HGVbaseG2P: a central genetic association database

Þórisson, Guðmundur Á.; Lancaster, Owen; Free, Robert C; Hastings, Robert K.; Sarmah, Pallavi; Dash, Debasis; Brahmachari, Samir K.; Brookes, Anthony J.

doi:10.1093/nar/gkn748

Cited by 62 publications

(50 citation statements)

References 22 publications

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“…For this reason GWAS results must be analyzed in the context of linkage disequilibrium (LD), which is defined as the non-random association of variants at two or more loci [34,35]. GWAS Central (http://www.gwascentral.org/) is a centralized database that collects the results from genetic association studies [36]. Unfortunately it provides data only for small-to medium-scale investigations and explicitly forbids using the data to create similar public resources.…”

Section: Disease-gene Association Databasesmentioning

confidence: 99%

DISEASES: Text mining and data integration of disease–gene associations

Pletscher-Frankild

Pallejà

Tsafou

et al. 2015

Methods

535

465

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Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease-gene associations from biomedical abstracts. The system consists of a highly efficient dictionary-based tagger for named entity recognition of human genes and diseases, which we combine with a scoring scheme that takes into account co-occurrences both within and between sentences. We show that this approach is able to extract half of all manually curated associations with a false positive rate of only 0.16%. Nonetheless, text mining should not stand alone, but be combined with other types of evidence. For this reason, we have developed the DISEASES resource, which integrates the results from text mining with manually curated disease-gene associations, cancer mutation data, and genome-wide association studies from existing databases. The DISEASES resource is accessible through a web interface at http://diseases.jensenlab.org/, where the text-mining software and all associations are also freely available for download.

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Section: Disease-gene Association Databasesmentioning

confidence: 99%

DISEASES: Text mining and data integration of disease–gene associations

Pletscher-Frankild

Pallejà

Tsafou

et al. 2015

Methods

535

465

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“…The creation of locus-specific variation databases (LSDBs), pioneered by the Human Genome Variation Society [29,30] along with standard guidelines to report and disseminate various variations, has been a major effort toward systematic curation of information in a standardized format. Recent availability of tools and aggregators, including efforts from the NCBI and the Gen2Phen consortium [31], is worth mentioning. Fortunately, in the area of pharmacogenomics, a number of resources like DrugBank [32] and PharmGKB [33] curate complementary evidence for genetic markers and traits.…”

Section: New Challengesmentioning

confidence: 99%

Pharmacogenomics for Precision Medicine in the Era of Collaborative Co-creation and Crowdsourcing

Hasija

Khan²,

Scaria

2014

Curr Genet Med Rep

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The whole gamut of new technologies in the past decade has revolutionized DNA sequencing, making it cheaper, efficient, and scalable. The consequent big-data in genomics have posed new challenges and opportunities. The transformation of internet as a fabric that intertwines multiple technological and social layers and the rise of platforms that can organize and integrate massively parallel human activities have transformed the workplaces in many industries and offers a new opportunity in the area of genomics. In this short review, we discuss the state-of-the art of crowdsourcing in genomics research with special focus on pharmacogenomics. We discuss the field, starting with an overview of technology and the major challenges. We also discuss a number of ongoing crowdsourcing approaches in the area of pharmacogenomics and personal genomics. We conclude with deliberating on the issues in genomics and how crowdsourcing could offer a plausible alternative to conventional approaches in genomics.

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“…http://bioinformatics.ua.pt/WAVe/ Highlights of work undertaken to date include data exchange and integration between LSDBs and Ensembl, facilitated by the aforementioned LRG standard. Also, exemplifying the power of the hybrid federation/centralization approach, GEN2PHEN has built HGVbaseG2P [Thorisson et al, 2008a] (http://www.hgvbaseg2p. org)-recently rebadged as ''GWAS Central'' (http://www.gwascentral.…”

Section: Toward a Unified G2p Data Infrastructurementioning

confidence: 99%