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, although suitable big data strategies still address mainly primary and secondary analysis. Hence, there is a pressing need for algorithms specifically designed to explore big omics datasets, capable of ensuring scalability and interoperability, possibly relying on high-performance computing infrastructures.

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

Cascianelli, Silvia;Masseroli, Marco
2022-01-01

Abstract

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, although suitable big data strategies still address mainly primary and secondary analysis. Hence, there is a pressing need for algorithms specifically designed to explore big omics datasets, capable of ensuring scalability and interoperability, possibly relying on high-performance computing infrastructures.
2022
Data scalability
Distribution transparency
Heterogeneous omics big data
Tertiary data analysis
Big Data
Cloud Computing
Genomics
Metadata
Software
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1224734
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