The Implicitome: A Resource for Rationalizing Gene-Disease Associations

High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerati...

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Bibliographic Details
Published in:PloS one 2016-02, Vol.11 (2), p.e0149621-e0149621
Main Authors: Hettne, Kristina M, Thompson, Mark, van Haagen, Herman H H B M, van der Horst, Eelke, Kaliyaperumal, Rajaram, Mina, Eleni, Tatum, Zuotian, Laros, Jeroen F J, van Mulligen, Erik M, Schuemie, Martijn, Aten, Emmelien, Li, Tong Shu, Bruskiewich, Richard, Good, Benjamin M, Su, Andrew I, Kors, Jan A, den Dunnen, Johan, van Ommen, Gert-Jan B, Roos, Marco, 't Hoen, Peter A C, Mons, Barend, Schultes, Erik A
Format: Article
Language:English
Subjects:
Acids
Bioinformatics
Biology and Life Sciences
Computational Biology - methods
Computer and Information Sciences
Computer applications
Databases, Genetic
Dictionaries
Experimental methods
Gene expression
Gene sequencing
Genetic diversity
Genetic Predisposition to Disease
Genetic susceptibility
Genetic variance
Genetic variation
Genetics
Genome-wide association studies
Genome-Wide Association Study
Genomes
Genomics
Health informatics
Humans
Identification methods
Jargon
Keywords
Medicine
Medicine and Health Sciences
Physiological aspects
Proteins
Research and Analysis Methods
Semantic web
Semantics
Studies
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Summary:High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome). Implicit relations are largely unknown to, or are even unintended by the original authors, but they vastly extend the reach of existing biomedical knowledge for identification and interpretation of gene-disease associations. The implicitome can be used in conjunction with experimental data resources to rationalize both known and novel associations. We demonstrate the usefulness of the implicitome by rationalizing known and novel gene-disease associations, including those from GWAS. To facilitate the re-use of implicit gene-disease associations, we publish our data in compliance with FAIR Data Publishing recommendations [https://www.force11.org/group/fairgroup] using nanopublications. An online tool (http://knowledge.bio) is available to explore established and potential gene-disease associations in the context of other biomedical relations.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0149621