charles, G.; Chen, J. -P.; Chetry, T.; Choi, Seonho; Ciullo, G.; Clark, L.; Colaneri, L.; Cole, P. L.; Compton, N.; Contalbrigo, M.; Crede, V.; D' Angelo, A.; Dashyan, N.; De Vita, R.; De Sanctis, E.; Djalali, C.; Dodge, G. E.; Dupre, R.; Egiyan, H.; El Alaoui, A.; Elouadrhiri, L.; Eugenio, P.; Fanchini, E.; Fedotov, G.; Fersch, R.; Filippi, A.; Fleming, J. A.; Gevorgyan, N.; Ghandilyan, Y.; Gilfoyle, G. P.; Giovanetti, K. L.; Girod, F. X.; Gleason, C.; Golovach, E.; Gothe, R. W.; Griffioen, K. A.; Guidal, M.; Guler, N.; Guo, L.; Hanretty, C.; Harrison, N.; Hattawy, M.; Hicks, K.; Holtrop, M.; Hughes, S. M.; Ilieva, Y.; Ireland, D. G.; Ishkhanov, B. S.; Isupov, E. L.; Jenkins, D.; Jiang, H.; Jo, H. S.; Joosten, S.; Keller, D.; Khachatryan, G.; Khandaker, M.; Kim, A.; Kim, W.; Klein, F. J.; Kubarovsky, V.; Lanza, L.; Lenisa, P.; Livingston, K.; MacGregor, I. J. D.; Markov, N.; McKinnon, B.; Mirazita, M.; Mokeev, V.; Movsisyan, A.; Munevar, E.; Munoz Camacho, C.; Murdoch, G.; Nadel-Turonski, P.; Net, L. A.; Ni, A.; Niccolai, S.; Niculescu, G.; Niculescu, I.; Osipenko, M.; Ostrovidov, A. I.; Paolone, M.; Paremuzyan, R.; Park, K.; Pasyuk, E.; Peng, P.; Pisano, S.; Pogorelko, O.; Price, J. W.; Puckett, A. J. R.; Raue, B. A.; Rizzo, A.; Rosner, G.; Rossi, P.; Roy, P.; Sabatié, F.; Salgado, C.; Schumacher, R. A.; Sharabian, Y. G.; Skorodumina, Iu.; Smith, G. D.; Sokhan, D.; Sparveris, N.; Stankovic, I.; Strakovsky, I. I.; Strauch, S.; Taiuti, M.; Tian, Ye; Ungaro, M.; Voskanyan, H.; Voutier, E.; Walford, N. K.; Watts, D. P.; Wei, X.; Weinstein, L. B.; Wood, M. H.; Zachariou, N.; Zhang, J.; Zonta, I.

2016-10-01

We report measurements of target- and double-spin asymmetries for the exclusive channel mi>e>→<mi>p→→eπ+(n>) in the nucleon resonance region at Jefferson Lab using the CEBAF Large Acceptance Spectrometer (CLAS). These asymmetries were extracted from data obtained using a longitudinally polarized NH3 target and a longitudinally polarized electron beam with energies 1.1, 1.3, 2.0, 2.3, and 3.0 GeV. The new results are consistent with previous CLAS publications but are extended to a low Q² range from 0.0065 to 0.35 (GeV/c)². The Q² access was made possible by a custom-built Cherenkov detector that allowed the detection of electrons for scattering angles as low as 6 degrees. These results are compared with the unitary isobar models JANR and MAID, the partial-wave analysis prediction from SAID, and the dynamic model DMT. In many kinematic regions our results, in particular results on the target asymmetry, help to constrain the polarization-dependent components of these models.

Construction and application of a Korean reference panel for imputing classical alleles and amino acids of human leukocyte antigen genes.

Science.gov (United States)

Kim, Kwangwoo; Bang, So-Young; Lee, Hye-Soon; Bae, Sang-Cheol

2014-01-01

Genetic variations of human leukocyte antigen (HLA) genes within the major histocompatibility complex (MHC) locus are strongly associated with disease susceptibility and prognosis for many diseases, including many autoimmune diseases. In this study, we developed a Korean HLA reference panel for imputing classical alleles and amino acid residues of several HLA genes. An HLA reference panel has potential for use in identifying and fine-mapping disease associations with the MHC locus in East Asian populations, including Koreans. A total of 413 unrelated Korean subjects were analyzed for single nucleotide polymorphisms (SNPs) at the MHC locus and six HLA genes, including HLA-A, -B, -C, -DRB1, -DPB1, and -DQB1. The HLA reference panel was constructed by phasing the 5,858 MHC SNPs, 233 classical HLA alleles, and 1,387 amino acid residue markers from 1,025 amino acid positions as binary variables. The imputation accuracy of the HLA reference panel was assessed by measuring concordance rates between imputed and genotyped alleles of the HLA genes from a subset of the study subjects and East Asian HapMap individuals. Average concordance rates were 95.6% and 91.1% at 2-digit and 4-digit allele resolutions, respectively. The imputation accuracy was minimally affected by SNP density of a test dataset for imputation. In conclusion, the Korean HLA reference panel we developed was highly suitable for imputing HLA alleles and amino acids from MHC SNPs in East Asians, including Koreans.

Construction and application of a Korean reference panel for imputing classical alleles and amino acids of human leukocyte antigen genes.

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Kwangwoo Kim

Full Text Available Genetic variations of human leukocyte antigen (HLA genes within the major histocompatibility complex (MHC locus are strongly associated with disease susceptibility and prognosis for many diseases, including many autoimmune diseases. In this study, we developed a Korean HLA reference panel for imputing classical alleles and amino acid residues of several HLA genes. An HLA reference panel has potential for use in identifying and fine-mapping disease associations with the MHC locus in East Asian populations, including Koreans. A total of 413 unrelated Korean subjects were analyzed for single nucleotide polymorphisms (SNPs at the MHC locus and six HLA genes, including HLA-A, -B, -C, -DRB1, -DPB1, and -DQB1. The HLA reference panel was constructed by phasing the 5,858 MHC SNPs, 233 classical HLA alleles, and 1,387 amino acid residue markers from 1,025 amino acid positions as binary variables. The imputation accuracy of the HLA reference panel was assessed by measuring concordance rates between imputed and genotyped alleles of the HLA genes from a subset of the study subjects and East Asian HapMap individuals. Average concordance rates were 95.6% and 91.1% at 2-digit and 4-digit allele resolutions, respectively. The imputation accuracy was minimally affected by SNP density of a test dataset for imputation. In conclusion, the Korean HLA reference panel we developed was highly suitable for imputing HLA alleles and amino acids from MHC SNPs in East Asians, including Koreans.

Comprehensive analysis of miRNAs expression profiles revealed potential key miRNA/mRNAs regulating colorectal cancer stem cell self-renewal.

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Xu, Peng; Wang, Junhua; Sun, Bo; Xiao, Zhongdang

2018-05-20

Self-renewal is essential for the malignant biological behaviors of colorectal cancer stem cells. While the self-renewal molecular mechanisms of colorectal cancer stem cells are not yet fully understood. Recently, miRNAs are reported to be relevant to the self-renewal ability of cancer stem cells. In this study, we first isolated colorectal cancer stem cell from colorectal cancer cell line HCT-116 by 1% low serum culture. Then we conducted a comprehensive analysis based on the miRNAs profiles data of both colorectal cancer stem cells and normal cultured colorectal cancer cells. Pathway analysis revealed multiple pathways including Jak-STAT, TGF-beta, PI3K-Akt and MAPK signaling pathway that are correlated to colorectal cancer. Further, we constructed a miRNA-mRNA network, based on which, several miRNA/mRNA pairs were ranked according to their impact index to the self-renewal of colorectal cancer stem cells. Further biological experiment showed that up-regulation of miR-92a-3p led to cell cycle arrest and reduced colony formation. This work provides clues to find the new potential biomarkers for colorectal cancer stem cell diagnosis and select effective miRNAs for targeted therapy. Copyright © 2018 Elsevier B.V. All rights reserved.

DTW-APPROACH FOR UNCORRELATED MULTIVARIATE TIME SERIES IMPUTATION

OpenAIRE

Phan , Thi-Thu-Hong; Poisson Caillault , Emilie; Bigand , André; Lefebvre , Alain

2017-01-01

International audience; Missing data are inevitable in almost domains of applied sciences. Data analysis with missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Some well-known methods for multivariate time series imputation require high correlations between series or their features. In this paper , we propose an approach based on the shape-behaviour relation in low/un-correlated multivariate time series under an assumption of...

MicroRNAs miR-17 and miR-20a inhibit T cell activation genes and are under-expressed in MS whole blood.

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Mathew B Cox

Full Text Available It is well established that Multiple Sclerosis (MS is an immune mediated disease. Little is known about what drives the differential control of the immune system in MS patients compared to unaffected individuals. MicroRNAs (miRNAs are small non-coding nucleic acids that are involved in the control of gene expression. Their potential role in T cell activation and neurodegenerative disease has recently been recognised and they are therefore excellent candidates for further studies in MS. We investigated the transcriptome of currently known miRNAs using miRNA microarray analysis in peripheral blood samples of 59 treatment naïve MS patients and 37 controls. Of these 59, 18 had a primary progressive, 17 a secondary progressive and 24 a relapsing remitting disease course. In all MS subtypes miR-17 and miR-20a were significantly under-expressed in MS, confirmed by RT-PCR. We demonstrate that these miRNAs modulate T cell activation genes in a knock-in and knock-down T cell model. The same T cell activation genes are also up-regulated in MS whole blood mRNA, suggesting these miRNAs or their analogues may provide useful targets for new therapeutic approaches.

Evaluating geographic imputation approaches for zip code level data: an application to a study of pediatric diabetes

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Puett Robin C

2009-10-01

Full Text Available Abstract Background There is increasing interest in the study of place effects on health, facilitated in part by geographic information systems. Incomplete or missing address information reduces geocoding success. Several geographic imputation methods have been suggested to overcome this limitation. Accuracy evaluation of these methods can be focused at the level of individuals and at higher group-levels (e.g., spatial distribution. Methods We evaluated the accuracy of eight geo-imputation methods for address allocation from ZIP codes to census tracts at the individual and group level. The spatial apportioning approaches underlying the imputation methods included four fixed (deterministic and four random (stochastic allocation methods using land area, total population, population under age 20, and race/ethnicity as weighting factors. Data included more than 2,000 geocoded cases of diabetes mellitus among youth aged 0-19 in four U.S. regions. The imputed distribution of cases across tracts was compared to the true distribution using a chi-squared statistic. Results At the individual level, population-weighted (total or under age 20 fixed allocation showed the greatest level of accuracy, with correct census tract assignments averaging 30.01% across all regions, followed by the race/ethnicity-weighted random method (23.83%. The true distribution of cases across census tracts was that 58.2% of tracts exhibited no cases, 26.2% had one case, 9.5% had two cases, and less than 3% had three or more. This distribution was best captured by random allocation methods, with no significant differences (p-value > 0.90. However, significant differences in distributions based on fixed allocation methods were found (p-value Conclusion Fixed imputation methods seemed to yield greatest accuracy at the individual level, suggesting use for studies on area-level environmental exposures. Fixed methods result in artificial clusters in single census tracts. For studies

MiR-210 disturbs mitotic progression through regulating a group of mitosis-related genes

OpenAIRE

He, Jie; Wu, Jiangbin; Xu, Naihan; Xie, Weidong; Li, Mengnan; Li, Jianna; Jiang, Yuyang; Yang, Burton B.; Zhang, Yaou

2012-01-01

MiR-210 is up-regulated in multiple cancer types but its function is disputable and further investigation is necessary. Using a bioinformatics approach, we identified the putative target genes of miR-210 in hypoxia-induced CNE cells from genome-wide scale. Two functional gene groups related to cell cycle and RNA processing were recognized as the major targets of miR-210. Here, we investigated the molecular mechanism and biological consequence of miR-210 in cell cycle regulation, particularly ...

miRSeqNovel

DEFF Research Database (Denmark)

Qian, Kui; Auvinen, Eeva; Greco, Dario

2012-01-01

We present miRSeqNovel, an R based workflow for miRNA sequencing data analysis. miRSeqNovel can process both colorspace (SOLiD) and basespace (Illumina/Solexa) data by different mapping algorithms. It finds differentially expressed miRNAs and gives conservative prediction of novel miRNA candidates...... with customized parameters. miRSeqNovel is freely available at http://sourceforge.net/projects/mirseq/files....

Circulating miRNAs miR-34a and miR-150 associated with colorectal cancer progression

Czech Academy of Sciences Publication Activity Database

Aherne, S.T.; Madden, S.F.; Hughes, D. J.; Pardini, B.; Naccarati, A.; Levý, M.; Vodička, Pavel; Neary, P.; Dowling, P.; Clynes, M.

2015-01-01

Roč. 15, apr 30 (2015), s. 2-13 ISSN 1471-2407 R&D Projects: GA ČR GAP304/10/1286 Institutional support: RVO:68378041 Keywords : colorectal cancer * circulating miRNAs * miR-34a * miR-150 * miR-923 Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 3.265, year: 2015

Emerging roles and regulation of MiT/TFE transcriptional factors.

Science.gov (United States)

Yang, Min; Liu, En; Tang, Li; Lei, Yuanyuan; Sun, Xuemei; Hu, Jiaxi; Dong, Hui; Yang, Shi-Ming; Gao, Mingfa; Tang, Bo

2018-06-15

The MiT/TFE transcription factors play a pivotal role in the regulation of autophagy and lysosomal biogenesis. The subcellular localization and activity of MiT/TFE proteins are primarily regulated through phosphorylation. And the phosphorylated protein is retained in the cytoplasm and subsequently translocates to the nucleus upon dephosphorylation, where it stimulates the expression of hundreds of genes, leading to lysosomal biogenesis and autophagy induction. The transcription factor-mediated lysosome-to-nucleus signaling can be directly controlled by several signaling molecules involved in the mTORC1, PKC, and AKT pathways. MiT/TFE family members have attracted much attention owing to their intracellular clearance of pathogenic factors in numerous diseases. Recently, multiple studies have also revealed the MiT/TFE proteins as master regulators of cellular metabolic reprogramming, converging on autophagic and lysosomal function and playing a critical role in cancer, suggesting that novel therapeutic strategies could be based on the modulation of MiT/TFE family member activity. Here, we present an overview of the latest research on MiT/TFE transcriptional factors and their potential mechanisms in cancer.

Advances in Roles of miR-132 in the Nervous System

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Yun Qian

2017-10-01

Full Text Available miR-132 is an endogenous small RNA and controls post-transcriptional regulation of gene expression via controlled degradation of mRNA or transcription inhibition. In the nervous system, miR-132 is significant for regulating neuronal differentiation, maturation and functioning, and widely participates in axon growth, neural migration, and plasticity. The miR-132 is affected by factors like mRNA expression, functional redundancy, and signaling cascades. It targets multiple downstream molecules to influence physiological and pathological neuronal activities. MiR-132 can influence the pathogenesis of many diseases, especially in the nervous system. The dysregulation of miR-132 results in the occurrence and exacerbation of neural developmental, degenerative diseases, like Alzheimer’s disease, Parkinson’s disease and epilepsy, neural infection and psychiatric disorders including disturbance of consciousness, cognition and memory, depression and schizophrenia. Regulation of miR-132 expression relieves symptoms, alleviates severity and finally effects a cure. This review aims to discuss the clinical potentials of miR-132 in the nervous system.

Tumor-Suppressing Effect of MiR-4458 on Human Hepatocellular Carcinoma

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Dan Tang

2015-03-01

Full Text Available Background: Besides multiple genetic and epigenetic changes of protein coding genes in hepatocellular carcinoma (HCC, growing evidence indicate that deregulation of miRNAs contribute to HCC development by influencing cell growth, apoptosis, migration, or invasion. IKBKE is amplified and over-expressed in a large percentage of human breast tumors and identified as an oncogene of human breast tumor. Microarray analysis showed that miR-4458 was down-regulated in HCC tissues. Methods: The level of miR-4458 was up-regulated by miR-4458 mimics transfection, or down-regulated by miR-4458 ASO transfection. Cell proliferation was assayed by MTT analysis. MiRNAs and mRNA expression were assayed by qRT-PCR. These potential targeted genes of miR-4458 were predicted by bioinformatic algorithms. Dual luciferase reporter assay system was used to analyze the interaction between miR-4458 and IKBKE. IKBKE protein level was assayed by Western blot. The role of miR-4458 or IKBKE in the survival of HCC patients were revealed by Kaplan-Meier plot of overall survival. Results: Lower miR-4458 expression level or higher IKBKE level in HCC tissues correlated with worse prognosis of HCC patients. Overexpression of miR-4458 inhibited the HCC cells growth and vice versa. MiR-4458 played its role via targeting 3'UTR of IKBKE. Conclusions: MiR-4458 or IKBKE may be potential predictors of HCC prognosis. Restoration of miR-4458 or inhibition of IKBKE could be a prospective therapeutic approach for HCC.

Identification of targets of miR-200b by a SILAC-based quantitative proteomic approach

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Arivusudar Marimuthu

2014-09-01

Full Text Available miRNAs regulate gene expression by binding to cognate mRNAs causing mRNA degradation or translational repression. Mass spectrometry-based proteomic analysis is being widely used to identify miRNA targets. The miR-200b miRNA cluster is often overexpressed in multiple cancer types, but the identity of the targets remains elusive. Using SILAC-based analysis, we examined the effects of overexpression of a miR-200b mimic or a control miRNA in fibrosarcoma cells. We identified around 300 potential targets of miR-200b based on a change in the expression of protein levels. We validated a subset of potential targets at the transcript level using quantitative PCR.

miREE: miRNA recognition elements ensemble

Science.gov (United States)

2011-01-01

Background Computational methods for microRNA target prediction are a fundamental step to understand the miRNA role in gene regulation, a key process in molecular biology. In this paper we present miREE, a novel microRNA target prediction tool. miREE is an ensemble of two parts entailing complementary but integrated roles in the prediction. The Ab-Initio module leverages upon a genetic algorithmic approach to generate a set of candidate sites on the basis of their microRNA-mRNA duplex stability properties. Then, a Support Vector Machine (SVM) learning module evaluates the impact of microRNA recognition elements on the target gene. As a result the prediction takes into account information regarding both miRNA-target structural stability and accessibility. Results The proposed method significantly improves the state-of-the-art prediction tools in terms of accuracy with a better balance between specificity and sensitivity, as demonstrated by the experiments conducted on several large datasets across different species. miREE achieves this result by tackling two of the main challenges of current prediction tools: (1) The reduced number of false positives for the Ab-Initio part thanks to the integration of a machine learning module (2) the specificity of the machine learning part, obtained through an innovative technique for rich and representative negative records generation. The validation was conducted on experimental datasets where the miRNA:mRNA interactions had been obtained through (1) direct validation where even the binding site is provided, or through (2) indirect validation, based on gene expression variations obtained from high-throughput experiments where the specific interaction is not validated in detail and consequently the specific binding site is not provided. Conclusions The coupling of two parts: a sensitive Ab-Initio module and a selective machine learning part capable of recognizing the false positives, leads to an improved balance between

About miRNAs, miRNA seeds, target genes and target pathways.

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Kehl, Tim; Backes, Christina; Kern, Fabian; Fehlmann, Tobias; Ludwig, Nicole; Meese, Eckart; Lenhof, Hans-Peter; Keller, Andreas

2017-12-05

miRNAs are typically repressing gene expression by binding to the 3' UTR, leading to degradation of the mRNA. This process is dominated by the eight-base seed region of the miRNA. Further, miRNAs are known not only to target genes but also to target significant parts of pathways. A logical line of thoughts is: miRNAs with similar (seed) sequence target similar sets of genes and thus similar sets of pathways. By calculating similarity scores for all 3.25 million pairs of 2,550 human miRNAs, we found that this pattern frequently holds, while we also observed exceptions. Respective results were obtained for both, predicted target genes as well as experimentally validated targets. We note that miRNAs target gene set similarity follows a bimodal distribution, pointing at a set of 282 miRNAs that seems to target genes with very high specificity. Further, we discuss miRNAs with different (seed) sequences that nonetheless regulate similar gene sets or pathways. Most intriguingly, we found miRNA pairs that regulate different gene sets but similar pathways such as miR-6886-5p and miR-3529-5p. These are jointly targeting different parts of the MAPK signaling cascade. The main goal of this study is to provide a general overview on the results, to highlight a selection of relevant results on miRNAs, miRNA seeds, target genes and target pathways and to raise awareness for artifacts in respective comparisons. The full set of information that allows to infer detailed results on each miRNA has been included in miRPathDB, the miRNA target pathway database (https://mpd.bioinf.uni-sb.de).

miRiadne: a web tool for consistent integration of miRNA nomenclature.

Science.gov (United States)

Bonnal, Raoul J P; Rossi, Riccardo L; Carpi, Donatella; Ranzani, Valeria; Abrignani, Sergio; Pagani, Massimiliano

2015-07-01

The miRBase is the official miRNA repository which keeps the annotation updated on newly discovered miRNAs: it is also used as a reference for the design of miRNA profiling platforms. Nomenclature ambiguities generated by loosely updated platforms and design errors lead to incompatibilities among platforms, even from the same vendor. Published miRNA lists are thus generated with different profiling platforms that refer to diverse and not updated annotations. This greatly compromises searches, comparisons and analyses that rely on miRNA names only without taking into account the mature sequences, which is particularly critic when such analyses are carried over automatically. In this paper we introduce miRiadne, a web tool to harmonize miRNA nomenclature, which takes into account the original miRBase versions from 10 up to 21, and annotations of 40 common profiling platforms from nine brands that we manually curated. miRiadne uses the miRNA mature sequence to link miRBase versions and/or platforms to prevent nomenclature ambiguities. miRiadne was designed to simplify and support biologists and bioinformaticians in re-annotating their own miRNA lists and/or data sets. As Ariadne helped Theseus in escaping the mythological maze, miRiadne will help the miRNA researcher in escaping the nomenclature maze. miRiadne is freely accessible from the URL http://www.miriadne.org. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

miR-200a-3p promotes β-Amyloid-induced neuronal apoptosis ...

Indian Academy of Sciences (India)

Qi-Shun Zhang

2017-07-20

Jul 20, 2017 ... through down-regulation of SIRT1 in Alzheimer's disease. QI-SHUN ... purpose of this study was to examine whether miR-200a-3p regulated ... miR-29b-1 can modulate b-site amyloid precursor protein- ... multiple important cellular processes, including cell meta- .... Institutes of Health, Bethesda, MD, USA).

Pharmaco-miR

DEFF Research Database (Denmark)

Rukov, Jakob Lewin; Wilentzik, Roni; Jaffe, Ishai

2014-01-01

MicroRNAs (miRNAs) are short regulatory RNAs that down-regulate gene expression. They are essential for cell homeostasis and active in many disease states. A major discovery is the ability of miRNAs to determine the efficacy of drugs, which has given rise to the field of 'miRNA pharmacogenomics......' through 'Pharmaco-miRs'. miRNAs play a significant role in pharmacogenomics by down-regulating genes that are important for drug function. These interactions can be described as triplet sets consisting of a miRNA, a target gene and a drug associated with the gene. We have developed a web server which...... links miRNA expression and drug function by combining data on miRNA targeting and protein-drug interactions. miRNA targeting information derive from both experimental data and computational predictions, and protein-drug interactions are annotated by the Pharmacogenomics Knowledge base (Pharm...

The MiRNA Journey from Theory to Practice as a CNS Biomarker

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Nicoleta eStoicea

2016-02-01

Full Text Available MicroRNAs (miRNAs, small nucleotide sequences that control gene transcription, have the potential to serve an expanded function as indicators in the diagnosis and progression of neurological disorders. Studies involving debilitating neurological diseases such as, Alzheimer’s disease, multiple sclerosis, traumatic brain injuries, Parkinson’s disease and CNS tumors, already provide validation for their clinical diagnostic use. These small nucleotide sequences have several features, making them favorable candidates as biomarkers, including function in multiple tissues, stability in bodily fluids, a role in pathogenesis, and the ability to be detected early in the disease course. Cerebrospinal fluid, with its cell-free environment, collection process that minimizes tissue damage, and direct contact with the brain and spinal cord, is a promising source of miRNA in the diagnosis of many neurological disorders. Despite the advantages of miRNA analysis, current analytic technology is not yet affordable as a clinically viable diagnostic tool and requires standardization. The goal of this review is to explore the prospective use of CSF miRNA as a reliable and affordable biomarker for different neurological disorders.

The MiRNA Journey from Theory to Practice as a CNS Biomarker

Science.gov (United States)

Stoicea, Nicoleta; Du, Amy; Lakis, D. Christie; Tipton, Courtney; Arias-Morales, Carlos E.; Bergese, Sergio D.

2016-01-01

MicroRNAs (miRNAs), small nucleotide sequences that control gene transcription, have the potential to serve an expanded function as indicators in the diagnosis and progression of neurological disorders. Studies involving debilitating neurological diseases such as, Alzheimer's disease, multiple sclerosis, traumatic brain injuries, Parkinson's disease and CNS tumors, already provide validation for their clinical diagnostic use. These small nucleotide sequences have several features, making them favorable candidates as biomarkers, including function in multiple tissues, stability in bodily fluids, a role in pathogenesis, and the ability to be detected early in the disease course. Cerebrospinal fluid, with its cell-free environment, collection process that minimizes tissue damage, and direct contact with the brain and spinal cord, is a promising source of miRNA in the diagnosis of many neurological disorders. Despite the advantages of miRNA analysis, current analytic technology is not yet affordable as a clinically viable diagnostic tool and requires standardization. The goal of this review is to explore the prospective use of CSF miRNA as a reliable and affordable biomarker for different neurological disorders. PMID:26904099

Four-miRNA signature as a prognostic tool for lung adenocarcinoma.

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Lin, Yan; Lv, Yufeng; Liang, Rong; Yuan, Chunling; Zhang, Jinyan; He, Dan; Zheng, Xiaowen; Zhang, Jianfeng

2018-01-01

The aim of this study was to generate a novel miRNA expression signature to accurately predict prognosis for patients with lung adenocarcinoma (LUAD). Using expression profiles downloaded from The Cancer Genome Atlas database, we identified multiple miRNAs with differential expression between LUAD and paired healthy tissues. We then evaluated the prognostic values of the differentially expressed miRNAs using univariate/multivariate Cox regression analysis. This analysis was ultimately used to construct a four-miRNA signature that effectively predicted patient survival. Finally, we analyzed potential functional roles of the target genes for these four miRNAs using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. Based on our cutoff criteria ( P 1.0), we identified a total of 187 differentially expressed miRNAs, including 148 that were upregulated in LUAD tissues and 39 that were downregulated. Four miRNAs (miR-148a-5p, miR-31-5p, miR-548v, and miR-550a-5p) were independently associated with survival based on Kaplan-Meier analysis. We generated a signature index based on the expression of these four miRNAs and stratified patients into low- and high-risk groups. Patients in the high-risk group had significantly shorter survival times than those in the low-risk group ( P =0.002). A functional enrichment analysis suggested that the target genes of these four miRNAs were involved in protein phosphorylation and the Hippo and sphingolipid signaling pathways. Taken together, our results suggest that our four-miRNA signature can be used as a prognostic tool for patients with LUAD.

FCMPSO: An Imputation for Missing Data Features in Heart Disease Classification

Science.gov (United States)

Salleh, Mohd Najib Mohd; Ashikin Samat, Nurul

2017-08-01

The application of data mining and machine learning in directing clinical research into possible hidden knowledge is becoming greatly influential in medical areas. Heart Disease is a killer disease around the world, and early prevention through efficient methods can help to reduce the mortality number. Medical data may contain many uncertainties, as they are fuzzy and vague in nature. Nonetheless, imprecise features data such as no values and missing values can affect quality of classification results. Nevertheless, the other complete features are still capable to give information in certain features. Therefore, an imputation approach based on Fuzzy C-Means and Particle Swarm Optimization (FCMPSO) is developed in preprocessing stage to help fill in the missing values. Then, the complete dataset is trained in classification algorithm, Decision Tree. The experiment is trained with Heart Disease dataset and the performance is analysed using accuracy, precision, and ROC values. Results show that the performance of Decision Tree is increased after the application of FCMSPO for imputation.

DLEU2, frequently deleted in malignancy, functions as a critical host gene of the cell cycle inhibitory microRNAs miR-15a and miR-16-1

International Nuclear Information System (INIS)

Lerner, Mikael; Harada, Masako; Loven, Jakob; Castro, Juan; Davis, Zadie; Oscier, David; Henriksson, Marie; Sangfelt, Olle; Grander, Dan; Corcoran, Martin M.

2009-01-01

The microRNAs miR-15a and miR-16-1 are downregulated in multiple tumor types and are frequently deleted in chronic lymphocytic leukemia (CLL), myeloma and mantle cell lymphoma. Despite their abundance in most cells the transcriptional regulation of miR-15a/16-1 remains unclear. Here we demonstrate that the putative tumor suppressor DLEU2 acts as a host gene of these microRNAs. Mature miR-15a/miR-16-1 are produced in a Drosha-dependent process from DLEU2 and binding of the Myc oncoprotein to two alterative DLEU2 promoters represses both the host gene transcript and levels of mature miR-15a/miR-16-1. In line with a functional role for DLEU2 in the expression of the microRNAs, the miR-15a/miR-16-1 locus is retained in four CLL cases that delete both promoters of this gene and expression analysis indicates that this leads to functional loss of mature miR-15a/16-1. We additionally show that DLEU2 negatively regulates the G1 Cyclins E1 and D1 through miR-15a/miR-16-1 and provide evidence that these oncoproteins are subject to miR-15a/miR-16-1-mediated repression under normal conditions. We also demonstrate that DLEU2 overexpression blocks cellular proliferation and inhibits the colony-forming ability of tumor cell lines in a miR-15a/miR-16-1-dependent way. Together the data illuminate how inactivation of DLEU2 promotes cell proliferation and tumor progression through functional loss of miR-15a/miR-16-1.

MicroRNAs regulate human adipocyte lipolysis: effects of miR-145 are linked to TNF-α.

Directory of Open Access Journals (Sweden)

Silvia Lorente-Cebrián

Full Text Available MicroRNAs (miRNAs are small non-coding RNAs that regulate gene expression and have multiple effects in various tissues including adipose inflammation, a condition characterized by increased local release of the pro-lipolytic cytokine tumor necrosis factor-alpha (TNF-α. Whether miRNAs regulate adipocyte lipolysis is unknown. We set out to determine whether miRNAs affect adipocyte lipolysis in human fat cells. To this end, eleven miRNAs known to be present in human adipose tissue were over-expressed in human in vitro differentiated adipocytes followed by assessments of TNF-α and glycerol levels in conditioned media after 48 h. Three miRNAs (miR-145, -26a and let-7d modulated both parameters in parallel. However, while miR-26a and let-7d decreased, miR-145 increased both glycerol release and TNF-α secretion. Further studies were focused therefore on miR-145 since this was the only stimulator of lipolysis and TNF-α secretion. Time-course analysis demonstrated that miR-145 over-expression up-regulated TNF-α expression/secretion followed by increased glycerol release. Increase in TNF-α production by miR-145 was mediated via activation of p65, a member of the NF-κB complex. In addition, miR-145 down-regulated the expression of the protease ADAM17, resulting in an increased fraction of membrane bound TNF-α, which is the more biologically active form of TNF-α. MiR-145 overexpression also increased the phosphorylation of activating serine residues in hormone sensitive lipase and decreased the mRNA expression of phosphodiesterase 3B, effects which are also observed upon TNF-α treatment in human adipocytes. We conclude that miR-145 regulates adipocyte lipolysis via multiple mechanisms involving increased production and processing of TNF-α in fat cells.

34A, miRNA-944, miRNA-101 and miRNA-218 in cervical cancer

African Journals Online (AJOL)

RNAs (21 - 24 nucleotides in length) that are critical for many important processes such as development, ... RNA extraction and reverse transcription. Total RNA was extracted from each of the experimental groups using ... used as an endogenous control to normalize the expression of miRNA-143, miRNA-34A, miRNA-.

Expression profiling of miR-96, miR-584 and miR-422a in colon ...

African Journals Online (AJOL)

. Lower miRNA ... Thus, the ratio of miR-96/miR-638 in plasma is a potential non- ... leading cause of cancer related deaths. ... breast cancer cells have revealed a total of 51 ... Corresponding negative control ..... The American Joint Committee.

Transcriptome Profiling of Neovascularized Corneas Reveals miR-204 as a Multi-target Biotherapy Deliverable by rAAVs

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Yi Lu

2018-03-01

Full Text Available Corneal neovascularization (NV is the major sight-threatening pathology caused by angiogenic stimuli. Current drugs that directly target pro-angiogenic factors to inhibit or reverse the disease require multiple rounds of administration and have limited efficacies. Here, we identify potential anti-angiogenic corneal microRNAs (miRNAs and demonstrate a framework that employs discovered miRNAs as biotherapies deliverable by recombinant adeno-associated viruses (rAAVs. By querying differentially expressed miRNAs in neovascularized mouse corneas induced by alkali burn, we have revealed 39 miRNAs that are predicted to target more than 5,500 differentially expressed corneal mRNAs. Among these, we selected miR-204 and assessed its efficacy and therapeutic benefit for treating injured corneas. Our results show that delivery of miR-204 by rAAV normalizes multiple novel target genes and biological pathways to attenuate vascularization of injured mouse cornea. Importantly, this gene therapy treatment alternative is efficacious and safe for mitigating corneal NV. Overall, our work demonstrates the discovery of potential therapeutic miRNAs in corneal disorders and their translation into viable treatment alternatives.

Shrimp miR-12 Suppresses White Spot Syndrome Virus Infection by Synchronously Triggering Antiviral Phagocytosis and Apoptosis Pathways

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Shu, Le; Zhang, Xiaobo

2017-01-01

Growing evidence has indicated that the innate immune system can be regulated by microRNAs (miRNAs). However, the mechanism underlying miRNA-mediated simultaneous activation of multiple immune pathways remains unknown. To address this issue, the role of host miR-12 in shrimp (Marsupenaeus japonicus) antiviral immune responses was characterized in the present study. The results indicated that miR-12 participated in virus infection, host phagocytosis, and apoptosis in defense against white spot syndrome virus invasion. miR-12 could simultaneously trigger phagocytosis, apoptosis, and antiviral immunity through the synchronous downregulation of the expression of shrimp genes [PTEN (phosphatase and tensin homolog) and BI-1(transmembrane BAX inhibitor motif containing 6)] and the viral gene (wsv024). Further analysis showed that miR-12 could synchronously mediate the 5′–3′ exonucleolytic degradation of its target mRNAs, and this degradation terminated in the vicinity of the 3′ untranslated region sequence complementary to the seed sequence of miR-12. Therefore, the present study showed novel aspects of the miRNA-mediated simultaneous regulation of multiple immune pathways. PMID:28824612

miR319, miR390, and miR393 Are Involved in Aluminum Response in Flax (Linum usitatissimum L.).

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Dmitriev, Alexey A; Kudryavtseva, Anna V; Bolsheva, Nadezhda L; Zyablitsin, Alexander V; Rozhmina, Tatiana A; Kishlyan, Natalya V; Krasnov, George S; Speranskaya, Anna S; Krinitsina, Anastasia A; Sadritdinova, Asiya F; Snezhkina, Anastasiya V; Fedorova, Maria S; Yurkevich, Olga Yu; Muravenko, Olga V; Belenikin, Maxim S; Melnikova, Nataliya V

2017-01-01

Acid soils limit agricultural production worldwide. Major reason of crop losses in acid soils is the toxicity of aluminum (Al). In the present work, we investigated expression alterations of microRNAs in flax ( Linum usitatissimum L.) plants under Al stress. Flax seedlings of resistant (TMP1919 and G1071/4_k) and sensitive (Lira and G1071/4_o) to Al cultivars and lines were exposed to AlCl 3 solution for 4 and 24 hours. Twelve small RNA libraries were constructed and sequenced using Illumina platform. In total, 97 microRNAs from 18 conserved families were identified. miR319, miR390, and miR393 revealed expression alterations associated with Al treatment of flax plants. Moreover, for miR390 and miR393, the alterations were distinct in sensitive and resistant to Al genotypes. Expression level changes of miR319 and miR390 were confirmed using qPCR analysis. In flax, potential targets of miR319 are TCPs, miR390-TAS3 and GRF5, and miR393-AFB2-coding transcripts. TCPs, TAS3, GRF5, and AFB2 participate in regulation of plant growth and development. The involvement of miR319, miR390, and miR393 in response to Al stress in flax was shown here for the first time. We speculate that these microRNAs play an important role in Al response via regulation of growth processes in flax plants.

Decreased Expression of miR-21, miR-26a, miR-29a, and miR-142-3p in CD4+ T Cells and Peripheral Blood from Tuberculosis Patients

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Schattling, Stefanie; Kohns, Malte; Sander-Jülch, Claudia; Walzl, Gerhard; Hesseling, Anneke; Mayatepek, Ertan; Fleischer, Bernhard; Marx, Florian M.; Jacobsen, Marc

2013-01-01

The vast majority of Mycobacterium tuberculosis (M. tuberculosis) infected individuals are protected from developing tuberculosis and T cells are centrally involved in this process. MicroRNAs (miRNA) regulate T-cell functions and are biomarker candidates of disease susceptibility and treatment efficacy in M. tuberculosis infection. We determined the expression profile of 29 selected miRNAs in CD4+ T cells from tuberculosis patients and contacts with latent M. tuberculosis infection (LTBI). These analyses showed lower expression of miR-21, miR-26a, miR-29a, and miR-142-3p in CD4+ T cells from tuberculosis patients. Whole blood miRNA candidate analyses verified decreased expression of miR-26a, miR-29a, and miR-142-3p in children with tuberculosis as compared to healthy children with LTBI. Despite marked variances between individual donor samples, trends of increased miRNA candidate expression during treatment and recovery were observed. Functional in vitro analysis identified increased miR-21 and decreased miR-26a expression after re-stimulation of T cells. In vitro polarized Interleukin-17 positive T-cell clones showed activation-dependent miR-29a up-regulation. In order to characterize the role of miR-29a (a described suppressor of Interferon-γ in tuberculosis), we analyzed M. tuberculosis specific Interferon-γ expressing T cells in children with tuberculosis and healthy contacts but detected no correlation between miR-29a and Interferon-γ expression. Suppression of miR-29a in primary human T cells by antagomirs indicated no effect on Interferon-γ expression after in vitro activation. Finally, classification of miRNA targets revealed only a moderate overlap between the candidates. This may reflect differential roles of miR-21, miR-26a, miR-29a, and miR-142-3p in T-cell immunity against M. tuberculosis infection and disease. PMID:23613882

Investigation of a miRNA-Induced Gene Silencing Technique in Petunia Reveals Alterations in miR173 Precursor Processing and the Accumulation of Secondary siRNAs from Endogenous Genes.

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Yao Han

Full Text Available MIGS (miRNA-induced gene silencing is a straightforward and efficient gene silencing technique in Arabidopsis. It works by exploiting miR173 to trigger the production of phasiRNAs (phased small interfering RNAs. MIGS can be used in plant species other than Arabidopsis by co-expression of miR173 and target gene fragments fused to an upstream miR173 target site. However, the efficiency and technical mechanisms have not been thoroughly investigated in other plants. In this work, two vectors, pMIGS-chs and pMIGS-pds, were constructed and transformed into petunia plants. The transgenic plants showed CHS (chalcone synthase and PDS (phytoene desaturase gene-silencing phenotypes respectively, indicating that MIGS functions in petunia. MIGS-chs plants were used to investigate the mechanisms of this technique in petunia. Results of 5'- RACE showed that the miR173 target site was cleaved at the expected position and that endogenous CHS genes were cut at multiple positions. Small RNA deep sequencing analysis showed that the processing of Arabidopsis miR173 precursors in MIGS-chs transgenic petunia plants did not occur in exactly the same way as in Arabidopsis, suggesting differences in the machinery of miRNA processing between plant species. Small RNAs in-phase with the miR173 cleavage register were produced immediately downstream from the cleavage site and out-of-phase small RNAs were accumulated at relatively high levels from processing cycle 5 onwards. Secondary siRNAs were generated from multiple sites of endogenous CHS-A and CHS-J genes, indicating that miR173 cleavage induced siRNAs have the same ability to initiate siRNA transitivity as the siRNAs functioning in co-suppression and hpRNA silencing. On account of the simplicity of vector construction and the transitive amplification of signals from endogenous transcripts, MIGS is a good alternative gene silencing method for plants, especially for silencing a cluster of homologous genes with redundant

Investigation of a miRNA-Induced Gene Silencing Technique in Petunia Reveals Alterations in miR173 Precursor Processing and the Accumulation of Secondary siRNAs from Endogenous Genes.

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Han, Yao; Zhang, Bin; Qin, Xiaoting; Li, Mingyang; Guo, Yulong

2015-01-01

MIGS (miRNA-induced gene silencing) is a straightforward and efficient gene silencing technique in Arabidopsis. It works by exploiting miR173 to trigger the production of phasiRNAs (phased small interfering RNAs). MIGS can be used in plant species other than Arabidopsis by co-expression of miR173 and target gene fragments fused to an upstream miR173 target site. However, the efficiency and technical mechanisms have not been thoroughly investigated in other plants. In this work, two vectors, pMIGS-chs and pMIGS-pds, were constructed and transformed into petunia plants. The transgenic plants showed CHS (chalcone synthase) and PDS (phytoene desaturase) gene-silencing phenotypes respectively, indicating that MIGS functions in petunia. MIGS-chs plants were used to investigate the mechanisms of this technique in petunia. Results of 5'- RACE showed that the miR173 target site was cleaved at the expected position and that endogenous CHS genes were cut at multiple positions. Small RNA deep sequencing analysis showed that the processing of Arabidopsis miR173 precursors in MIGS-chs transgenic petunia plants did not occur in exactly the same way as in Arabidopsis, suggesting differences in the machinery of miRNA processing between plant species. Small RNAs in-phase with the miR173 cleavage register were produced immediately downstream from the cleavage site and out-of-phase small RNAs were accumulated at relatively high levels from processing cycle 5 onwards. Secondary siRNAs were generated from multiple sites of endogenous CHS-A and CHS-J genes, indicating that miR173 cleavage induced siRNAs have the same ability to initiate siRNA transitivity as the siRNAs functioning in co-suppression and hpRNA silencing. On account of the simplicity of vector construction and the transitive amplification of signals from endogenous transcripts, MIGS is a good alternative gene silencing method for plants, especially for silencing a cluster of homologous genes with redundant functions.

MiR-122 Induces Radiosensitization in Non-Small Cell Lung Cancer Cell Line

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Debin Ma

2015-09-01

Full Text Available MiR-122 is a novel tumor suppresser and its expression induces cell cycle arrest, or apoptosis, and inhibits cell proliferation in multiple cancer cells, including non-small cell lung cancer (NSCLC cells. Radioresistance of cancer cell leads to the major drawback of radiotherapy for NSCLC and the induction of radiosensitization could be a useful strategy to fix this problem. The present work investigates the function of miR-122 in inducing radiosensitization in A549 cell, a type of NSCLC cells. MiR-122 induces the radiosensitization of A549 cells. MiR-122 also boosts the inhibitory activity of ionizing radiation (IR on cancer cell anchor-independent growth and invasion. Moreover, miR-122 reduced the expression of its targeted genes related to tumor-survival or cellular stress response. These results indicate that miR-122 would be a novel strategy for NSCLC radiation-therapy.

Missing data methods for dealing with missing items in quality of life questionnaires. A comparison by simulation of personal mean score, full information maximum likelihood, multiple imputation, and hot deck techniques applied to the SF-36 in the French 2003 decennial health survey.

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Peyre, Hugo; Leplège, Alain; Coste, Joël

2011-03-01

Missing items are common in quality of life (QoL) questionnaires and present a challenge for research in this field. It remains unclear which of the various methods proposed to deal with missing data performs best in this context. We compared personal mean score, full information maximum likelihood, multiple imputation, and hot deck techniques using various realistic simulation scenarios of item missingness in QoL questionnaires constructed within the framework of classical test theory. Samples of 300 and 1,000 subjects were randomly drawn from the 2003 INSEE Decennial Health Survey (of 23,018 subjects representative of the French population and having completed the SF-36) and various patterns of missing data were generated according to three different item non-response rates (3, 6, and 9%) and three types of missing data (Little and Rubin's "missing completely at random," "missing at random," and "missing not at random"). The missing data methods were evaluated in terms of accuracy and precision for the analysis of one descriptive and one association parameter for three different scales of the SF-36. For all item non-response rates and types of missing data, multiple imputation and full information maximum likelihood appeared superior to the personal mean score and especially to hot deck in terms of accuracy and precision; however, the use of personal mean score was associated with insignificant bias (relative bias personal mean score appears nonetheless appropriate for dealing with items missing from completed SF-36 questionnaires in most situations of routine use. These results can reasonably be extended to other questionnaires constructed according to classical test theory.

Improved Correction of Misclassification Bias With Bootstrap Imputation.

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van Walraven, Carl

2018-07-01

Diagnostic codes used in administrative database research can create bias due to misclassification. Quantitative bias analysis (QBA) can correct for this bias, requires only code sensitivity and specificity, but may return invalid results. Bootstrap imputation (BI) can also address misclassification bias but traditionally requires multivariate models to accurately estimate disease probability. This study compared misclassification bias correction using QBA and BI. Serum creatinine measures were used to determine severe renal failure status in 100,000 hospitalized patients. Prevalence of severe renal failure in 86 patient strata and its association with 43 covariates was determined and compared with results in which renal failure status was determined using diagnostic codes (sensitivity 71.3%, specificity 96.2%). Differences in results (misclassification bias) were then corrected with QBA or BI (using progressively more complex methods to estimate disease probability). In total, 7.4% of patients had severe renal failure. Imputing disease status with diagnostic codes exaggerated prevalence estimates [median relative change (range), 16.6% (0.8%-74.5%)] and its association with covariates [median (range) exponentiated absolute parameter estimate difference, 1.16 (1.01-2.04)]. QBA produced invalid results 9.3% of the time and increased bias in estimates of both disease prevalence and covariate associations. BI decreased misclassification bias with increasingly accurate disease probability estimates. QBA can produce invalid results and increase misclassification bias. BI avoids invalid results and can importantly decrease misclassification bias when accurate disease probability estimates are used.

miRNA-Processing Gene Methylation and Cancer Risk.

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Joyce, Brian T; Zheng, Yinan; Zhang, Zhou; Liu, Lei; Kocherginsky, Masha; Murphy, Robert; Achenbach, Chad J; Musa, Jonah; Wehbe, Firas; Just, Allan; Shen, Jincheng; Vokonas, Pantel; Schwartz, Joel; Baccarelli, Andrea A; Hou, Lifang

2018-05-01

Background: Dysregulation of miRNA and methylation levels are epigenetic hallmarks of cancer, potentially linked via miRNA-processing genes. Studies have found genetic alterations to miRNA-processing genes in cancer cells and human population studies. Our objective was to prospectively examine changes in DNA methylation of miRNA-processing genes and their associations with cancer risk. Methods: We examined cohort data from the Department of Veterans' Affairs Normative Aging Study. Participants were assessed every 3 to 5 years starting in 1999 through 2013 including questionnaires, medical record review, and blood collection. Blood from 686 consenting participants was analyzed using the Illumina 450K BeadChip array to measure methylation at CpG sites throughout the genome. We selected 19 genes based on a literature review, with 519 corresponding CpG sites. We then used Cox proportional hazards models to examine associations with cancer incidence, and generalized estimating equations to examine associations with cancer prevalence. Associations at false discovery rate time to cancer development (positively for cg06751583, inversely for cg23230564 and cg21034183), whereas methylation of one CpG site ( DROSHA : cg16131300) was positively associated with cancer prevalence. Conclusions: DNA methylation of DROSHA , a key miRNA-processing gene, and TNRC6B may play a role in early carcinogenesis. Impact: Changes in miRNA processing may exert multiple effects on cancer development, including protecting against it via altered global miRNAs, and may be a useful early detection biomarker of cancer. Cancer Epidemiol Biomarkers Prev; 27(5); 550-7. ©2018 AACR . ©2018 American Association for Cancer Research.

Alu-miRNA interactions modulate transcript isoform diversity in stress response and reveal signatures of positive selection

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Pandey, Rajesh; Bhattacharya, Aniket; Bhardwaj, Vivek; Jha, Vineet; Mandal, Amit K.; Mukerji, Mitali

2016-09-01

Primate-specific Alus harbor different regulatory features, including miRNA targets. In this study, we provide evidence for miRNA-mediated modulation of transcript isoform levels during heat-shock response through exaptation of Alu-miRNA sites in mature mRNA. We performed genome-wide expression profiling coupled with functional validation of miRNA target sites within exonized Alus, and analyzed conservation of these targets across primates. We observed that two miRNAs (miR-15a-3p and miR-302d-3p) elevated in stress response, target RAD1, GTSE1, NR2C1, FKBP9 and UBE2I exclusively within Alu. These genes map onto the p53 regulatory network. Ectopic overexpression of miR-15a-3p downregulates GTSE1 and RAD1 at the protein level and enhances cell survival. This Alu-mediated fine-tuning seems to be unique to humans as evident from the absence of orthologous sites in other primate lineages. We further analyzed signatures of selection on Alu-miRNA targets in the genome, using 1000 Genomes Phase-I data. We found that 198 out of 3177 Alu-exonized genes exhibit signatures of selection within Alu-miRNA sites, with 60 of them containing SNPs supported by multiple evidences (global-FST > 0.3, pair-wise-FST > 0.5, Fay-Wu’s H  2.0, high ΔDAF) and implicated in p53 network. We propose that by affecting multiple genes, Alu-miRNA interactions have the potential to facilitate population-level adaptations in response to environmental challenges.

Job characteristics in relation to the prevalence of myocardial infarction in the US Health Examination Survey (HES) and the Health and Nutrition Examination Survey (HANES).

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Karasek, R A; Theorell, T; Schwartz, J E; Schnall, P L; Pieper, C F; Michela, J L

1988-08-01

Associations between psychosocial job characteristics and past myocardial infarction (MI) prevalence for employed males were tested with the Health Examination Survey (HES) 1960-61, N = 2,409, and the Health and Nutrition Examination Survey (HANES) 1971-75, N = 2,424. A new estimation method is used which imputes to census occupation codes, job characteristic information from national surveys of job characteristics (US Department of Labor, Quality of Employment Surveys). Controlling for age, we find that employed males with jobs which are simultaneously low in decision latitude and high in psychological work load (a multiplicative product term isolating 20 per cent of the population) have a higher prevalence of myocardial infarction in both data bases. In a logistic regression analysis, using job measures adjusted for demographic factors and controlling for age, race, education, systolic blood pressure, serum cholesterol, smoking (HANES only), and physical exertion, we find a low decision latitude/high psychological demand multiplicative product term associated with MI in both data bases. Additional multiple logistic regressions show that low decision latitude is associated with increased prevalence of MI in both the HES and the HANES. Psychological workload and physical exertion are significant only in the HANES.

Estimating past hepatitis C infection risk from reported risk factor histories: implications for imputing age of infection and modeling fibrosis progression

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Busch Michael P

2007-12-01

Full Text Available Abstract Background Chronic hepatitis C virus infection is prevalent and often causes hepatic fibrosis, which can progress to cirrhosis and cause liver cancer or liver failure. Study of fibrosis progression often relies on imputing the time of infection, often as the reported age of first injection drug use. We sought to examine the accuracy of such imputation and implications for modeling factors that influence progression rates. Methods We analyzed cross-sectional data on hepatitis C antibody status and reported risk factor histories from two large studies, the Women's Interagency HIV Study and the Urban Health Study, using modern survival analysis methods for current status data to model past infection risk year by year. We compared fitted distributions of past infection risk to reported age of first injection drug use. Results Although injection drug use appeared to be a very strong risk factor, models for both studies showed that many subjects had considerable probability of having been infected substantially before or after their reported age of first injection drug use. Persons reporting younger age of first injection drug use were more likely to have been infected after, and persons reporting older age of first injection drug use were more likely to have been infected before. Conclusion In cross-sectional studies of fibrosis progression where date of HCV infection is estimated from risk factor histories, modern methods such as multiple imputation should be used to account for the substantial uncertainty about when infection occurred. The models presented here can provide the inputs needed by such methods. Using reported age of first injection drug use as the time of infection in studies of fibrosis progression is likely to produce a spuriously strong association of younger age of infection with slower rate of progression.

Identification of miR-508-3p and miR-509-3p that are associated with cell invasion and migration and involved in the apoptosis of renal cell carcinoma

International Nuclear Information System (INIS)

Zhai, Qingna; Zhou, Liang; Zhao, Chunjuan; Wan, Jun; Yu, Zhendong; Guo, Xin; Qin, Jie; Chen, Jing; Lu, Ruijing

2012-01-01

Highlights: ► Previous method was the second-generation sequencing technology. ► miR-508-3p and miR-509-3p were significantly down-regulated in RCC tissues. ► They can inhibit cell proliferation and migration and promote cell apoptosis. ► The expression of miR-508-3p was significantly decreased in RCC patients plasma. ► miR-508-3p may be a novel diagnostic marker of RCC. -- Abstract: MicroRNAs (miRNAs) have emerged as powerful regulators of multiple processes linked to human cancer, including cell apoptosis, proliferation and migration, suggesting that the regulation of miRNA function could play a critical role in cancer progression. Recent studies have found that human serum/plasma contains stably expressed miRNAs. If they prove indicative of disease states, miRNAs measured from peripheral blood samples may be a source for routine clinical detection of cancer. Our studies showed that both miR-508-3p and miR-509-3p were down-regulated in renal cancer tissues. The level of miR-508-3p but not miR-509-3p in renal cell carcinoma (RCC) patient plasma demonstrated significant differences from that in control plasma. In addition, the overexpression of miR-508-3p and miR-509-3p suppressed the proliferation of RCC cells (786-0), induced cell apoptosis and inhibited cell migration in vitro. Our data demonstrated that miR-508-3p and miR-509-3p played an important role as tumor suppressor genes during tumor formation and that they may serve as novel diagnostic markers for RCC.

Circulating miR-1, miR-133a, and miR-206 levels are increased after a half-marathon run.

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Gomes, Clarissa P C; Oliveira, Getúlio P; Madrid, Bibiano; Almeida, Jeeser A; Franco, Octávio L; Pereira, Rinaldo W

2014-11-01

Circulating miRNAs are potential biomarkers that can be important molecules driving cell-to-cell communication. To investigate circulating muscle-specific miRNAs in recreational athletes. Three miRNAs from whole plasma before and after a half-marathon were analyzed by qPCR. MiR-1, -133a, and -206 significantly increased after the race. Increased levels of miRNAs after exercise point to potential biomarkers and to the possibility of being functional players following endurance training. These miRNAs are potential biomarkers of muscle damage or adaptation to exercise.

Multiple Intelligences or Multiply Misleading: The Critic's View of the Multiple Intelligences Theory

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Peariso, Jamon F.

2008-01-01

Howard Gardner's Multiple Intelligences (MI) theory has been widely accepted in the field of education for the past two decades. Most educators have been subjugated to the MI theory and to the many issues that its implementation in the classroom brings. This is often done without ever looking at or being presented the critic's view or research on…

Using imputed genotype data in the joint score tests for genetic association and gene-environment interactions in case-control studies.

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Song, Minsun; Wheeler, William; Caporaso, Neil E; Landi, Maria Teresa; Chatterjee, Nilanjan

2018-03-01

Genome-wide association studies (GWAS) are now routinely imputed for untyped single nucleotide polymorphisms (SNPs) based on various powerful statistical algorithms for imputation trained on reference datasets. The use of predicted allele counts for imputed SNPs as the dosage variable is known to produce valid score test for genetic association. In this paper, we investigate how to best handle imputed SNPs in various modern complex tests for genetic associations incorporating gene-environment interactions. We focus on case-control association studies where inference for an underlying logistic regression model can be performed using alternative methods that rely on varying degree on an assumption of gene-environment independence in the underlying population. As increasingly large-scale GWAS are being performed through consortia effort where it is preferable to share only summary-level information across studies, we also describe simple mechanisms for implementing score tests based on standard meta-analysis of "one-step" maximum-likelihood estimates across studies. Applications of the methods in simulation studies and a dataset from GWAS of lung cancer illustrate ability of the proposed methods to maintain type-I error rates for the underlying testing procedures. For analysis of imputed SNPs, similar to typed SNPs, the retrospective methods can lead to considerable efficiency gain for modeling of gene-environment interactions under the assumption of gene-environment independence. Methods are made available for public use through CGEN R software package. © 2017 WILEY PERIODICALS, INC.

Whose Music of a Century? Performance, History and Multiple Voices

African Journals Online (AJOL)

It's the small words that do the most cultural work. THE MUSIC OF A CENTURY, the title of the conference for which this paper was written, imputes a spurious singularity to a multiplicity of cultural practices, and begs the question of in whose interests this singularity is being constructed. An alternative question, 'WHOSE ...

Exploring the miRNA regulatory network using evolutionary correlations.

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Benedikt Obermayer

2014-10-01

Full Text Available Post-transcriptional regulation by miRNAs is a widespread and highly conserved phenomenon in metazoans, with several hundreds to thousands of conserved binding sites for each miRNA, and up to two thirds of all genes under miRNA regulation. At the same time, the effect of miRNA regulation on mRNA and protein levels is usually quite modest and associated phenotypes are often weak or subtle. This has given rise to the notion that the highly interconnected miRNA regulatory network exerts its function less through any individual link and more via collective effects that lead to a functional interdependence of network links. We present a Bayesian framework to quantify conservation of miRNA target sites using vertebrate whole-genome alignments. The increased statistical power of our phylogenetic model allows detection of evolutionary correlation in the conservation patterns of site pairs. Such correlations could result from collective functions in the regulatory network. For instance, co-conservation of target site pairs supports a selective benefit of combinatorial regulation by multiple miRNAs. We find that some miRNA families are under pronounced co-targeting constraints, indicating a high connectivity in the regulatory network, while others appear to function in a more isolated way. By analyzing coordinated targeting of different curated gene sets, we observe distinct evolutionary signatures for protein complexes and signaling pathways that could reflect differences in control strategies. Our method is easily scalable to analyze upcoming larger data sets, and readily adaptable to detect high-level selective constraints between other genomic loci. We thus provide a proof-of-principle method to understand regulatory networks from an evolutionary perspective.

Discovery and Fine-Mapping of Glycaemic and Obesity-Related Trait Loci Using High-Density Imputation.

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Horikoshi, Momoko; Mӓgi, Reedik; van de Bunt, Martijn; Surakka, Ida; Sarin, Antti-Pekka; Mahajan, Anubha; Marullo, Letizia; Thorleifsson, Gudmar; Hӓgg, Sara; Hottenga, Jouke-Jan; Ladenvall, Claes; Ried, Janina S; Winkler, Thomas W; Willems, Sara M; Pervjakova, Natalia; Esko, Tõnu; Beekman, Marian; Nelson, Christopher P; Willenborg, Christina; Wiltshire, Steven; Ferreira, Teresa; Fernandez, Juan; Gaulton, Kyle J; Steinthorsdottir, Valgerdur; Hamsten, Anders; Magnusson, Patrik K E; Willemsen, Gonneke; Milaneschi, Yuri; Robertson, Neil R; Groves, Christopher J; Bennett, Amanda J; Lehtimӓki, Terho; Viikari, Jorma S; Rung, Johan; Lyssenko, Valeriya; Perola, Markus; Heid, Iris M; Herder, Christian; Grallert, Harald; Müller-Nurasyid, Martina; Roden, Michael; Hypponen, Elina; Isaacs, Aaron; van Leeuwen, Elisabeth M; Karssen, Lennart C; Mihailov, Evelin; Houwing-Duistermaat, Jeanine J; de Craen, Anton J M; Deelen, Joris; Havulinna, Aki S; Blades, Matthew; Hengstenberg, Christian; Erdmann, Jeanette; Schunkert, Heribert; Kaprio, Jaakko; Tobin, Martin D; Samani, Nilesh J; Lind, Lars; Salomaa, Veikko; Lindgren, Cecilia M; Slagboom, P Eline; Metspalu, Andres; van Duijn, Cornelia M; Eriksson, Johan G; Peters, Annette; Gieger, Christian; Jula, Antti; Groop, Leif; Raitakari, Olli T; Power, Chris; Penninx, Brenda W J H; de Geus, Eco; Smit, Johannes H; Boomsma, Dorret I; Pedersen, Nancy L; Ingelsson, Erik; Thorsteinsdottir, Unnur; Stefansson, Kari; Ripatti, Samuli; Prokopenko, Inga; McCarthy, Mark I; Morris, Andrew P

2015-07-01

Reference panels from the 1000 Genomes (1000G) Project Consortium provide near complete coverage of common and low-frequency genetic variation with minor allele frequency ≥0.5% across European ancestry populations. Within the European Network for Genetic and Genomic Epidemiology (ENGAGE) Consortium, we have undertaken the first large-scale meta-analysis of genome-wide association studies (GWAS), supplemented by 1000G imputation, for four quantitative glycaemic and obesity-related traits, in up to 87,048 individuals of European ancestry. We identified two loci for body mass index (BMI) at genome-wide significance, and two for fasting glucose (FG), none of which has been previously reported in larger meta-analysis efforts to combine GWAS of European ancestry. Through conditional analysis, we also detected multiple distinct signals of association mapping to established loci for waist-hip ratio adjusted for BMI (RSPO3) and FG (GCK and G6PC2). The index variant for one association signal at the G6PC2 locus is a low-frequency coding allele, H177Y, which has recently been demonstrated to have a functional role in glucose regulation. Fine-mapping analyses revealed that the non-coding variants most likely to drive association signals at established and novel loci were enriched for overlap with enhancer elements, which for FG mapped to promoter and transcription factor binding sites in pancreatic islets, in particular. Our study demonstrates that 1000G imputation and genetic fine-mapping of common and low-frequency variant association signals at GWAS loci, integrated with genomic annotation in relevant tissues, can provide insight into the functional and regulatory mechanisms through which their effects on glycaemic and obesity-related traits are mediated.

miRNAtools: Advanced Training Using the miRNA Web of Knowledge.

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Stępień, Ewa Ł; Costa, Marina C; Enguita, Francisco J

2018-02-16

Micro-RNAs (miRNAs) are small non-coding RNAs that act as negative regulators of the genomic output. Their intrinsic importance within cell biology and human disease is well known. Their mechanism of action based on the base pairing binding to their cognate targets have helped the development not only of many computer applications for the prediction of miRNA target recognition but also of specific applications for functional assessment and analysis. Learning about miRNA function requires practical training in the use of specific computer and web-based applications that are complementary to wet-lab studies. In order to guide the learning process about miRNAs, we have created miRNAtools (http://mirnatools.eu), a web repository of miRNA tools and tutorials. This article compiles tools with which miRNAs and their regulatory action can be analyzed and that function to collect and organize information dispersed on the web. The miRNAtools website contains a collection of tutorials that can be used by students and tutors engaged in advanced training courses. The tutorials engage in analyses of the functions of selected miRNAs, starting with their nomenclature and genomic localization and finishing with their involvement in specific cellular functions.

miR-371, miR-138, miR-544, miR-145, and miR-214 could modulate Th1/Th2 balance in asthma through the combinatorial regulation of Runx3.

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Qiu, Yu-Ying; Zhang, Ying-Wei; Qian, Xiu-Fen; Bian, Tao

2017-01-01

Asthma is tightly related to the imbalance of Th1/Th2 cells, and Runx3 plays a pivotal role in the differentiation of T helper cells. The present study aimed to investigate dysregulated microRNAs that may target Runx3 in CD4 + T cells from asthmatic patients and reveal Runx3 function in Th1/Th2 balance regulation. We detected the levels of Th1- and Th2-related cytokines by ELISA and analyzed the differentiation marker gene of T helper cells by qRT-PCR. Results indicated that an imbalance of Th1/Th2 cells was present in our asthmatic subject. Runx3 expression was reduced in the CD4 + T cells from asthmatic patients. Overexpression of Runx3 could restore the Th1/Th2 balance. After performing microRNA microarray assay, we found a series of microRNAs that were considerably altered in the CD4 + T cells from asthmatic patients. Among these upregulated microRNAs, eight microRNAs that may target Runx3 were selected by bioinformatics prediction. Five microRNAs, namely miR-371, miR-138, miR-544, miR-145, and miR-214, were confirmed by qRT-PCR and selected as candidate microRNAs. Luciferase reporter assay showed that these five microRNAs could directly target the 3'-UTR of Runx3. However, only simultaneous inhibition of these five microRNAs could alter the expression of Runx3. Most importantly, only simultaneous inhibition could improve the Th1/Th2 balance. Thus, we suggest that miR-371, miR-138, miR-544, miR-145, and miR-214 can modulate the Th1/Th2 balance in asthma by regulating Runx3 in a combinatorial manner.

Loss of miR-141/200c ameliorates hepatic steatosis and inflammation by reprogramming multiple signaling pathways in NASH

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Tran, Melanie; Lee, Sang-Min; Shin, Dong-Ju

2017-01-01

Accumulation of lipid droplets and inflammatory cell infiltration is the hallmark of nonalcoholic steatohepatitis (NASH). The roles of noncoding RNAs in NASH are less known. We aim to elucidate the function of miR-141/200c in diet-induced NASH. WT and miR-141/200c–/– mice were fed a methionine and choline deficient (MCD) diet for 2 weeks to assess markers of steatosis, liver injury, and inflammation. Hepatic miR-141 and miR-200c RNA levels were highly induced in human patients with NASH fatty liver and in WT MCD mice. miR-141/200c–/– MCD mice had reduced liver weights and triglyceride (TG) levels, which was associated with increased microsomal TG transfer protein (MTTP) and PPARα but reduced SREBP1c and FAS expression. Inflammation was attenuated and F4/80 macrophage activation was suppressed in miR-141/200c–/– mice, as evidenced by decreased serum aminotransferases and IL-6 and reduced hepatic proinflammatory, neutrophil, and profibrotic genes. Treatment with LPS in BM-derived macrophages isolated from miR-200c/141–/– mice polarized macrophages toward the M2 antiinflammatory state by increasing Arg1 and IL-10 levels while decreasing the M1 marker iNOS. In addition, elevated phosphorylated AMPK (p-AMPK), p-AKT, and p-GSK3β and diminished TLR4 and p-mTOR/p-4EBP1 proteins were observed. Lipidomics and metabolomics revealed alterations of TG and phosphatidylcholine (PC) lipid species by miR-141/200c deficiency. In summary, miR-141/200c deficiency diminished NASH-associated hepatic steatosis and inflammation by reprogramming lipid and inflammation signaling pathways. PMID:29093267

Defining, evaluating, and removing bias induced by linear imputation in longitudinal clinical trials with MNAR missing data.

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Helms, Ronald W; Reece, Laura Helms; Helms, Russell W; Helms, Mary W

2011-03-01

Missing not at random (MNAR) post-dropout missing data from a longitudinal clinical trial result in the collection of "biased data," which leads to biased estimators and tests of corrupted hypotheses. In a full rank linear model analysis the model equation, E[Y] = Xβ, leads to the definition of the primary parameter β = (X'X)(-1)X'E[Y], and the definition of linear secondary parameters of the form θ = Lβ = L(X'X)(-1)X'E[Y], including, for example, a parameter representing a "treatment effect." These parameters depend explicitly on E[Y], which raises the questions: What is E[Y] when some elements of the incomplete random vector Y are not observed and MNAR, or when such a Y is "completed" via imputation? We develop a rigorous, readily interpretable definition of E[Y] in this context that leads directly to definitions of β, Bias(β) = E[β] - β, Bias(θ) = E[θ] - Lβ, and the extent of hypothesis corruption. These definitions provide a basis for evaluating, comparing, and removing biases induced by various linear imputation methods for MNAR incomplete data from longitudinal clinical trials. Linear imputation methods use earlier data from a subject to impute values for post-dropout missing values and include "Last Observation Carried Forward" (LOCF) and "Baseline Observation Carried Forward" (BOCF), among others. We illustrate the methods of evaluating, comparing, and removing biases and the effects of testing corresponding corrupted hypotheses via a hypothetical but very realistic longitudinal analgesic clinical trial.

Potential role of miR-9 and miR-223 in recurrent ovarian cancer

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McGuinness Eamonn

2008-04-01

Full Text Available Abstract Background MicroRNAs (miRNAs are small, noncoding RNAs that negatively regulate gene expression by binding to target mRNAs. miRNAs have not been comprehensively studied in recurrent ovarian cancer, yet an incurable disease. Results Using real-time RT-PCR, we obtained distinct miRNA expression profiles between primary and recurrent serous papillary ovarian adenocarcinomas (n = 6 in a subset of samples previously used in a transcriptome approach. Expression levels of top dysregulated miRNA genes, miR-223 and miR-9, were examined using TaqMan PCR in independent cohorts of fresh frozen (n = 18 and FFPE serous ovarian tumours (n = 22. Concordance was observed on TaqMan analysis for miR-223 and miR-9 between the training cohort and the independent test cohorts. Target prediction analysis for the above miRNA "recurrent metastatic signature" identified genes previously validated in our transcriptome study. Common biological pathways well characterised in ovarian cancer were shared by miR-9 and miR-223 lists of predicted target genes. We provide strong evidence that miR-9 acts as a putative tumour suppressor gene in recurrent ovarian cancer. Components of the miRNA processing machinery, such as Dicer and Drosha are not responsible for miRNA deregulation in recurrent ovarian cancer, as deluded by TaqMan and immunohistochemistry. Conclusion We propose a miRNA model for the molecular pathogenesis of recurrent ovarian cancer. Some of the differentially deregulated miRNAs identified correlate with our previous transcriptome findings. Based on integrated transcriptome and miRNA analysis, miR-9 and miR-223 can be of potential importance as biomarkers in recurrent ovarian cancer.

Decreased expression of miR-21, miR-26a, miR-29a, and miR-142-3p in CD4⁺ T cells and peripheral blood from tuberculosis patients.

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Kleinsteuber, Katja; Heesch, Kerrin; Schattling, Stefanie; Kohns, Malte; Sander-Jülch, Claudia; Walzl, Gerhard; Hesseling, Anneke; Mayatepek, Ertan; Fleischer, Bernhard; Marx, Florian M; Jacobsen, Marc

2013-01-01

The vast majority of Mycobacterium tuberculosis (M. tuberculosis) infected individuals are protected from developing tuberculosis and T cells are centrally involved in this process. MicroRNAs (miRNA) regulate T-cell functions and are biomarker candidates of disease susceptibility and treatment efficacy in M. tuberculosis infection. We determined the expression profile of 29 selected miRNAs in CD4(+) T cells from tuberculosis patients and contacts with latent M. tuberculosis infection (LTBI). These analyses showed lower expression of miR-21, miR-26a, miR-29a, and miR-142-3p in CD4(+) T cells from tuberculosis patients. Whole blood miRNA candidate analyses verified decreased expression of miR-26a, miR-29a, and miR-142-3p in children with tuberculosis as compared to healthy children with LTBI. Despite marked variances between individual donor samples, trends of increased miRNA candidate expression during treatment and recovery were observed. Functional in vitro analysis identified increased miR-21 and decreased miR-26a expression after re-stimulation of T cells. In vitro polarized Interleukin-17 positive T-cell clones showed activation-dependent miR-29a up-regulation. In order to characterize the role of miR-29a (a described suppressor of Interferon-γ in tuberculosis), we analyzed M. tuberculosis specific Interferon-γ expressing T cells in children with tuberculosis and healthy contacts but detected no correlation between miR-29a and Interferon-γ expression. Suppression of miR-29a in primary human T cells by antagomirs indicated no effect on Interferon-γ expression after in vitro activation. Finally, classification of miRNA targets revealed only a moderate overlap between the candidates. This may reflect differential roles of miR-21, miR-26a, miR-29a, and miR-142-3p in T-cell immunity against M. tuberculosis infection and disease.

Applying an efficient K-nearest neighbor search to forest attribute imputation

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Andrew O. Finley; Ronald E. McRoberts; Alan R. Ek

2006-01-01

This paper explores the utility of an efficient nearest neighbor (NN) search algorithm for applications in multi-source kNN forest attribute imputation. The search algorithm reduces the number of distance calculations between a given target vector and each reference vector, thereby, decreasing the time needed to discover the NN subset. Results of five trials show gains...

Structural profiles of human miRNA families from pairwise clustering

DEFF Research Database (Denmark)

Kaczkowski, Bogumil; Þórarinsson, Elfar; Reiche, Kristin

2009-01-01

secondary structure already predicted, little is known about the patterns of structural conservation among pre-miRNAs. We address this issue by clustering the human pre-miRNA sequences based on pairwise, sequence and secondary structure alignment using FOLDALIGN, followed by global multiple alignment...... of obtained clusters by WAR. As a result, the common secondary structure was successfully determined for four FOLDALIGN clusters: the RF00027 structural family of the Rfam database and three clusters with previously undescribed consensus structures. Availability: http://genome.ku.dk/resources/mirclust...

Altered expression of four miRNA (miR-1238-3p, miR-202-3p, miR-630 and miR-766-3p) and their potential targets in peripheral blood from vitiligo patients.

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Shang, Zhiwei; Li, Hongwen

2017-10-01

Vitiligo is an acquired skin disease with pigmentary disorder. Autoimmune destruction of melanocytes is thought to be major factor in the etiology of vitiligo. miRNA-based regulators of gene expression have been reported to play crucial roles in autoimmune disease. Therefore, we attempt to profile the miRNA expressions and predict their potential targets, assessing the biological functions of differentially expressed miRNA. Total RNA was extracted from peripheral blood of vitiligo (experimental group, n = 5) and non-vitiligo (control group, n = 5) age-matched patients. Samples were hybridized to a miRNA array. Box, scatter and principal component analysis plots were performed, followed by unsupervised hierarchical clustering analysis to classify the samples. Quantitative reverse transcription polymerase chain reaction (RT-PCR) was conducted for validation of microarray data. Three different databases, TargetScan, PITA and microRNA.org, were used to predict the potential target genes. Gene ontology (GO) annotation and pathway analysis were performed to assess the potential functions of predicted genes of identified miRNA. A total of 100 (29 upregulated and 71 downregulated) miRNA were filtered by volcano plot analysis. Four miRNA were validated by quantitative RT-PCR as significantly downregulated in the vitiligo group. The functions of predicted target genes associated with differentially expressed miRNA were assessed by GO analysis, showing that the GO term with most significantly enriched target genes was axon guidance, and that the axon guidance pathway was most significantly correlated with these miRNA. In conclusion, we identified four downregulated miRNA in vitiligo and assessed the potential functions of target genes related to these differentially expressed miRNA. © 2017 Japanese Dermatological Association.

Local exome sequences facilitate imputation of less common variants and increase power of genome wide association studies.

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Peter K Joshi

Full Text Available The analysis of less common variants in genome-wide association studies promises to elucidate complex trait genetics but is hampered by low power to reliably detect association. We show that addition of population-specific exome sequence data to global reference data allows more accurate imputation, particularly of less common SNPs (minor allele frequency 1-10% in two very different European populations. The imputation improvement corresponds to an increase in effective sample size of 28-38%, for SNPs with a minor allele frequency in the range 1-3%.

MiR-338-3p regulates neuronal maturation and suppresses glioblastoma proliferation.

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James R Howe

Full Text Available Neurogenesis is a highly-regulated process occurring in the dentate gyrus that has been linked to learning, memory, and antidepressant efficacy. MicroRNAs (miRNAs have been previously shown to play an important role in the regulation of neuronal development and neurogenesis in the dentate gyrus via modulation of gene expression. However, this mode of regulation is both incompletely described in the literature thus far and highly multifactorial. In this study, we designed sensors and detected relative levels of expression of 10 different miRNAs and found miR-338-3p was most highly expressed in the dentate gyrus. Comparison of miR-338-3p expression with neuronal markers of maturity indicates miR-338-3p is expressed most highly in the mature neuron. We also designed a viral "sponge" to knock down in vivo expression of miR-338-3p. When miR-338-3p is knocked down, neurons sprout multiple primary dendrites that branch off of the soma in a disorganized manner, cellular proliferation is upregulated, and neoplasms form spontaneously in vivo. Additionally, miR-338-3p overexpression in glioblastoma cell lines slows their proliferation in vitro. Further, low miR-338-3p expression is associated with increased mortality and disease progression in patients with glioblastoma. These data identify miR-338-3p as a clinically relevant tumor suppressor in glioblastoma.

Differential expression of miR-1, a putative tumor suppressing microRNA, in cancer resistant and cancer susceptible mice

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Jessica L. Fleming

2013-04-01

Full Text Available Mus spretus mice are highly resistant to several types of cancer compared to Mus musculus mice. To determine whether differences in microRNA (miRNA expression account for some of the differences in observed skin cancer susceptibility between the strains, we performed miRNA expression profiling of skin RNA for over 300 miRNAs. Five miRNAs, miR-1, miR-124a-3, miR-133a, miR-134, miR-206, were differentially expressed by array and/or qPCR. miR-1 was previously shown to have tumor suppressing abilities in multiple tumor types. We found miR-1 expression to be lower in mouse cutaneous squamous cell carcinomas (cSCCs compared to normal skin. Based on the literature and our expression data, we performed detailed studies on predicted miR-1 targets and evaluated the effect of miR-1 expression on two murine cSCC cell lines, A5 and B9. Following transfection of miR-1, we found decreased mRNA expression of three validated miR-1 targets, Met, Twf1 and Ets1 and one novel target Bag4. Decreased expression of Ets1 was confirmed by Western analysis and by 3’ reporter luciferase assays containing wildtype and mutated Ets1 3’UTR. We evaluated the effect of miR-1 on multiple tumor phenotypes including apoptosis, proliferation, cell cycle and migration. In A5 cells, expression of miR-1 led to decreased proliferation compared to a control miR. miR-1 expression also led to increased apoptosis at later time points (72 and 96 h and to a decrease in cells in S-phase. In summary, we identified five miRNAs with differential expression between cancer resistant and cancer susceptible mice and found that miR-1, a candidate tumor suppressor, has targets with defined roles in tumorigenesis.

ARMOUR - A Rice miRNA: mRNA Interaction Resource.

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Sanan-Mishra, Neeti; Tripathi, Anita; Goswami, Kavita; Shukla, Rohit N; Vasudevan, Madavan; Goswami, Hitesh

2018-01-01

ARMOUR was developed as A Rice miRNA:mRNA interaction resource. This informative and interactive database includes the experimentally validated expression profiles of miRNAs under different developmental and abiotic stress conditions across seven Indian rice cultivars. This comprehensive database covers 689 known and 1664 predicted novel miRNAs and their expression profiles in more than 38 different tissues or conditions along with their predicted/known target transcripts. The understanding of miRNA:mRNA interactome in regulation of functional cellular machinery is supported by the sequence information of the mature and hairpin structures. ARMOUR provides flexibility to users in querying the database using multiple ways like known gene identifiers, gene ontology identifiers, KEGG identifiers and also allows on the fly fold change analysis and sequence search query with inbuilt BLAST algorithm. ARMOUR database provides a cohesive platform for novel and mature miRNAs and their expression in different experimental conditions and allows searching for their interacting mRNA targets, GO annotation and their involvement in various biological pathways. The ARMOUR database includes a provision for adding more experimental data from users, with an aim to develop it as a platform for sharing and comparing experimental data contributed by research groups working on rice.

Silencing of Stress-Regulated miRNAs in Plants by Short Tandem Target Mimic (STTM) Approach.

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Teotia, Sachin; Tang, Guiliang

2017-01-01

In plants, microRNAs (miRNAs) regulate more than hundred target genes comprising largely transcription factors that control growth and development as well as stress responses. However, the exact functions of miRNA families could not be deciphered because each miRNA family has multiple loci in the genome, thus are functionally redundant. Therefore, an ideal approach to study the function of a miRNA family is to silence the expression of all members simultaneously, which is a daunting task. However, this can be partly overcome by Target Mimic (TM) approach that can knockdown an entire miRNA family. STTM is a modification of TM approach and complements it. STTMs have been successfully used in monocots and dicots to block miRNA functions. miR159 has been shown to be differentially regulated by various abiotic stresses including ABA in various plant species. Here, we describe in detail the protocol for designing STTM construct to block miR159 functions in Arabidopsis, with the potential to apply this technique on a number of other stress-regulated miRNAs in plants.

MiR-210 disturbs mitotic progression through regulating a group of mitosis-related genes.

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He, Jie; Wu, Jiangbin; Xu, Naihan; Xie, Weidong; Li, Mengnan; Li, Jianna; Jiang, Yuyang; Yang, Burton B; Zhang, Yaou

2013-01-07

MiR-210 is up-regulated in multiple cancer types but its function is disputable and further investigation is necessary. Using a bioinformatics approach, we identified the putative target genes of miR-210 in hypoxia-induced CNE cells from genome-wide scale. Two functional gene groups related to cell cycle and RNA processing were recognized as the major targets of miR-210. Here, we investigated the molecular mechanism and biological consequence of miR-210 in cell cycle regulation, particularly mitosis. Hypoxia-induced up-regulation of miR-210 was highly correlated with the down-regulation of a group of mitosis-related genes, including Plk1, Cdc25B, Cyclin F, Bub1B and Fam83D. MiR-210 suppressed the expression of these genes by directly targeting their 3'-UTRs. Over-expression of exogenous miR-210 disturbed mitotic progression and caused aberrant mitosis. Furthermore, miR-210 mimic with pharmacological doses reduced tumor formation in a mouse metastatic tumor model. Taken together, these results implicate that miR-210 disturbs mitosis through targeting multi-genes involved in mitotic progression, which may contribute to its inhibitory role on tumor formation.

The Association of Circulating MiR-29b and Interleukin-6 with Subclinical Atherosclerosis

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Yu-qing Huang

2017-12-01

Full Text Available Background/Aims: Although it is widely acknowledged that atherosclerosis is mainly a chronic inflammatory process, in which both miR-29b and interleukin-6 (IL-6 play multifaceted roles, the association between miR-29b and IL-6 remains unknown. The aim of the present study was to explore the relationship between miR-29b and IL-6 and to test whether circulating levels of miR-29b and IL-6 could predict atherosclerosis. Methods: A total of 170 participants were divided into two groups according to carotid intima-media thickness (CIMT: study group (CIMT ≥ 0.9mm and control group (CIMT < 0.9mm. Levels of circulating miR-29b and IL-6 were measured by quantitative real-time polymerase chain reaction (qRT-PCR and enzyme-linked immunosorbent assay (ELISA, respectively. The association of miR-29b and IL-6 levels with CIMT was assessed using Spearman correlation analysis and multiple linear regression analysis. Results: The study group showed higher miR-29b levels (31.61 ± 3.05 vs. 27.91 ± 1.71 Ct, p < 0.001 and IL-6 levels (3.40 ± 0.67 vs. 2.99 ± 0.37 pg/ml, p < 0.001, compared with the control group. CIMT was positively correlated with miR-29b (r = 0.587, p < 0.001 and IL-6 (r = 0.410, p < 0.001, and miR-29b levels were also correlated with IL-6 (r = 0.242, p = 0.001. Multiple linear regression analysis also showed that CIMT was positively correlated with miR-29b and IL-6. After adjustment for age, body mass index, systolic blood pressure, total cholesterol and C-reactive protein, CIMT was still closely correlated with miR-29b and IL-6. The combination of miR-29b and IL-6 (AUC = 0.901, p < 0.001 offered a better predictive index for atherosclerosis than either miR-29b (AUC = 0.867, p < 0.001 or IL-6 (AUC = 0.747, p < 0.001 alone. Conclusion: Circulating levels of miR-29b and IL-6 may be independently correlated with subclinical atherosclerosis, and may serve as novel biomarkers for the identification of atherosclerosis.

Missing data in clinical trials: control-based mean imputation and sensitivity analysis.

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Mehrotra, Devan V; Liu, Fang; Permutt, Thomas

2017-09-01

In some randomized (drug versus placebo) clinical trials, the estimand of interest is the between-treatment difference in population means of a clinical endpoint that is free from the confounding effects of "rescue" medication (e.g., HbA1c change from baseline at 24 weeks that would be observed without rescue medication regardless of whether or when the assigned treatment was discontinued). In such settings, a missing data problem arises if some patients prematurely discontinue from the trial or initiate rescue medication while in the trial, the latter necessitating the discarding of post-rescue data. We caution that the commonly used mixed-effects model repeated measures analysis with the embedded missing at random assumption can deliver an exaggerated estimate of the aforementioned estimand of interest. This happens, in part, due to implicit imputation of an overly optimistic mean for "dropouts" (i.e., patients with missing endpoint data of interest) in the drug arm. We propose an alternative approach in which the missing mean for the drug arm dropouts is explicitly replaced with either the estimated mean of the entire endpoint distribution under placebo (primary analysis) or a sequence of increasingly more conservative means within a tipping point framework (sensitivity analysis); patient-level imputation is not required. A supplemental "dropout = failure" analysis is considered in which a common poor outcome is imputed for all dropouts followed by a between-treatment comparison using quantile regression. All analyses address the same estimand and can adjust for baseline covariates. Three examples and simulation results are used to support our recommendations. Copyright © 2017 John Wiley & Sons, Ltd.

Polymorphisms in miRNA genes and their involvement in autoimmune diseases susceptibility.

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Latini, Andrea; Ciccacci, Cinzia; Novelli, Giuseppe; Borgiani, Paola

2017-08-01

MicroRNAs (miRNAs) are small non-coding RNA molecules that negatively regulate the expression of multiple protein-encoding genes at the post-transcriptional level. MicroRNAs are involved in different pathways, such as cellular proliferation and differentiation, signal transduction and inflammation, and play crucial roles in the development of several diseases, such as cancer, diabetes, and cardiovascular diseases. They have recently been recognized to play a role also in the pathogenesis of autoimmune diseases. Although the majority of studies are focused on miRNA expression profiles investigation, a growing number of studies have been investigating the role of polymorphisms in miRNA genes in the autoimmune diseases development. Indeed, polymorphisms affecting the miRNA genes can modify the set of targets they regulate or the maturation efficiency. This review is aimed to give an overview about the available studies that have investigated the association of miRNA gene polymorphisms with the susceptibility to various autoimmune diseases and to their clinical phenotypes.

A path-based measurement for human miRNA functional similarities using miRNA-disease associations

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Ding, Pingjian; Luo, Jiawei; Xiao, Qiu; Chen, Xiangtao

2016-09-01

Compared with the sequence and expression similarity, miRNA functional similarity is so important for biology researches and many applications such as miRNA clustering, miRNA function prediction, miRNA synergism identification and disease miRNA prioritization. However, the existing methods always utilized the predicted miRNA target which has high false positive and false negative to calculate the miRNA functional similarity. Meanwhile, it is difficult to achieve high reliability of miRNA functional similarity with miRNA-disease associations. Therefore, it is increasingly needed to improve the measurement of miRNA functional similarity. In this study, we develop a novel path-based calculation method of miRNA functional similarity based on miRNA-disease associations, called MFSP. Compared with other methods, our method obtains higher average functional similarity of intra-family and intra-cluster selected groups. Meanwhile, the lower average functional similarity of inter-family and inter-cluster miRNA pair is obtained. In addition, the smaller p-value is achieved, while applying Wilcoxon rank-sum test and Kruskal-Wallis test to different miRNA groups. The relationship between miRNA functional similarity and other information sources is exhibited. Furthermore, the constructed miRNA functional network based on MFSP is a scale-free and small-world network. Moreover, the higher AUC for miRNA-disease prediction indicates the ability of MFSP uncovering miRNA functional similarity.

Random Forest as an Imputation Method for Education and Psychology Research: Its Impact on Item Fit and Difficulty of the Rasch Model

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Golino, Hudson F.; Gomes, Cristiano M. A.

2016-01-01

This paper presents a non-parametric imputation technique, named random forest, from the machine learning field. The random forest procedure has two main tuning parameters: the number of trees grown in the prediction and the number of predictors used. Fifty experimental conditions were created in the imputation procedure, with different…

PPARγ inhibits ovarian cancer cells proliferation through upregulation of miR-125b

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Luo, Shuang, E-mail: luoshuangsch@163.com [Department of Obstetrics and Gynecology, Suining Central Hospital, Suining (China); Wang, Jidong [Department of Gynecology and Obsterics, Jinan Central Hospital, Jinan (China); Ma, Ying [Department of Otorhinolaryngolgy, Suining Central Hospital, Suining (China); Yao, Zhenwei [Department of Gynecology and Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing (China); Pan, Hongjuan [Department of Gynecology and Obsterics, Zhongshan Hospital, Wuhan (China)

2015-06-26

miR-125b has essential roles in coordinating tumor proliferation, angiogenesis, invasiveness, metastasis and chemotherapy recurrence. In ovarian cancer miR-125b has been shown to be downregulated and acts as a tumor suppressor by targeting proto-oncogene BCL3. PPARγ, a multiple functional transcription factor, has been reported to have anti-tumor effects through inhibition of proliferation and induction of differentiation and apoptosis by targeting the tumor related genes. However, it is unclear whether miR-125b is regulated by PPARγ in ovarian cancer. In this study, we demonstrated that the miR-125b downregulated in ovarian cancer tissues and cell lines. Ligands-activated PPARγ suppressed proliferation of ovarian cancer cells and this PPARγ-induced growth inhibition is mediated by the upregulation of miR-125b. PPARγ promoted the expression of miR-125b by directly binding to the responsive element in miR-125b gene promoter region. Thus, our results suggest that PPARγ can induce growth suppression of ovarian cancer by upregulating miR-125b which inhibition of proto-oncogene BCL3. These findings will extend our understanding of the function of PPARγ in tumorigenesis and miR-125b may be a therapeutic intervention of ovarian cancer. - Highlights: • miR-125b is down-regulated in ovarian cancer tissues and cells. • PPARγ upregulates miR-125b and downregulates its target gene BCL3 expression. • Silence of miR-125b attenuates PPARγ-mediated growth suppression of ovarian cancer cells. • PPARγ promotes the transcription of miR-125b via binding to PPARE in miR-125b gene promoter region.

Evaluation of miR-21 and miR-375 as prognostic biomarkers in esophageal cancer

DEFF Research Database (Denmark)

Winther, Mette; Alsner, Jan; Tramm, Trine

2015-01-01

analyses identified miR-21 as an independent prognostic marker for DSS in EAC [HR 3.52 (95% CI 1.06-11.69)]. High miR-375 was not correlated with improved prognosis in either histology. However, Forest plots demonstrated that both miR-21 and miR-375 were of prognostic impact in ESCC. CONCLUSION...... chemotherapy were analyzed. Expression levels of miR-21 and miR-375 were quantified using Affymetrix GeneChip miRNA 1.0 Array. The Cox proportional hazards model was used to assess the correlation of miR-21 and miR-375 with disease-specific survival (DSS) and overall survival (OS). Forest plots were performed...... to evaluate the prognostic impact of miR-21 and miR-375 in the present study and previously published reports. RESULTS: In ESCC, patients with miR-21 expression levels above median showed a trend towards poorer DSS and OS. When dividing miR-21 expression by tertiles, high levels of miR-21 significantly...

Early Sámi visual artists - Western fine art meets Sámi culture

Directory of Open Access Journals (Sweden)

Tuija Hautala-Hirvioja

2014-04-01

Full Text Available Johan Turi (1854–1936, Nils Nilsson Skum (1872–1951 and John Savio (1902–1938 were among the first Sámi visual artists. The production of their art work occurred between the 1910s and the early 1950s. Sámi aesthetics had its basis in folklore, i.e., handicraft or duodji, which did not follow the principle of art for art’s sake but combined beauty and practicality. Art was part of community life. Not until the 1970s was the word daidda, which is Finnish in origin and which means “art”, adopted into the Sámi language. Turi and Skum became famous through their books. They drew and wrote in order to pass the traditional knowledge of their people on to succeeding generations. They also wanted to introduce Sámi life and culture to non-Sámi people. One typical feature of their work is that they depicted Sáminess in a realistic way and sought to strengthen and preserve the Sámi identity through their art. In Turi and Skum’s work, both the documentation of community life and their own personal expression were strongly present and equally important; for this reason their pictures and texts have both practical and aesthetic dimensions. They did not attend school and were self-taught artists. The third pioneer of Sámi visual arts was John Savio, who, unlike the other two, attended secondary school and studied visual arts both independently and under the guidance of a mentor. He expressively combined Western ways of depiction with Sámi subjects. My article examines what made these early Sámi artists change over from Sámi handicraft, duodji, to Western visual arts, how they used Western pictorial conventions in dealing with their Sámi subjects, and the significance of their art for Sámi identity and culture. They lived and worked under cross pressure: the first few decades of the 20th century were characterized by racial theories that denigrated Sámi people, and the period following World War II was marked by demands for

A Meta-Path-Based Prediction Method for Human miRNA-Target Association

Directory of Open Access Journals (Sweden)

Jiawei Luo

2016-01-01

Full Text Available MicroRNAs (miRNAs are short noncoding RNAs that play important roles in regulating gene expressing, and the perturbed miRNAs are often associated with development and tumorigenesis as they have effects on their target mRNA. Predicting potential miRNA-target associations from multiple types of genomic data is a considerable problem in the bioinformatics research. However, most of the existing methods did not fully use the experimentally validated miRNA-mRNA interactions. Here, we developed RMLM and RMLMSe to predict the relationship between miRNAs and their targets. RMLM and RMLMSe are global approaches as they can reconstruct the missing associations for all the miRNA-target simultaneously and RMLMSe demonstrates that the integration of sequence information can improve the performance of RMLM. In RMLM, we use RM measure to evaluate different relatedness between miRNA and its target based on different meta-paths; logistic regression and MLE method are employed to estimate the weight of different meta-paths. In RMLMSe, sequence information is utilized to improve the performance of RMLM. Here, we carry on fivefold cross validation and pathway enrichment analysis to prove the performance of our methods. The fivefold experiments show that our methods have higher AUC scores compared with other methods and the integration of sequence information can improve the performance of miRNA-target association prediction.

miR-7 and miR-218 epigenetically control tumor suppressor genes RASSF1A and Claudin-6 by targeting HoxB3 in breast cancer

International Nuclear Information System (INIS)

Li, Qiaoyan; Zhu, Fufan; Chen, Puxiang

2012-01-01

Highlights: ► Both miR-7 and miR-218 down-regulates HoxB3 expression by targeting the 3′-UTR of HoxB3 mRNA. ► A reverse correlation between the levels of endogenous miR-7, miR218 and HoxB3 expression. ► Epigenetic changes involve in the reactivation of HoxB3. ► Both miRNAs inhibits the cell cycle and clone formation of breast cancer cells. -- Abstract: Many microRNAs have been implicated as key regulators of cellular growth and differentiation and have been found to dysregulate proliferation in human tumors, including breast cancer. Cancer-linked microRNAs also alter the epigenetic landscape by way of DNA methylation and post-translational modifications of histones. Aberrations in Hox gene expression are important for oncogene or tumor suppressor during abnormal development and malignancy. Although recent studies suggest that HoxB3 is critical in breast cancer, the putative role(s) of microRNAs impinging on HoxB3 is not yet fully understood. In this study, we found that the expression levels of miR-7 and miR-218 were strongly and reversely associated with HoxB3 expression. Stable overexpression of miR-7 and miR-218 was accompanied by reactivation of tumor suppressor genes including RASSF1A and Claudin-6 by means of epigenetic switches in DNA methylation and histone modification, giving rise to inhibition of the cell cycle and clone formation of breast cancer cells. The current study provides a novel link between overexpression of collinear Hox genes and multiple microRNAs in human breast malignancy.

Diagnostic potential of miR-126, miR-143, miR-145, and miR-652 in malignant pleural mesothelioma

DEFF Research Database (Denmark)

Andersen, Morten; Grauslund, Morten; Ravn, Jesper

2014-01-01

Malignant pleural mesothelioma (MPM) is difficult to distinguish from reactive mesothelial proliferations (RMPs). It is uncertain whether miRNAs are useful biomarkers for differentiating MPM from RMPs. Thus, we screened with a quantitative RT-PCR (RT-qPCR)-based platform the expression of 742 miR...

Evaluation of miR-122 as a Serum Biomarker for Hepatotoxicity in Investigative Rat Toxicology Studies.

Science.gov (United States)

Sharapova, T; Devanarayan, V; LeRoy, B; Liguori, M J; Blomme, E; Buck, W; Maher, J

2016-01-01

MicroRNAs are short noncoding RNAs involved in regulation of gene expression. Certain microRNAs, including miR-122, seem to have ideal properties as biomarkers due to good stability, high tissue specificity, and ease of detection across multiple species. Recent reports have indicated that miR-122 is a highly liver-specific marker detectable in serum after liver injury. The purpose of the current study was to assess the performance of miR-122 as a serum biomarker for hepatotoxicity in short-term (5-28 days) repeat-dose rat toxicology studies when benchmarked against routine clinical chemistry and histopathology. A total of 23 studies with multiple dose levels of experimental compounds were examined, and they included animals with or without liver injury and with various hepatic histopathologic changes. Serum miR-122 levels were quantified by reverse transcription quantitative polymerase chain reaction. Increases in circulating miR-122 levels highly correlated with serum elevations of liver enzymes, such as alanine aminotransferase (ALT), aspartate aminotransferase (AST) and glutamate dehydrogenase (GLDH). Statistical analysis showed that miR-122 outperformed ALT as a biomarker for histopathologically confirmed liver toxicity and was equivalent in performance to AST and GLDH. Additionally, an increase of 4% in predictive accuracy was obtained using a multiparameter approach incorporating miR-122 with ALT, AST, and GLDH. In conclusion, serum miR-122 levels can be utilized as a biomarker of hepatotoxicity in acute and subacute rat toxicology studies, and its performance can rival or exceed those of standard enzyme biomarkers such as the liver transaminases. © The Author(s) 2015.

Discovery and Fine-Mapping of Glycaemic and Obesity-Related Trait Loci Using High-Density Imputation.

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Momoko Horikoshi

2015-07-01

Full Text Available Reference panels from the 1000 Genomes (1000G Project Consortium provide near complete coverage of common and low-frequency genetic variation with minor allele frequency ≥0.5% across European ancestry populations. Within the European Network for Genetic and Genomic Epidemiology (ENGAGE Consortium, we have undertaken the first large-scale meta-analysis of genome-wide association studies (GWAS, supplemented by 1000G imputation, for four quantitative glycaemic and obesity-related traits, in up to 87,048 individuals of European ancestry. We identified two loci for body mass index (BMI at genome-wide significance, and two for fasting glucose (FG, none of which has been previously reported in larger meta-analysis efforts to combine GWAS of European ancestry. Through conditional analysis, we also detected multiple distinct signals of association mapping to established loci for waist-hip ratio adjusted for BMI (RSPO3 and FG (GCK and G6PC2. The index variant for one association signal at the G6PC2 locus is a low-frequency coding allele, H177Y, which has recently been demonstrated to have a functional role in glucose regulation. Fine-mapping analyses revealed that the non-coding variants most likely to drive association signals at established and novel loci were enriched for overlap with enhancer elements, which for FG mapped to promoter and transcription factor binding sites in pancreatic islets, in particular. Our study demonstrates that 1000G imputation and genetic fine-mapping of common and low-frequency variant association signals at GWAS loci, integrated with genomic annotation in relevant tissues, can provide insight into the functional and regulatory mechanisms through which their effects on glycaemic and obesity-related traits are mediated.

Decreased miR-128 and increased miR-21 synergistically cause podocyte injury in sepsis.

Science.gov (United States)

Wang, Shanshan; Wang, Jun; Zhang, Zengdi; Miao, Hongjun

2017-08-01

Glomerular podocytes are injured in sepsis. We studied, in a sepsis patient, whether microRNAs (miRNAs) play a role in the podocyte injury. Podocytes were cultured and treated with lipopolysaccharide (LPS). Filtration barrier function of podocyte was analyzed with albumin influx assay. Nephrin level was analyzed with reverse transcription polymerase chain reaction (RT-PCR) and western blot. MiRNAs were detected using miRNAs PCR Array and in situ hybridization. MiRNA target sites were evaluated with luciferase reporter assays. LPS impaired the filtration barrier function of podocytes. MiR-128 level was decreased and miR-21 level was increased in podocytes in vitro and in the sepsis patient. The decrease in miR-128 was sufficient to induce the loss of nephrin and the impairment of filtration barrier function, while the increase of miR-21 exacerbated the process. Snail and phosphatase and tensin homolog (PTEN) were identified as the targets of miR-128 and miR-21. Decreased miR-128 induced Snail expression, and the increased miR-21 stabilized Snail by regulating the PTEN/Akt/GSK3β pathway. Supplementation of miR-128 and inhibition of miR-21 suppressed Snail expression and prevented the podocyte injury induced by LPS. Our study suggests that decreased miR-128 and increased miR-21 synergistically cause podocyte injury and are the potential therapeutic targets in sepsis.

Age at menopause: imputing age at menopause for women with a hysterectomy with application to risk of postmenopausal breast cancer

Science.gov (United States)

Rosner, Bernard; Colditz, Graham A.

2011-01-01

Purpose Age at menopause, a major marker in the reproductive life, may bias results for evaluation of breast cancer risk after menopause. Methods We follow 38,948 premenopausal women in 1980 and identify 2,586 who reported hysterectomy without bilateral oophorectomy, and 31,626 who reported natural menopause during 22 years of follow-up. We evaluate risk factors for natural menopause, impute age at natural menopause for women reporting hysterectomy without bilateral oophorectomy and estimate the hazard of reaching natural menopause in the next 2 years. We apply this imputed age at menopause to both increase sample size and to evaluate the relation between postmenopausal exposures and risk of breast cancer. Results Age, cigarette smoking, age at menarche, pregnancy history, body mass index, history of benign breast disease, and history of breast cancer were each significantly related to age at natural menopause; duration of oral contraceptive use and family history of breast cancer were not. The imputation increased sample size substantially and although some risk factors after menopause were weaker in the expanded model (height, and alcohol use), use of hormone therapy is less biased. Conclusions Imputing age at menopause increases sample size, broadens generalizability making it applicable to women with hysterectomy, and reduces bias. PMID:21441037

Measurement of the Single Top Quark Production Cross Section and mi>Vtb>| in Events with One Charged Lepton, Large Missing Transverse Energy, and Jets at CDF

Energy Technology Data Exchange (ETDEWEB)

Aaltonen, T.; Amerio, S.; Amidei, D.; Anastassov, A.; Annovi, A.; Antos, J.; Apollinari, G.; Appel, J. A.; Arisawa, T.; Artikov, A.; Asaadi, J.; Ashmanskas, W.; Auerbach, B.; Aurisano, A.; Azfar, F.; Badgett, W.; Bae, T.; Barbaro-Galtieri, A.; Barnes, V. E.; Barnett, B. A.; Barria, P.; Bartos, P.; Bauce, M.; Bedeschi, F.; Behari, S.; Bellettini, G.; Bellinger, J.; Benjamin, D.; Beretvas, A.; Bhatti, A.; Bland, K. R.; Blumenfeld, B.; Bocci, A.; Bodek, A.; Bortoletto, D.; Boudreau, J.; Boveia, A.; Brigliadori, L.; Bromberg, C.; Brucken, E.; Budagov, J.; Budd, H. S.; Burkett, K.; Busetto, G.; Bussey, P.; Butti, P.; Buzatu, A.; Calamba, A.; Camarda, S.; Campanelli, M.; Canelli, F.; Carls, B.; Carlsmith, D.; Carosi, R.; Carrillo, S.; Casal, B.; Casarsa, M.; Castro, A.; Catastini, P.; Cauz, D.; Cavaliere, V.; Cerri, A.; Cerrito, L.; Chen, Y. C.; Chertok, M.; Chiarelli, G.; Chlachidze, G.; Cho, K.; Chokheli, D.; Clark, A.; Clarke, C.; Convery, M. E.; Conway, J.; Corbo, M.; Cordelli, M.; Cox, C. A.; Cox, D. J.; Cremonesi, M.; Cruz, D.; Cuevas, J.; Culbertson, R.; d’Ascenzo, N.; Datta, M.; de Barbaro, P.; Demortier, L.; Deninno, M.; D’Errico, M.; Devoto, F.; Di Canto, A.; Di Ruzza, B.; Dittmann, J. R.; Donati, S.; D’Onofrio, M.; Dorigo, M.; Driutti, A.; Ebina, K.; Edgar, R.; Elagin, A.; Erbacher, R.; Errede, S.; Esham, B.; Farrington, S.; Fernández Ramos, J. P.; Field, R.; Flanagan, G.; Forrest, R.; Franklin, M.; Freeman, J. C.; Frisch, H.; Funakoshi, Y.; Galloni, C.; Garfinkel, A. F.; Garosi, P.; Gerberich, H.; Gerchtein, E.; Giagu, S.; Giakoumopoulou, V.; Gibson, K.; Ginsburg, C. M.; Giokaris, N.; Giromini, P.; Glagolev, V.; Glenzinski, D.; Gold, M.; Goldin, D.; Golossanov, A.; Gomez, G.; Gomez-Ceballos, G.; Goncharov, M.; González López, O.; Gorelov, I.; Goshaw, A. T.; Goulianos, K.; Gramellini, E.; Grosso-Pilcher, C.; Group, R. C.; Guimaraes da Costa, J.; Hahn, S. R.; Han, J. Y.; Happacher, F.; Hara, K.; Hare, M.; Harr, R. F.; Harrington-Taber, T.; Hatakeyama, K.; Hays, C.; Heinrich, J.; Herndon, M.; Hirschbuehl, D.; Hocker, A.; Hong, Z.; Hopkins, W.; Hou, S.; Hughes, R. E.; Husemann, U.; Hussein, M.; Huston, J.; Introzzi, G.; Iori, M.; Ivanov, A.; James, E.; Jang, D.; Jayatilaka, B.; Jeon, E. J.; Jindariani, S.; Jones, M.; Joo, K. K.; Jun, S. Y.; Junk, T. R.; Kambeitz, M.; Kamon, T.; Karchin, P. E.; Kasmi, A.; Kato, Y.; Ketchum, W.; Keung, J.; Kilminster, B.; Kim, D. H.; Kim, H. S.; Kim, J. E.; Kim, M. J.; Kim, S. H.; Kim, S. B.; Kim, Y. J.; Kim, Y. K.; Kimura, N.; Kirby, M.; Knoepfel, K.; Kondo, K.; Kong, D. J.; Konigsberg, J.; Kotwal, A. V.; Kreps, M.; Kroll, J.; Kruse, M.; Kuhr, T.; Kurata, M.; Laasanen, A. T.; Lammel, S.; Lancaster, M.; Lannon, K.; Latino, G.; Lee, H. S.; Lee, J. S.; Leo, S.; Leone, S.; Lewis, J. D.; Limosani, A.; Lipeles, E.; Lister, A.; Liu, H.; Liu, Q.; Liu, T.; Lockwitz, S.; Loginov, A.; Lucchesi, D.; Lucà, A.; Lueck, J.; Lujan, P.; Lukens, P.; Lungu, G.; Lys, J.; Lysak, R.; Madrak, R.; Maestro, P.; Malik, S.; Manca, G.; Manousakis-Katsikakis, A.; Marchese, L.; Margaroli, F.; Marino, P.; Matera, K.; Mattson, M. E.; Mazzacane, A.; Mazzanti, P.; McNulty, R.; Mehta, A.; Mehtala, P.; Mesropian, C.; Miao, T.; Mietlicki, D.; Mitra, A.; Miyake, H.; Moed, S.; Moggi, N.; Moon, C. S.; Moore, R.; Morello, M. J.; Mukherjee, A.; Muller, Th.; Murat, P.; Mussini, M.; Nachtman, J.; Nagai, Y.; Naganoma, J.; Nakano, I.; Napier, A.; Nett, J.; Neu, C.; Nigmanov, T.; Nodulman, L.; Noh, S. Y.; Norniella, O.; Oakes, L.; Oh, S. H.; Oh, Y. D.; Oksuzian, I.; Okusawa, T.; Orava, R.; Ortolan, L.; Pagliarone, C.; Palencia, E.; Palni, P.; Papadimitriou, V.; Parker, W.; Pauletta, G.; Paulini, M.; Paus, C.; Phillips, T. J.; Pianori, E.; Pilot, J.; Pitts, K.; Plager, C.; Pondrom, L.; Poprocki, S.; Potamianos, K.; Pranko, A.; Prokoshin, F.; Ptohos, F.; Punzi, G.; Redondo Fernández, I.; Renton, P.; Rescigno, M.; Rimondi, F.; Ristori, L.; Robson, A.; Rodriguez, T.; Rolli, S.; Ronzani, M.; Roser, R.; Rosner, J. L.; Ruffini, F.; Ruiz, A.; Russ, J.; Rusu, V.; Sakumoto, W. K.; Sakurai, Y.; Santi, L.; Sato, K.; Saveliev, V.; Savoy-Navarro, A.; Schlabach, P.; Schmidt, E. E.; Schwarz, T.; Scodellaro, L.; Scuri, F.; Seidel, S.; Seiya, Y.; Semenov, A.; Sforza, F.; Shalhout, S. Z.; Shears, T.; Shepard, P. F.; Shimojima, M.; Shochet, M.; Shreyber-Tecker, I.; Simonenko, A.; Sliwa, K.; Smith, J. R.; Snider, F. D.; Song, H.; Sorin, V.; St. Denis, R.; Stancari, M.; Stentz, D.; Strologas, J.; Sudo, Y.; Sukhanov, A.; Suslov, I.; Takemasa, K.; Takeuchi, Y.; Tang, J.; Tecchio, M.; Teng, P. K.; Thom, J.; Thomson, E.; Thukral, V.; Toback, D.; Tokar, S.; Tollefson, K.; Tomura, T.; Tonelli, D.; Torre, S.; Torretta, D.; Totaro, P.; Trovato, M.; Ukegawa, F.; Uozumi, S.; Vázquez, F.; Velev, G.; Vellidis, C.; Vernieri, C.; Vidal, M.; Vilar, R.; Vizán, J.; Vogel, M.; Volpi, G.; Wagner, P.; Wallny, R.; Wang, S. M.; Waters, D.; Wester, W. C.; Whiteson, D.; Wicklund, A. B.; Wilbur, S.; Williams, H. H.; Wilson, J. S.; Wilson, P.; Winer, B. L.; Wittich, P.; Wolbers, S.; Wolfe, H.; Wright, T.; Wu, X.; Wu, Z.; Yamamoto, K.; Yamato, D.; Yang, T.; Yang, U. K.; Yang, Y. C.; Yao, W. -M.; Yeh, G. P.; Yi, K.; Yoh, J.; Yorita, K.; Yoshida, T.; Yu, G. B.; Yu, I.; Zanetti, A. M.; Zeng, Y.; Zhou, C.; Zucchelli, S.

2014-12-31

We report a measurement of single top quark production in proton-antiproton collisions at a center-of-mass energy of mi>s>=1.96 mi>TeV> using a data set corresponding to 7.5 mi>fb>-1 of integrated luminosity collected by the Collider Detector at Fermilab. We select events consistent with the single top quark decay process mi>t>→mi>Wb>→ℓmi>νb> by requiring the presence of an electron or muon, a large imbalance of transverse momentum indicating the presence of a neutrino, and two or three jets including at least one originating from a bottom quark. An artificial neural network is used to discriminate the signal from backgrounds. We measure a single top quark production cross section of 3.04-0.53+0.57 mi>pb> and set a lower limit on the magnitude of the coupling between the top quark and bottom quark |mi>V

miRNAs in brain development

International Nuclear Information System (INIS)

Petri, Rebecca; Malmevik, Josephine; Fasching, Liana; Åkerblom, Malin; Jakobsson, Johan

2014-01-01

MicroRNAs (miRNAs) are small, non-coding RNAs that negatively regulate gene expression at the post-transcriptional level. In the brain, a large number of miRNAs are expressed and there is a growing body of evidence demonstrating that miRNAs are essential for brain development and neuronal function. Conditional knockout studies of the core components in the miRNA biogenesis pathway, such as Dicer and DGCR8, have demonstrated a crucial role for miRNAs during the development of the central nervous system. Furthermore, mice deleted for specific miRNAs and miRNA-clusters demonstrate diverse functional roles for different miRNAs during the development of different brain structures. miRNAs have been proposed to regulate cellular functions such as differentiation, proliferation and fate-determination of neural progenitors. In this review we summarise the findings from recent studies that highlight the importance of miRNAs in brain development with a focus on the mouse model. We also discuss the technical limitations of current miRNA studies that still limit our understanding of this family of non-coding RNAs and propose the use of novel and refined technologies that are needed in order to fully determine the impact of specific miRNAs in brain development. - Highlights: • miRNAs are essential for brain development and neuronal function. • KO of Dicer is embryonically lethal. • Conditional Dicer KO results in defective proliferation or increased apoptosis. • KO of individual miRNAs or miRNA families is necessary to determine function

ARMOUR – A Rice miRNA: mRNA Interaction Resource

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Neeti Sanan-Mishra

2018-05-01

Full Text Available ARMOUR was developed as ARice miRNA:mRNA interaction resource. This informative and interactive database includes the experimentally validated expression profiles of miRNAs under different developmental and abiotic stress conditions across seven Indian rice cultivars. This comprehensive database covers 689 known and 1664 predicted novel miRNAs and their expression profiles in more than 38 different tissues or conditions along with their predicted/known target transcripts. The understanding of miRNA:mRNA interactome in regulation of functional cellular machinery is supported by the sequence information of the mature and hairpin structures. ARMOUR provides flexibility to users in querying the database using multiple ways like known gene identifiers, gene ontology identifiers, KEGG identifiers and also allows on the fly fold change analysis and sequence search query with inbuilt BLAST algorithm. ARMOUR database provides a cohesive platform for novel and mature miRNAs and their expression in different experimental conditions and allows searching for their interacting mRNA targets, GO annotation and their involvement in various biological pathways. The ARMOUR database includes a provision for adding more experimental data from users, with an aim to develop it as a platform for sharing and comparing experimental data contributed by research groups working on rice.

Cell type-specific deficiency of c-kit gene expression in mutant mice of mi/mi genotype.

Science.gov (United States)

Isozaki, K.; Tsujimura, T.; Nomura, S.; Morii, E.; Koshimizu, U.; Nishimune, Y.; Kitamura, Y.

1994-01-01

The mi locus of mice encodes a novel member of the basic-helix-loop-helix-leucine zipper protein family of transcription factors (hereafter called mi factor). In addition to microphthalmus, osteopetrosis, and lack of melanocytes, mice of mi/mi genotype are deficient in mast cells. Since the c-kit receptor tyrosine kinase plays an important role in the development of mast cells, and since the c-kit expression by cultured mast cells from mi/mi mice is deficient in both mRNA and protein levels, the mast cell deficiency of mi/mi mice has been attributed at least in part to the deficient expression of c-kit. However, it remained to be examined whether the c-kit expression was also deficient in tissues of mi/mi mice. In the present study, we examined the c-kit expression by mi/mi skin mast cells using in situ hybridization and immunohistochemistry. Moreover, we examined the c-kit expression by various cells other than mast cells in tissues of mi/mi mice. We found that the c-kit expression was deficient in mast cells but not in erythroid precursors, testicular germ cells, and neurons of mi/mi mice. This suggested that the regulation of the c-kit transcription by the mi factor was dependent on cell types. Mice of mi/mi genotype appeared to be a useful model to analyze the function of transcription factors in the whole-animal level. Images Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 PMID:7524330

Which DTW Method Applied to Marine Univariate Time Series Imputation

OpenAIRE

Phan , Thi-Thu-Hong; Caillault , Émilie; Lefebvre , Alain; Bigand , André

2017-01-01

International audience; Missing data are ubiquitous in any domains of applied sciences. Processing datasets containing missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Therefore, the aim of this paper is to build a framework for filling missing values in univariate time series and to perform a comparison of different similarity metrics used for the imputation task. This allows to suggest the most suitable methods for the imp...

Towards a more efficient representation of imputation operators in TPOT

OpenAIRE

Garciarena, Unai; Mendiburu, Alexander; Santana, Roberto

2018-01-01

Automated Machine Learning encompasses a set of meta-algorithms intended to design and apply machine learning techniques (e.g., model selection, hyperparameter tuning, model assessment, etc.). TPOT, a software for optimizing machine learning pipelines based on genetic programming (GP), is a novel example of this kind of applications. Recently we have proposed a way to introduce imputation methods as part of TPOT. While our approach was able to deal with problems with missing data, it can prod...

Hypermethylation of MIR21 in CD4+ T cells from patients with relapsing-remitting multiple sclerosis associates with lower miRNA-21 levels and concomitant up-regulation of its target genes

KAUST Repository

Ruhrmann, Sabrina

2017-08-02

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system caused by genetic and environmental factors. DNA methylation, an epigenetic mechanism that controls genome activity, may provide a link between genetic and environmental risk factors.We sought to identify DNA methylation changes in CD4+ T cells in patients with relapsing-remitting (RR-MS) and secondary-progressive (SP-MS) disease and healthy controls (HC).We performed DNA methylation analysis in CD4+ T cells from RR-MS, SP-MS, and HC and associated identified changes with the nearby risk allele, smoking, age, and gene expression.We observed significant methylation differences in the VMP1/MIR21 locus, with RR-MS displaying higher methylation compared to SP-MS and HC. VMP1/MIR21 methylation did not correlate with a known MS risk variant in VMP1 or smoking but displayed a significant negative correlation with age and the levels of mature miR-21 in CD4+ T cells. Accordingly, RR-MS displayed lower levels of miR-21 compared to SP-MS, which might reflect differences in age between the groups, and healthy individuals and a significant enrichment of up-regulated miR-21 target genes.Disease-related changes in epigenetic marking of MIR21 in RR-MS lead to differences in miR-21 expression with a consequence on miR-21 target genes.

miRNA signature and Dicer requirement during human endometrial stromal decidualization in vitro.

Directory of Open Access Journals (Sweden)

Carlos Estella

Full Text Available Decidualization is a morphological and biochemical transformation of endometrial stromal fibroblast into differentiated decidual cells, which is critical for embryo implantation and pregnancy establishment. The complex regulatory networks have been elucidated at both the transcriptome and the proteome levels, however very little is known about the post-transcriptional regulation of this process. miRNAs regulate multiple physiological pathways and their de-regulation is associated with human disorders including gynaecological conditions such as endometriosis and preeclampsia. In this study we profile the miRNAs expression throughout human endometrial stromal (hESCs decidualization and analyze the requirement of the miRNA biogenesis enzyme Dicer during this process. A total of 26 miRNAs were upregulated and 17 miRNAs downregulated in decidualized hESCs compared to non-decidualized hESCs. Three miRNAs families, miR-181, miR-183 and miR-200, are down-regulated during the decidualization process. Using miRNAs target prediction algorithms we have identified the potential targets and pathways regulated by these miRNAs. The knockdown of Dicer has a minor effect on hESCs during in vitro decidualization. We have analyzed a battery of decidualization markers such as cell morphology, Prolactin, IGFBP-1, MPIF-1 and TIMP-3 secretion as well as HOXA10, COX2, SP1, C/EBPß and FOXO1 expression in decidualized hESCs with decreased Dicer function. We found decreased levels of HOXA10 and altered intracellular organization of actin filaments in Dicer knockdown decidualized hESCs compared to control. Our results provide the miRNA signature of hESC during the decidualization process in vitro. We also provide the first functional characterization of Dicer during human endometrial decidualization although surprisingly we found that Dicer plays a minor role regulating this process suggesting that alternative biogenesis miRNAs pathways must be involved in human

Serum MiRNA Biomarkers serve as a Fingerprint for Proliferative Diabetic Retinopathy

Directory of Open Access Journals (Sweden)

Shao Qing

2014-11-01

Full Text Available Background: Diabetic retinopathy (DR is a retinopathy resulting from diabetes mellitus (DM which was classified into non-proliferative DR (NPDR and proliferative DR (PDR. Without an early screening and effective diagnosis, patients with PDR will develop serious complications. Therefore, we sought to identify special serum microRNAs (miRNAs that can serve as a novel non-invasive screening signature of PDR and test its specificity and sensitivity in the early diagnosis of PDR. Methods: In total, we obtained serum samples from 90 PDR cases, 90 matched NPDR patients and 20 controls. An initial screening of miRNA expression was performed through TaqMan Low Density Array (TLDA. The candidate miRNAs were validated by individual reverse transcription quantitative real-time PCR (RT-qPCR arranged in an initial and a two-stage validation sets. Moreover, additional double-blind testing was performed in 20 patients clinically suspected of having DR to evaluate the diagnostic value and accuracy of the serum miRNA profiling system in predicting PDR. Results: Three miRNAs were significantly increased in patients with PDR compared with NPDR after the multiple stages. The areas under the receiver operating characteristic (ROC curves of the validated three-serum miRNAs signature were 0.830, 0.803 and 0.873 in the initial and two validation sets, respectively. Combination of miR-21, miR-181c, and miR-1179 possessed a moderate ability to discrimination between PDR and NPDR with an area under ROC value of 0.89. The accuracy rate of the three-miRNA profile as PDR signature was 82.6%. Conclusions: These data provide evidence that serum miRNAs have the potential to be sensitive, cost-effective biomarkers for the early detection of PDR. These biomarkers could serve as a dynamic monitoring factor for detecting the progression of PDR from NPDR.

miRNAFold: a web server for fast miRNA precursor prediction in genomes.

Science.gov (United States)

Tav, Christophe; Tempel, Sébastien; Poligny, Laurent; Tahi, Fariza

2016-07-08

Computational methods are required for prediction of non-coding RNAs (ncRNAs), which are involved in many biological processes, especially at post-transcriptional level. Among these ncRNAs, miRNAs have been largely studied and biologists need efficient and fast tools for their identification. In particular, ab initio methods are usually required when predicting novel miRNAs. Here we present a web server dedicated for miRNA precursors identification at a large scale in genomes. It is based on an algorithm called miRNAFold that allows predicting miRNA hairpin structures quickly with high sensitivity. miRNAFold is implemented as a web server with an intuitive and user-friendly interface, as well as a standalone version. The web server is freely available at: http://EvryRNA.ibisc.univ-evry.fr/miRNAFold. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Functional miRNAs in breast cancer drug resistance

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Hu WZ

2018-03-01

Full Text Available Weizi Hu,1–3,* Chunli Tan,1–3,* Yunjie He,4 Guangqin Zhang,2 Yong Xu,3,5 Jinhai Tang1 1Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, 2School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, 3Nanjing Medical University Affiliated Cancer Hospital, 4The First Clinical School of Nanjing Medical University, 5Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Nanjing Medical University, Nanjing, People’s Republic of China *These authors contributed equally to this work Abstract: Owing to improved early surveillance and advanced therapy strategies, the current death rate due to breast cancer has decreased; nevertheless, drug resistance and relapse remain obstacles on the path to successful systematic treatment. Multiple mechanisms responsible for drug resistance have been elucidated, and miRNAs seem to play a major part in almost every aspect of cancer progression, including tumorigenesis, metastasis, and drug resistance. In recent years, exosomes have emerged as novel modes of intercellular signaling vehicles, initiating cell–cell communication through their fusion with target cell membranes, delivering functional molecules including miRNAs and proteins. This review particularly focuses on enumerating functional miRNAs involved in breast cancer drug resistance as well as their targets and related mechanisms. Subsequently, we discuss the prospects and challenges of miRNA function in drug resistance and highlight valuable approaches for the investigation of the role of exosomal miRNAs in breast cancer progression and drug resistance. Keywords: microRNA, exosome, breast cancer, drug resistance

Imputation-based analysis of association studies: candidate regions and quantitative traits.

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Bertrand Servin

2007-07-01

Full Text Available We introduce a new framework for the analysis of association studies, designed to allow untyped variants to be more effectively and directly tested for association with a phenotype. The idea is to combine knowledge on patterns of correlation among SNPs (e.g., from the International HapMap project or resequencing data in a candidate region of interest with genotype data at tag SNPs collected on a phenotyped study sample, to estimate ("impute" unmeasured genotypes, and then assess association between the phenotype and these estimated genotypes. Compared with standard single-SNP tests, this approach results in increased power to detect association, even in cases in which the causal variant is typed, with the greatest gain occurring when multiple causal variants are present. It also provides more interpretable explanations for observed associations, including assessing, for each SNP, the strength of the evidence that it (rather than another correlated SNP is causal. Although we focus on association studies with quantitative phenotype and a relatively restricted region (e.g., a candidate gene, the framework is applicable and computationally practical for whole genome association studies. Methods described here are implemented in a software package, Bim-Bam, available from the Stephens Lab website http://stephenslab.uchicago.edu/software.html.

Selective inhibition of miR-92 in hippocampal neurons alters contextual fear memory.

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Vetere, Gisella; Barbato, Christian; Pezzola, Silvia; Frisone, Paola; Aceti, Massimiliano; Ciotti, MariaTeresa; Cogoni, Carlo; Ammassari-Teule, Martine; Ruberti, Francesca

2014-12-01

Post-transcriptional gene regulation mediated by microRNAs (miRNAs) is implicated in memory formation; however, the function of miR-92 in this regulation is uncharacterized. The present study shows that training mice in contextual fear conditioning produces a transient increase in miR-92 levels in the hippocampus and decreases several miR-92 gene targets, including: (i) the neuronal Cl(-) extruding K(+) Cl(-) co-transporter 2 (KCC2) protein; (ii) the cytoplasmic polyadenylation protein (CPEB3), an RNA-binding protein regulator of protein synthesis in neurons; and (iii) the transcription factor myocyte enhancer factor 2D (MEF2D), one of the MEF2 genes which negatively regulates memory-induced structural plasticity. Selective inhibition of endogenous miR-92 in CA1 hippocampal neurons, by a sponge lentiviral vector expressing multiple sequences imperfectly complementary to mature miR-92 under the control of the neuronal specific synapsin promoter, leads to up-regulation of KCC2, CPEB3 and MEF2D, impairs contextual fear conditioning, and prevents a memory-induced increase in the spine density. Taken together, the results indicate that neuronal-expressed miR-92 is an endogenous fine regulator of contextual fear memory in mice. © 2014 Wiley Periodicals, Inc.

Perspectives on Sámi historiography

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Lars Ivar Hansen

2017-09-01

Full Text Available The article focuses on Sámi history and historical methods. The main results and central aspects of Sámi history, in its relational context, are gone through. What effects and consequences — regarding both methodology and narrative styles — these aspects have had, and ought to have, for the processes of doing research on and writing Sámi history? The focus is on the politics of Sámi history and research. The issues, who is “allowed” to write Sámi history and the way Sámi research is demanded to stand in the service of different societal-cultural needs of the Sámi is dealt with. This expectation of applicability concerns Sámi history in general, and the more delimited efforts of presenting situated accounts of Sámi cultural practices, traditions and experience with relations to other folk groups. Finally, methodological considerations and recommendations of Sámi history are presented, in which a number of methodological competences and in-depth usage of numerous source categories are called for.

Measurement of Ratios of mi>νμ> Charged-Current Cross Sections on C, Fe, and Pb to CH at Neutrino Energies 2–20 GeV

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Tice, B. G.; Datta, M.; Mousseau, J.; Aliaga, L.; Altinok, O.; Barrios Sazo, M. G.; Betancourt, M.; Bodek, A.; Bravar, A.; Brooks, W. K.; Budd, H.; Bustamante, M. J.; Butkevich, A.; Martinez Caicedo, D. A.; Castromonte, C. M.; Christy, M. E.; Chvojka, J.; da Motta, H.; Devan, J.; Dytman, S. A.; Díaz, G. A.; Eberly, B.; Felix, J.; Fields, L.; Fiorentini, G. A.; Gago, A. M.; Gallagher, H.; Gran, R.; Harris, D. A.; Higuera, A.; Hurtado, K.; Jerkins, M.; Kafka, T.; Kordosky, M.; Kulagin, S. A.; Le, T.; Maggi, G.; Maher, E.; Manly, S.; Mann, W. A.; Marshall, C. M.; Martin Mari, C.; McFarland, K. S.; McGivern, C. L.; McGowan, A. M.; Miller, J.; Mislivec, A.; Morfín, J. G.; Muhlbeier, T.; Naples, D.; Nelson, J. K.; Norrick, A.; Osta, J.; Palomino, J. L.; Paolone, V.; Park, J.; Patrick, C. E.; Perdue, G. N.; Rakotondravohitra, L.; Ransome, R. D.; Ray, H.; Ren, L.; Rodrigues, P. A.; Savage, D. G.; Schellman, H.; Schmitz, D. W.; Simon, C.; Snider, F. D.; Solano Salinas, C. J.; Tagg, N.; Valencia, E.; Velásquez, J. P.; Walton, T.; Wolcott, J.; Zavala, G.; Zhang, D.; Ziemer, B. P.

2014-06-01

We present measurements of mi>νμ> charged-current cross section ratios on carbon, iron, and lead relative to a scintillator (CH) using the fine-grained MINERvA detector exposed to the NuMI neutrino beam at Fermilab. The measurements utilize events of energies 2<mi>Eν><20mi>GeVmi>, with (mi>Eν>)=8mi>GeV>, which have a reconstructed mi>μ>- scattering angle less than 17° to extract ratios of inclusive total cross sections as a function of neutrino energy mi>Eν> and flux-integrated differential cross sections with respect to the Bjorken scaling variable mi>x>. These results provide the first high-statistics direct measurements of nuclear effects in neutrino scattering using different targets in the same neutrino beam. Measured cross section ratios exhibit a relative

Knock-down of miR-221 and miR-222 in the radiosensitization of breast cancer cells

International Nuclear Information System (INIS)

Zhang Chunzhi; Kang Chunsheng; Cao Yongzhen; Pu Peiyu; Lu Zhonghong; Du Yue

2009-01-01

Objective: To investigate the radiosensitizing effect of knock-down of miR-221 miR-222 on MCF-7 human breast cancer cells and explore the possible mechanism. Methods: Antisense oligonucleotides of miR-221 and miR-222 (AS-miR-221 and AS-miR-222), mediated by lipofectamine, were transfected to MCF-7 cells to knock down miR-221 and miR-222, Northern blotting was conducted to detect the expression of miR-221 and miR-222 in transfected cells. The cell apoptosis was detected by flow cytometry and Caspase-3 and Caspase-7 activity assay. Clonogenic assay was used to measure the sensitizing enhancement ratio. Target genes of miR-221 and miR-222 relevant to radio-sensitivity were searched using bioinformatics analysis. The targeted protein expression was determined by Western blot analysis. Results: The expression of miR-221 and miR-222 in the AS-miR-221/222 cells determined by Northern blotting was significantly reduced. Compared with the control group, the cell apoptosis and mitotic cell death after the radiation were significantly higher in AS-miR-221/222 cells. The sensitizing enhancement ratio was 1.87. Based on bioinformatics analysis, PTEN was a target gene of miR-221 and miR-222 which could enhance the radiosensitivity of MCF-7 cells. In AS-miR-221/222 cells, the expression of PTEN was up-regulated while pAkt down-regulated. Conclusions: AS-miR-221 and AS-miR-222 may enhance the radiosensitivity of MCF-7 breast cancer cells by up-regulating the expression of PTEN. (authors)

Imputation of genotypes from low density (50,000 markers) to high density (700,000 markers) of cows from research herds in Europe, North America, and Australasia using 2 reference populations

DEFF Research Database (Denmark)

Pryce, J E; Johnston, J; Hayes, B J

2014-01-01

detection in genome-wide association studies and the accuracy of genomic selection may increase when the low-density genotypes are imputed to higher density. Genotype data were available from 10 research herds: 5 from Europe [Denmark, Germany, Ireland, the Netherlands, and the United Kingdom (UK)], 2 from...... reference populations. Although it was not possible to use a combined reference population, which would probably result in the highest accuracies of imputation, differences arising from using 2 high-density reference populations on imputing 50,000-marker genotypes of 583 animals (from the UK) were...... information exploited. The UK animals were also included in the North American data set (n = 1,579) that was imputed to high density using a reference population of 2,018 bulls. After editing, 591,213 genotypes on 5,999 animals from 10 research herds remained. The correlation between imputed allele...

Exploring the Application of Multiple Intelligences Theory to Career Counseling

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Shearer, C. Branton; Luzzo, Darrell Anthony

2009-01-01

This article demonstrates the practical value of applying H. Gardner's (1993) theory of multiple intelligences (MI) to the practice of career counseling. An overview of H. Gardner's MI theory is presented, and the ways in which educational and vocational planning can be augmented by the integration of MI theory in career counseling contexts are…

Evaluation of miR-182/miR-100 Ratio for Diagnosis and Survival Prediction in Bladder Cancer.

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Chen, Zhanguo; Wu, Lili; Lin, Qi; Shi, Jing; Lin, Xiangyang; Shi, Liang

2016-09-01

Abnormal expression of microRNAs (miRNAs) plays an important role in development of several cancer types, including bladder cancer (BCa). However, the relationship between the ratio of miR-181/miR-100 and the prognosis of BCa has not been studied yet. The aim of this study was to evaluate the expression of miR-182, miR-100 and their clinical significance in BCa. Upregulation of miR-182 and down-regulation of miR-100 were validated in tissue specimens of 134 BCa cases compared with 148 normal bladder epithelia (NBE) specimens  using TaqMan-based real-time reverse transcription quantitative PCR (RT-qPCR). The diagnostic and prognostic evaluation of miR-182, miR-100, and miR-182/miR-100 ratio was also performed. miR-182 was upregulated in BCa and miR-100 was down-regulated in BCa compared with NBE (P ratio increased the diagnostic performance, yielding an AUC of 0.981 (97.01% sensitivity and 90.54% specificity). Moreover, miR-182/miR-100 ratio was associated with pT-stage, histological grade, BCa recurrence and carcinoma in situ (P analysis indicated that miR-182/miR-100 ratio was an independent prognostic factor for overall survival (Hazard ratio: 7.142; 95% CI: 2.106 - 9.891; P analysis revealed that high-level of miR-182/miR-100 ratio was significantly correlated with shortened survival time for BCa patients (P ratio may serve as a novel promising biomarker for diagnosis and survival prediction in BCa. Further studies are needed to elucidate the role of miR-182/miR-100 ratio as a non‑invasive diagnostic tool for BCa.

MDRL lncRNA regulates the processing of miR-484 primary transcript by targeting miR-361.

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Kun Wang

2014-07-01

Full Text Available Long noncoding RNAs (lncRNAs are emerging as new players in gene regulation, but whether lncRNAs operate in the processing of miRNA primary transcript is unclear. Also, whether lncRNAs are involved in the regulation of the mitochondrial network remains to be elucidated. Here, we report that a long noncoding RNA, named mitochondrial dynamic related lncRNA (MDRL, affects the processing of miR-484 primary transcript in nucleus and regulates the mitochondrial network by targeting miR-361 and miR-484. The results showed that miR-361 that predominantly located in nucleus can directly bind to primary transcript of miR-484 (pri-miR-484 and prevent its processing by Drosha into pre-miR-484. miR-361 is able to regulate mitochondrial fission and apoptosis by regulating miR-484 levels. In exploring the underlying molecular mechanism by which miR-361 is regulated, we identified MDRL and demonstrated that it could directly bind to miR-361 and downregulate its expression levels, which promotes the processing of pri-miR-484. MDRL inhibits mitochondrial fission and apoptosis by downregulating miR-361, which in turn relieves inhibition of miR-484 processing by miR-361. Our present study reveals a novel regulating model of mitochondrial fission program which is composed of MDRL, miR-361 and miR-484. Our work not only expands the function of the lncRNA pathway in gene regulation but also establishes a new mechanism for controlling miRNA expression.

Differential expression of miR-139, miR-486 and miR-21 in breast cancer patients sub-classified according to lymph node status

DEFF Research Database (Denmark)

Rask, Lene; Balslev, Eva; Søkilde, Rolf

2014-01-01

PURPOSE: Therapeutic decisions in breast cancer are increasingly guided by prognostic and predictive biomarkers. Non-protein-coding microRNAs (miRNAs) have recently been found to be deregulated in breast cancers and, in addition, to be correlated with several clinico-pathological features. One...... of the most consistently up-regulated miRNAs is miR-21. Here, we specifically searched for differentially expressed miRNAs in high-risk breast cancer patients as compared to low-risk breast cancer patients. In the same patients, we also compared miR-21 expression with the expression of its presumed target...... PTEN. METHODS: Both microarray and RT-qPCR techniques were used to assess miRNA expression levels in lymph node-positive and -negative human invasive ductal carcinoma tissues. Simultaneously, PTEN protein expression levels were assessed using immunohistochemistry. RESULTS: miR-486-5p and miR-139-5p...

Global miRNA expression analysis of serous and clear cell ovarian carcinomas identifies differentially expressed miRNAs including miR-200c-3p as a prognostic marker

International Nuclear Information System (INIS)

Vilming Elgaaen, Bente; Olstad, Ole Kristoffer; Haug, Kari Bente Foss; Brusletto, Berit; Sandvik, Leiv; Staff, Anne Cathrine; Gautvik, Kaare M; Davidson, Ben

2014-01-01

Improved insight into the molecular characteristics of the different ovarian cancer subgroups is needed for developing a more individualized and optimized treatment regimen. The aim of this study was to a) identify differentially expressed miRNAs in high-grade serous ovarian carcinoma (HGSC), clear cell ovarian carcinoma (CCC) and ovarian surface epithelium (OSE), b) evaluate selected miRNAs for association with clinical parameters including survival and c) map miRNA-mRNA interactions. Differences in miRNA expression between HGSC, CCC and OSE were analyzed by global miRNA expression profiling (Affymetrix GeneChip miRNA 2.0 Arrays, n = 12, 9 and 9, respectively), validated by RT-qPCR (n = 35, 19 and 9, respectively), and evaluated for associations with clinical parameters. For HGSC, differentially expressed miRNAs were linked to differentially expressed mRNAs identified previously. Differentially expressed miRNAs (n = 78) between HGSC, CCC and OSE were identified (FDR < 0.01%), of which 18 were validated (p < 0.01) using RT-qPCR in an extended cohort. Compared with OSE, miR-205-5p was the most overexpressed miRNA in HGSC. miR-200 family members and miR-182-5p were the most overexpressed in HGSC and CCC compared with OSE, whereas miR-383 was the most underexpressed. miR-205-5p and miR-200 members target epithelial-mesenchymal transition (EMT) regulators, apparently being important in tumor progression. miR-509-3-5p, miR-509-5p, miR-509-3p and miR-510 were among the strongest differentiators between HGSC and CCC, all being significantly overexpressed in CCC compared with HGSC. High miR-200c-3p expression was associated with poor progression-free (p = 0.031) and overall (p = 0.026) survival in HGSC patients. Interacting miRNA and mRNA targets, including those of a TP53-related pathway presented previously, were identified in HGSC. Several miRNAs differentially expressed between HGSC, CCC and OSE have been identified, suggesting a carcinogenetic role for these mi

MiMiR: a comprehensive solution for storage, annotation and exchange of microarray data

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Rahman Fatimah

2005-11-01

Full Text Available Abstract Background The generation of large amounts of microarray data presents challenges for data collection, annotation, exchange and analysis. Although there are now widely accepted formats, minimum standards for data content and ontologies for microarray data, only a few groups are using them together to build and populate large-scale databases. Structured environments for data management are crucial for making full use of these data. Description The MiMiR database provides a comprehensive infrastructure for microarray data annotation, storage and exchange and is based on the MAGE format. MiMiR is MIAME-supportive, customised for use with data generated on the Affymetrix platform and includes a tool for data annotation using ontologies. Detailed information on the experiment, methods, reagents and signal intensity data can be captured in a systematic format. Reports screens permit the user to query the database, to view annotation on individual experiments and provide summary statistics. MiMiR has tools for automatic upload of the data from the microarray scanner and export to databases using MAGE-ML. Conclusion MiMiR facilitates microarray data management, annotation and exchange, in line with international guidelines. The database is valuable for underpinning research activities and promotes a systematic approach to data handling. Copies of MiMiR are freely available to academic groups under licence.

MiR-34a regulates the invasive capacity of canine osteosarcoma cell lines.

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Lopez, Cecilia M; Yu, Peter Y; Zhang, Xiaoli; Yilmaz, Ayse Selen; London, Cheryl A; Fenger, Joelle M

2018-01-01

Osteosarcoma (OSA) is the most common bone tumor in children and dogs; however, no substantial improvement in clinical outcome has occurred in either species over the past 30 years. MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression and play a fundamental role in cancer. The purpose of this study was to investigate the potential contribution of miR-34a loss to the biology of canine OSA, a well-established spontaneous model of the human disease. RT-qPCR demonstrated that miR-34a expression levels were significantly reduced in primary canine OSA tumors and canine OSA cell lines as compared to normal canine osteoblasts. In canine OSA cell lines stably transduced with empty vector or pre-miR-34a lentiviral constructs, overexpression of miR-34a inhibited cellular invasion and migration but had no effect on cell proliferation or cell cycle distribution. Transcriptional profiling of canine OSA8 cells possessing enforced miR-34a expression demonstrated dysregulation of numerous genes, including significant down-regulation of multiple putative targets of miR-34a. Moreover, gene ontology analysis of down-regulated miR-34a target genes showed enrichment of several biological processes related to cell invasion and motility. Lastly, we validated changes in miR-34a putative target gene expression, including decreased expression of KLF4, SEM3A, and VEGFA transcripts in canine OSA cells overexpressing miR-34a and identified KLF4 and VEGFA as direct target genes of miR-34a. Concordant with these data, primary canine OSA tumor tissues demonstrated increased expression levels of putative miR-34a target genes. These data demonstrate that miR-34a contributes to invasion and migration in canine OSA cells and suggest that loss of miR-34a may promote a pattern of gene expression contributing to the metastatic phenotype in canine OSA.

MiR-34a regulates the invasive capacity of canine osteosarcoma cell lines.

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Cecilia M Lopez

Full Text Available Osteosarcoma (OSA is the most common bone tumor in children and dogs; however, no substantial improvement in clinical outcome has occurred in either species over the past 30 years. MicroRNAs (miRNAs are small non-coding RNAs that regulate gene expression and play a fundamental role in cancer. The purpose of this study was to investigate the potential contribution of miR-34a loss to the biology of canine OSA, a well-established spontaneous model of the human disease.RT-qPCR demonstrated that miR-34a expression levels were significantly reduced in primary canine OSA tumors and canine OSA cell lines as compared to normal canine osteoblasts. In canine OSA cell lines stably transduced with empty vector or pre-miR-34a lentiviral constructs, overexpression of miR-34a inhibited cellular invasion and migration but had no effect on cell proliferation or cell cycle distribution. Transcriptional profiling of canine OSA8 cells possessing enforced miR-34a expression demonstrated dysregulation of numerous genes, including significant down-regulation of multiple putative targets of miR-34a. Moreover, gene ontology analysis of down-regulated miR-34a target genes showed enrichment of several biological processes related to cell invasion and motility. Lastly, we validated changes in miR-34a putative target gene expression, including decreased expression of KLF4, SEM3A, and VEGFA transcripts in canine OSA cells overexpressing miR-34a and identified KLF4 and VEGFA as direct target genes of miR-34a. Concordant with these data, primary canine OSA tumor tissues demonstrated increased expression levels of putative miR-34a target genes.These data demonstrate that miR-34a contributes to invasion and migration in canine OSA cells and suggest that loss of miR-34a may promote a pattern of gene expression contributing to the metastatic phenotype in canine OSA.

Inhibition of miR-155 Protects Against LPS-induced Cardiac Dysfunction and Apoptosis in Mice

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Hui Wang

2016-01-01

Full Text Available Sepsis-induced myocardial dysfunction represents a major cause of death in intensive care units. Dysregulated microRNAs (miR-155 has been implicated in multiple cardiovascular diseases and miR-155 can be induced by lipopolysaccharide (LPS. However, the role of miR-155 in LPS-induced cardiac dysfunction is unclear. Septic cardiac dysfunction in mice was induced by intraperitoneal injection of LPS (5 mg/kg and miR-155 was found to be significantly increased in heart challenged with LPS. Pharmacological inhibition of miR-155 using antagomiR improved cardiac function and suppressed cardiac apoptosis induced by LPS in mice as determined by echocardiography, terminal deoxynucleotidyl transferase nick-end labeling (TUNEL assay, and Western blot for Bax and Bcl-2, while overexpression of miR-155 using agomiR had inverse effects. Pea15a was identified as a target gene of miR-155, mediating its effects in controlling apoptosis of cardiomyocytes as evidenced by luciferase reporter assays, quantitative real time-polymerase chain reaction, Western blot, and TUNEL staining. Noteworthy, miR-155 was also found to be upregulated in the plasma of patients with septic cardiac dysfunction compared to sepsis patients without cardiac dysfunction, indicating a potential clinical relevance of miR-155. The receiver-operator characteristic curve indicated that plasma miR-155 might be a biomarker for sepsis patients developing cardiac dysfunction. Therefore, inhibition of miR-155 represents a novel therapy for septic myocardial dysfunction.

miR-24 and miR-205 expression is dependent on HPV onco-protein expression in keratinocytes

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McKenna, Declan J., E-mail: dj.mckenna@ulster.ac.uk [Biomedical Sciences Research Institute, University of Ulster, Coleraine, Co. Derry BT52 1SA (United Kingdom); Centre for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Science, Queen' s University Belfast, Belfast BT9 7BL (United Kingdom); Patel, Daksha, E-mail: d.patel@qub.ac.uk [Centre for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Science, Queen' s University Belfast, Belfast BT9 7BL (United Kingdom); McCance, Dennis J., E-mail: d.mccance@qub.ac.uk [Centre for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Science, Queen' s University Belfast, Belfast BT9 7BL (United Kingdom)

2014-01-05

A screen of microRNA (miRNA) expression following differentiation in human foreskin keratinocytes (HFKs) identified changes in several miRNAs, including miR-24 and miR-205. We investigated how expression of Human Papilloma Virus Type-16 (HPV16) onco-proteins E6 and E7 affected expression of miR-24 and miR-205 during proliferation and differentiation of HFKs. We show that the induction of both miR-24 and miR-205 observed during differentiation of HFKs is lost in HFKs expressing E6 and E7. We demonstrate that the effect on miR-205 is due to E7 activity, as miR-205 expression is dependent on pRb expression. Finally, we provide evidence that miR-24 effects in the cell may be due to targeting of cyclin dependent kinase inhibitor p27. In summary, these results indicate that expression of both miR-24 and miR-205 are impacted by E6 and/or E7 expression, which may be one mechanism by which HPV onco-proteins can disrupt the balance between proliferation and differentiation in keratinocytes. - Highlights: • miR-24 and miR-205 are induced during keratinocyte differentiation. • This induction is lost in keratinocytes expressing HPV onco-proteins E6 and E7. • miR-205 is dependent upon pRb expression. • miR-24 targets p27 in cycling keratinocytes.

miR-24 and miR-205 expression is dependent on HPV onco-protein expression in keratinocytes

International Nuclear Information System (INIS)

McKenna, Declan J.; Patel, Daksha; McCance, Dennis J.

2014-01-01

A screen of microRNA (miRNA) expression following differentiation in human foreskin keratinocytes (HFKs) identified changes in several miRNAs, including miR-24 and miR-205. We investigated how expression of Human Papilloma Virus Type-16 (HPV16) onco-proteins E6 and E7 affected expression of miR-24 and miR-205 during proliferation and differentiation of HFKs. We show that the induction of both miR-24 and miR-205 observed during differentiation of HFKs is lost in HFKs expressing E6 and E7. We demonstrate that the effect on miR-205 is due to E7 activity, as miR-205 expression is dependent on pRb expression. Finally, we provide evidence that miR-24 effects in the cell may be due to targeting of cyclin dependent kinase inhibitor p27. In summary, these results indicate that expression of both miR-24 and miR-205 are impacted by E6 and/or E7 expression, which may be one mechanism by which HPV onco-proteins can disrupt the balance between proliferation and differentiation in keratinocytes. - Highlights: • miR-24 and miR-205 are induced during keratinocyte differentiation. • This induction is lost in keratinocytes expressing HPV onco-proteins E6 and E7. • miR-205 is dependent upon pRb expression. • miR-24 targets p27 in cycling keratinocytes

Regulation of Pattern Formation and Gene Amplification During Drosophila Oogenesis by the miR-318 microRNA

DEFF Research Database (Denmark)

Ge, Wanzhong; Deng, Qiannan; Guo, Ting

2015-01-01

Pattern formation during epithelial development requires the coordination of multiple signaling pathways. Here, we investigate the functions of an ovary-enriched miRNA, miR-318, in epithelial development during Drosophila oogenesis. miR-318 maternal loss-of-function mutants were female sterile...... and laid eggs with abnormal morphology. Removal of miR-318 disrupted the dorsal-anterior follicle cell patterning, resulting in abnormal dorsal appendages. miR-318 mutant females also produced thin and fragile eggshells, due to impaired chorion gene amplification. We provide evidence that the ecdysone......RNAs in maintaining cell fate and promoting the developmental transition in the female follicular epithelium....

miRNA Expression Profile after Status Epilepticus and Hippocampal Neuroprotection by Targeting miR-132

Science.gov (United States)

Jimenez-Mateos, Eva M.; Bray, Isabella; Sanz-Rodriguez, Amaya; Engel, Tobias; McKiernan, Ross C.; Mouri, Genshin; Tanaka, Katsuhiro; Sano, Takanori; Saugstad, Julie A.; Simon, Roger P.; Stallings, Raymond L.; Henshall, David C.

2011-01-01

When an otherwise harmful insult to the brain is preceded by a brief, noninjurious stimulus, the brain becomes tolerant, and the resulting damage is reduced. Epileptic tolerance develops when brief seizures precede an episode of prolonged seizures (status epilepticus). MicroRNAs (miRNAs) are small, noncoding RNAs that function as post-transcriptional regulators of gene expression. We investigated how prior seizure preconditioning affects the miRNA response to status epilepticus evoked by intra-amygdalar kainic acid in mice. The miRNA was extracted from the ipsilateral CA3 subfield 24 hours after focal-onset status epilepticus in animals that had previously received either seizure preconditioning (tolerance) or no preconditioning (injury), and mature miRNA levels were measured using TaqMan low-density arrays. Expression of 21 miRNAs was increased, relative to control, after status epilepticus alone, and expression of 12 miRNAs was decreased. Increased miR-132 levels were matched with increased binding to Argonaute-2, a constituent of the RNA-induced silencing complex. In tolerant animals, expression responses of >40% of the injury-group-detected miRNAs differed, being either unchanged relative to control or down-regulated, and this included miR-132. In vivo microinjection of locked nucleic acid-modified oligonucleotides (antagomirs) against miR-132 depleted hippocampal miR-132 levels and reduced seizure-induced neuronal death. Thus, our data strongly suggest that miRNAs are important regulators of seizure-induced neuronal death. PMID:21945804

miR-20b, miR-98, miR-125b-1*, and let-7e* as new potential diagnostic biomarkers in ulcerative colitis

DEFF Research Database (Denmark)

Coskun, Mehmet; Bjerrum, Jacob Tveiten; Seidelin, Jakob Benedict

2013-01-01

were obtained endoscopically from patients with active UC or CD, quiescent UC or CD, as well as healthy controls. Total RNA was isolated and miRNA expression assessed using the miRNA microarray Geniom Biochip miRNA Homo sapiens (Febit GmbH, Heidelberg, Germany). Data analysis was carried out...... genes involved in various pathways, such as mitogen-activated protein kinase and cytokine signaling, which are both key signaling pathways in UC. CONCLUSION: The present study provides the first evidence that miR-20b, miR-98, miR-125b-1*, and let-7e* are deregulated in patients with UC. The level...

miRSponge: a manually curated database for experimentally supported miRNA sponges and ceRNAs.

Science.gov (United States)

Wang, Peng; Zhi, Hui; Zhang, Yunpeng; Liu, Yue; Zhang, Jizhou; Gao, Yue; Guo, Maoni; Ning, Shangwei; Li, Xia

2015-01-01

In this study, we describe miRSponge, a manually curated database, which aims at providing an experimentally supported resource for microRNA (miRNA) sponges. Recent evidence suggests that miRNAs are themselves regulated by competing endogenous RNAs (ceRNAs) or 'miRNA sponges' that contain miRNA binding sites. These competitive molecules can sequester miRNAs to prevent them interacting with their natural targets to play critical roles in various biological and pathological processes. It has become increasingly important to develop a high quality database to record and store ceRNA data to support future studies. To this end, we have established the experimentally supported miRSponge database that contains data on 599 miRNA-sponge interactions and 463 ceRNA relationships from 11 species following manual curating from nearly 1200 published articles. Database classes include endogenously generated molecules including coding genes, pseudogenes, long non-coding RNAs and circular RNAs, along with exogenously introduced molecules including viral RNAs and artificial engineered sponges. Approximately 70% of the interactions were identified experimentally in disease states. miRSponge provides a user-friendly interface for convenient browsing, retrieval and downloading of dataset. A submission page is also included to allow researchers to submit newly validated miRNA sponge data. Database URL: http://www.bio-bigdata.net/miRSponge. © The Author(s) 2015. Published by Oxford University Press.

miR-132 and miR-212 are increased in pancreatic cancer and target the retinoblastoma tumor suppressor

International Nuclear Information System (INIS)

Park, Jong-Kook; Henry, Jon C.; Jiang, Jinmai; Esau, Christine; Gusev, Yuriy; Lerner, Megan R.; Postier, Russell G.; Brackett, Daniel J.; Schmittgen, Thomas D.

2011-01-01

Research highlights: → The expression of miR-132 and miR-212 are significantly increased in pancreatic cancer. → miR-132 and miR-212 target the tumor suppressor pRb, resulting in enhanced proliferation. → miR-132 and miR-212 expression is increased by a β2 adrenergic receptor agonist, suggesting a novel mechanism for pancreatic cancer progression. -- Abstract: Numerous microRNAs (miRNAs) are reported as differentially expressed in cancer, however the consequence of miRNA deregulation in cancer is unknown for many miRNAs. We report that two miRNAs located on chromosome 17p13, miR-132 and miR-212, are over-expressed in pancreatic adenocarcinoma (PDAC) tissues. Both miRNAs are predicted to target the retinoblastoma tumor suppressor, Rb1. Validation of this interaction was confirmed by luciferase reporter assay and western blot in a pancreatic cancer cell line transfected with pre-miR-212 and pre-miR-132 oligos. Cell proliferation was enhanced in Panc-1 cells transfected with pre-miR-132/-212 oligos. Conversely, antisense oligos to miR-132/-212 reduced cell proliferation and caused a G 2 /M cell cycle arrest. The mRNA of a number of E2F transcriptional targets were increased in cells over expressing miR-132/-212. Exposing Panc-1 cells to the β2 adrenergic receptor agonist, terbutaline, increased the miR-132 and miR-212 expression by 2- to 4-fold. We report that over-expression of miR-132 and miR-212 result in reduced pRb protein in pancreatic cancer cells and that the increase in cell proliferation from over-expression of these miRNAs is likely due to increased expression of several E2F target genes. The β2 adrenergic pathway may play an important role in this novel mechanism.

Expression of miR-15a, miR-145, and miR-182 in granulosa-lutein cells, follicular fluid, and serum of women with polycystic ovary syndrome (PCOS).

Science.gov (United States)

Naji, Mohammad; Nekoonam, Saeid; Aleyasin, Ashraf; Arefian, Ehsan; Mahdian, Reza; Azizi, Elham; Shabani Nashtaei, Maryam; Amidi, Fardin

2018-01-01

Polycystic ovary syndrome (PCOS) is one of the most common endocrinopathies that affects women in reproductive age. MicroRNAs (miRNAs) play crucial roles in normal function of female reproductive system and folliculogenesis. Deregulated expression of miRNAs in PCOS condition may be significantly implicated in the pathogenesis of PCOS. We determined relative expression of miR-15a, miR-145, and miR-182 in granulosa-lutein cells (GLCs), follicular fluid (FF), and serum of PCOS patients. Human subjects were divided into PCOS (n = 20) and control (n = 21) groups. GLCs, FF, and serum were isolated and stored. RNA isolation was performed and cDNA was reversely transcribed using specific stem-loop RT primers. Relative expression of miRNAs was calculated after normalization against U6 expression. Correlation of miRNAs' expression level with basic clinical features and predictive value of miRNAs in FF and serum were appraised. Relative expression of miR-145 and miR-182 in GLCs was significantly decreased in PCOS, but miR-182 in FF of PCOS patients revealed up-regulated levels. Significant correlations between level of miRNAs in FF and serum and hormonal profile of subjects were observed. MiR-182 in FF showed a significant predictive value with AUC of 0.73, 76.4% sensitivity, and 70.5% specificity which was improved after combination of miR-182 and miR-145. A significant dysregulation of miR-145 and miR-182 in GLCs of PCOS may indicate their involvement in pathogenesis of PCOS. Differential up-regulation of miR-182 in FF of PCOS patients with its promising predictive values for discrimination of PCOS reinforced the importance of studying miRNAs' profile in FF.

A Study of Chinese Undergraduates' MI Distribution in EFL Class

Science.gov (United States)

Liu, Ning

2008-01-01

This paper initiates an investigation of the college students' MI (multiple intelligences) distribution in English class. The participants are a group of Chinese sophomores from different majors: city planning, tourism, software engineering, financial administration and arts of English. With a view to make the investigation more specified in…

Investigation of miRNA Biology by Bioinformatic Tools and Impact of miRNAs in Colorectal Cancer: Regulatory Relationship of c-Myc and p53 with miRNAs

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Yaguang Xi

2007-01-01

Full Text Available MicroRNAs (miRNAs are a class of small non-coding RNAs that mediate gene expression at the posttranscriptional and translational levels and have been demonstrated to be involved in diverse biological functions. Mounting evidence in recent years has shown that miRNAs play key roles in tumorigenesis due to abnormal expression of and mutations in miRNAs. High throughput miRNA expression profiling of several major tumor types has identified miRNAs associated with clinical diagnosis and prognosis of cancer treatment. Previously our group has discovered a novel regulatory relationship between tumor suppressor gene p53 with miRNAs expression and a number of miRNA promoters contain putative p53 binding sites. In addition, others have reported that c-myc can mediate a large number of miRNAs expression. In this review, we will emphasize algorithms to identify mRNA targets of miRNAs and the roles of miRNAs in colorectal cancer. In particular, we will discuss a novel regulatory relationship of miRNAs with tumor suppressor p53 and c-myc. miRNAs are becoming promising novel targets and biomarkers for future cancer therapeutic development and clinical molecular diagnosis.

miR-199a-3p displays tumor suppressor functions in papillary thyroid carcinoma.

Science.gov (United States)

Minna, Emanuela; Romeo, Paola; De Cecco, Loris; Dugo, Matteo; Cassinelli, Giuliana; Pilotti, Silvana; Degl'Innocenti, Debora; Lanzi, Cinzia; Casalini, Patrizia; Pierotti, Marco A; Greco, Angela; Borrello, Maria Grazia

2014-05-15

Thyroid cancer incidence is rapidly increasing. Papillary Thyroid Carcinoma (PTC), the most frequent hystotype, usually displays good prognosis, but no effective therapeutic options are available for the fraction of progressive PTC patients. BRAF and RET/PTC are the most frequent driving genetic lesions identified in PTC. We developed two complementary in vitro models based on RET/PTC1 oncogene, starting from the hypothesis that miRNAs modulated by a driving PTC-oncogene are likely to have a role in thyroid neoplastic processes. Through this strategy, we identified a panel of deregulated miRNAs. Among these we focused on miR-199a-3p and showed its under-expression in PTC specimens and cell lines. We demonstrated that miR-199a-3p restoration in PTC cells reduces MET and mTOR protein levels, impairs migration and proliferation and, more interesting, induces lethality through an unusual form of cell death similar to methuosis, caused by macropinocytosis dysregulation. Silencing MET or mTOR, both involved in survival pathways, does not recapitulate miR-199a-3p-induced cell lethality, thus suggesting that the cooperative regulation of multiple gene targets is necessary. Integrated analysis of miR-199a-3p targets unveils interesting networks including HGF and macropinocytosis pathways. Overall our results indicate miR-199a-3p as a tumor suppressor miRNA in PTC.

MiR-132 prohibits proliferation, invasion, migration, and metastasis in breast cancer by targeting HN1

Energy Technology Data Exchange (ETDEWEB)

Zhang, Zhan-Guo, E-mail: zhang_zhanguo@hotmail.com; Chen, Wei-Xun, E-mail: chenweixunclark@163.com; Wu, Yan-Hui, E-mail: wuyanhui84@126.com; Liang, Hui-Fang, E-mail: lianghuifang1997@126.com; Zhang, Bi-Xiang, E-mail: bixiangzhang@163.com

2014-11-07

Highlights: • MiR-132 is down-regulated in breast cancer tissues and cell lines. • MiR-132 directly regulates HN1 by binding its 3′ UTR. • MiR-132 shows regulatory role in proliferation, invasion, migration and metastasis. • HN1 is involved in miR-132-mediated cell behavior. • Aberrant HN1 is associated with worse overall survival of breast cancer patients. - Abstract: Accumulating evidence indicates that miRNAs play critical roles in tumorigenesis and cancer progression. This study aims to investigate the role and the underlying mechanism of miR-132 in breast cancer. Here, we report that miR-132 is significantly down-regulated in breast cancer tissues and cancer cell lines. Additional study identifies HN1 as a novel direct target of miR-132. MiR-132 down-regulates HN1 expression by binding to the 3′ UTR of HN1 transcript, thereby, suppressing multiple oncogenic traits such as cancer cell proliferation, invasion, migration and metastasis in vivo and in vitro. Overexpression of HN1 restores miR-132-suppressed malignancy. Importantly, higher HN1 expression is significantly associated with worse overall survival of breast cancer patients. Taken together, our data demonstrate a critical role of miR-132 in prohibiting cell proliferation, invasion, migration and metastasis in breast cancer through direct suppression of HN1, supporting the potential utility of miR-132 as a novel therapeutic strategy against breast cancer.

Cointegrating MiDaS Regressions and a MiDaS Test

OpenAIRE

J. Isaac Miller

2011-01-01

This paper introduces cointegrating mixed data sampling (CoMiDaS) regressions, generalizing nonlinear MiDaS regressions in the extant literature. Under a linear mixed-frequency data-generating process, MiDaS regressions provide a parsimoniously parameterized nonlinear alternative when the linear forecasting model is over-parameterized and may be infeasible. In spite of potential correlation of the error term both serially and with the regressors, I find that nonlinear least squares consistent...

Potential impact of miR-137 and its targets in schizophrenia

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Carrie eWright

2013-04-01

Full Text Available The significant impact of microRNAs (miRNAs on disease pathology is becoming increasingly evident. These small non-coding RNAs have the ability to post-transcriptionally silence the expression of thousands of genes. Therefore, dysregulation of even a single miRNA could confer a large polygenic effect. Schizophrenia is a genetically complex illness thought to involve multiple genes each contributing a small risk. Large genome-wide association studies identified miR-137, a miRNA shown to be involved in neuronal maturation, as one of the top risk genes. To assess the potential mechanism of impact of miR-137 in this disorder and identify its targets, we used a combination of literature searches, Ingenuity Pathway Analysis (IPA, and freely accessible bioinformatics resources. Using TargetScan and the Schizophrenia Gene Resource (SZGR database, we found that in addition to CSMD1, C10orf26, CACNA1C, TCF4, and ZNF804A, five schizophrenia risk genes whose transcripts are also validated miR-137 targets, there are other schizophrenia-associated genes that may be targets of miR-137, including ERBB4, GABRA1, GRIN2A, GRM5, GSK3B, NRG2 and HTR2C. IPA analyses of all the potential targets identified several nervous system functions as the top canonical pathways including synaptic long-term potentiation, a process implicated in learning and memory mechanisms and recently shown to be altered in patients with schizophrenia. Among the subset of targets involved in nervous system development and function, the top scoring pathways were ephrin receptor signaling and axonal guidance, processes that are critical for proper circuitry formation and were shown to be disrupted in schizophrenia. These results suggest that miR-137 may indeed play a substantial role in the genetic etiology of schizophrenia by regulating networks involved in neural development and brain function.

Mapping change of older forest with nearest-neighbor imputation and Landsat time-series

Science.gov (United States)

Janet L. Ohmann; Matthew J. Gregory; Heather M. Roberts; Warren B. Cohen; Robert E. Kennedy; Zhiqiang. Yang

2012-01-01

The Northwest Forest Plan (NWFP), which aims to conserve late-successional and old-growth forests (older forests) and associated species, established new policies on federal lands in the Pacific Northwest USA. As part of monitoring for the NWFP, we tested nearest-neighbor imputation for mapping change in older forest, defined by threshold values for forest attributes...

Neurotrophin-3 mRNA a putative target of miR21 following status epilepticus.

Science.gov (United States)

Risbud, Rashmi M; Lee, Carolyn; Porter, Brenda E

2011-11-18

Status epilepticus induces a cascade of protein expression changes contributing to the subsequent development of epilepsy. By identifying the cascade of molecular changes that contribute to the development of epilepsy we hope to be able to design therapeutics for preventing epilepsy. MicroRNAs influence gene expression by altering mRNA stability and/or translation and have been implicated in the pathology of multiple diseases. MiR21 and its co-transcript miR21, microRNAs produced from either the 5' or 3' ends of the same precursor RNA strand, are increased in the hippocampus following status epilepticus. We have identified a miR21 binding site, in the 3' UTR of neurotrophin-3 that inhibits translation. Neurotrophin-3 mRNA levels decrease in the hippocampus following SE concurrent with the increase in miR21. MiR21 levels in cultured hippocampal neurons inversely correlate with neurotrophin-3 mRNA levels. Treatment of hippocampal neuronal cultures with excess K(+)Cl(-), a depolarizing agent mimicking the episode of status epilepticus, also results in an increase in miR21 and a decrease in neurotrophin-3 mRNA. MiR21 is a candidate for regulating neurotrophin-3 signaling in the hippocampus following status epilepticus. Copyright © 2011 Elsevier B.V. All rights reserved.

Inference for multivariate regression model based on multiply imputed synthetic data generated via posterior predictive sampling

Science.gov (United States)

Moura, Ricardo; Sinha, Bimal; Coelho, Carlos A.

2017-06-01

The recent popularity of the use of synthetic data as a Statistical Disclosure Control technique has enabled the development of several methods of generating and analyzing such data, but almost always relying in asymptotic distributions and in consequence being not adequate for small sample datasets. Thus, a likelihood-based exact inference procedure is derived for the matrix of regression coefficients of the multivariate regression model, for multiply imputed synthetic data generated via Posterior Predictive Sampling. Since it is based in exact distributions this procedure may even be used in small sample datasets. Simulation studies compare the results obtained from the proposed exact inferential procedure with the results obtained from an adaptation of Reiters combination rule to multiply imputed synthetic datasets and an application to the 2000 Current Population Survey is discussed.

Does Multiple Intelligence Improve Performance? Evidence from a ...

African Journals Online (AJOL)

This study reports the findings of a study that investigated the relationship between multiple intelligence (MI) and academic performance in higher education. It addresses one question: does MI improve academic performance? Taking the case of the finalist cohort of the university's Faculty of Education of the academic year ...

miR-132 and miR-212 are increased in pancreatic cancer and target the retinoblastoma tumor suppressor

Energy Technology Data Exchange (ETDEWEB)

Park, Jong-Kook [College of Pharmacy, Ohio State University, Columbus, OH 43210 (United States); Henry, Jon C. [Department of Surgery, Ohio State University, Columbus, OH 43210 (United States); Jiang, Jinmai [College of Pharmacy, Ohio State University, Columbus, OH 43210 (United States); Esau, Christine [Regulus Therapeutics, Carlsbad, CA (United States); Gusev, Yuriy [Lombardi Cancer Center, Georgetown University, Washington, DC (United States); Lerner, Megan R. [Veterans Affairs Medical Center, Oklahoma City, OK (United States); Postier, Russell G. [Department of Surgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK (United States); Brackett, Daniel J. [Veterans Affairs Medical Center, Oklahoma City, OK (United States); Schmittgen, Thomas D., E-mail: Schmittgen.2@osu.edu [College of Pharmacy, Ohio State University, Columbus, OH 43210 (United States)

2011-03-25

Research highlights: {yields} The expression of miR-132 and miR-212 are significantly increased in pancreatic cancer. {yields} miR-132 and miR-212 target the tumor suppressor pRb, resulting in enhanced proliferation. {yields} miR-132 and miR-212 expression is increased by a {beta}2 adrenergic receptor agonist, suggesting a novel mechanism for pancreatic cancer progression. -- Abstract: Numerous microRNAs (miRNAs) are reported as differentially expressed in cancer, however the consequence of miRNA deregulation in cancer is unknown for many miRNAs. We report that two miRNAs located on chromosome 17p13, miR-132 and miR-212, are over-expressed in pancreatic adenocarcinoma (PDAC) tissues. Both miRNAs are predicted to target the retinoblastoma tumor suppressor, Rb1. Validation of this interaction was confirmed by luciferase reporter assay and western blot in a pancreatic cancer cell line transfected with pre-miR-212 and pre-miR-132 oligos. Cell proliferation was enhanced in Panc-1 cells transfected with pre-miR-132/-212 oligos. Conversely, antisense oligos to miR-132/-212 reduced cell proliferation and caused a G{sub 2}/M cell cycle arrest. The mRNA of a number of E2F transcriptional targets were increased in cells over expressing miR-132/-212. Exposing Panc-1 cells to the {beta}2 adrenergic receptor agonist, terbutaline, increased the miR-132 and miR-212 expression by 2- to 4-fold. We report that over-expression of miR-132 and miR-212 result in reduced pRb protein in pancreatic cancer cells and that the increase in cell proliferation from over-expression of these miRNAs is likely due to increased expression of several E2F target genes. The {beta}2 adrenergic pathway may play an important role in this novel mechanism.

Oligoasthenoteratozoospermia and infertility in mice deficient for miR-34b/c and miR-449 loci.

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Stefano Comazzetto

2014-10-01

Full Text Available Male fertility requires the continuous production of high quality motile spermatozoa in abundance. Alterations in all three metrics cause oligoasthenoteratozoospermia, the leading cause of human sub/infertility. Post-mitotic spermatogenesis inclusive of several meiotic stages and spermiogenesis (terminal spermatozoa differentiation are transcriptionally inert, indicating the potential importance for the post-transcriptional microRNA (miRNA gene-silencing pathway therein. We found the expression of miRNA generating enzyme Dicer within spermatogenesis peaks in meiosis with critical functions in spermatogenesis. In an expression screen we identified two miRNA loci of the miR-34 family (miR-34b/c and miR-449 that are specifically and highly expressed in post-mitotic male germ cells. A reduction in several miRNAs inclusive of miR-34b/c in spermatozoa has been causally associated with reduced fertility in humans. We found that deletion of both miR34b/c and miR-449 loci resulted in oligoasthenoteratozoospermia in mice. MiR-34bc/449-deficiency impairs both meiosis and the final stages of spermatozoa maturation. Analysis of miR-34bc-/-;449-/- pachytene spermatocytes revealed a small cohort of genes deregulated that were highly enriched for miR-34 family target genes. Our results identify the miR-34 family as the first functionally important miRNAs for spermatogenesis whose deregulation is causal to oligoasthenoteratozoospermia and infertility.

The regulatory effect of miRNAs is a heritable genetic trait in humans

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Geeleher Paul

2012-08-01

Full Text Available Abstract Background microRNAs (miRNAs have been shown to regulate the expression of a large number of genes and play key roles in many biological processes. Several previous studies have quantified the inhibitory effect of a miRNA indirectly by considering the expression levels of genes that are predicted to be targeted by the miRNA and this approach has been shown to be robust to the choice of prediction algorithm. Given a gene expression dataset, Cheng et al. defined the regulatory effect score (RE-score of a miRNA as the difference in the gene expression rank of targets of the miRNA compared to non-targeted genes. Results Using microarray data from parent-offspring trios from the International HapMap project, we show that the RE-score of most miRNAs is correlated between parents and offspring and, thus, inter-individual variation in RE-score has a genetic component in humans. Indeed, the mean RE-score across miRNAs is correlated between parents and offspring, suggesting genetic differences in the overall efficiency of the miRNA biogenesis pathway between individuals. To explore the genetics of this quantitative trait further, we carried out a genome-wide association study of the mean RE-score separately in two HapMap populations (CEU and YRI. No genome-wide significant associations were discovered; however, a SNP rs17409624, in an intron of DROSHA, was significantly associated with mean RE-score in the CEU population following permutation-based control for multiple testing based on all SNPs mapped to the canonical miRNA biogenesis pathway; of 244 individual miRNA RE-scores assessed in the CEU, 214 were associated (p p = 0.04 with mean RE-score in the YRI population. Interestingly, the same SNP was associated with 17 (8.5% of all expressed miRNA expression levels in the CEU. We also show here that the expression of the targets of most miRNAs is more highly correlated with global changes in miRNA regulatory effect than with the expression of

MiRNA-155 and miRNA-132 as potential diagnostic biomarkers for pulmonary tuberculosis: A preliminary study.

Science.gov (United States)

Zheng, Meng-Li; Zhou, Nai-Kang; Luo, Cheng-Hua

2016-11-01

In our study, we aimed to profile a panel microRNAs (miRNAs) as potential biomarkers for the early diagnosis of pulmonary tuberculosis (PTB) and to illuminate the molecular mechanisms in the development of PTB. Firstly, gene expression profile of E-GEOD-49951 was downloaded from ArrayExpress database, and quantile-adjusted conditional maximum likelihood method was utilized to identify statistical difference between miRNAs of Mycobacterium tuberculosis (MTB)-infected individuals and healthy subjects. Furthermore, in order to assess the performance of our methodology, random forest (RF) classification model was utilized to identify the top 10 miRNAs with better Area Under The Curve (AUC) using 10-fold cross-validation method. Additionally, Monte Carlo Cross-Validation was repeated 50 times to explore the best miRNAs. In order to learn more about the differentially-expressed miRNAs, the target genes of differentially-expressed miRNAs were retrieved from TargetScan database and Ingenuity Pathways Analysis (IPA) was used to screen out biological pathways where target genes were involved. After normalization, a total of 478 miRNAs with higher than 0.25-fold quantile average across all samples were required. Based on the differential expression analysis, 38 differentially expressed miRNAs were identified when the significance was set as false discovery rate (FDR) < 0.01. Among the top 10 differentially expressed miRNAs, miRNA-155 obtained a highest AUC value 0.976, showing a good performance between PTB and control groups. Similarly, miRNA-449a, miRNA-212 and miRNA-132 revealed also a good performance with AUC values 0.947, 0.931 and 0.930, respectively. Moreover, miRNA-155, miRNA-449a, miRNA-29b-1* and miRNA-132 appeared in 50, 49, 49 and 48 bootstraps. Thus, miRNA-155 and miRNA-132 might be important in the progression of PTB and thereby, might present potential signatures for diagnosis of PTB. Copyright © 2016 Elsevier Ltd. All rights reserved.

miRge - A Multiplexed Method of Processing Small RNA-Seq Data to Determine MicroRNA Entropy.

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Alexander S Baras

Full Text Available Small RNA RNA-seq for microRNAs (miRNAs is a rapidly developing field where opportunities still exist to create better bioinformatics tools to process these large datasets and generate new, useful analyses. We built miRge to be a fast, smart small RNA-seq solution to process samples in a highly multiplexed fashion. miRge employs a Bayesian alignment approach, whereby reads are sequentially aligned against customized mature miRNA, hairpin miRNA, noncoding RNA and mRNA sequence libraries. miRNAs are summarized at the level of raw reads in addition to reads per million (RPM. Reads for all other RNA species (tRNA, rRNA, snoRNA, mRNA are provided, which is useful for identifying potential contaminants and optimizing small RNA purification strategies. miRge was designed to optimally identify miRNA isomiRs and employs an entropy based statistical measurement to identify differential production of isomiRs. This allowed us to identify decreasing entropy in isomiRs as stem cells mature into retinal pigment epithelial cells. Conversely, we show that pancreatic tumor miRNAs have similar entropy to matched normal pancreatic tissues. In a head-to-head comparison with other miRNA analysis tools (miRExpress 2.0, sRNAbench, omiRAs, miRDeep2, Chimira, UEA small RNA Workbench, miRge was faster (4 to 32-fold and was among the top-two methods in maximally aligning miRNAs reads per sample. Moreover, miRge has no inherent limits to its multiplexing. miRge was capable of simultaneously analyzing 100 small RNA-Seq samples in 52 minutes, providing an integrated analysis of miRNA expression across all samples. As miRge was designed for analysis of single as well as multiple samples, miRge is an ideal tool for high and low-throughput users. miRge is freely available at http://atlas.pathology.jhu.edu/baras/miRge.html.

A viral microRNA down-regulates multiple cell cycle genes through mRNA 5'UTRs.

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Finn Grey

2010-06-01

Full Text Available Global gene expression data combined with bioinformatic analysis provides strong evidence that mammalian miRNAs mediate repression of gene expression primarily through binding sites within the 3' untranslated region (UTR. Using RNA induced silencing complex immunoprecipitation (RISC-IP techniques we have identified multiple cellular targets for a human cytomegalovirus (HCMV miRNA, miR-US25-1. Strikingly, this miRNA binds target sites primarily within 5'UTRs, mediating significant reduction in gene expression. Intriguingly, many of the genes targeted by miR-US25-1 are associated with cell cycle control, including cyclin E2, BRCC3, EID1, MAPRE2, and CD147, suggesting that miR-US25-1 is targeting genes within a related pathway. Deletion of miR-US25-1 from HCMV results in over expression of cyclin E2 in the context of viral infection. Our studies demonstrate that a viral miRNA mediates translational repression of multiple cellular genes by targeting mRNA 5'UTRs.

Early diagnostic evaluation of miR-122 and miR-224 as biomarkers for hepatocellular carcinoma

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Khalda S. Amr

2017-12-01

Full Text Available Hepatocellular carcinoma (HCC is one of the common lethal types of tumor all over the world. The lethality of HCC accounts for many reasons. One of them, the lack of reliable diagnostic markers at the early stage, in this context, serum miRNAs became promising diagnostic biomarkers. Herein, we aimed to identify the predictive value of two miRNAs (miR-122 and miR-224 in plasma of patients with HCC preceded by chronic HCV infection. Taqman miRNA assays specific for hsa-miR-122 and hsa-miR-224 were used to assess the expression levels of the chosen miRNAs in plasma samples collected from three groups; 40 patients with HCC related to HCV, 40 with CHC patients and 20 healthy volunteers. This study revealed that the mean plasma values of miRNA-122 were significantly lower among HCC group when compared to CHC and control groups (P 1.2 (RQ and (AUC = 0.93, P < 0.001, while the accuracy of AFP to diagnose HCC was (AUC: 0.619; P = 0.06. In conclusion, the expression plasma of miR-122 and miR-224 could be used as noninvasive biomarkers for the early prediction of developing HCC at the early stage.

Growth inhibitory effects of miR-221 and miR-222 in non-small cell lung cancer cells

International Nuclear Information System (INIS)

Yamashita, Ryo; Sato, Mitsuo; Kakumu, Tomohiko; Hase, Tetsunari; Yogo, Naoyuki; Maruyama, Eiichi; Sekido, Yoshitaka; Kondo, Masashi; Hasegawa, Yoshinori

2015-01-01

Both pro- and anti-oncogenic roles of miR-221 and miR-222 microRNAs are reported in several types of human cancers. A previous study suggested their oncogenic role in invasiveness in lung cancer, albeit only one cell line (H460) was used. To further evaluate involvement of miR-221 and miR-222 in lung cancer, we investigated the effects of miR-221 and miR-222 overexpression on six lung cancer cell lines, including H460, as well as one immortalized normal human bronchial epithelial cell line, HBEC4. miR-221 and miR-222 induced epithelial-to-mesenchymal transition (EMT)-like changes in a minority of HBEC4 cells but, unexpectedly, both the microRNAs rather suppressed their invasiveness. Consistent with the prior report, miR-221 and miR-222 promoted growth in H460; however, miR-221 suppressed growth in four other cell lines with no effects in one, and miR-222 suppressed growth in three cell lines but promoted growth in two. These are the first results to show tumor-suppressive effects of miR-221 and miR-222 in lung cancer cells, and we focused on clarifying the mechanisms. Cell cycle and apoptosis analyses revealed that growth suppression by miR-221 and miR-222 occurred through intra-S-phase arrest and/or apoptosis. Finally, lung cancer cell lines transfected with miR-221 or miR-222 became more sensitive to the S-phase targeting drugs, possibly due to an increased S-phase population. In conclusion, our data are the first to show tumor-suppressive effects of miR-221 and miR-222 on lung cancer, warranting testing their potential as therapeutics for the disease

Altering β-cell number through stable alteration of miR-21 and miR-34a expression

DEFF Research Database (Denmark)

Backe, Marie Balslev; Novotny, Guy Wayne; Christensen, Dan Ploug

2014-01-01

RNAs, miR-21 and miR-34a, may be involved in mediating cytokine-induced β-cell dysfunction. Therefore, manipulation of miR-21 and miR-34a levels may potentially be beneficial to β cells. To study the effect of long-term alterations of miR-21 or miR-34a levels upon net β-cell number, we stably overexpressed...

Data of expression status of miR- 29a and its putative target mitochondrial apoptosis regulatory gene DRP1 upon miR-15a and miR-214 inhibition

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Muhammad Ishtiaq Jan

2018-02-01

Full Text Available Data is about the mitochondrial apoptosis regulatory framework genes PUMA, DRP1 (apoptotic, and ARC (anti-apoptotic analysis after the employment of their controlling miRNAs inhibitors. The data represents putative conserved targeting of seed regions of miR-15a, miR-29a, and miR-214 with respective target genes PUMA, DRP1, and ARC. Data is of cross interference in expression levels of one miRNA family, miR-29a and its putative target DRP1 upon the inhibitory treatment of other miRNAs 15a and 214. Keywords: DRP1, miR-15a, Apoptosis, miRNAs inhibition

Deregulation of miR-100, miR-99a and miR-199b in tissues and plasma coexists with increased expression of mTOR kinase in endometrioid endometrial carcinoma

International Nuclear Information System (INIS)

Torres, Anna; Torres, Kamil; Pesci, Anna; Ceccaroni, Marcello; Paszkowski, Tomasz; Cassandrini, Paola; Zamboni, Giuseppe; Maciejewski, Ryszard

2012-01-01

Alterations of mTOR gene expression have been implicated in the pathogenesis of endometrioid endometrial cancer however only few studies explored the cause of increased mTOR activation in this malignancy. miRNAs are small, noncoding RNAs, which were proven to regulated gene expression at the posttranscriptional level. The study aimed to explore deregulation of miRNAs targeting mTOR kinase (miR-99a, miR-100 and miR-199b) as a possible cause of its altered expression in EEC tissues. In addition expression of the three miRNAs was investigated in plasma of EEC patients and was assessed in terms of diagnostic and prognostic utility. We investigated expression of mTOR kinase transcripts in 46 fresh tissue samples. Expression of miR-99a, miR-100 and miR-199b was investigated in the same group of fresh samples, and in additional 58 FFPE sections as well as in 48 plasma samples using qPCR. Relative quantification was performed using experimentally validated endogenous controls. mTOR kinase expression was increased in EEC tissues and was accompanied by decreased expression of all three miRNAs. Down-regulation of the investigated miRNAs was discovered in plasma of EEC patients and miRNA signatures classified EEC tissues (miR-99a/miR-100/miR-199b) and plasma (miR-99a/miR-199b) samples with higher accuracy in comparison to single miRNAs. We also revealed that miR-100 was an independent prognostic marker of overall survival. We conclude that increased expression of mTOR kinase coexists with down-regulation of its targeting miRNAs, which could suggest a new mechanism of mTOR pathway alterations in EEC. In addition, our findings implicate that miRNA signatures can be considered promising biomarkers for early detection and prognosis of endometrioid endometrial carcinoma