- A major bulk of published experimental data, referred to as 'Gold Standard' data, is available in a format that cannot be easily accessed by computers unless effectively curated. Most curation techniques bank on mining the text for information. Here we propose and demonstrate the efficacy of curating the experimental data itself. The data models facilitate digitization of the every aspect of the information associated with the experimental data. The models utilize several universally accepted ontologies as well as in-house developed alphanumeric notations for digitizing different aspect of the data. The data models have sufficient flexibility to address the extensive variability in experimental data. They have a very generic nature and can be used to curate and digitize experimental data from any organism. The digitized data is easily stored in a relational database management system and can thus be rapidly searched and integrated. These models have been successfully used to digitize data from over 20,000 experiments spanning over 500 research articles on rice biology. The entire dataset is available as a database entitled 'Manually Curated Database of Rice Proteins' at www.genomeindia.org/biocuration.
A ‘Systems’ level understanding of any organism requires
integration and analysis of multi-dimensional experimental
data [
II. DESCRIPTION OF THE DATA PRESENTATION FORMATS Here we address these issue by proposing formats or datamodels for digitization and semantic representation of the experimental data in biological sciences. These formats have a very generic nature and are flexible enough to be used for digitization of diverse experimental data. In essence, these models attempt to represent every aspect of the data in terms of an alpha-numeric notation which can be an ontology term or custom notation. As depicted in Fig. 1, every experimental data is composed of several data units, which are usually represented by a single ‘bar’ of a bar-chart or a ‘band’ of a gel profile. Each of these data-points is associated with two types of information. The first is the actual value of the data-unit (height of bar) while the other is an array of information such as Gene id., plant type, tissue, growth condition etc. Depending on the complexity of the experiment each data-unit may be associated with upto 10-15 different types of information. As per the data-models each of these information types is represented by either a custom alpha-numerical notation or an ontology term. Thus, every data unit is now represented as a collection or group of alpha-numeric terms. This collection can be easily visualized as an array or equation as depicted in Fig. 1. Every term of this array is actually stored in a specific relational database table. Consequently, since experimental data consists of several data-units, there will be as many groups of alpha-numerical notations, which can be easily stored in an relational database table.
The data models extensively utilize several ontologies such as
plant ontology, environment ontology, trait ontology as well
as gene ontology terms to digitize various aspects of the
information associated with every data-unit [
Based on the aforementioned principles, manual data curation
and digitization portals were created. These portals were used
to digitize experimental data from over 500 published research
articles on rice biology. The entire data set has been organized
as a database entitled ‘Manually Curated Database of Rice
Proteins’ (MCDRP) [
Since every aspect of information associated with data-unit of an experiment has been digitized as alpha-numeric notations, can be retrieved from several different aspects. For example, in the browse page one can easily retrieve all the genes that have been studied in a particular plant part or developmental stage. Similarly, all genes that have been associated or studied for a particular trait can be easily accessed.
The process of digitization is primarily dependent on the depth of various ontologies. During the process of digitization, it was realized that there is an extensive amount of untapped variability in all aspects of data that is not completely described by the current ontologies. Consequently, a significant number of terms used in the curation endeavor had been coined anew (www.genomeindia.org/biocuration). While several new terms had to be coined since there was no equivalent term, many were formulated since the existing terms had a different perspective. Thus, there is a requirement for consistent efforts to enrich and modify the existing ontologies by defining more and more terms. Further, several new ontologies might also be required such as an ontology describing different methodologies. This is important since the interpretation of the data can only be done in light of the precise information about the experimental methodology. It was also observed, that many times that description of the experimental data in published literature is not very precise. This has major consequences in terms of data integration. Thus, usage of precise ontology terms to describe different aspect of the experimental data must be encouraged.
VI. FUTURE The data digitization formats for the experimental data in biological sciences enable a very precise digitization of the data. This makes the experimental data (Gold standard data) amenable to computerized search and integration. Thus, consistent curation efforts must be done to digitize the already published experimental data. Further, while currently, the data models are being used for digitization of published experimental data, the main aim is to develop data curation and exchange formats that can be implemented in every lab itself even before publication. Thus, at the time of publication all the experimental data is in a pre-defined digitized format than can be easily integrated in any database or public repository.
Seamless availability and semantic integration of experimental data is essential to comprehend the complex behavior of biological systems. The data digitization fundamentals briefly explained in this article facilitate digitization of almost every aspect of the experimental data and thus should prove instrumental in achieving a higher understanding of any biological system, if implemented universally. The basic fundamental is very generic in nature and can be applied to data from any biological system. In essence, the data models facilitate digitization of every data-unit of the experimental data in terms of an organized array of alpha-numeric notations. The structure of the array is important since same notation can be used in different positions to mean differently. For example, a plant ontology term can be used to describe the tissue wherein expression of a particular gene has been studied. It can also be used to qualify a gene ontology term to associate a particular ‘molecular activity’ in a specialized tissue or developmental stage. Thus, same notation can be used to signify different aspect of the information. This gives flexibility as well as universality to the concept.
One of the basic aims of this endeavor is to develop data
digitization, archiving and exchange formats that can be used
at a very early stage (pre-publication) to organize the
experimental data. The idea is to implement these data models
as lab data management portals/softwares. This will greatly
facilitate rapid and seamless access of the experimental data
since there would be no or very minimal need for
postpublication data curation [
We regard our study as a step of a much wider area of research, since experimental data has several dimensions of interpretations. So far we have only addressed the initial digitization and archiving of the data. Models for correlating the digitized data from different studies need to be worked out. Nevertheless, it was possible to demonstrate an effective digitization of experimental data in biological sciences.
ACKNOWLEDGMENT
The author acknowledgs financial support from Department of Biotechnology, Government of India.