Posts Tagged OBO Foundry
In a recent Nature editorial entitled Standardizing data, several projects were highlighted that are forfeiting there chances of winning a Nobel prize (according to Quackenbush) and championing the blue collar science of data standardization.in the life-sciences.
I wanted to take the article a step further highlight three significant properties of scientific data that I believe to be fundamental in considering how to curate, standardize or simply represent scientific data; from primary data, to lab books, to publication. These significant properties of scientific data are the content, syntax, and semantics, or more simply put -What do we want to say? How do we say it? What does it all mean? These three significant properties of data are what I refer to as the Triumvirate of scientific data.
Content: What do we want to say?
Data Content is defined as the items, topics or information that is “contained in” or represented by a data object. What is, should or must be said. Generic data content standards exists, such as Dublin Core, as well as more focused or domain specific standards. Most aspects of the research life-cycle have a content standard. For example, when submitting a manuscript to a scientific publisher you are required to conform to a content standard for that Journal. For example, PlosOne calls their content standard Criteria for Publication and lists seven points to conform to.
The Minimum Information about [insert favourite technology] are efforts by the relevant communities to define content standards for their experiments. These do (should) not define how the content is represented (in a database or file format) rather they state what information is required to describe an experiment. Collecting and defining content standards for the life-sciences is the premise of the MIBBI project.
Syntax: How do we say it?
The content of data is independent of any structure, language implementation or semantics. For example when viewing a journal article on Biomed central you typically have the option to view or download the “Full Text” which is often represented in HTML or you have the option of viewing the PDF file or XML. Each representation has the same scientific content to a human but is structured and then rendered (or “presented”) to the user in three different syntax.
The majority of the structural of syntactic representation of scientific data is largely database centric. However, alternative methods can be identified such as Wikis (OpenWetWare, UsefulChem), Blogs (LaBLog), XML, (GelML), RDF (UniProt export) or described as a data model (FuGE) which can be realised in multiple syntax
Semantics: What do we mean?
The explicit meaning of data is very difficult to get right and is a difficult problem in the life-sciences. One word can have many meanings and one meaning can be described by many words. A good example of a failure to correctly determine the semantics of data is described in the paper by Zeeberg et al 2004. In the paper they describe the mis-interpretation of the semantics of gene names. This mis-interpretation of semantics resulted in an irreversible conversion to date-format by Excel and which percolated through to the curated LocusLink public repository.
Within the life-sciences the issue of semantics is being addressed via the use of Controlled vocabularies and ontologies.
According to the Neurocommons definition; A controlled vocabulary is an association between formal names (identifiers) and their definitions. A ontology is a controlled vocabulary augmented with logical constraints that describe their interrelationships. Not only do we need semantics for data, we need shared semantics, so that we are able to describe data consistently, within laboratories, across collaborations and transcending scientific domains. The OBO Foundry is one of the projects tasked with fostering the orthogonal development of ontologies – one term only appears in one ontology and is referenced by others – with the goal of shared semantics.
When considering how to curate, standardize or represent scientific data, either internally within laboratories, or externally for publication, the three significant properties of content, syntax and semantics should be considered carefully for the specific data. Consistent representation of data conforming to the Triumvirate of scientific data will provide a platform for the dissemination, interpretation, evaluation and advancement of scientific knowledge.
Thanks to Phil Lord for helpful discussions on the Triumvirate of data
Conflict of interest
I am involved in the MIBBI project, the development of GelML and a member of the OBO Foundry via the OBI project.
OK, So that is a relatively inflammatory and controversial headline, edging on the side of tabloid sensationalism. What is refers to is probably a situation that I may never find myself in again, which is in this months edition of Nature Biotechnology I am an author on two, biological standards related publications.
The first is a letter advertising the PSI’s MIAPE Guidelines for reporting the use of gel electrophoresis in proteomics. This letter is also accompanied by letters referring to the MIAPE guidelines for Mass Spectrometry, Mass Spectrometry Informatics and protein modification data.
The second is a paper on the Minimum Information about a Biomedical or Biological Investigations (MIBBI) registry entitled Promoting coherent minimum reporting guidelines for biological and biomedical investigations: the MIBBI project.
The following press release describes this paper in more detail.
More than 20 grass-roots standardisation groups, led by scientists at the European Bioinformatics Institute (EMBL-EBI) and the Centre for Ecology & Hydrology (CEH), have combined forces to form the “Minimum Information about a Biomedical or Biological Investigation” (MIBBI) initiative. Their aim is to harmonise standards for high-throughput biology, and their methodology is described in a Commentary article, published today in the journal Nature Biotechnology.
Data standards are increasingly vital to scientific progress, as groups from around the world look to share their data and mine it more effectively. But the proliferation of projects to build “Minimum Information” checklists that describe experimental procedures was beginning to create problems. “There was no way of even finding all the current checklist projects without days of googling,” says the EMBL-EBI’s Chris Taylor, who shares first authorship of the paper with Dawn Field (CEH) and Susanna-Assunta Sansone (EMBL-EBI). “As a result, much of the great work that’s going into developing community standards was being overlooked, and different communities were at risk of developing mutually incompatible standards. MIBBI will help to prevent them from reinventing the wheel.
The MIBBI Portal already offers a one-stop shop for researchers, funders, journals and reviewers searching for a comprehensive list of minimum information checklists. The next step will be to build the MIBBI Foundry, which will bring together diverse communities to rationalise and streamline standardisation efforts. “Communities working together through MIBBI will produce non-overlapping minimal information modules,” says CEH’s Dawn Field. “The idea is that each checklist will fit neatly into a jigsaw, with each community being able to take the pieces that are relevant to them.” Some, such as checklists describing the nature of a biological sample used for an experiment, will be relevant to many communities, whereas others, such as standards for describing a flow cytometry experiment, may be developed and used by a subset of communities.
“MIBBI represents the first new effort taking the Open Biomedical Ontologies (OBO) as its role model”, says Susanna-Assunta Sansone. “The MIBBI Portal operates in a manner analogous to OBO as an open information resource, while the MIBBI Foundry fosters collaborative development and integration of checklists into self-contained modules just like the OBO Foundry does for the ontologies”.
There is a growing understanding of the value of such minimal information standards among biologists and an increased willingness to work together across disciplinary boundaries. The benefits include making experimental data more reproducible and allowing more powerful analyses over diverse sets of data. New checklist communities are encouraged to register with MIBBI and consider joining the MIBBI Foundry.
Press release issued by the EMBL-European Bioinformatics Institute and the Centre for Ecology and Hydrology, UK.