Dataset Information

Enlight: web-based integration of GWAS results with biological annotations.

ABSTRACT: Identifying causal variants remains a key challenge in post-GWAS (genome-wide association study) era, as many GWAS single-nucleotide polymorphisms (SNPs) (including imputed ones) fall into non-coding regions, making it difficult to associate statistical significance with predicted functionality. Therefore, we created a web-based tool, Enlight, which overlays functional annotation information, such as histone modification states, methylation patterns, transcription factor binding sites, eQTL and higher-order chromosomal structure, to GWAS results.Accessible by a Web browser at http://enlight.usc.edu.

SUBMITTER: Guo Y

PROVIDER: S-EPMC4287951 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

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Publications

Enlight: web-based integration of GWAS results with biological annotations.

Guo Yunfei Y Conti David V DV Wang Kai K

Bioinformatics (Oxford, England) 20140926 2

<h4>Unlabelled</h4>Identifying causal variants remains a key challenge in post-GWAS (genome-wide association study) era, as many GWAS single-nucleotide polymorphisms (SNPs) (including imputed ones) fall into non-coding regions, making it difficult to associate statistical significance with predicted functionality. Therefore, we created a web-based tool, Enlight, which overlays functional annotation information, such as histone modification states, methylation patterns, transcription factor bindi ...[more]

PMID: 25262152

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Project description:MotivationBiological knowledge is widely represented in the form of ontology-based annotations: ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a (kind of) biological entity with a set of phenomena within the domain. The structure and information contained in ontologies and their annotations make them valuable for developing machine learning, data analysis and knowledge extraction algorithms; notably, semantic similarity is widely used to identify relations between biological entities, and ontology-based annotations are frequently used as features in machine learning applications.ResultsWe propose the Onto2Vec method, an approach to learn feature vectors for biological entities based on their annotations to biomedical ontologies. Our method can be applied to a wide range of bioinformatics research problems such as similarity-based prediction of interactions between proteins, classification of interaction types using supervised learning, or clustering. To evaluate Onto2Vec, we use the gene ontology (GO) and jointly produce dense vector representations of proteins, the GO classes to which they are annotated, and the axioms in GO that constrain these classes. First, we demonstrate that Onto2Vec-generated feature vectors can significantly improve prediction of protein-protein interactions in human and yeast. We then illustrate how Onto2Vec representations provide the means for constructing data-driven, trainable semantic similarity measures that can be used to identify particular relations between proteins. Finally, we use an unsupervised clustering approach to identify protein families based on their Enzyme Commission numbers. Our results demonstrate that Onto2Vec can generate high quality feature vectors from biological entities and ontologies. Onto2Vec has the potential to significantly outperform the state-of-the-art in several predictive applications in which ontologies are involved.Availability and implementationhttps://github.com/bio-ontology-research-group/onto2vec.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Enlight: web-based integration of GWAS results with biological annotations.

Publications

Enlight: web-based integration of GWAS results with biological annotations.

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