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An Ontology-Based GIS for Genomic Data Management of Rumen Microbes

Overview of attention for article published in Genomics & Informatics, March 2015
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Title
An Ontology-Based GIS for Genomic Data Management of Rumen Microbes
Published in
Genomics & Informatics, March 2015
DOI 10.5808/gi.2015.13.1.7
Pubmed ID
Authors

Saber Jelokhani-Niaraki, Mojtaba Tahmoorespur, Zarrin Minuchehr, Mohammad Reza Nassiri

Abstract

During recent years, there has been exponential growth in biological information. With the emergence of large datasets in biology, life scientists are encountering bottlenecks in handling the biological data. This study presents an integrated geographic information system (GIS)-ontology application for handling microbial genome data. The application uses a linear referencing technique as one of the GIS functionalities to represent genes as linear events on the genome layer, where users can define/change the attributes of genes in an event table and interactively see the gene events on a genome layer. Our application adopted ontology to portray and store genomic data in a semantic framework, which facilitates data-sharing among biology domains, applications, and experts. The application was developed in two steps. In the first step, the genome annotated data were prepared and stored in a MySQL database. The second step involved the connection of the database to both ArcGIS and Protégé as the GIS engine and ontology platform, respectively. We have designed this application specifically to manage the genome-annotated data of rumen microbial populations. Such a GIS-ontology application offers powerful capabilities for visualizing, managing, reusing, sharing, and querying genome-related data.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 21%
Researcher 3 21%
Student > Ph. D. Student 3 21%
Student > Doctoral Student 1 7%
Student > Bachelor 1 7%
Other 2 14%
Unknown 1 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 2 14%
Earth and Planetary Sciences 2 14%
Medicine and Dentistry 2 14%
Computer Science 2 14%
Chemical Engineering 1 7%
Other 2 14%
Unknown 3 21%