Comparisons of computational methods for differential alternative splicing detection using RNA-seq in plant systems

BackgroundAlternative Splicing (AS) as a post-transcription regulation mechanism is an important application of RNA-seq studies in eukaryotes. A number of software and computational methods have been developed for detecting AS. Most of the methods, however, are designed and tested on animal data, such as human and mouse. Plants genes differ from those of animals in many ways, e.g., the average intron size and preferred AS types. These differences may require different computational approaches and raise questions about their effectiveness on plant data. The goal of this paper is to benchmark existing computational differential splicing (or transcription) detection methods so that biologists can choose the most suitable tools to accomplish their goals. ResultsThis study compares the eight popular public available software packages for differential splicing analysis using both simulated and real Arabidopsis thaliana RNA-seq data. All software are freely available. The study examines the effect of varying AS ratio, read depth, dispersion pattern, AS types, sample sizes and the influence of annotation. Using a real data, the study looks at the consistences between the packages and verifies a subset of the detected AS events using PCR studies. ConclusionsNo single method performs the best in all situations. The accuracy of annotation has a major impact on which method should be chosen for AS analysis. DEXSeq performs well in the simulated data when the AS signal is relative strong and annotation is accurate. Cufflinks achieve a better tradeoff between precision and recall and turns out to be the best one when incomplete annotation is provided. Some methods perform inconsistently for different AS types. Complex AS events that combine several simple AS events impose problems for most methods, especially for MATS. MATS stands out in the analysis of real RNA-seq data when all the AS events being evaluated are simple AS events

Supporting professional learning and development through international collaboration in the co-construction of an undergraduate teaching qualification

This is an Accepted Manuscript of an article published by Taylor & Francis in Professional Development in Education on 19 May 2015, available at: http://dx.doi.org/10.1080/19415257.2015.1026454.This article explores one thread from a larger, longitudinal research project that investigated the views and experiences of teacher educators in Malaysia and from the United Kingdom who were involved in collaboration for the co-construction of a Bachelor of Education (Honours) in Primary Mathematics, with English and health and physical education as minor subjects. The article examines the impact of the approach taken to collaboration, which included the development and sharing of a pedagogical model for teacher education (ARM: action, reflection, modelling) and reflects on the value of this to professional learning and development. The research findings suggest that this co-constructive approach was effective in enabling senior managers and teacher educators involved in the project to critique their own practice and to further develop their understanding of effective teacher education. These findings have implications for developing the pedagogy of teacher educators in other contexts: the co-construction of a programme with colleagues who had different understandings of the nature of teacher education enabled new insight into participants’ own practice.Peer reviewe

Reconstructing genome-wide regulatory network of E. coli using transcriptome data and predicted transcription factor activities

<p>Abstract</p> <p>Background</p> <p>Gene regulatory networks play essential roles in living organisms to control growth, keep internal metabolism running and respond to external environmental changes. Understanding the connections and the activity levels of regulators is important for the research of gene regulatory networks. While relevance score based algorithms that reconstruct gene regulatory networks from transcriptome data can infer genome-wide gene regulatory networks, they are unfortunately prone to false positive results. Transcription factor activities (TFAs) quantitatively reflect the ability of the transcription factor to regulate target genes. However, classic relevance score based gene regulatory network reconstruction algorithms use models do not include the TFA layer, thus missing a key regulatory element.</p> <p>Results</p> <p>This work integrates TFA prediction algorithms with relevance score based network reconstruction algorithms to reconstruct gene regulatory networks with improved accuracy over classic relevance score based algorithms. This method is called Gene expression and Transcription factor activity based Relevance Network (GTRNetwork). Different combinations of TFA prediction algorithms and relevance score functions have been applied to find the most efficient combination. When the integrated GTRNetwork method was applied to <it>E. coli </it>data, the reconstructed genome-wide gene regulatory network predicted 381 new regulatory links. This reconstructed gene regulatory network including the predicted new regulatory links show promising biological significances. Many of the new links are verified by known TF binding site information, and many other links can be verified from the literature and databases such as EcoCyc. The reconstructed gene regulatory network is applied to a recent transcriptome analysis of <it>E. coli </it>during isobutanol stress. In addition to the 16 significantly changed TFAs detected in the original paper, another 7 significantly changed TFAs have been detected by using our reconstructed network.</p> <p>Conclusions</p> <p>The GTRNetwork algorithm introduces the hidden layer TFA into classic relevance score-based gene regulatory network reconstruction processes. Integrating the TFA biological information with regulatory network reconstruction algorithms significantly improves both detection of new links and reduces that rate of false positives. The application of GTRNetwork on <it>E. coli </it>gene transcriptome data gives a set of potential regulatory links with promising biological significance for isobutanol stress and other conditions.</p

Fuzzy Navigation Engine: Mitigating the Cognitive Demands of Semi-Natural Locomotion

Many interfaces exist for locomotion in virtual reality, although they are rarely considered fully natural. Past research has found that using such interfaces places cognitive demands on the user, with unnatural actions and concurrent tasks competing for finite cognitive resources. Notably, using semi-natural interfaces leads to poor performance on concurrent tasks requiring spatial working memory. This paper presents an adaptive system designed to track a user\u27s concurrent cognitive task load and adjust interface parameters accordingly, varying the extent to which movement is fully natural. A fuzzy inference system is described and the results of an initial validation study are presented. Users of this adaptive interface demonstrated better performance than users of a baseline interface on several movement metrics, indicating that the adaptive interface helped users manage the demands of concurrent spatial tasks in a virtual environment. However, participants experienced some unexpected difficulties when faced with a concurrent verbal task

Network motif comparison rationalizes Sec1/Munc18-SNARE regulation mechanism in exocytosis

BackgroundNetwork motifs, recurring subnetwork patterns, provide significant insight into the biological networks which are believed to govern cellular processes. MethodsWe present a comparative network motif experimental approach, which helps to explain complex biological phenomena and increases the understanding of biological functions at the molecular level by exploring evolutionary design principles of network motifs. ResultsUsing this framework to analyze the SM (Sec1/Munc18)-SNARE (N-ethylmaleimide-sensitive factor activating protein receptor) system in exocytic membrane fusion in yeast and neurons, we find that the SM-SNARE network motifs of yeast and neurons show distinct dynamical behaviors. We identify the closed binding mode of neuronal SM (Munc18-1) and SNARE (syntaxin-1) as the key factor leading to mechanistic divergence of membrane fusion systems in yeast and neurons. We also predict that it underlies the conflicting observations in SM overexpression experiments. Furthermore, hypothesis-driven lipid mixing assays validated the prediction. ConclusionTherefore this study provides a new method to solve the discrepancies and to generalize the functional role of SM proteins

MetaBlast! Virtual Cell: A Pedagogical Convergence between Game Design and Science Education

MetaBlast! Virtual Cell (from now on referred to as VC) is a game design solution to a specific scientific and educational problem; expressly, how to make advanced, university level plant biology instruction on molecular and anatomical levels an exciting, efficient learning experience. The advanced technologies of 3D modeling and animation, computer programming and game design are united and tempered with strong, scientific guidance for accuracy and art direction for a powerful visual and audio simulation. The additional strength of intense gaming as a powerful tool aiding memory, logic and problem solving has recently become well recognized. Virtual Cell will provide a unique gaming experience, while transparently teaching scientifically accurate facts and concepts about, in this case, a soybean plant’s inner workings and dependant mechanisms on multiple scales and levels of complexity. Virtual Cell (from now on referred to as VC) in the future may prove to be a reference for other scientific/education endeavors as scientists battle for a more prominent mind share among average citizens. This paper will discuss the difficulties of developing VC, its structure, intended game and educational goals along with additional benefits to both the sciences and gaming industry

MetNetGE: interactive views of biological networks and ontologies

Background Linking high-throughput experimental data with biological networks is a key step for understanding complex biological systems. Currently, visualization tools for large metabolic networks often result in a dense web of connections that is difficult to interpret biologically. The MetNetGE application organizes and visualizes biological networks in a meaningful way to improve performance and biological interpretability. Results MetNetGE is an interactive visualization tool based on the Google Earth platform. MetNetGE features novel visualization techniques for pathway and ontology information display. Instead of simply showing hundreds of pathways in a complex graph, MetNetGE gives an overview of the network using the hierarchical pathway ontology using a novel layout, called the Enhanced Radial Space-Filling (ERSF) approach that allows the network to be summarized compactly. The non-tree edges in the pathway or gene ontology, which represent pathways or genes that belong to multiple categories, are linked using orbital connections in a third dimension. Biologists can easily identify highly activated pathways or gene ontology categories by mapping of summary experiment statistics such as coefficient of variation and overrepresentation values onto the visualization. After identifying such pathways, biologists can focus on the corresponding region to explore detailed pathway structure and experimental data in an aligned 3D tiered layout. In this paper, the use of MetNetGE is illustrated with pathway diagrams and data from E. coli and Arabidopsis. Conclusions MetNetGE is a visualization tool that organizes biological networks according to a hierarchical ontology structure. The ERSF technique assigns attributes in 3D space, such as color, height, and transparency, to any ontological structure. For hierarchical data, the novel ERSF layout enables the user to identify pathways or categories that are differentially regulated in particular experiments. MetNetGE also displays complex biological pathway in an aligned 3D tiered layout for exploration