ICBO_2018_51: Manually Curated Database of Rice Proteins: a case of digitized experimental data via structured use of ontologies

TitleICBO_2018_51: Manually Curated Database of Rice Proteins: a case of digitized experimental data via structured use of ontologies
Publication TypeConference Paper
Year of Publication2018
AuthorsRaghuvanshi, S
Conference NameInternational Conference on Biomedical Ontology (ICBO 2018)
Date Published08/06/2018
PublisherInternational Conference on Biological Ontology
KeywordsDigitization, Experimental data, manual curation, rice
Abstract

The ‘Manually Curated Database of Rice Proteins’ (www.genomeindia.org/biocuration) is a data resource based on digitized experimental data on rice proteins. More than 15,000 published experimental datasets (consisting of over 90,000 data-points) from >550 published articles have been digitized in a manual curation exercise. The experimental datasets originate from over 150 different types of experimental techniques. Various combinations of ontologies have been used to represent different aspects of every data point of the experimental datasets. Thus, each data point could be imagined to be represented by an equation consisting of various ontology terms. Each ontology term represents a unique aspect of the data points. In original publication these data sets are represented as an image or a graph which cannot be searched computationally. As a consequence of this curation procedure data from numerous experimental techniques such as enzymatic assays, RT-PCR assays, localization analysis and trait analysis can be rapidly searched from a collection of hundred of curated research publications. The data can either be browsed from various perspectives (tissue, developmental stage, experimental technique, function, treatment etc.) or searched with the help of any ontology term or definition. All the experimental data can be browsed by either the gene name, plant developmental stage, plant tissue, environmental condition (treatment), trait (molecular or biochemical) or gene function. Figure 1. illustrates the interoperability and connectivity of the digitized experimental datasets. As an example one can start browsing with a list of ‘traits’. Selection of any one trait will give the list of all the genes that have been associated with this trait in published literature. It also shows the actual experimental dataset and the experimental technique that was used for this association. Further selecting any one of these takes the user to the actual digitized data. Thus, the database provides a seamless access and search capability to experimental data that was earlier published as an image or graph and thus could not be searched (accessed) computationally. Continuous efforts are being done to add more and more published datasets to the database and to also extend the exercise to other crops as well.

URLhttp://icbo2018.cgrb.oregonstate.edu/