FORG3D: Force-directed 3D graph editor for visualization of integrated genome scale data

<p>Abstract</p> <p>Background</p> <p>Genomics research produces vast amounts of experimental data that needs to be integrated in order to understand, model, and interpret the underlying biological phenomena. Interpreting these large and complex data sets is challenging and different visualization methods are needed to help produce knowledge from the data.</p> <p>Results</p> <p>To help researchers to visualize and interpret integrated genomics data, we present a novel visualization method and bioinformatics software tool called FORG3D that is based on real-time three-dimensional force-directed graphs. FORG3D can be used to visualize integrated networks of genome scale data such as interactions between genes or gene products, signaling transduction, metabolic pathways, functional interactions and evolutionary relationships. Furthermore, we demonstrate its utility by exploring gene network relationships using integrated data sets from a <it>Caenorhabditis elegans </it>Parkinson's disease model.</p> <p>Conclusion</p> <p>We have created an open source software tool called FORG3D that can be used for visualizing and exploring integrated genome scale data.</p

An Improved, Bias-Reduced Probabilistic Functional Gene Network of Baker's Yeast, Saccharomyces cerevisiae

Background: Probabilistic functional gene networks are powerful theoretical frameworks for integrating heterogeneous functional genomics and proteomics data into objective models of cellular systems. Such networks provide syntheses of millions of discrete experimental observations, spanning DNA microarray experiments, physical protein interactions, genetic interactions, and comparative genomics; the resulting networks can then be easily applied to generate testable hypotheses regarding specific gene functions and associations. Methodology/Principal Findings: We report a significantly improved version (v. 2) of a probabilistic functional gene network [1] of the baker's yeast, Saccharomyces cerevisiae. We describe our optimization methods and illustrate their effects in three major areas: the reduction of functional bias in network training reference sets, the application of a probabilistic model for calculating confidences in pair-wise protein physical or genetic interactions, and the introduction of simple thresholds that eliminate many false positive mRNA co-expression relationships. Using the network, we predict and experimentally verify the function of the yeast RNA binding protein Puf6 in 60S ribosomal subunit biogenesis. Conclusions/Significance: YeastNet v. 2, constructed using these optimizations together with additional data, shows significant reduction in bias and improvements in precision and recall, in total covering 102,803 linkages among 5,483 yeast proteins (95% of the validated proteome). YeastNet is available from http://www.yeastnet.org.This work was supported by grants from the N.S.F. (IIS-0325116, EIA-0219061), N.I.H. (GM06779-01,GM076536-01), Welch (F-1515), and a Packard Fellowship (EMM). These agencies were not involved in the design and conduct of the study, in the collection, analysis, and interpretation of the data, or in the preparation, review, or approval of the manuscript.Cellular and Molecular Biolog

A Genome-Wide Gene Function Prediction Resource for Drosophila melanogaster

Predicting gene functions by integrating large-scale biological data remains a challenge for systems biology. Here we present a resource for Drosophila melanogaster gene function predictions. We trained function-specific classifiers to optimize the influence of different biological datasets for each functional category. Our model predicted GO terms and KEGG pathway memberships for Drosophila melanogaster genes with high accuracy, as affirmed by cross-validation, supporting literature evidence, and large-scale RNAi screens. The resulting resource of prioritized associations between Drosophila genes and their potential functions offers a guide for experimental investigations

Cell-specific microarray profiling experiments reveal a comprehensive picture of gene expression in the C. elegans nervous system

A novel strategy for profiling Caenorhabditis elegans cells identifies transcripts highly enriched in either the embryonic or larval C. elegans nervous system, including 19 conserved transcripts of unknown function that are also expressed in the mammalian brain

Does trust play a role when it comes to donations? A comparison of Italian and US higher education institutions

Higher education institutions (HEIs) have experienced severe cutbacks in funding over the past few years, with universities examining options for alternative funding streams, such as alumni funding. Identifying the factors influencing their alumni's intentions to invest in their alma mater can be of significant importance when establishing a sustainable revenue stream. Within this context, empirical research on the potential role of trust is scarce. This paper aims to deepen the analysis of the relationship between alumni trust and engagement as well as three outcomes, namely support, commitment, and attitude toward donation. A structural equation model was tested on two samples of US (  = 318) and Italian (  = 314) alumni. Although both countries are affluent and developed countries, the USA has an established tradition of alumni donations, which is not such a developed practice in Italy. For both countries, results confirm that engagement is an antecedent of trust, which in turn leads to the three investigated outcomes (support, commitment, and attitude toward donations). In contrast, the effect of commitment on attitude toward donations is significant only for the USA universities. The paper has interesting theoretical and managerial implications. From a theoretical point of view, the study aims to address a gap concerning the role of trust in the HE context. Managerially, the study has significant implications for universities that want to change alumni attitude toward donations. [Abstract copyright: © Springer Nature B.V. 2020.