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Biomedicines
Volume 10
Issue 3
10.3390/biomedicines10030590
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Article

Functional Enrichment Analysis of Regulatory Elements

by
Adrian Garcia-Moreno
1,†,
Raul López-Domínguez
1,2,†,
Juan Antonio Villatoro-García
1,2,
Alberto Ramirez-Mena
1,
Ernesto Aparicio-Puerta
3,4,
Michael Hackenberg
3,4,
Alberto Pascual-Montano
5 and
Pedro Carmona-Saez
1,2,*
1
Bioinformatics Unit, Centre for Genomics and Oncological Research, GENYO, Pfizer/University of Granada/Andalusian Regional Government, PTS, 18016 Granada, Spain
2
Department of Statistics and Operational Research, University of Granada, 18071 Granada, Spain
3
Department of Genetics, University of Granada, 18071 Granada, Spain
4
Bioinformatics Laboratory, Biotechnology Institute, CIBM, Avda. del Conocimiento s/n, 18100 Granada, Spain
5
Data Science & Analytics at IDBS (Danaher Group), 68 Chertsey Road, Woking GU21 5BJ, UK
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Biomedicines 2022, 10(3), 590; https://doi.org/10.3390/biomedicines10030590
Submission received: 27 January 2022 / Revised: 22 February 2022 / Accepted: 25 February 2022 / Published: 3 March 2022
(This article belongs to the Special Issue Omics Data Analysis and Integration in Complex Diseases)
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Abstract

Statistical methods for enrichment analysis are important tools to extract biological information from omics experiments. Although these methods have been widely used for the analysis of gene and protein lists, the development of high-throughput technologies for regulatory elements demands dedicated statistical and bioinformatics tools. Here, we present a set of enrichment analysis methods for regulatory elements, including CpG sites, miRNAs, and transcription factors. Statistical significance is determined via a power weighting function for target genes and tested by the Wallenius noncentral hypergeometric distribution model to avoid selection bias. These new methodologies have been applied to the analysis of a set of miRNAs associated with arrhythmia, showing the potential of this tool to extract biological information from a list of regulatory elements. These new methods are available in GeneCodis 4, a web tool able to perform singular and modular enrichment analysis that allows the integration of heterogeneous information.
Keywords: gene set analysis; regulation; web tool; enrichment analysis; functional analysis gene set analysis; regulation; web tool; enrichment analysis; functional analysis

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MDPI and ACS Style

Garcia-Moreno, A.; López-Domínguez, R.; Villatoro-García, J.A.; Ramirez-Mena, A.; Aparicio-Puerta, E.; Hackenberg, M.; Pascual-Montano, A.; Carmona-Saez, P. Functional Enrichment Analysis of Regulatory Elements. Biomedicines 2022, 10, 590. https://doi.org/10.3390/biomedicines10030590

AMA Style

Garcia-Moreno A, López-Domínguez R, Villatoro-García JA, Ramirez-Mena A, Aparicio-Puerta E, Hackenberg M, Pascual-Montano A, Carmona-Saez P. Functional Enrichment Analysis of Regulatory Elements. Biomedicines. 2022; 10(3):590. https://doi.org/10.3390/biomedicines10030590

Chicago/Turabian Style

Garcia-Moreno, Adrian, Raul López-Domínguez, Juan Antonio Villatoro-García, Alberto Ramirez-Mena, Ernesto Aparicio-Puerta, Michael Hackenberg, Alberto Pascual-Montano, and Pedro Carmona-Saez. 2022. "Functional Enrichment Analysis of Regulatory Elements" Biomedicines 10, no. 3: 590. https://doi.org/10.3390/biomedicines10030590

APA Style

Garcia-Moreno, A., López-Domínguez, R., Villatoro-García, J. A., Ramirez-Mena, A., Aparicio-Puerta, E., Hackenberg, M., Pascual-Montano, A., & Carmona-Saez, P. (2022). Functional Enrichment Analysis of Regulatory Elements. Biomedicines, 10(3), 590. https://doi.org/10.3390/biomedicines10030590

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