The next-generation Open Targets Platform: reimagined, redesigned, rebuilt

Ochoa, David; Hercules, Andrew; Carmona, Miguel; Süveges, Dániel; Baker, Jarrod; Malangone, Claudio; López, Irene; Miranda, Alfredo; Cruz-Castillo, Carlos; Fumis, Luca; Bernal-Llinares, Manuel; Tsukanov, Kirill; Cornu, Helena; Tsirigos, Konstantinos; Razuvayevskaya, Olesya; Buniello, Annalisa; Schwartzentruber, Jeremy; Karim, Mohd Anisul; Ariano, Bruno; Osorio, Ricardo Esteban Martinez; Ferrer, Javier; Ge, Xin; Machlitt-Northen, Sandra; Gonzalez-Uriarte, Asier; Saha, Shyamasree; Tirunagari, Santosh; Mehta, Chintan R.; Roldán-Romero, Juan María; Horswell, Stuart; Young, Sarah; Ghoussaini, Maya; Hulcoop, David G.; Dunham, Ian; McDonagh, Ellen M.

doi:10.1093/nar/gkac1046

Cited by 201 publications

(138 citation statements)

References 42 publications

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“…5A, Table S3 ). In parallel, using the OpenTargets and Omnipath databases (24,25), we identified 834 LR pairs ( Table S4 ) associated with genetic risk for SCZ and prioritized 18 inter- and intra-cellular interactions where both counterparts showed disease association, including 9 interactions involving the protein tyrosine kinase, FYN ( Fig. 5A, Fig S40 ).…”

Section: Resultsmentioning

confidence: 99%

Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex

Huuki-Myers

Spangler

Eagles

et al. 2023

Preprint

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The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.

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Section: Resultsmentioning

confidence: 99%

Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex

Huuki-Myers

Spangler

Eagles

et al. 2023

Preprint

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“…Drug repurposing. Independent genetic signals from the cross-ancestry meta-analysis were searched in OpenTargets.org 39 for druggability and medication target status based on nearest genes. Among them, OPRM1 implicated naltrexone and GABRA4 implicated acamprosate, both current treatments for AUD.…”

Section: Transcriptome-wide Association Analyses (Twas)mentioning

confidence: 99%

Multi-ancestry study of the genetics of problematic alcohol use in >1 million individuals

Zhou

Kember

Deak

et al. 2023

Preprint

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Problematic alcohol use (PAU) is a leading cause of death and disability worldwide. To improve our understanding of the genetics of PAU, we conducted a large cross-ancestry meta-analysis of PAU in 1,079,947 individuals. We observed a high degree of cross-ancestral similarity in the genetic architecture of PAU and identified 110 independent risk variants in within- and cross-ancestry analyses. Cross-ancestry fine-mapping improved the identification of likely causal variants. Prioritizing genes through gene expression and/or chromatin interaction in brain tissues identified multiple genes associated with PAU. We identified existing medications for potential pharmacological studies by drug repurposing analysis. Cross-ancestry polygenic risk scores (PRS) showed better performance in independent sample than single-ancestry PRS. Genetic correlations between PAU and other traits were observed in multiple ancestries, with other substance use traits having the highest correlations. The analysis of diverse ancestries contributed significantly to the findings, and fills an important gap in the literature.

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“…For example, biological processes “regulation of lysosomal lumen pH” (GO:0035751) and “lysosomal lumen pH elevation” both regulate the biological attribute (trait) “lysosomal lumen pH” (OBA:0000091). OBA trait terms imported into EFO can facilitate computational drug target identification via the Open Targets Platform 60 . For example, OBA, in tandem with other ontologies, has proved useful for computational drug target identification in a study of drug-induced adverse events in animal models 61 .…”

Section: Resultsmentioning

confidence: 99%

The Ontology of Biological Attributes (OBA) - Computational Traits for the Life Sciences

Stefancsik

Balhoff

Balk

et al. 2023

Preprint

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Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focused measurable trait data. Moreover, variations in gene expression in response to environmental disturbances even without any genetic alterations can also be associated with particular biological attributes. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

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The next-generation Open Targets Platform: reimagined, redesigned, rebuilt

Cited by 201 publications

References 42 publications

Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex

Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex

Multi-ancestry study of the genetics of problematic alcohol use in >1 million individuals

The Ontology of Biological Attributes (OBA) - Computational Traits for the Life Sciences

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