SUMMARY: Cells are organized as a hierarchy of macromolecular assemblies, ranging from small protein complexes to entire organelles. Various technologies have been developed to elucidate subcellular architecture at different scales, such as mass spectrometry approaches for mapping protein biophysical interactions and immunofluorescence imaging for mapping protein localization. We present the Cell Mapping Toolkit, which is designed to systematically integrate data from different modalities into unified hierarchical maps of subcellular organization. The toolkit facilitates an end-to-end pipeline including processing datasets, integrating modalities, and visualizing the final cell map with rich metadata including provenance documentation at each step. The Cell Mapping Toolkit provides researchers with tools for analyzing, integrating, and visualizing diverse protein datasets in a robust and reproducible framework. AVAILABILITY AND IMPLEMENTATION: The code is freely available and is hosted on GitHub at https://github.com/idekerlab/cellmaps_pipeline. Comprehensive documentation and practical examples are provided at https://cellmaps-pipeline.readthedocs.io/.

BACKGROUND: Genome-wide experiments are routinely conducted to measure gene expression, DNA-protein interactions and epigenetic status. Structured metadata for these experiments is imperative for a complete understanding of experimental conditions, to enable consistent data processing and to allow retrieval, comparison, and integration of experimental results. Even though several repositories have been developed for genomics data, only a few provide annotation of samples and assays using controlled vocabularies. Moreover, many of them are tailored for a single type of technology or measurement and do not support the integration of multiple data types. RESULTS: We have developed eXframe - a reusable web-based framework for genomics experiments that provides 1) the ability to publish structured data compliant with accepted standards 2) support for multiple data types including microarrays and next generation sequencing 3) query, analysis and visualization integration tools (enabled by consistent processing of the raw data and annotation of samples) and is available as open-source software. We present two case studies where this software is currently being used to build repositories of genomics experiments - one contains data from hematopoietic stem cells and another from Parkinson's disease patients. CONCLUSION: The web-based framework eXframe offers structured annotation of experiments as well as uniform processing and storage of molecular data from microarray and next generation sequencing platforms. The framework allows users to query and integrate information across species, technologies, measurement types and experimental conditions. Our framework is reusable and freely modifiable - other groups or institutions can deploy their own custom web-based repositories based on this software. It is interoperable with the most important data formats in this domain. We hope that other groups will not only use eXframe, but also contribute their own useful modifications.

Currently a large number of Web sites are driven by Content Management Systems (CMS) which manage textual and multimedia content but also - inherently - carry valuable information about a site’s structure and content model. Exposing this structured information to the Web of Data has so far required considerable expertise in RDF and OWL modelling and additional programming effort. In this paper we tackle one of the most popular CMS: Drupal. We enable site administrators to export their site content model and data to the Web of Data without requiring extensive knowledge on Semantic Web technologies. Our modules create RDFa annotations and – optionally – a SPARQL endpoint for any Drupal site out of the box. Likewise, we add the means to map the site data to existing ontologies on the Web with a search interface to find commonly used ontology terms. We also allow a Drupal site administrator to include existing RDF data from remote SPARQL endpoints on the Web in the site. When brought together, these features allow networked RDF Drupal sites that reuse and enrich Linked Data. We finally discuss the adoption of our modules and report on a use case in the biomedical field and the current status of its deployment. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Web-based biomedical communities are becoming an increasingly popular vehicle for sharing information amongst researchers and are fast gaining an online presence. However, information organization and exchange in such communities is usually unstructured, rendering interoperability between communities difficult. Furthermore, specialized software to create such communities at low cost-targeted at the specific common information requirements of biomedical researchers-has been largely lacking. At the same time, a growing number of biological knowledge bases and biomedical resources are being structured for the Semantic Web. Several groups are creating reference ontologies for the biomedical domain, actively publishing controlled vocabularies and making data available in Resource Description Framework (RDF) language. We have developed the Science Collaboration Framework (SCF) as a reusable platform for advanced structured online collaboration in biomedical research that leverages these ontologies and RDF resources. SCF supports structured 'Web 2.0' style community discourse amongst researchers, makes heterogeneous data resources available to the collaborating scientist, captures the semantics of the relationship among the resources and structures discourse around the resources. The first instance of the SCF framework is being used to create an open-access online community for stem cell research-StemBook (http://www.stembook.org). We believe that such a framework is required to achieve optimal productivity and leveraging of resources in interdisciplinary scientific research. We expect it to be particularly beneficial in highly interdisciplinary areas, such as neurodegenerative disease and neurorepair research, as well as having broad utility across the natural sciences.

Nothing great is ever accomplished in isolation. – Yo-Yo Ma As Neumann and Prusak note (this issue), there are now literally thousands of communities of practice in biomedical research and healthcare, knit together by the Web. These communities rely extensively upon digital resources of many kinds, in many formats, as mediators of their work. The current non-semantic web stitches these resources together at their edges. It has been left to human web navigators—mostly—to integrate the content as knowledge. Knowledge integration occurs in part through the activities of reading, comparing, evaluating, discussing, hypothesis construction, and validation, and extending … inevitable and irreducible activities in science. The social fabric and mediating resources for these activities have been called the knowledge ecosystem of science. To this has been added, almost organically, over the last 10–15 years, a dense web of aligned activities in bioinformatics and applied web technology, which is now beginning to include the semantic web. The essential questions for semantic web technology applied to bioscience are: The six articles for our special issue fall into two groups by general topic. Can semantic web provide deeper and more uniform integration for science on the web? What new social forms of collaboration in science can this enable? Will this require us to change any of our approaches, either to science or to technology development? In the first group are exemplars of new technical approaches for knowledge integration via semantic web. These include persistence mechanisms for semantic data (Feigenbaum et al.); provenance capture for scientific data from in-silico experiments (Stevens, Zhao and Goble) and multi-schema information integration (Quan). The open-source triple store Boca (Feigenbaum et al.), is one of several persistence and query mechanisms now available for dealing with RDF [3], the lingua franca of semantic web. Quan's article discusses technology based on the Haystack platform [4, 5] for integrating and navigating information within the browser based on its semantics. Stevens, Zhao and Goble on management of experimental data provenance introduce a key technology for any attempt to integrate knowledge via computation, and a key question: what was the data, and what was computed on it? [6]. In the second group are discussions of the kinds of facilities and new capabilities that are now being built with these and related technologies. Neumann and Prusak step slightly away from semantic web per se to discuss the general topic of how ‘knowledge networks’ form and are enabled on the web. Both the SenseLab (Crasto et al.) and the Alzforum-SWAN (Kinoshita and Clark) articles discuss different types of knowledge bases provided on the web by and for scientific web communities in neuroscience. We find in each of these papers, a significant role for formalization of biological knowledge schemas, or biological ontologies [7]. There is now a substantial body of W3C technology [8–10] to support this happening on the web. And there are many science-technology collaborations emerging to provide social organization and support for these activities, under the auspices of W3C (http://www.w3.org/), the National Center for Biomedical Ontology (http://www.bioontology.org/) and other organizations. Any data organization for computing will have a schema of some kind, explicitly or implicitly. Initially this resided in the application, later in a database schema. Successive generations of technology have attempted to externalize and share the schema so that it could be used and re-used by multiple applications; and to formalize it for purposes of robust reasoning. What we now potentially have with ontologies conforming to W3C standards is the ability to externalize and share multiple, partially intersecting, logically robust schemas on the web in association with the data they describe. These ontologies will have been developed by and for communities of practice—knowledge networks in Neumann and Prusak's terms—in which there is a strong requirement to re-use the ontologized data outside of its original purpose. This kind of network is a component of the larger scientific knowledge ecosystem. Why is it that scientists need to re-use data in such complex ways that it must be shared with associated ontologies? This brings us from ontology to epistemology, which is, perhaps, where we should have begun. The scientific method believes in a commonly accessible external world, subject to rational laws, which can be inferred from phenomena and tested by experiment. Philosophers of science define knowledge as ‘justified true belief’ [11]. This differentiates ‘knowledge’ from mere ‘belief’ based on two criteria applied to belief. Knowledge is a true statement of belief, experimentally verifiable as truth by the activities of others. And knowledge is a justifiable statement of belief, because the process by which it was arrived at is a replicable activity of materialistic enquiry, which can be recounted and repeated. A guess is not knowledge, even if correct. The digital resources we create, publish, consume, critique and argue about in science all purport to contain knowledge, and we believe we can integrate them to produce further knowledge, but over time, part of this involves validating the assertions of authors that what they have published is, indeed, true. A winnowing process is constantly in play, in which we partially invalidate the content of some of these resources. At all times in our knowledge networks there will be both knowledge and error, closely commingled, and in process of being sorted out by human activities. This is what scientists do. It should not be at all unfamiliar to people who spend half their lives debugging and refactoring code! We also have to accept that there will exist at all times a very important intermediate state of ‘knowledge in embryo’ which both is, and is not, real knowledge, as it is in process of being validated. This situation becomes easier for us to work with if we think of the evolving knowledge ecosystem of science as now consisting of two interconnected ecosystems, one scientific and one technical. The contents of each ecosystem are continually being internally aligned, aligned against the other ecosystem and debugged. Software for the web, including formal ontologies, will continue to evolve more and more closely in conjunction with the needs and participation of scientists. At the same time scientific knowledge will more and more make use of software and ontologies to develop, validate, publish and integrate its findings. For this process to work well requires a very agile and flexible collaboration of scientific web communities, and cooperating communities of web technologists focused on scientific problems. Many of the authors of this issue's articles will be participating in an ambitious proof of concept demonstration at WWW2007 (http://www2007.org/), sponsored by HCLS, the W3C Health Care Life Sciences Task Force (http://www.w3.org/2001/sw/hcls/). HCLS is just such a collaboration of science and technology communities. The demonstration will show interoperability across many semantic web resources focused on assisting in the solution of a pair of scientific use cases in Alzheimer Disease and Parkinson's Disease research. Neuromedicine was selected for the domain of the demonstration in part because it attempts to study and intervene in such complex phenomena, requiring the integration of many subdomains of inquiry. Connecting experts and expert knowledge flexibly across these subdomains remains challenging, and is not only a problem of technology, but of learning and the creation of shared culture [12, 13]. We believe this experiment will be a step toward a continuously-evolving close collaboration of biomedical researchers, health care providers, bioinformaticians and web technologists in the future. We sincerely hope that HCLS and many similar working groups will be a proving ground for a continuously developing and highly productive extended ecosystem of biomedical researchers, web technologists and bioinformaticians.

Brief Bioinform. Advance Access published on May 25, 2007. doi: 10.1093/bib/bbm019 The editorial should have been included in Briefings in Bioinformatics Volume 8, Issue 3. The publisher apologizes for this error.

Scientists drove the early development of the World Wide Web, primarily as a means for rapid communication, document sharing and data access. They have been far slower to adopt the web as a medium for building research communities. Yet, web-based communities hold great potential for accelerating the pace of scientific research. In this article, we will describe the 10-year experience of the Alzheimer Research Forum ('Alzforum'), a unique example of a thriving scientific web community, and explain the features that contributed to its success. We will then outline the SWAN (Semantic Web Applications in Neuromedicine) project, in which Alzforum curators are collaborating with informatics researchers to develop novel approaches that will enable communities to share richly contextualized information about scientific data, claims and hypotheses.

BACKGROUND: Neuroscientists often need to access a wide range of data sets distributed over the Internet. These data sets, however, are typically neither integrated nor interoperable, resulting in a barrier to answering complex neuroscience research questions. Domain ontologies can enable the querying heterogeneous data sets, but they are not sufficient for neuroscience since the data of interest commonly span multiple research domains. To this end, e-Neuroscience seeks to provide an integrated platform for neuroscientists to discover new knowledge through seamless integration of the very diverse types of neuroscience data. Here we present a Semantic Web approach to building this e-Neuroscience framework by using the Resource Description Framework (RDF) and its vocabulary description language, RDF Schema (RDFS), as a standard data model to facilitate both representation and integration of the data. RESULTS: We have constructed a pilot ontology for BrainPharm (a subset of SenseLab) using RDFS and then converted a subset of the BrainPharm data into RDF according to the ontological structure. We have also integrated the converted BrainPharm data with existing RDF hypothesis and publication data from a pilot version of SWAN (Semantic Web Applications in Neuromedicine). Our implementation uses the RDF Data Model in Oracle Database 10g release 2 for data integration, query, and inference, while our Web interface allows users to query the data and retrieve the results in a convenient fashion. CONCLUSION: Accessing and integrating biomedical data which cuts across multiple disciplines will be increasingly indispensable and beneficial to neuroscience researchers. The Semantic Web approach we undertook has demonstrated a promising way to semantically integrate data sets created independently. It also shows how advanced queries and inferences can be performed over the integrated data, which are hard to achieve using traditional data integration approaches. Our pilot results suggest that our Semantic Web approach is suitable for realizing e-Neuroscience and generic enough to be applied in other biomedical fields.

BACKGROUND: A fundamental goal of the U.S. National Institute of Health (NIH) "Roadmap" is to strengthen Translational Research, defined as the movement of discoveries in basic research to application at the clinical level. A significant barrier to translational research is the lack of uniformly structured data across related biomedical domains. The Semantic Web is an extension of the current Web that enables navigation and meaningful use of digital resources by automatic processes. It is based on common formats that support aggregation and integration of data drawn from diverse sources. A variety of technologies have been built on this foundation that, together, support identifying, representing, and reasoning across a wide range of biomedical data. The Semantic Web Health Care and Life Sciences Interest Group (HCLSIG), set up within the framework of the World Wide Web Consortium, was launched to explore the application of these technologies in a variety of areas. Subgroups focus on making biomedical data available in RDF, working with biomedical ontologies, prototyping clinical decision support systems, working on drug safety and efficacy communication, and supporting disease researchers navigating and annotating the large amount of potentially relevant literature. RESULTS: We present a scenario that shows the value of the information environment the Semantic Web can support for aiding neuroscience researchers. We then report on several projects by members of the HCLSIG, in the process illustrating the range of Semantic Web technologies that have applications in areas of biomedicine. CONCLUSION: Semantic Web technologies present both promise and challenges. Current tools and standards are already adequate to implement components of the bench-to-bedside vision. On the other hand, these technologies are young. Gaps in standards and implementations still exist and adoption is limited by typical problems with early technology, such as the need for a critical mass of practitioners and installed base, and growing pains as the technology is scaled up. Still, the potential of interoperable knowledge sources for biomedicine, at the scale of the World Wide Web, merits continued work.

The World-Wide Web provides a globally distributed communication framework that is essential for almost all scientific collaboration, including bioinformatics. However, several limits and inadequacies have become apparent, one of which is the inability to programmatically identify locally named objects that may be widely distributed over the network. This shortcoming limits our ability to integrate multiple knowledgebases, each of which gives partial information of a shared domain, as is commonly seen in bioinformatics. The Life Science Identifier (LSID) and LSID Resolution System (LSRS) provide simple and elegant solutions to this problem, based on the extension of existing internet technologies. LSID and LSRS are consistent with next-generation semantic web and semantic grid approaches. This article describes the syntax, operations, infrastructure compatibility considerations, use cases and potential future applications of LSID and LSRS. We see the adoption of these methods as important steps toward simpler, more elegant and more reliable integration of the world's biological knowledgebases, and as facilitating stronger global collaboration in biology.

An initiative to increase biopharmaceutical research productivity by capturing, sharing and computationally integrating proprietary scientific discoveries with public knowledge is described. This initiative involves both organisational process change and multiple interoperating software systems. The software components rely on mutually supporting integration techniques. These include a richly structured ontology, statistical analysis of experimental data against stored conclusions, natural language processing of public literature, secure document repositories with lightweight metadata, web services integration, enterprise web portals and relational databases. This approach has already begun to increase scientific productivity in our enterprise by creating an organisational memory (OM) of internal research findings, accessible on the web. Through bringing together these components it has also been possible to construct a very large and expanding repository of biological pathway information linked to this repository of findings which is extremely useful in analysis of DNA microarray data. This repository, in turn, enables our research paradigm to be shifted towards more comprehensive systems-based understandings of drug action.