RGMQL: scalable and interoperable computing of heterogeneous omics big data and metadata in R/Bioconductor

RGMQL: scalable and interoperable computing of heterogeneous omics big data and metadata in R/Bioconductor

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Background: Heterogeneous omics data, increasingly collected through high-throughput technologies, can contain hidden answers to very important and still unsolved biomedical questions. Their integration and processing are crucial mostly for tertiary analysis of Next Generation Sequencing data, altho...

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Bibliographic Details
Main Authors: Cascianelli, S. (Author), Masseroli, M. (Author), Pallotta, S. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2022
Subjects:
article
big data
Big data
Bioconductors
cloud computing
Computational efficiency
data analysis
Data and metadata
Data handling
Data scalability
Distribution transparency
Heterogeneous omic big data
Heterogeneous omics big data
High throughput technology
human
human experiment
Information management
Interoperability
language
metadata
Metadata
Next-generation sequencing
'omics'
Pressung
Query languages
Scalability
scale up
Tertiary data analyse
Tertiary data analysis
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