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Updated: Sep 27, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

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

Simone Pallotta¹, Silvia Cascianelli², Marco Masseroli¹

¹Dipartimento di Elettronica, Informazione e Bioingegneria, Via Ponzio 34/5, 20133, Milan, Italy.

BMC Bioinformatics

|April 8, 2022

Summary

This summary is machine-generated.

RGMQL is a new R/Bioconductor package that integrates and processes heterogeneous omics datasets. It enhances big omics data analysis by providing scalable and interoperable tools for tertiary analysis.

Keywords:

Data scalability Distribution transparency Heterogeneous omics big data Tertiary data analysis

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Last Updated: Sep 27, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

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Published on: May 9, 2017

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

High-throughput technologies generate vast amounts of heterogeneous omics data, crucial for solving biomedical questions.
Current big data strategies primarily focus on primary and secondary omics data analysis, neglecting tertiary analysis.
There is a need for scalable and interoperable algorithms for exploring large omics datasets, leveraging high-performance computing.

Purpose of the Study:

To introduce RGMQL, an R/Bioconductor package for extracting, combining, processing, and comparing diverse omics datasets and their metadata.
To provide a procedural approach for omics data management within R, overcoming limitations of declarative syntax.
To ensure full interoperability with the R/Bioconductor framework and genomic data structures.

Main Methods:

RGMQL is built upon the GenoMetric Query Language (GMQL) engine.
It utilizes GMQL's curated repository and cloud resources, with options for remote computation outsourcing.
The package offers specialized functions for omics data manipulation and analysis within R.

Main Results:

RGMQL integrates GMQL's query expressiveness and computational efficiency with R's processing capabilities.
It provides full interoperability and extensibility with existing R/Bioconductor packages and genomic data structures.
Demonstrated scalability from local to parallel and cloud computing for heterogeneous omics data analysis.

Conclusions:

RGMQL offers a comprehensive processing flow for omics data analysis within the R environment.
The package facilitates transparent combination and analysis of heterogeneous omics data from various sources.
Reproducible use cases highlight RGMQL's flexibility, interoperability, and scalability for biological research.