PosMed: ranking genes and bioresources based on Semantic Web Association Study

Yuko Makita,Terue Takatsuki,Hiroshi Masuya,Tetsuro Toyoda,Zolzaya Khadbaatar,Rinki Bhatia,Hajime Watabe,Yoshiki Mochizuki,Yuko Yoshida,Koji Doi,Akihiro Matsushima,Koro Nishikata,Satoshi Takahashi,Norio Kobayashi,Manabu Ishii

doi:10.1093/nar/gkt474

Abstract

Positional MEDLINE (PosMed; http://biolod.org/PosMed) is a powerful Semantic Web Association Study engine that ranks biomedical resources such as genes, metabolites, diseases and drugs, based on the statistical significance of associations between user-specified phenotypic keywords and resources connected directly or inferentially through a Semantic Web of biological databases such as MEDLINE, OMIM, pathways, co-expressions, molecular interactions and ontology terms. Since 2005, PosMed has long been used for in silico positional cloning studies to infer candidate disease-responsible genes existing within chromosomal intervals. PosMed is redesigned as a workbench to discover possible functional interpretations for numerous genetic variants found from exome sequencing of human disease samples. We also show that the association search engine enhances the value of mouse bioresources because most knockout mouse resources have no phenotypic annotation, but can be associated inferentially to phenotypes via genes and biomedical documents. For this purpose, we established text-mining rules to the biomedical documents by careful human curation work, and created a huge amount of correct linking between genes and documents. PosMed associates any phenotypic keyword to mouse resources with 20 public databases and four original data sets as of May 2013.

Highlights

Mouse bioresources contribute to the study of human genes and diseases [1,2]
To take advantage of the Semantic Web of biological linked data, we propose to extend the methodology of association studies to the methodology called a ‘Semantic Web Association Study (SWAS)’ (Figure 1)
Several eminent databases have released Resource Description Framework (RDF) files, but not so many scientists use Semantic Web technology actively. This may be partially because bioinformaticians like to calculate the statistical significance of associations of the RDF connections rather than a simple Boolean retrieval of the connections

Summary

Introduction

Mouse bioresources contribute to the study of human genes and diseases [1,2]. To elucidate the function of all mouse genes, the International Knockout Mouse Consortium systematically generates mutant embryonic stem cells for every protein-coding gene [3], and the International Mouse Phenotype Consortium produces knockout mice and carries out high-throughput phenotyping of each line [4]. To enhance the value of bioresources, we applied our original statistical search engine called the General and Rapid Association Study Engine (GRASE) and provided this as a web-oriented service called Positional MEDLINE (PosMed) [6,7,8,9]. PosMed allows users to retrieve mouse bioresources directly with phenotypic keywords described in bioresource annotations, and inferentially through corresponding documents for genes, diseases, drugs, ontologies, pathways, metabolites, molecular interactions and MEDLINE abstracts.

Results

Conclusion