ArrayWiki: an enabling technology for sharing public microarray data repositories and meta-analyses

© 2008 Stokes et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.DOI: 10.1186/1471-2105-9-S6-S18Background. A survey of microarray databases reveals that most of the repository contents and data models are heterogeneous (i.e., data obtained from different chip manufacturers), and that the repositories provide only basic biological keywords linking to PubMed. As a result, it is difficult to find datasets using research context or analysis parameters information beyond a few keywords. For example, to reduce the "curse-of-dimension" problem in microarray analysis, the number of samples is often increased by merging array data from different datasets. Knowing chip data parameters such as pre-processing steps (e.g., normalization, artefact removal, etc), and knowing any previous biological validation of the dataset is essential due to the heterogeneity of the data. However, most of the microarray repositories do not have meta-data information in the first place, and do not have a a mechanism to add or insert this information. Thus, there is a critical need to create "intelligent" microarray repositories that (1) enable update of meta-data with the raw array data, and (2) provide standardized archiving protocols to minimize bias from the raw data sources. Results. To address the problems discussed, we have developed a community maintained system called ArrayWiki that unites disparate meta-data of microarray meta-experiments from multiple primary sources with four key features. First, ArrayWiki provides a user-friendly knowledge management interface in addition to a programmable interface using standards developed by Wikipedia. Second, ArrayWiki includes automated quality control processes (caCORRECT) and novel visualization methods (BioPNG, Gel Plots), which provide extra information about data quality unavailable in other microarray repositories. Third, it provides a user-curation capability through the familiar Wiki interface. Fourth, ArrayWiki provides users with simple text-based searches across all experiment meta-data, and exposes data to search engine crawlers (Semantic Agents) such as Google to further enhance data discovery. Conclusions. Microarray data and meta information in ArrayWiki are distributed and visualized using a novel and compact data storage format, BioPNG. Also, they are open to the research community for curation, modification, and contribution. By making a small investment of time to learn the syntax and structure common to all sites running MediaWiki software, domain scientists and practioners can all contribute to make better use of microarray technologies in research and medical practices. ArrayWiki is available at http://www.bio-miblab.org/arraywiki

A Transparent, Reputation-Based Architecture for Semantic Web Annotation

New forms of conceiving the web such as web 2.0 and the semantic web have emerged for numerous purposes ranging from professional activities to leisure. The semantic web is based on associating concepts with web pages, rather than only identifying hyperlinks and repeated literals. ITACA is a project whose aim is to add semantic annotations to web pages, where semantic annotations are Wikipedia URLs. Therefore, users can write, read and vote on semantic annotations of a webpage. Semantic annotations of a webpage are ranked according to users' votes. Building upon the ITACA project, we propose a transparent, reputation-based architecture. With this proposal, semantic annotations are stored in the users' local machines instead of web servers, so that web servers transparency is preserved. To achieve transparency, an indexing server is added to the architecture to locate semantic annotations. Moreover, users are grouped into reputation domains, providing accurate semantic annotation ranking when retrieving annotations of a web page. Cache copies of semantic annotations in annotation servers are done to improve eficiency of the algorithm, reducing the number of sent messages

A Transparent, Reputation-Based Architecture for Semantic Web Annotation

New forms of conceiving the web such as web 2.0 and the semantic web have emerged for numerous purposes ranging from professional activities to leisure. The semantic web is based on associating concepts with web pages, rather than only identifying hyperlinks and repeated literals. ITACA is a project whose aim is to add semantic annotations to web pages, where semantic annotations are Wikipedia URLs. Therefore, users can write, read and vote on semantic annotations of a webpage. Semantic annotations of a webpage are ranked according to users' votes. Building upon the ITACA project, we propose a transparent, reputation-based architecture. With this proposal, semantic annotations are stored in the users' local machines instead of web servers, so that web servers transparency is preserved. To achieve transparency, an indexing server is added to the architecture to locate semantic annotations. Moreover, users are grouped into reputation domains, providing accurate semantic annotation ranking when retrieving annotations of a web page. Cache copies of semantic annotations in annotation servers are done to improve eficiency of the algorithm, reducing the number of sent messages