The MO-LD project, Enhancing the FAIRness of Yeast and other model organism data

Presenting Author Information 

Name

Michel Dumontier

Institution

Stanford University

BD2K Grant Number

PI

Michel Dumontier

ORCID (optional)

Email

michel.dumontier@stanford.edu

Phone Number

(650) 497-3260

Additional Author Information 

Maxime Deraspe², Gail Binkley³, Kalpana Karra³, Gos Micklem⁴, Julie Sullivan⁴, Michael J. Cherry⁴, and Michel Dumontier¹

¹Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, United States;

²Department of Molecular Medicine, Université Laval, Quebec, Canada;

³Department of Genetics, Stanford University, Stanford, United States; 4Department of Genetics, University of Cambridge, Cambridge, United Kingdom

Is there an additional contact person?

No

Additional information 

Please choose the topic that best fits your abstract (posters will be grouped according to your selection). Detailed session descriptions can be found in the Abstract Guidelines.

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Abstract Information

Poster presentations may be submitted electronically in order to reach a wider audience and be available after the All hands meeting. Do you plan to submit your poster as a digital submission in addition to bringing a physical copy?

No

Abstract Title

The MO-LD project, Enhancing the FAIRness of Yeast and other model organism data

Abstract Description

Model organisms such as budding yeast provide a common platform to interrogate and understand cellular and physiological processes. Knowledge about model organisms, whether generated during the course of scientific investigations, or extracted from published articles, are integrated and made available by model organism databases (MODs) such as the Saccharomyces Genome Database (SGD). SGD and many MODs use InterMine, a system for integrating, analysing, and republishing biological data from multiple sources that also enables data-driven bioinformatic analyses through a web user interface and programmatic web services.

Here, we developed a cloud-ready dockerized platform that uses Semantic Web technologies to transform and make available model organism data in a manner that makes it easier to discover, explore, and query. First, we developed a pipeline to extract, transform, and load a Linked Data representation of the InterMine store. Second, we use Docker to package both software and data for local or remote deployment. Third, we built a lightweight dashboard that packages together existing and SPARQL-aware applications to search, browse, explore, and query the InterMine-based data. Our work extends the InterMine platform, and supports new query functionality across InterMine installations and the network of open Linked Data.