Xufei Qian

A cell-centered database for electron tomographic data.

Electron tomography is providing a wealth of 3D structural data on biological components ranging from molecules to cells. We are developing a web-accessible database tailored to high-resolution cellular level structural and protein localization data derived from electron tomography. The Cell Centered Database or CCDB is built on an object-relational framework using Oracle 8i and is housed on a server at the San Diego Supercomputer Center at the University of California, San Diego. Data can be deposited and accessed via a web interface. Each volume reconstruction is stored with a full set of descriptors along with tilt images and any derived products such as segmented objects and animations. Tomographic data are supplemented by high-resolution light microscopic data in order to provide correlated data on higher-order cellular and tissue structure. Every object segmented from a reconstruction is included as a distinct entity in the database along with measurements such as volume, surface area, diameter, and length and amount of protein labeling, allowing the querying of image-specific attributes. Data sets obtained in response to a CCDB query are retrieved via the Storage Resource Broker, a data management system for transparent access to local and distributed data collections. The CCDB is designed to provide a resource for structural biologists and to make tomographic data sets available to the scientific community at large.

The cell-centered database: a database for multiscale structural and protein localization data from light and electron microscopy.

The creation of structured shared data repositories for molecular data in the form of web-accessible databases like GenBank has been a driving force behind the genomic revolution. These resources serve not only to organize and manage molecular data being created by researchers around the globe, but also provide the starting point for data mining operations to uncover interesting information present in the large amount of sequence and structural data. To realize the full impact of the genomic and proteomic efforts of the last decade, similar resources are needed for structural and biochemical complexity in biological systems beyond the molecular level, where proteins and macromolecular complexes are situated within their cellular and tissue environments. In this review, we discuss our efforts in the development of neuroinformatics resources for managing and mining cell level imaging data derived from light and electron microscopy. We describe the main features of our web-accessible database, the Cell Centered Database (CCDB; http://ncmir.ucsd.edu/CCDB/), designed for structural and protein localization information at scales ranging from large expanses of tissue to cellular microdomains with their associated macromolecular constituents. The CCDB was created to make 3D microscopic imaging data available to the scientific community and to serve as a resource for investigating structural and macromolecular complexity of cells and tissues, particularly in the rodent nervous system.

Federated Access to Heterogeneous Information Resources in the Neuroscience Information Framework (NIF)

The overarching goal of the NIF (Neuroscience Information Framework) project is to be a one-stop-shop for Neuroscience. This paper provides a technical overview of how the system is designed. The technical goal of the first version of the NIF system was to develop an information system that a neuroscientist can use to locate relevant information from a wide variety of information sources by simple keyword queries. Although the user would provide only keywords to retrieve information, the NIF system is designed to treat them as concepts whose meanings are interpreted by the system. Thus, a search for term should find a record containing synonyms of the term. The system is targeted to find information from web pages, publications, databases, web sites built upon databases, XML documents and any other modality in which such information may be published. We have designed a system to achieve this functionality. A central element in the system is an ontology called NIFSTD (for NIF Standard) constructed by amalgamating a number of known and newly developed ontologies. NIFSTD is used by our ontology management module, called OntoQuest to perform ontology-based search over data sources. The NIF architecture currently provides three different mechanisms for searching heterogeneous data sources including relational databases, web sites, XML documents and full text of publications. Version 1.0 of the NIF system is currently in beta test and may be accessed through http://nif.nih.gov.

PathSys: integrating molecular interaction graphs for systems biology

BackgroundThe goal of information integration in systems biology is to combine information from a number of databases and data sets, which are obtained from both high and low throughput experiments, under one data management scheme such that the cumulative information provides greater biological insight than is possible with individual information sources considered separately.ResultsHere we present PathSys, a graph-based system for creating a combined database of networks of interaction for generating integrated view of biological mechanisms. We used PathSys to integrate over 14 curated and publicly contributed data sources for the budding yeast (S. cerevisiae) and Gene Ontology. A number of exploratory questions were formulated as a combination of relational and graph-based queries to the integrated database. Thus, PathSys is a general-purpose, scalable, graph-data warehouse of biological information, complete with a graph manipulation and a query language, a storage mechanism and a generic data-importing mechanism through schema-mapping.ConclusionResults from several test studies demonstrate the effectiveness of the approach in retrieving biologically interesting relations between genes and proteins, the networks connecting them, and of the utility of PathSys as a scalable graph-based warehouse for interaction-network integration and a hypothesis generator system. The PathSyss client software, named BiologicalNetworks, developed for navigation and analyses of molecular networks, is available as a Java Web Start application at http://brak.sdsc.edu/pub/BiologicalNetworks.

Semantically Based Data Integration Environment for Biomedical Research

This paper presents an overview of the data integration mediation system developed as part of the Biomedical Informatics Research Network (BIRN; http://www.nbirn.net) project. BIRN is sponsored by the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH). A core BIRN goal is the development of a multi-institution information management system to support biomedical research. Each participating institution maintains a database of their experimental or computationally derived data, and the data integration system performs semantic integration over the databases to enable researchers to perform analyses based on larger and broader datasets than would be available from any single institutions data. This demonstration paper describes architecture, implementation, and capabilities of the semantically based data integration system for BIRN

Editorial: BioDB: An ontology-enhanced information system for heterogeneous biological information

This paper presents BIODB, an ontology-enhanced information system to manage heterogeneous data. An ontology-enhanced system is a system where ad hoc data is imported into the system by a user, annotated by the user to connect the data to an ontology or other data sources, and then all data connected through the ontology can be queried in a federated manner. The BIODB system enables multi-model data federation, i.e., it federate data that can be in different data models including, relational, XML and RDF, sequence data and so on. It uses an ontologically enhanced system catalog, an ontological data index, an association index to facilitate cross-model data mapping, and a new algorithm for ontology-assisted keyword queries with ranking. The paper describes these components in detail, and presents an evaluation of the architecture in the context of an actual application.

A practical approach for microscopy imaging data management (MIDM) in neuroscience

Current data management approaches can easily handle the relatively simple requirements for molecular biology research but not the more varied and sophisticated microscopy imaging data in neuroscience research. We developed a project-oriented experimental imaging data management system through integration of the object-relational Oracle DBMS (database management system) and a distributed file management system, the storage resource broker (SRB). The data model we developed on Oracle9i supports semantic and analytical queries and image content mining. The MIDM provides comprehensive descriptive, structural, spatial and administrative information on microscopy image datasets. The current MIDM is web accessible at http://ncmir.ucsd.edu/CCDB. This paper describes the MIDM architecture and data mode in MIDM.

Navigating Virtual Information Sources with Know-ME

In many application domains such as biological sciences, information integration faces a challenge usually not observed in simpler applications. Here, the tobe-integrated information sources come from very different sub-specialties (e.g., anatomy and behavioral neuroscience) and have widely diverse schema, often with little or no overlap in attributes. Yet, they can be conceptually integrated because they refer to different aspects of the same physical objects or phenomena. We have proposed model-based mediation (MBM) as an information integration paradigm where information sources with hard-to-correlate schemas may be integrated using auxiliary expert knowledge to hold together widely different data schemas. The expert knowledge is captured in a graph structure called the Knowledge Map. In MBM, we extend the global-as-view architecture by lifting exported source data to conceptual models (CMs) that represent more source specific knowledge than a logical schema. The mediators IVDs are defined in terms of source CMs and make use of a semantically richer model involving class hierarchies, complex object structure, and rule-defined semantic integrity constraints. Additionally, sources specify object contexts, i.e., formulas that relate a sources conceptual schema with the global domain knowledge maintained at the mediator. In this paper, we introduce a tool called Knowledge Map Explorer (Know-ME) for a user to explore both the domain knowledge, and all data sources that have been integrated using it.

A System for Managing Alternate Models in Model-Based Mediation

In [1,3], we have described the problem of model-based mediation (MBM) as an extension of the global-as-view paradigm of information integration. The need for this extension arises in many application domains where the information sources to be integrated not only differ in their export formats, data models, and query capabilities, but have widely different schema with very little overlap in attributes. In scientific applications, the information sources come from different subdisciplines, and despite their poorly overlapping schema, can be integrated because they capture different aspects of the same scientific objects or phenomena, and can be conceptually integrated due to scientific reasons. In the MBM paradigm, a “mediation engineer” consults with domain experts to explicitly model the “glue knowledge” using a set of facts and rules at the mediator. Integrated views are defined in MBM on top of the exported schemas from the information sources together with the glue knowledge source that ties them together. We have successfully applied the MBM technique to develop the KIND mediator or Neuroscience information sources [1]-[4]. To accomplish this, sources in the MBM framework export their conceptual models (CMs), consisting of the logical schema, domain constraints, and object contexts, i.e., formulas that relate their conceptual schema with the global domain knowledge maintained at the mediator. Thus model-based mediation has a hybrid approach to information integration - on the one hand at the mediator integrated views are defined over source CMs and the Knowledge Map using a global-as-view approach; on the other hand, object-contexts of the source are defined as local-as-view.

Semantic query reformulation: the NIF experience

The NIF system is a semantic search engine that uses an ontology to improve search quality. In this experience paper we present SKEYQL, our semantic keyword query language and describe a number of ontology-based query reformulation strategies that go beyond standard query expansion techniques. We also present a set of lessons learnt and strategies that did not work. We reaffirm the importance of pre-annotating data to ensure quality query results.