NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways

The network analysis tools (NeAT) (http://rsat.ulb.ac.be/neat/) provide a user-friendly web access to a collection of modular tools for the analysis of networks (graphs) and clusters (e.g. microarray clusters, functional classes, etc.). A first set of tools supports basic operations on graphs (comparison between two graphs, neighborhood of a set of input nodes, path finding and graph randomization). Another set of programs makes the connection between networks and clusters (graph-based clustering, cliques discovery and mapping of clusters onto a network). The toolbox also includes programs for detecting significant intersections between clusters/classes (e.g. clusters of co-expression versus functional classes of genes). NeAT are designed to cope with large datasets and provide a flexible toolbox for analyzing biological networks stored in various databases (protein interactions, regulation and metabolism) or obtained from high-throughput experiments (two-hybrid, mass-spectrometry and microarrays). The web interface interconnects the programs in predefined analysis flows, enabling to address a series of questions about networks of interest. Each tool can also be used separately by entering custom data for a specific analysis. NeAT can also be used as web services (SOAP/WSDL interface), in order to design programmatic workflows and integrate them with other available resources

NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways

The network analysis tools (NeAT) (http://rsat.ulb.ac.be/neat/) provide a user-friendly web access to a collection of modular tools for the analysis of networks (graphs) and clusters (e.g. microarray clusters, functional classes, etc.). A first set of tools supports basic operations on graphs (comparison between two graphs, neighborhood of a set of input nodes, path finding and graph randomization). Another set of programs makes the connection between networks and clusters (graph-based clustering, cliques discovery and mapping of clusters onto a network). The toolbox also includes programs for detecting significant intersections between clusters/classes (e.g. clusters of co-expression versus functional classes of genes). NeAT are designed to cope with large datasets and provide a flexible toolbox for analyzing biological networks stored in various databases (protein interactions, regulation and metabolism) or obtained from high-throughput experiments (two-hybrid, mass-spectrometry and microarrays). The web interface interconnects the programs in predefined analysis flows, enabling to address a series of questions about networks of interest. Each tool can also be used separately by entering custom data for a specific analysis. NeAT can also be used as web services (SOAP/WSDL interface), in order to design programmatic workflows and integrate them with other available resources

Crystallography data analysis at LCLS

Serial femtosecond crystallography is the process for which an ultrafast free-electron x-ray laser diffracts a protein crystal into its atomic structural basis. From these atomic structures, scientists can better understand the functions of the diffracted proteins. This is important because the protein structures obtained from this process can also provide valuable information that can help redesign medicinal drugs that will target diseases caused by certain proteins. One of the objectives for this research project is to work with a computational crystallography toolbox (CCBTX) that is still in development towards becoming an automated data analyzing software. We collect data by manually running the built in programs on python and analyzing the outputs given through a variety of parameters for commands such as averaging dark images and masking out bad pixels. This helps determine the efficiency of the built in software of the computational crystallography toolbox. Another objective was to use mathematical theory to calculate the sigma level of an automated peak finding threshold on a given peak value. Further more, we calculated the the probability of obtaining the peak value desired and verified the accuracy of the mathematical derivation through a python simulation on Jupyter Notebook

Supporting the Everyday Work of Scientists: Automating Scientific Workflows

This paper describes an action research project that we undertook with National Research Council Canada (NRC) scientists. Based on discussions about their \ud difficulties in using software to collect data and manage processes, we identified three requirements for increasing research productivity: ease of use for end- \ud users; managing scientific workflows; and facilitating software interoperability. Based on these requirements, we developed a software framework, Sweet, to \ud assist in the automation of scientific workflows. \ud \ud Throughout the iterative development process, and through a series of structured interviews, we evaluated how the framework was used in practice, and identified \ud increases in productivity and effectiveness and their causes. While the framework provides resources for writing application wrappers, it was easier to code the applications’ functionality directly into the framework using OSS components. Ease of use for the end-user and flexible and fully parameterized workflow representations were key elements of the framework’s success. \u

3D Reconstruction of CT Scans For Visualization in Virtual Reality

Computed tomography allows analyzing the internal structure of an object, which is useful especially in medicine. The standard visualization displays scans in the 2D plane. 3D reconstruction of scans provides a complex image of the morphology of the scanned object. Matlab is a software commonly used for image processing and analysis. It includes Medical Image Processing Toolbox for displaying data from CT scan in DICOM format. However, it is not possible with this toolbox to export the dataset of the image as a 3D object. Therefore, the aim of the paper is the implementation of a toolbox for loading and displaying data as a 3D reconstruction. This toolbox allows the user to export the data in OBJ or STL format. That allows the user (i) to visualize the 3D models in virtual reality and (ii) to prepare the model for 3D printing. The OBJ model is imported to Blender and then exported out with a texture as an object file. In Unity, we created a 3D scene and imported model. The advantage of displaying the 3D model in virtual reality is a more realistic view of the shape and dimension of an object.Výpočetní tomografie umožňuje studovat vnitřní strukturu objektu, což je využíváno především v medicíně. Standardní zobrazovací techniky promítají snímky ve 2D rovině. 3D rekonstrukce snímků přináší komplexní pohled na morfologii snímané tkáně. Matlab je software běžně užívaný v oblasti zpracování a analýze obrazových dat. Zároveň obsahuje nástroj “Image Procesessing Toolbox”, který umožňuje zobrazit CT snímky uchované ve formátu DICOM. Tento nástroj však neumožňuje vyexportovat zobrazený model jako 3D objekt. Cílem tohoto projektu bylo vytvoření nástroje pro načítání a zobrazení zrekonstruovaných 3D modelů. Tento nástroj umožňuje uživateli vyexportovat data v OBJ nebo STL formátu, který umožňuje (i) vizualizovat 3D model ve virtuální realitě a (ii) připravit model vhodný pro 3D tisk. V editor Unity byla vytvořena 3D scéna a do ní byl importován vygenerovaný model. Výhodou zobrazení 3D modelu ve virtuální realitě je přirozený pohled na prostorové uspořádání objektu

Keeping track of worm trackers

C. elegans is used extensively as a model system in the neurosciences due to its well defined nervous system. However, the seeming simplicity of this nervous system in anatomical structure and neuronal connectivity, at least compared to higher animals, underlies a rich diversity of behaviors. The usefulness of the worm in genome-wide mutagenesis or RNAi screens, where thousands of strains are assessed for phenotype, emphasizes the need for computational methods for automated parameterization of generated behaviors. In addition, behaviors can be modulated upon external cues like temperature, O2 and CO2 concentrations, mechanosensory and chemosensory inputs. Different machine vision tools have been developed to aid researchers in their efforts to inventory and characterize defined behavioral “outputs”. Here we aim at providing an overview of different worm-tracking packages or video analysis tools designed to quantify different aspects of locomotion such as the occurrence of directional changes (turns, omega bends), curvature of the sinusoidal shape (amplitude, body bend angles) and velocity (speed, backward or forward movement)

Evaluation of crime prevention initiatives

This third toolbox in the series published by the EUCPN Secretariat focuses on the main theme of the Irish Presidency, which is the evaluation of crime prevention initiatives. The theme is explored and elaborated in various ways through: a literature review; two workshops with international experts and practitioners during which the strengths and weaknesses of programme evaluation were discussed in detail; a screening of existing guidelines and manuals on evaluation; and finally, a call which was launched by the EUCPN Secretariat to the Member States to collect some practices on the evaluation of crime prevention initiatives

A framework for working with digitized cultural heritage artefacts

In this paper, we present our work in designing, implementing, and evaluating a set of 3D interactive spatial measurement tools in the context of Cultural Heritage Toolbox (CH Toolbox), a framework for computer-aided cultural heritage research. Our application utilizes a bi-manual, spaceball and mouse driven user interface to help the user manage visualized 3D models digitized from real artifacts. We have developed a virtual radius estimator, useful for analyzing incomplete pieces of radial artifacts, and a virtual tape measure, useful in measurement of geodesic distances between two points on the surface of an artifact. We tested the tools on the special case of pottery analysis

Security Toolbox for Detecting Novel and Sophisticated Android Malware

This paper presents a demo of our Security Toolbox to detect novel malware in Android apps. This Toolbox is developed through our recent research project funded by the DARPA Automated Program Analysis for Cybersecurity (APAC) project. The adversarial challenge ("Red") teams in the DARPA APAC program are tasked with designing sophisticated malware to test the bounds of malware detection technology being developed by the research and development ("Blue") teams. Our research group, a Blue team in the DARPA APAC program, proposed a "human-in-the-loop program analysis" approach to detect malware given the source or Java bytecode for an Android app. Our malware detection apparatus consists of two components: a general-purpose program analysis platform called Atlas, and a Security Toolbox built on the Atlas platform. This paper describes the major design goals, the Toolbox components to achieve the goals, and the workflow for auditing Android apps. The accompanying video (http://youtu.be/WhcoAX3HiNU) illustrates features of the Toolbox through a live audit.Comment: 4 pages, 1 listing, 2 figure