RGMQL

RGMQL extends the GenoMetric Query Language into the R environment to enable scalable integration, processing, and tertiary analysis of heterogeneous omics datasets, including Next Generation Sequencing data.

Key Features:

• Integration and Processing: Enables extraction, combination, processing, and comparison of omics datasets and their metadata from local and remote sources.
• Scalability and Performance: Leverages the GMQL computational engine and cloud-computing technologies to scale analyses from local to parallel and cloud environments for big-data workloads.
• Interoperability and Extensibility: Ensures interoperability with R/Bioconductor packages and supports common genomic data structures and processing functions.
• Procedural Approach: Provides a procedural programming interface within R while extending GMQL's declarative query capabilities.
• Transparent Data Handling: Supports combining and analyzing heterogeneous public and private omics datasets without manual integration of underlying data sources.
• Repository and Cloud Resources: Allows access to GMQL's open curated repository and cloud-based resources to enhance computational efficiency and expressiveness.

Scientific Applications:

• Tertiary Analysis of NGS Data: Supports tertiary analysis of Next Generation Sequencing data to derive biological insights from processed genomic signals and annotations.
• Large-scale Omics Exploration: Enables exploration and comparative analysis of large-scale heterogeneous omics datasets and associated metadata.
• Scalable Genomic Analyses on HPC and Cloud: Facilitates execution of scalable genomic analyses on high-performance computing infrastructures and cloud platforms.
• Reproducible Computational Experiments: Enables reproducible, scriptable analyses and scalable execution of computational use cases involving multi-sample omics data.

Methodology:

RGMQL extends GMQL into R, implements extraction/combination/processing/comparison operations on omics datasets and metadata, provides a procedural R interface alongside GMQL's declarative model, and leverages the GMQL computational engine, GMQL's open curated repository, and cloud-computing technologies to scale from local to parallel and cloud environments.