To make full use of research data, the bioscience community needs to adopt technologies and reward mechanisms that support interoperability and promote the growth of an open ‘data commoning’ culture. Here we describe the prerequisites for data commoning and present an established and growing ecosystem of solutions using the shared ‘Investigation-Study-Assay’ framework to support that vision.

In Huntington's disease (HD; MIM ID #143100), a fatal neurodegenerative disorder, transcriptional dysregulation is a key pathogenic feature. Histone modifications are altered in multiple cellular and animal models of HD suggesting a potential mechanism for the observed changes in transcriptional levels. In particular, previous work has suggested an important link between decreased histone acetylation, particularly acetylated histone H3 (AcH3; H3K9K14ac), and downregulated gene expression. However, the question remains whether changes in histone modifications correlate with transcriptional abnormalities across the entire transcriptome. Using chromatin immunoprecipitation paired with microarray hybridization (ChIP-chip), we interrogated AcH3-gene interactions genome-wide in striata of 12-week old wild-type (WT) and transgenic (TG) R6/2 mice, an HD mouse model, and correlated these interactions with gene expression levels. At the level of the individual gene, we found decreases in the number of sites occupied by AcH3 in the TG striatum. In addition, the total number of genes bound by AcH3 was decreased. Surprisingly, the loss of AcH3 binding sites occurred within the coding regions of the genes rather than at the promoter region. We also found that the presence of AcH3 at any location within a gene strongly correlated with the presence of its transcript in both WT and TG striatum. In the TG striatum, treatment with histone deacetylase (HDAC) inhibitors increased global AcH3 levels with concomitant increases in transcript levels; however, AcH3 binding at select gene loci increased only slightly. This study demonstrates that histone H3 acetylation at lysine residues 9 and 14 and active gene expression are intimately tied in the rodent brain, and that this fundamental relationship remains unchanged in an HD mouse model despite genome-wide decreases in histone H3 acetylation.

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.

Background

There is currently a gap between the rich and expressive collection of published biomedical ontologies, and the natural language expression of biomedical papers consumed on a daily basis by scientific researchers. The purpose of this paper is to provide an open, shareable structure for dynamic integration of biomedical domain ontologies with the scientific document, in the form of an Annotation Ontology (AO), thus closing this gap and enabling application of formal biomedical ontologies directly to the literature as it emerges.

Methods

Initial requirements for AO were elicited by analysis of integration needs between biomedical web communities, and of needs for representing and integrating results of biomedical text mining. Analysis of strengths and weaknesses of previous efforts in this area was also performed. A series of increasingly refined annotation tools were then developed along with a metadata model in OWL, and deployed for feedback and additional requirements the ontology to users at a major pharmaceutical company and a major academic center. Further requirements and critiques of the model were also elicited through discussions with many colleagues and incorporated into the work.

Results

This paper presents Annotation Ontology (AO), an open ontology in OWL-DL for annotating scientific documents on the web. AO supports both human and algorithmic content annotation. It enables “stand-off” or independent metadata anchored to specific positions in a web document by any one of several methods. In AO, the document may be annotated but is not required to be under update control of the annotator. AO contains a provenance model to support versioning, and a set model for specifying groups and containers of annotation. AO is freely available under open source license at http://purl.org/ao/, and extensive documentation including screencasts is available on AO’s Google Code page: http://code.google.com/p/annotation-ontology/ .

Conclusions

The Annotation Ontology meets critical requirements for an open, freely shareable model in OWL, of annotation metadata created against scientific documents on the Web. We believe AO can become a very useful common model for annotation metadata on Web documents, and will enable biomedical domain ontologies to be used quite widely to annotate the scientific literature. Potential collaborators and those with new relevant use cases are invited to contact the authors.