Improving gene regulatory network inference and assessment: The importance of using network structure.

Autor:	Escorcia-Rodríguez JM; Regulatory Systems Biology Research Group, Program of Systems Biology, Center for Genomic Sciences, Universidad Nacional Autónoma de México, Cuernavaca, Mexico., Gaytan-Nuñez E; Regulatory Systems Biology Research Group, Program of Systems Biology, Center for Genomic Sciences, Universidad Nacional Autónoma de México, Cuernavaca, Mexico.; Undergraduate Program in Genomic Sciences, Center for Genomic Sciences, Universidad Nacional Autónoma de México, Cuernavaca, Mexico., Hernandez-Benitez EM; Regulatory Systems Biology Research Group, Program of Systems Biology, Center for Genomic Sciences, Universidad Nacional Autónoma de México, Cuernavaca, Mexico.; Undergraduate Program in Genomic Sciences, Center for Genomic Sciences, Universidad Nacional Autónoma de México, Cuernavaca, Mexico., Zorro-Aranda A; Regulatory Systems Biology Research Group, Program of Systems Biology, Center for Genomic Sciences, Universidad Nacional Autónoma de México, Cuernavaca, Mexico.; Department of Chemical Engineering, Universidad de Antioquia, Medellín, Colombia., Tello-Palencia MA; Regulatory Systems Biology Research Group, Program of Systems Biology, Center for Genomic Sciences, Universidad Nacional Autónoma de México, Cuernavaca, Mexico.; Undergraduate Program in Genomic Sciences, Center for Genomic Sciences, Universidad Nacional Autónoma de México, Cuernavaca, Mexico., Freyre-González JA; Regulatory Systems Biology Research Group, Program of Systems Biology, Center for Genomic Sciences, Universidad Nacional Autónoma de México, Cuernavaca, Mexico.
Jazyk:	angličtina
Zdroj:	Frontiers in genetics [Front Genet] 2023 Feb 27; Vol. 14, pp. 1143382. Date of Electronic Publication: 2023 Feb 27 (Print Publication: 2023).
DOI:	10.3389/fgene.2023.1143382
Abstrakt:	Gene regulatory networks are graph models representing cellular transcription events. Networks are far from complete due to time and resource consumption for experimental validation and curation of the interactions. Previous assessments have shown the modest performance of the available network inference methods based on gene expression data. Here, we study several caveats on the inference of regulatory networks and methods assessment through the quality of the input data and gold standard, and the assessment approach with a focus on the global structure of the network. We used synthetic and biological data for the predictions and experimentally-validated biological networks as the gold standard (ground truth). Standard performance metrics and graph structural properties suggest that methods inferring co-expression networks should no longer be assessed equally with those inferring regulatory interactions. While methods inferring regulatory interactions perform better in global regulatory network inference than co-expression-based methods, the latter is better suited to infer function-specific regulons and co-regulation networks. When merging expression data, the size increase should outweigh the noise inclusion and graph structure should be considered when integrating the inferences. We conclude with guidelines to take advantage of inference methods and their assessment based on the applications and available expression datasets. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Escorcia-Rodríguez, Gaytan-Nuñez, Hernandez-Benitez, Zorro-Aranda, Tello-Palencia and Freyre-González.)
Databáze:	MEDLINE
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