Seedling Development in Species of \u3ci\u3eChamaesyce\u3c/i\u3e (Euphorbiaceae) with Erect Growth Habits

Seedling development is described for Chamaesyce hirta, C. hypericifolia, and C. mesembrianthemifolia as discerned by light microscopy and scanning electron microscopy. Although these species ultimately develop erect to ascending growth habits, epicotyl development is limited to the production of a single pair ofleaves located immediately superjacent to and decussate with the cotyledons. The shoot system develops from one or more buds located in the axils of the cotyledons. In all respects, seedling ontogeny is very similar to that of previously studied prostrate species of Chamaesyce. Evidence from seedling ontogeny thus contradicts a hypothesis concerning homologies of plant form pertinent to the origin of Chamaesyce from Euphorbia that was first articulated by Roeper in 1824. These results support an alternative hypothesis based on proliferation of branches from the cotyledonary node in hypothetical ancestral elements within Euphorbia where this morphology can be found in perennial hemicryptophytes as well as certain annual species

Graphle: Interactive exploration of large, dense graphs

<p>Abstract</p> <p>Background</p> <p>A wide variety of biological data can be modeled as network structures, including experimental results (e.g. protein-protein interactions), computational predictions (e.g. functional interaction networks), or curated structures (e.g. the Gene Ontology). While several tools exist for visualizing large graphs at a global level or small graphs in detail, previous systems have generally not allowed interactive analysis of dense networks containing thousands of vertices at a level of detail useful for biologists. Investigators often wish to explore specific portions of such networks from a detailed, gene-specific perspective, and balancing this requirement with the networks' large size, complex structure, and rich metadata is a substantial computational challenge.</p> <p>Results</p> <p>Graphle is an online interface to large collections of arbitrary undirected, weighted graphs, each possibly containing tens of thousands of vertices (e.g. genes) and hundreds of millions of edges (e.g. interactions). These are stored on a centralized server and accessed efficiently through an interactive Java applet. The Graphle applet allows a user to examine specific portions of a graph, retrieving the relevant neighborhood around a set of query vertices (genes). This neighborhood can then be refined and modified interactively, and the results can be saved either as publication-quality images or as raw data for further analysis. The Graphle web site currently includes several hundred biological networks representing predicted functional relationships from three heterogeneous data integration systems: <it>S. cerevisiae </it>data from bioPIXIE, <it>E. coli </it>data using MEFIT, and <it>H. sapiens </it>data from HEFalMp.</p> <p>Conclusions</p> <p>Graphle serves as a search and visualization engine for biological networks, which can be managed locally (simplifying collaborative data sharing) and investigated remotely. The Graphle framework is freely downloadable and easily installed on new servers, allowing any lab to quickly set up a Graphle site from which their own biological network data can be shared online.</p

Interpretable neural architecture search and transfer learning for understanding CRISPR/Cas9 off-target enzymatic reactions

Finely-tuned enzymatic pathways control cellular processes, and their dysregulation can lead to disease. Creating predictive and interpretable models for these pathways is challenging because of the complexity of the pathways and of the cellular and genomic contexts. Here we introduce Elektrum, a deep learning framework which addresses these challenges with data-driven and biophysically interpretable models for determining the kinetics of biochemical systems. First, it uses in vitro kinetic assays to rapidly hypothesize an ensemble of high-quality Kinetically Interpretable Neural Networks (KINNs) that predict reaction rates. It then employs a novel transfer learning step, where the KINNs are inserted as intermediary layers into deeper convolutional neural networks, fine-tuning the predictions for reaction-dependent in vivo outcomes. Elektrum makes effective use of the limited, but clean in vitro data and the complex, yet plentiful in vivo data that captures cellular context. We apply Elektrum to predict CRISPR-Cas9 off-target editing probabilities and demonstrate that Elektrum achieves state-of-the-art performance, regularizes neural network architectures, and maintains physical interpretability.Comment: 23 pages, 4 figure

Paving the Way Towards a Successful and Fulfilling Career in Computational Biology

Most of us will spend a significant amount of time and effort throughout our lives in improving our career. The decisions we make shape how our career progresses, and the right decisions can ensure it is successful and fulfilling. Early decisions can have a strong influence, especially in today’s competitive job market, where a university degree will not guarantee the best job. It is vital these early decisions are well informed and based on access to as much information as possible. As part of an effort to ensure that computational biologists and students are guided into the right career paths, the Regional Student Group (RSG) program, an arm of the International Society for Computational Biology (ISCB), has provided a range of activities to assist computational biologists and bioinformatics researchers in their career development. These include organizing prac�tical workshops and seminars presented by leading experts on how to broaden the scope of career options and guarantee success. This article provides insight on some of these activities and highlights the benefits gained through the shared experiences of RSGs in running career-related activities

Visualization methods for statistical analysis of microarray clusters

BACKGROUND: The most common method of identifying groups of functionally related genes in microarray data is to apply a clustering algorithm. However, it is impossible to determine which clustering algorithm is most appropriate to apply, and it is difficult to verify the results of any algorithm due to the lack of a gold-standard. Appropriate data visualization tools can aid this analysis process, but existing visualization methods do not specifically address this issue. RESULTS: We present several visualization techniques that incorporate meaningful statistics that are noise-robust for the purpose of analyzing the results of clustering algorithms on microarray data. This includes a rank-based visualization method that is more robust to noise, a difference display method to aid assessments of cluster quality and detection of outliers, and a projection of high dimensional data into a three dimensional space in order to examine relationships between clusters. Our methods are interactive and are dynamically linked together for comprehensive analysis. Further, our approach applies to both protein and gene expression microarrays, and our architecture is scalable for use on both desktop/laptop screens and large-scale display devices. This methodology is implemented in GeneVAnD (Genomic Visual ANalysis of Datasets) and is available at . CONCLUSION: Incorporating relevant statistical information into data visualizations is key for analysis of large biological datasets, particularly because of high levels of noise and the lack of a gold-standard for comparisons. We developed several new visualization techniques and demonstrated their effectiveness for evaluating cluster quality and relationships between clusters

Integrated functional networks of process, tissue, and developmental stage specific interactions in Arabidopsis thaliana

<p>Abstract</p> <p>Background</p> <p>Recent years have seen an explosion in plant genomics, as the difficulties inherent in sequencing and functionally analyzing these biologically and economically significant organisms have been overcome. <it>Arabidopsis thaliana</it>, a versatile model organism, represents an opportunity to evaluate the predictive power of biological network inference for plant functional genomics.</p> <p>Results</p> <p>Here, we provide a compendium of functional relationship networks for <it>Arabidopsis thaliana </it>leveraging data integration based on over 60 microarray, physical and genetic interaction, and literature curation datasets. These include tissue, biological process, and development stage specific networks, each predicting relationships specific to an individual biological context. These biological networks enable the rapid investigation of uncharacterized genes in specific tissues and developmental stages of interest and summarize a very large collection of <it>A. thaliana </it>data for biological examination. We found validation in the literature for many of our predicted networks, including those involved in disease resistance, root hair patterning, and auxin homeostasis.</p> <p>Conclusions</p> <p>These context-specific networks demonstrate that highly specific biological hypotheses can be generated for a diversity of individual processes, developmental stages, and plant tissues in <it>A. thaliana</it>. All predicted functional networks are available online at <url>http://function.princeton.edu/arathGraphle</url>.</p

Systemic approach to business administration of innovation-oriented enterprise

The authors use the method of regression analysis, with the help of which they determine the dependence of the level and rates of economic growth of modern economic systems on development of innovation-oriented entrepreneurship. The additional methodological instrumentarium includes the proprietary method of evaluation of effectiveness of business administration of innovation-oriented enterprise. The authors offer a systemic approach to business administration of innovation-oriented enterprise and prove its high effectiveness as compared to the usual approach by the example of modern Russian innovation-oriented enterprises.peer-reviewe