Generic and queryable data integration schema for transcriptomics and epigenomics studies

The expansion of multi-omics datasets raises significant challenges for data integration and querying. To overcome these challenges, we developed a generic RDF-based integration schema that connects various types of differential -omics data, epigenomics, and regulatory information. This schema employs the FALDO ontology to enable querying based on genomic locations. It is designed to be fully or partially populated, providing both flexibility and extensibility while supporting complex queries. We validated the schema by reproducing two recently published studies, one in biomedicine and the other in environmental science, proving its genericity and its ability to integrate data efficiently. This schema serves as an effective tool for managing and querying a wide range of multi-omics datasets.

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Publication Details

Journal
Computational and Structural Biotechnology Journal
Published
2024-11-19
DOI
https://doi.org/10.1016/j.csbj.2024.11.022
Primary Topic
Gene expression and cancer classification
Type
article
Field-Weighted Citation Impact
0.00

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article

Generic and queryable data integration schema for transcriptomics and epigenomics studies

E. Becker, Matéo Boudet, Fabrice Legeai, Olivier Dameron et al.
Computational and Structural Biotechnology Journal
Gene expression and cancer classification
article

Generic and queryable data integration schema for transcriptomics and epigenomics studies

E. Becker, Matéo Boudet, Fabrice Legeai, Olivier Dameron, Yael Tirlet
article en

Abstract

The expansion of multi-omics datasets raises significant challenges for data integration and querying. To overcome these challenges, we developed a generic RDF-based integration schema that connects various types of differential -omics data, epigenomics, and regulatory information. This schema employs the FALDO ontology to enable querying based on genomic locations. It is designed to be fully or partially populated, providing both flexibility and extensibility while supporting complex queries. We validated the schema by reproducing two recently published studies, one in biomedicine and the other in environmental science, proving its genericity and its ability to integrate data efficiently. This schema serves as an effective tool for managing and querying a wide range of multi-omics datasets.

Computational and Structural Biotechnology JournalVol. 23
Centre National de la Recherche Scientifique (FR), Institut de Recherche en Informatique et Systèmes Aléatoires (FR), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Institut de Génétique, Environnement et Protection des Plantes (FR), L'Institut Agro (FR), Université de Rennes (FR)
Agence Nationale de la Recherche
Openalex Percentile: Top 81%
Gene expression and cancer classification
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Generic and queryable data integration schema for transcriptomics and epigenomics studies — E. Becker, Matéo Boudet, et al. · Computational and Structural Biotechnology Journal (2024) | TGRS Research Map | TGRS