ArticleBMC bioinformatics2019
PyGMQL: scalable data extraction and analysis for heterogeneous genomic datasets.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Toward a Common Set of Interface Requirements for Genomic Data Management: Scoping Review.Journal of medical Internet research · 2026Article
- Processing genome-wide association studies within a repository of heterogeneous genomic datasets.BMC genomic data · 2023Article
- Framing Apache Spark in life sciences.Heliyon · 2023Review
- Genomic data integration and user-defined sample-set extraction for population variant analysis.BMC bioinformatics · 2022Article
- GeMI: interactive interface for transformer-based Genomic Metadata Integration.Database : the journal of biological databases and curation · 2022Article
- RGMQL: scalable and interoperable computing of heterogeneous omics big data and metadata in R/Bioconductor.BMC bioinformatics · 2022Article
- Data Integration Challenges for Machine Learning in Precision Medicine.Frontiers in medicine · 2021Review
- Spatial patterns of CTCF sites define the anatomy of TADs and their boundaries.Genome biology · 2020Article
- GenoSurf: metadata driven semantic search system for integrated genomic datasets.Database : the journal of biological databases and curation · 2019Article
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Authors and funding
4 authors.
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Abstract
backgroundWith the growth of available sequenced datasets, analysis of heterogeneous processed data can answer increasingly relevant biological and clinical questions. Scientists are challenged in performing efficient and reproducible data extraction and analysis pipelines over heterogeneously processed datasets. Available software packages are suitable for analyzing experimental files from such datasets one by one, but do not scale to thousands of experiments. Moreover, they lack proper support for metadata manipulation.
resultsWe present PyGMQL, a novel software for the manipulation of region-based genomic files and their relative metadata, built on top of the GMQL genomic big data management system. PyGMQL provides a set of expressive functions for the manipulation of region data and their metadata that can scale to arbitrary clusters and implicitly apply to thousands of files, producing millions of regions. PyGMQL provides data interoperability, distribution transparency and query outsourcing. The PyGMQL package integrates scalable data extraction over the Apache Spark engine underlying the GMQL implementation with native Python support for interactive data analysis and visualization. It supports data interoperability, solving the impedance mismatch between executing set-oriented queries and programming in Python. PyGMQL provides distribution transparency (the ability to address a remote dataset) and query outsourcing (the ability to assign processing to a remote service) in an orthogonal way. Outsourced processing can address cloud-based installations of the GMQL engine.
conclusionsPyGMQL is an effective and innovative tool for supporting tertiary data extraction and analysis pipelines. We demonstrate the expressiveness and performance of PyGMQL through a sequence of biological data analysis scenarios of increasing complexity, which highlight reproducibility, expressive power and scalability.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.