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- W2897920636 abstract "In the last ten years, genomic computing has made gigantic steps due to Next Generation Sequencing (NGS), a high-throughput, massively parallel technology; the cost of producing a complete human sequence dropped to 1000 US$ in 2015 and is expected to drop below 100 US$ by 2020. Several new methods have recently become available for extracting heterogeneous datasets from the genome, revealing data signals such as variations from a reference sequence, levels of expression of coding regions, or protein binding enrichments ('peaks') with their statistical or geometric properties. Huge collections of such datasets are made available by large international consortia. In this new context, we developed GenoMetric Query Language (GMQL), a new data extraction and integration language. GMQL supports queries over thousands of heterogeneous datasets; as such, it is key to genomic data analysis. GMQL queries are executed on the cloud, after being translated and optimized; our best deployment uses Spark over Hadoop. Datasets are described by the Genomic Data Model (GDM), which provides interoperability between many data formats; GDM combines abstractions for genomic region data with the associated experimental, biological and clinical metadata. GMQL is targeted to the bio-informatics community for facilitating data exploration and for integrating data extraction and data analysis; this demonstration highlights its usability and expressive power. We show GMQL at work from a Web-based user interface and from a language embedding (Python)." @default.
- W2897920636 created "2018-10-26" @default.
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- W2897920636 date "2018-10-17" @default.
- W2897920636 modified "2023-10-03" @default.
- W2897920636 title "Demonstration of GenoMetric Query Language" @default.
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- W2897920636 doi "https://doi.org/10.1145/3269206.3269217" @default.
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