Amplification of the Gene Ontology annotation of Affymetrix probe sets

BACKGROUND: The annotations of Affymetrix DNA microarray probe sets with Gene Ontology terms are carefully selected for correctness. This results in very accurate but incomplete annotations which is not always desirable for microarray experiment evaluation. RESULTS: Here we present a protocol to amplify the set of Gene Ontology annotations associated to Affymetrix DNA microarray probe sets using information from related databases. CONCLUSION: Predicted novel annotations and the evidence producing them can be accessed at Probe2GO: . Scripts are available on demand

Uncertainty quantification of transition operators in the empirical shell model

While empirical shell model calculations have successfully described low-lying nuclear data for decades, only recently has significant effort been made to quantify the uncertainty in such calculations. Here we quantify the statistical error in effective parameters for transition operators in empirical calculations in the $sd$ ($1s_{1/2}$-$0d_{3/2}$-$0d_{5/2}$) valence space, specifically the quenching of Gamow-Teller transitions, effective charges for electric quadrupole (E2) transitions, and the effective orbital and spin couplings for magnetic dipole (M1) transitions. We find the quenching factor for Gamow-Teller transitions relative to free-space values is tightly constrained and that the isoscalar coupling of E2 is much more tightly constrained than the isovector coupling. For effective M1 couplings, we found isovector components more constrained than isoscalar, but that to get any sensible result we had to fix one of four couplings. This detailed quantification of uncertainties, while highly empirical, nonetheless is an important step towards interpretation of experiments

Adaptive and architecture-independent task granularity for recursive applications

In the last few decades, modern applications have become larger and more complex. Among the users of these applications, the need to simplify the process of identifying units of work increased as well. With the approach of tasking models, this want has been satisfied. These models make scheduling units of work much more user-friendly. However, with the arrival of tasking models, came granularity management. Discovering an application’s optimal granularity is a frequent and sometimes challenging task for a wide range of recursive algorithms. Often, finding the optimal granularity will cause a substantial increase in performance. With that in mind, the quest for optimality is no easy task. Many aspects have to be considered that are directly related to lack or excess of parallelism in applications. There is no general solution as the optimal granularity depends on both algorithm and system characteristics. One commonly used method to find an optimal granularity consists in experimentally tuning an application with different granularities until an optimal is found. This paper proposes several heuristics which, combined with the appropriate monitoring techniques, allow a runtime system to automatically tune the granularity of recursive applications. The solution is independent of the architecture, execution environment or application being tested. A reference implementation in OmpSs—a task-parallel programming model—shows the programmability, ease of use and competitive performance of the proposed solution. Results show that the proposed solution is able to achieve, for any scenario, at least 75% of the performance of optimally tuned applications.This work has been supported by the Spanish Ministry of Science and Innovation (contract TIN2015-65316), the grant SEV-2015-0493 of Severo Ochoa Program awarded by the Spanish Government, and by Generalitat de Catalunya (contract 2014-SGR-1051)Peer ReviewedPostprint (author's final draft

A method for cell type marker discovery by high-throughput gene expression analysis of mixed cell populations

BACKGROUND: Gene transcripts specifically expressed in a particular cell type (cell-type specific gene markers) are useful for its detection and isolation from a tissue or other cell mixtures. However, finding informative marker genes can be problematic when working with a poorly characterized cell type, as markers can only be unequivocally determined once the cell type has been isolated. We propose a method that could identify marker genes of an uncharacterized cell type within a mixed cell population, provided that the proportion of the cell type of interest in the mixture can be estimated by some indirect method, such as a functional assay. RESULTS: We show that cell-type specific gene markers can be identified from the global gene expression of several cell mixtures that contain the cell type of interest in a known proportion by their high correlation to the concentration of the corresponding cell type across the mixtures. CONCLUSIONS: Genes detected using this high-throughput strategy would be candidate markers that may be useful in detecting or purifying a cell type from a particular biological context. We present an experimental proof-of-concept of this method using cell mixtures of various well-characterized hematopoietic cell types, and we evaluate the performance of the method in a benchmark that explores the requirements and range of validity of the approach

Worldwide impact of economic cycles on suicide trends over 3 decades: Differences according to level of development. A mixed effect model study

Objectives: To investigate the trends and correlations of gross domestic product (GDP) adjusted for purchasing power parity (PPP) per capita on suicide rates in 10 WHO regions during the past 30 years. Design: Analyses of databases of PPP-adjusted GDP per capita and suicide rates. Countries were grouped according to the Global Burden of Disease regional classification system. Data sources: World Bank’s official website and WHO’s mortality database. Statistical analyses: After graphically displaying PPP-adjusted GDP per capita and suicide rates, mixed effect models were used for representing and analysing clustered data. Results: Three different groups of countries, based on the correlation between the PPP-adjusted GDP per capita and suicide rates, are reported: (1) positive correlation: developing (lower middle and upper middle income) Latin-American and Caribbean countries, developing countries in the South East Asian Region including India, some countries in the Western Pacific Region (such as China and South Korea) and high-income Asian countries, including Japan; (2) negative correlation: high-income and developing European countries, Canada, Australia and New Zealand and (3) no correlation was found in an African country. Conclusions: PPP-adjusted GDP per capita may offer a simple measure for designing the type of preventive interventions aimed at lowering suicide rates that can be used across countries. Public health interventions might be more suitable for developing countries. In high-income countries, however, preventive measures based on the medical model might prove more usefulAll authors have completed the Unified Competing Interest form. Dr. Blasco-Fontecilla acknowledges the Spanish Ministry of Health (Rio Hortega CMO8/00170; SAF2010-21849), Alicia Koplowitz Foundation and Conchita Rabago Foundation for funding his post-doctoral stage at CHRU, Montpellier, France

Recent developments in StemBase: a tool to study gene expression in human and murine stem cells

<p>Abstract</p> <p>Background</p> <p>Currently one of the largest online repositories for human and mouse stem cell gene expression data, StemBase was first designed as a simple web-interface to DNA microarray data generated by the Canadian Stem Cell Network to facilitate the discovery of gene functions relevant to stem cell control and differentiation.</p> <p>Findings</p> <p>Since its creation, StemBase has grown in both size and scope into a system with analysis tools that examine either the whole database at once, or slices of data, based on tissue type, cell type or gene of interest. As of September 1, 2008, StemBase contains gene expression data (microarray and Serial Analysis of Gene Expression) from 210 stem cell samples in 60 different experiments.</p> <p>Conclusion</p> <p>StemBase can be used to study gene expression in human and murine stem cells and is available at <url>http://www.stembase.ca</url>.</p