The Image Data Resource: A Bioimage Data Integration and Publication Platform (original) (raw)
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Computers in biology and medicine, 2018
Molecular imaging serves as an important tool for researchers and clinicians to visualize and investigate complex biochemical phenomena using specialized instruments; these instruments are either used individually or in combination with targeted imaging agents to obtain images related to specific diseases with high sensitivity, specificity, and signal-to-noise ratios. However, molecular imaging, which is a multidisciplinary research field, faces several challenges, including the integration of imaging informatics with bioinformatics and medical informatics, requirement of reliable and robust image analysis algorithms, effective quality control of imaging facilities, and those related to individualized disease mapping, data sharing, software architecture, and knowledge management. As a cost-effective and open-source approach to address these challenges related to molecular imaging, we develop a flexible, transparent, and secure infrastructure, named MIRA, which stands for Molecular I...
Database, 2013
Genomic data and biomedical imaging data are undergoing exponential growth. However, our understanding of the phenotype-genotype connection linking the two types of data is lagging behind. While there are many types of software that enable the manipulation and analysis of image data and genomic data as separate entities, there is no framework established for linking the two. We present a generic set of software tools, BioDIG, that allows linking of image data to genomic data. BioDIG tools can be applied to a wide range of research problems that require linking images to genomes. BioDIG features the following: rapid construction of web-based workbenches, community-based annotation, user management, and web-services. By using BioDIG to create websites, researchers and curators can rapidly annotate large number of images with genomic information. Here we present the BioDIG software tools that include an image module, a genome module and a user management module. We also introduce a BioDIG-based website, MyDIG, which is being used to annotate images of Mycoplasma.
BioSig: an imaging bioinformatics system for phenotypic analysis
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2003
Organisms express their genomes in a cell-specific manner, resulting in a variety of cellular phenotypes or phenomes. Mapping cell phenomes under a variety of experimental conditions is necessary in order to understand the responses of organisms to stimuli. Representing such data requires an integrated view of experimental and informatic protocols. The proposed system, named BioSig, provides the foundation for cataloging cellular responses as a function of specific conditioning, treatment, staining, etc. for either fixed tissue or living cell studies. A data model has been developed to capture experimental variables and map them to image collections and their computed representation. This representation is hierarchical and spans across sample tissues, cells, and organelles, which are imaged with light microscopy. At each layer, content is represented with an attributed graph, which contains information about cellular morphology, protein localization, and cellular organization in tissue or cell culture. The web-based multilayer informatics architecture uses the data model to provide guided workflow access for content exploration.
Journal of structural …, 2008
Databases have become integral parts of data management, dissemination and mining in biology. At the Second Annual Conference on Electron Tomography, held in Amsterdam in 2001, we proposed that electron tomography data should be shared in a manner analogous to structural data at the protein and sequence scales. At that time, we outlined our progress in creating a database to bring together cell level imaging data across scales, The Cell Centered Database (CCDB). The CCDB was formally launched in 2002 as an on-line repository of high-resolution 3D light and electron microscopic reconstructions of cells and subcellular structures. It contains 2D, 3D and 4D structural and protein distribution information from confocal, multiphoton and electron microscopy, including correlated light and electron microscopy. Many of the data sets are derived from electron tomography of cells and tissues. In the five years since its debut, we have moved the CCDB from a prototype to a stable resource and expanded the scope of the project to include data management and knowledge engineering. Here we provide an update on the CCDB and how it is used by the scientific community. We also describe our work in developing additional knowledge tools, e.g., ontologies, for annotation and query of electron microscopic data.
Informatics methods to enable sharing of quantitative imaging research data
Magnetic Resonance Imaging, 2012
Introduction: The National Cancer Institute Quantitative Research Network (QIN) is a collaborative research network whose goal is to share data, algorithms and research tools to accelerate quantitative imaging research. A challenge is the variability in tools and analysis platforms used in quantitative imaging. Our goal was to understand the extent of this variation and to develop an approach to enable sharing data and to promote reuse of quantitative imaging data in the community. Methods: We performed a survey of the current tools in use by the QIN member sites for representation and storage of their QIN research data including images, image meta-data and clinical data. We identified existing systems and standards for data sharing and their gaps for the QIN use case. We then proposed a system architecture to enable data sharing and collaborative experimentation within the QIN. Results: There are a variety of tools currently used by each QIN institution. We developed a general information system architecture to support the QIN goals. We also describe the remaining architecture gaps we are developing to enable members to share research images and image meta-data across the network. Conclusions: As a research network, the QIN will stimulate quantitative imaging research by pooling data, algorithms and research tools. However, there are gaps in current functional requirements that will need to be met by future informatics development. Special attention must
The ImageJ ecosystem: An open platform for biomedical image analysis
Molecular Reproduction and Development, 2015
Technology in microscopy advances rapidly, enabling increasingly affordable, faster, and more precise quantitative biomedical imaging, which necessitates correspondingly more-advanced image processing and analysis techniques. A wide range of software is availableÀ Àfrom commercial to academic, special-purpose to Swiss army knife, small to largeÀ Àbut a key characteristic of software that is suitable for scientific inquiry is its accessibility. Open-source software is ideal for scientific endeavors because it can be freely inspected, modified, and redistributed; in particular, the opensoftware platform ImageJ has had a huge impact on the life sciences, and continues to do so. From its inception, ImageJ has grown significantly due largely to being freely available and its vibrant and helpful user community. Scientists as diverse as interested hobbyists, technical assistants, students, scientific staff, and advanced biology researchers use ImageJ on a daily basis, and exchange knowledge via its dedicated mailing list. Uses of ImageJ range from data visualization and teaching to advanced image processing and statistical analysis. The software's extensibility continues to attract biologists at all career stages as well as computer scientists who wish to effectively implement specific image-processing algorithms. In this review, we use the ImageJ project as a case study of how open-source software fosters its suites of software tools, making multitudes of image-analysis technology easily accessible to the scientific community. We specifically explore what makes ImageJ so popular, how it impacts the life sciences, how it inspires other projects, and how it is selfinfluenced by coevolving projects within the ImageJ ecosystem.
BioSig: An Imaging Bioinformatics System for
Organisms express their genomes in a cell-specific manner, resulting in a variety of cellular phenotypes or phenomes. Mapping cell phenomes under a variety of experimental conditions is necessary in order to understand the responses of organisms to stimuli. Representing such data requires an integrated view of experimental and informatic protocols. The proposed system, named BioSig, provides the foundation for cataloging cellular responses as a function of specific conditioning, treatment, staining, etc. for either fixed tissue or living cell studies. A data model has been developed to capture experimental variables and map them to image collections and their computed representation. This representation is hierarchical and spans across sample tissues, cells, and organelles, which are imaged with light microscopy. At each layer, content is represented with an attributed graph, which contains information about cellular morphology, protein localization, and cellular organization in tissue or cell culture. The web-based multilayer informatics architecture uses the data model to provide guided workflow access for content exploration.
BioSig: An Imaging Bioinformatics System for Studying Phenomics
2002
F unctional genomics-understanding how a genome is expressed to produce myriad cell phenotypes-presents the core challenge of the postgenomic era. Using genomic information to understand the biology of complex organisms requires comprehensive knowledge of the dynamics of phenotype generation and maintenance. A phenotype results from selective expression of the genome, creating a history of the cell and its response to the extracellular environment. Defining cell phenomes requires tracking the kinetics and quantities of multiple constituent proteins, their cellular context, and their morphological features in large populations. Such studies should also include responses to stimuli for use in generating and testing functional models.