Phaedra, a protocol-driven system for analysis and validation of high-content imaging and flow cytometry

High-content screening has brought new dimensions to cellular assays by generating rich data sets that characterize cell populations in great detail and detect subtle phenotypes. To derive relevant, reliable conclusions from these complex data, it is crucial to have informatics tools supporting quality control, data reduction, and data mining. These tools must reconcile the complexity of advanced analysis methods with the user-friendliness demanded by the user community. After review of existing applications, we realized the possibility of adding innovative new analysis options. Phaedra was developed to support workflows for drug screening and target discovery, interact with several laboratory information management systems, and process data generated by a range of techniques including high-content imaging, multicolor flow cytometry, and traditional high-throughput screening assays. The application is modular and flexible, with an interface that can be tuned to specific user roles. It offers user-friendly data visualization and reduction tools for HCS but also integrates Matlab for custom image analysis and the Konstanz Information Miner (KNIME) framework for data mining. Phaedra features efficient JPEG2000 compression and full drill-down functionality from dose-response curves down to individual cells, with exclusion and annotation options, cell classification, statistical quality controls, and reporting.     

Integrated web visualizations for protein-protein interaction databases

Background

Understanding living systems is crucial for curing diseases. To achieve this task we have to understand biological networks based on protein-protein interactions. Bioinformatics has come up with a great amount of databases and tools that support analysts in exploring protein-protein interactions on an integrated level for knowledge discovery. They provide predictions and correlations, indicate possibilities for future experimental research and fill the gaps to complete the picture of biochemical processes. There are numerous and huge databases of protein-protein interactions used to gain insights into answering some of the many questions of systems biology. Many computational resources integrate interaction data with additional information on molecular background. However, the vast number of diverse Bioinformatics resources poses an obstacle to the goal of understanding. We present a survey of databases that enable the visual analysis of protein networks.

Results

We selected M =10 out of N =53 resources supporting visualization, and we tested against the following set of criteria: interoperability, data integration, quantity of possible interactions, data visualization quality and data coverage. The study reveals differences in usability, visualization features and quality as well as the quantity of interactions. StringDB is the recommended first choice. CPDB presents a comprehensive dataset and IntAct lets the user change the network layout. A comprehensive comparison table is available via web. The supplementary table can be accessed on http://tinyurl.com/PPI-DB-Comparison-2015.

Conclusions

Only some web resources featuring graph visualization can be successfully applied to interactive visual analysis of protein-protein interaction. Study results underline the necessity for further enhancements of visualization integration in biochemical analysis tools. Identified challenges are data comprehensiveness, confidence, interactive feature and visualization maturing.     

Reactome pathway analysis to enrich biological discovery in proteomics data sets

Reactome (http://www.reactome.org) is an open-source, expert-authored, peer-reviewed, manually curated database of reactions, pathways and biological processes. We provide an intuitive web-based user interface to pathway knowledge and a suite of data analysis tools. The Pathway Browser is a Systems Biology Graphical Notation-like visualization system that supports manual navigation of pathways by zooming, scrolling and event highlighting, and that exploits PSI Common Query Interface web services to overlay pathways with molecular interaction data from the Reactome Functional Interaction Network and interaction databases such as IntAct, ChEMBL and BioGRID. Pathway and expression analysis tools employ web services to provide ID mapping, pathway assignment and over-representation analysis of user-supplied data sets. By applying Ensembl Compara to curated human proteins and reactions, Reactome generates pathway inferences for 20 other species. The Species Comparison tool provides a summary of results for each of these species as a table showing numbers of orthologous proteins found by pathway from which users can navigate to inferred details for specific proteins and reactions. Reactome's diverse pathway knowledge and suite of data analysis tools provide a platform for data mining, modeling and analysis of large-scale proteomics data sets. This Tutorial is part of the International Proteomics Tutorial Programme (IPTP 8).     

A visual data flow environment for macromolecular crystallographic computing

This article describes the integration of programs from the widely used CCP4 macromolecular crystallography package into a modern data flow visualization environment (application visualization system [AVS]), which provides a simple graphical user interface, a visual programming paradigm, and a variety of 1-, 2-, and 3-D data visualization tools for the display of graphical information and the results of crystallographic calculations, such as electron density and Patterson maps. The CCP4 suite comprises a number of separate Fortran 77 programs, which communicate via common file formats. Each program is encapsulated into an AVS macro module, and may be linked to others in a data flow network, reflecting the nature of many crystallo-graphic calculations. Named pipes are used to pass input parameters from a graphical user interface to the program module, and also to intercept line printer output, which can be filtered to extract graphical information and significant numerical parameters. These may be passed to downstream modules, permitting calculations to be automated if no user interaction is required, or giving the user the opportunity to make selections in an interactive manner.     

Dynamic Data Visualization with Weave and Brain Choropleths

This article introduces the neuroimaging community to the dynamic visualization workbench, Weave (https://www.oicweave.org/), and a set of enhancements to allow the visualization of brain maps. The enhancements comprise a set of brain choropleths and the ability to display these as stacked slices, accessible with a slider. For the first time, this allows the neuroimaging community to take advantage of the advanced tools already available for exploring geographic data. Our brain choropleths are modeled after widely used geographic maps but this mashup of brain choropleths with extant visualization software fills an important neuroinformatic niche. To date, most neuroinformatic tools have provided online databases and atlases of the brain, but not good ways to display the related data (e.g., behavioral, genetic, medical, etc). The extension of the choropleth to brain maps allows us to leverage general-purpose visualization tools for concurrent exploration of brain images and related data. Related data can be represented as a variety of tables, charts and graphs that are dynamically linked to each other and to the brain choropleths. We demonstrate that the simplified region-based analyses that underlay choropleths can provide insights into neuroimaging data comparable to those achieved by using more conventional methods. In addition, the interactive interface facilitates additional insights by allowing the user to filter, compare, and drill down into the visual representations of the data. This enhanced data visualization capability is useful during the initial phases of data analysis and the resulting visualizations provide a compelling way to publish data as an online supplement to journal articles.     

PGV - Prokaryotic Genome Viewer

Recent sequencing of genomes of several microorganisms provides an opportunity to have access to huge volumes of data stored in various databases. This has resulted in the development of various computational and visualization tools to aid in retrieval and analysis of data. Development of user friendly genome data mapping and visualization tools facilitates researchers to closely examine various features of genes and make inferences from the displayed data efficiently. PGV - Prokaryotic Genome Viewer is a Java based web application tool capable of generating high quality interactive circular chromosome maps. With simple mouse roll over tasks on the interested region on the displayed map, the user is provided with features such as feature labeling, multi-fold zooming, image rotation and hyperlinking to different information resources. The tool is capable of instantaneously generating maps using user-supplied sequence data.     

Gene expression data preprocessing

We present an interactive web tool for preprocessing microarray gene expression data. It analyses the data, suggests the most appropriate transformations and proceeds with them after user agreement. The normal preprocessing steps include scale transformations, management of missing values, replicate handling, flat pattern filtering and pattern standardization and they are required before performing any pattern analysis. The processed data set can be sent to other pattern analysis tools.     

Protein kinase resource: an integrated environment for phosphorylation research

The protein kinase superfamily is an important group of enzymes controlling cellular signaling cascades. The increasing amount of available experimental data provides a foundation for deeper understanding of details of signaling systems and the underlying cellular processes. Here, we describe the Protein Kinase Resource, an integrated online service that provides access to information relevant to cell signaling and enables kinase researchers to visualize and analyze the data directly in an online environment. The data set is synchronized with Uniprot and Protein Data Bank (PDB) databases and is regularly updated and verified. Additional annotation includes interactive display of domain composition, cross-references between orthologs and functional mapping to OMIM records. The Protein Kinase Resource provides an integrated view of the protein kinase superfamily by linking data with their visual representation. Thus, human kinases can be mapped onto the human kinome tree via an interactive display. Sequence and structure data can be easily displayed using applications developed for the PKR and integrated with the website and the underlying database. Advanced search mechanisms, such as multiparameter lookup, sequence pattern, and blast search, enable fast access to the desired information, while statistics tools provide the ability to analyze the relationships among the kinases under study. The integration of data presentation and visualization implemented in the Protein Kinase Resource can be adapted by other online providers of scientific data and should become an effective way to access available experimental information.     

Database design for ecologists: Composing core entities with observations

The ecoinformatics community recognizes that ecological synthesis across studies, space, and time will require new informatics tools and infrastructure. Recent advances have been encouraging, but many problems still face ecologists who manage their own datasets, prepare data for archiving, and search data stores for synthetic research. In this paper, we describe how work by the Canopy Database Project (CDP) might enable use of database technology by field ecologists: increasing the quality of database design, improving data validation, and providing structural and semantic metadata — all of which might improve the quality of data archives and thereby help drive ecological synthesis.The CDP has experimented with conceptual components for database design, templates, to address information technology issues facing ecologists. Templates represent forest structures and observational measurements on these structures. Using our software, researchers select templates to represent their study’s data and can generate normalized relational databases. Information hidden in those databases is used by ancillary tools, including data intake forms and simple data validation, data visualization, and metadata export. The primary question we address in this paper is, which templates are the right templates.We argue for defining simple templates (with relatively few attributes) that describe the domain's major entities, and for coupling those with focused and flexible observation templates. We present a conceptual model for the observation data type, and show how we have implemented the model as an observation entity in the DataBank database designer and generator. We show how our visualization tool CanopyView exploits metadata made explicit by DataBank to help scientists with analysis and synthesis. We conclude by presenting future plans for tools to conduct statistical calculations common to forest ecology and to enhance data mining with DataBank databases.DataBank could be extended to another domain by replacing our forest–ecology-specific templates with those for the new domain. This work extends the basic computer science idea of abstract data types and user-defined types to ecology-specific database design tools for individual users, and applies to ecoinformatics the software engineering innovations of domain-specific languages, software patterns, components, refactoring, and end-user programming.     

UCSF ChimeraX: Structure visualization for researchers,educators, and developers

UCSF ChimeraX is the next‐generation interactive visualization program from the Resource for Biocomputing, Visualization, and Informatics (RBVI), following UCSF Chimera. ChimeraX brings (a) significant performance and graphics enhancements; (b) new implementations of Chimera's most highly used tools, many with further improvements; (c) several entirely new analysis features; (d) support for new areas such as virtual reality, light‐sheet microscopy, and medical imaging data; (e) major ease‐of‐use advances, including toolbars with icons to perform actions with a single click, basic “undo” capabilities, and more logical and consistent commands; and (f) an app store for researchers to contribute new tools. ChimeraX includes full user documentation and is free for noncommercial use, with downloads available for Windows, Linux, and macOS from https://www.rbvi.ucsf.edu/chimerax .     

PIVOT: protein interacions visualizatiOn tool

Protein Interaction VisualizatiOn Tool (PIVOT) is a visualization tool for protein-protein interactions. It allows the user to create personal data sets of interactions by combining information from private and public data sources. The user can gradually access the interactions' data using a clear interactive map that is focused on the researcher's protein of interest, and is reshaped and expanded in response to his/her queries. It also offers several visual enhancements and intelligent queries that help the user efficiently study it. PIVOT allows the user to search the interactions data set for paths connecting proteins that are expected to co-operate. The user can also employ PIVOT to predict unknown interactions among proteins, based on interactions among their homologous proteins in other species.     

Visualizing biomolecular electrostatics in virtual reality with UnityMol‐APBS

Virtual reality is a powerful tool with the ability to immerse a user within a completely external environment. This immersion is particularly useful when visualizing and analyzing interactions between small organic molecules, molecular inorganic complexes, and biomolecular systems such as redox proteins and enzymes. A common tool used in the biomedical community to analyze such interactions is the Adaptive Poisson‐Boltzmann Solver (APBS) software, which was developed to solve the equations of continuum electrostatics for large biomolecular assemblages. Numerous applications exist for using APBS in the biomedical community including analysis of protein ligand interactions and APBS has enjoyed widespread adoption throughout the biomedical community. Currently, typical use of the full APBS toolset is completed via the command line followed by visualization using a variety of two‐dimensional external molecular visualization software. This process has inherent limitations: visualization of three‐dimensional objects using a two‐dimensional interface masks important information within the depth component. Herein, we have developed a single application, UnityMol‐APBS, that provides a dual experience where users can utilize the full range of the APBS toolset, without the use of a command line interface, by use of a simple graphical user interface (GUI) for either a standard desktop or immersive virtual reality experience.     

A visual environment for the manipulation and integration of JAVA beans

MOTIVATION: Visual programming has the potential to allow non- programmers to redesign and rebuild applications to suit their individual needs. We have built such a visual programming environment, which allows non-programmers to interrogate and combine software components graphically to form new applications. As the needs of the biological community grow, so too will the need for more powerful and easy to use software tools. Intelligent visual programming environments will allow users to design and develop applications easily, so that they can concentrate on the application they wish to build rather than how it is to be done. RESULTS: The environment can read in JAVA Beans, and present relevant information about the beans to the user. The user can then graphically specify how they would like information to flow between the beans by performing simple docking operations. Unnecessary complexities associated with such visual design have been removed by providing intelligent docking of components and visual feedback. With such mechanisms, the complexities of building new applications are reduced. When the biologist has finished the visual construction, the design system is able to generate the new application automatically. The system has been designed specifically to meet the needs of the biological community, and a range of 'BioBeans' are being developed. These include beans for visualization (sequence displays and data visualizers), analysis (feature recognition, error detection) and communication (database access, URL retrieval, DDE communication). AVAILABILITY: Freely available. CONTACT: boyle@synomics.com      

Marine systems analysis and modeling

Oceanography and marine ecology have a considerable history in the use of computers for modeling both physical and ecological processes. With increasing stress on the marine environment due to human activities such as fisheries and numerous forms of pollution, the analysis of marine problems must increasingly and jointly consider physical, ecological and socio-economic aspects in a broader systems framework that transcends more traditional disciplinary boundaries. This often introduces difficult-to-quantify, “soft” elements, such as values and perceptions, into formal analysis. Thus, the problem domain combines a solid foundation in the physical sciences, with strong elements of ecological, socio-economic and political considerations. At the same time, the domain is also characterized by both a very large volume of some data, and an extremely datapoor situation for other variables, as well as a very high degree of uncertainty, partly due to the temporal and spatial heterogeneity of the marine environment. Consequently, marine systems analysis and management require tools that can integrate these diverse aspects into efficient information systems that can support research as well as planning and also policy- and decisionmaking processes. Supporting scientific research, as well as decision-making processes and the diverse groups and actors involved, requires better access and direct understanding of the information basis as well as easy-to-use, but powerful tools for analysis. Advanced information technology provides the tools to design and implement smart software where, in a broad sense, the emphasis is on the man-machine interface. Symbolic and analogous, graphical interaction, visual representation of problems, integrated data sources, and built-in domain knowledge can effectively support users of complex and complicated software systems. Integration, interaction, visualization and intelligence are key concepts that are discussed in detail, using an operational software example of a coastal water quality model. The model comprises components of a geographical information and mapping system, data bases, dynamic simulation models, and an integrated expert system. An interactive graphical user interface, dynamic visualization of model results, and a hyper-text-based help-and-explain system illustrate some of the features of new and powerful software tools for marine systems analysis and modeling.     

The ImageJ ecosystem: Open‐source software for image visualization,processing, and analysis

For decades, biologists have relied on software to visualize and interpret imaging data. As techniques for acquiring images increase in complexity, resulting in larger multidimensional datasets, imaging software must adapt. ImageJ is an open‐source image analysis software platform that has aided researchers with a variety of image analysis applications, driven mainly by engaged and collaborative user and developer communities. The close collaboration between programmers and users has resulted in adaptations to accommodate new challenges in image analysis that address the needs of ImageJ's diverse user base. ImageJ consists of many components, some relevant primarily for developers and a vast collection of user‐centric plugins. It is available in many forms, including the widely used Fiji distribution. We refer to this entire ImageJ codebase and community as the ImageJ ecosystem. Here we review the core features of this ecosystem and highlight how ImageJ has responded to imaging technology advancements with new plugins and tools in recent years. These plugins and tools have been developed to address user needs in several areas such as visualization, segmentation, and tracking of biological entities in large, complex datasets. Moreover, new capabilities for deep learning are being added to ImageJ, reflecting a shift in the bioimage analysis community towards exploiting artificial intelligence. These new tools have been facilitated by profound architectural changes to the ImageJ core brought about by the ImageJ2 project. Therefore, we also discuss the contributions of ImageJ2 to enhancing multidimensional image processing and interoperability in the ImageJ ecosystem.     

A practical guide to interpreting and generating bottom-up proteomics data visualizations

Mass-spectrometry based bottom-up proteomics is the main method to analyze proteomes comprehensively and the rapid evolution of instrumentation and data analysis has made the technology widely available. Data visualization is an integral part of the analysis process and it is crucial for the communication of results. This is a major challenge due to the immense complexity of MS data. In this review, we provide an overview of commonly used visualizations, starting with raw data of traditional and novel MS technologies, then basic peptide and protein level analyses, and finally visualization of highly complex datasets and networks. We specifically provide guidance on how to critically interpret and discuss the multitude of different proteomics data visualizations. Furthermore, we highlight Python-based libraries and other open science tools that can be applied for independent and transparent generation of customized visualizations. To further encourage programmatic data visualization, we provide the Python code used to generate all data figures in this review on GitHub ( https://github.com/MannLabs/ProteomicsVisualization ).