Platform Independent Real-Time X3D Shaders and their Applications in Bioinformatics Visualization

Since the introduction of programmable Graphics Processing Units (GPUs) and procedural shaders, hardware vendors have each developed their own individual real-time shading language standard. None of these shading languages is fully platform independent. Although this real-time programmable shader technology could be developed into 3D application on a single system, this platform dependent limitation keeps the shader technology away from 3D Internet applications. The primary purpose of this dissertation is to design a framework for translating different shader formats to platform independent shaders and embed them into the eXtensible 3D (X3D) scene for 3D web applications. This framework includes a back-end core shader converter, which translates shaders among different shading languages with a middle XML layer. Also included is a shader library containing a basic set of shaders that developers can load and add shaders to. This framework will then be applied to some applications in Biomolecular Visualization

Website Review: Protein-Protein Interactions on the Web

We present a brief guide to resources on the Internet relating to Protein-Protein Interactions. These include databases containing experimentally verified and computationally inferred physical and functional interactions. There are also tools for predicting interactions and for extracting information on interactions from the literature, and organism specific databases

Genome dynamics of the human embryonic kidney 293 lineage in response to cell biology manipulations

The HEK293 human cell lineage is widely used in cell biology and biotechnology. Here we use whole-genome resequencing of six 293 cell lines to study the dynamics of this aneuploid genome in response to the manipulations used to generate common 293 cell derivatives, such as transformation and stable clone generation (293T); suspension growth adaptation (293S); and cytotoxic lectin selection (293SG). Remarkably, we observe that copy number alteration detection could identify the genomic region that enabled cell survival under selective conditions (i.c. ricin selection). Furthermore, we present methods to detect human/vector genome breakpoints and a user-friendly visualization tool for the 293 genome data. We also establish that the genome structure composition is in steady state for most of these cell lines when standard cell culturing conditions are used. This resource enables novel and more informed studies with 293 cells, and we will distribute the sequenced cell lines to this effect

Implementing inquiry based computational modeling curriculum in the secondary science classroom

textBetter visualization of micro-level structures and processes can greatly enhance student understanding of key biological functions such as the central dogma. Previous research has demonstrated a need of introducing novel methods to increase student understanding of these concepts. The intention of this report is to show how computational modeling programs (CMPs) can be successfully used as an innovative method of teaching biology concepts that occur at a molecular level. The use of computers and web-based lessons are not new topics in secondary education studies but there is not an abundance of research related to computational modeling alone. We began by researching the many studies that have already indicated the benefits of using computers in the classroom with an emphasis on CMPs and simulations. Of these, we focused mostly on the ones that showed increased student engagement and influenced understanding of core science concepts. Based on the literature reviewed, a framework for curriculum designed around CMPs is proposed. Lastly, a model lesson is discussed to provide an example of how these professional grade tools can be employed in the classroom. This report provides a basis for the continued development of constructivist curriculum built around the use of professional grade computational tools in secondary science classrooms.Science and Mathematics Educatio

ECOMICS: A Web-Based Toolkit for Investigating the Biomolecular Web in Ecosystems Using a Trans-omics Approach

Ecosystems can be conceptually thought of as interconnected environmental and metabolic systems, in which small molecules to macro-molecules interact through diverse networks. State-of-the-art technologies in post-genomic science offer ways to inspect and analyze this biomolecular web using omics-based approaches. Exploring useful genes and enzymes, as well as biomass resources responsible for anabolism and catabolism within ecosystems will contribute to a better understanding of environmental functions and their application to biotechnology. Here we present ECOMICS, a suite of web-based tools for ECosystem trans-OMICS investigation that target metagenomic, metatranscriptomic, and meta-metabolomic systems, including biomacromolecular mixtures derived from biomass. ECOMICS is made of four integrated webtools. E-class allows for the sequence-based taxonomic classification of eukaryotic and prokaryotic ribosomal data and the functional classification of selected enzymes. FT2B allows for the digital processing of NMR spectra for downstream metabolic or chemical phenotyping. Bm-Char allows for statistical assignment of specific compounds found in lignocellulose-based biomass, and HetMap is a data matrix generator and correlation calculator that can be applied to trans-omics datasets as analyzed by these and other web tools. This web suite is unique in that it allows for the monitoring of biomass metabolism in a particular environment, i.e., from macromolecular complexes (FT2DB and Bm-Char) to microbial composition and degradation (E-class), and makes possible the understanding of relationships between molecular and microbial elements (HetMap). This website is available to the public domain at: https://database.riken.jp/ecomics/

A virtual reality classroom to teach and explore crystal solid state structures

We present an educational application of virtual reality that we created to help students gain an in-depth understanding of the internal structure of crystals and related key concepts. Teachers can use it to give lectures to small groups (10-15) of students in a shared virtual environment, both remotely (with teacher and students in different locations) and locally (while sharing the same physical space). Lectures can be recorded, stored in an online repository, and shared with students who can either review a recorded lecture in the same virtual environment or can use the application for self-studying by exploring a large collection of available crystal structures. We validated our application with human subjects receiving positive feedback

Interactivity:the missing link between virtual reality technology and drug discovery pipelines

The potential of virtual reality (VR) to contribute to drug design and development has been recognised for many years. Hardware and software developments now mean that this potential is beginning to be realised, and VR methods are being actively used in this sphere. A recent advance is to use VR not only to visualise and interact with molecular structures, but also to interact with molecular dynamics simulations of 'on the fly' (interactive molecular dynamics in VR, IMD-VR), which is useful not only for flexible docking but also to examine binding processes and conformational changes. iMD-VR has been shown to be useful for creating complexes of ligands bound to target proteins, e.g., recently applied to peptide inhibitors of the SARS-CoV-2 main protease. In this review, we use the term 'interactive VR' to refer to software where interactivity is an inherent part of the user VR experience e.g., in making structural modifications or interacting with a physically rigorous molecular dynamics (MD) simulation, as opposed to simply using VR controllers to rotate and translate the molecule for enhanced visualisation. Here, we describe these methods and their application to problems relevant to drug discovery, highlighting the possibilities that they offer in this arena. We suggest that the ease of viewing and manipulating molecular structures and dynamics, and the ability to modify structures on the fly (e.g., adding or deleting atoms) makes modern interactive VR a valuable tool to add to the armoury of drug development methods.Comment: 19 pages, 3 figure

a versatile tool for the analysis and integrative visualization of DNA copy number variants

Background The analysis of DNA copy number variants (CNV) has increasing impact in the field of genetic diagnostics and research. However, the interpretation of CNV data derived from high resolution array CGH or NGS platforms is complicated by the considerable variability of the human genome. Therefore, tools for multidimensional data analysis and comparison of patient cohorts are needed to assist in the discrimination of clinically relevant CNVs from others. Results We developed GenomeCAT, a standalone Java application for the analysis and integrative visualization of CNVs. GenomeCAT is composed of three modules dedicated to the inspection of single cases, comparative analysis of multidimensional data and group comparisons aiming at the identification of recurrent aberrations in patients sharing the same phenotype, respectively. Its flexible import options ease the comparative analysis of own results derived from microarray or NGS platforms with data from literature or public depositories. Multidimensional data obtained from different experiment types can be merged into a common data matrix to enable common visualization and analysis. All results are stored in the integrated MySQL database, but can also be exported as tab delimited files for further statistical calculations in external programs. Conclusions GenomeCAT offers a broad spectrum of visualization and analysis tools that assist in the evaluation of CNVs in the context of other experiment data and annotations. The use of GenomeCAT does not require any specialized computer skills. The various R packages implemented for data analysis are fully integrated into GenomeCATs graphical user interface and the installation process is supported by a wizard. The flexibility in terms of data import and export in combination with the ability to create a common data matrix makes the program also well suited as an interface between genomic data from heterogeneous sources and external software tools. Due to the modular architecture the functionality of GenomeCAT can be easily extended by further R packages or customized plug-ins to meet future requirements

Intuitive Visualization and Analysis of Multi-Omics Data and Application to Escherichia coli Carbon Metabolism

Combinations of ‘omics’ investigations (i.e, transcriptomic, proteomic, metabolomic and/or fluxomic) are increasingly applied to get comprehensive understanding of biological systems. Because the latter are organized as complex networks of molecular and functional interactions, the intuitive interpretation of multi-omics datasets is difficult. Here we describe a simple strategy to visualize and analyze multi-omics data. Graphical representations of complex biological networks can be generated using Cytoscape where all molecular and functional components could be explicitly represented using a set of dedicated symbols. This representation can be used i) to compile all biologically-relevant information regarding the network through web link association, and ii) to map the network components with multi-omics data. A Cytoscape plugin was developed to increase the possibilities of both multi-omic data representation and interpretation. This plugin allowed different adjustable colour scales to be applied to the various omics data and performed the automatic extraction and visualization of the most significant changes in the datasets. For illustration purpose, the approach was applied to the central carbon metabolism of Escherichia coli. The obtained network contained 774 components and 1232 interactions, highlighting the complexity of bacterial multi-level regulations. The structured representation of this network represents a valuable resource for systemic studies of E. coli, as illustrated from the application to multi-omics data. Some current issues in network representation are discussed on the basis of this work