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Palombo V, Milanesi M, Sferra G, et al. PANEV: an R package for a pathway-based network visualization. BMC Bioinformatics. 2020;21(1):46doi: 10.1186/s12859-020-3371-7.
Palombo, V., Milanesi, M., Sferra, G., Capomaccio, S., Sgorlon, S., & D'Andrea, M. (2020). PANEV: an R package for a pathway-based network visualization. BMC bioinformatics, 21(1), 46. https://doi.org/10.1186/s12859-020-3371-7
Palombo, Valentino, et al. "PANEV: an R package for a pathway-based network visualization." BMC bioinformatics vol. 21,1 (2020): 46. doi: https://doi.org/10.1186/s12859-020-3371-7
Palombo V, Milanesi M, Sferra G, Capomaccio S, Sgorlon S, D'Andrea M. PANEV: an R package for a pathway-based network visualization. BMC Bioinformatics. 2020 Feb 06;21(1):46. doi: 10.1186/s12859-020-3371-7. PMID: 32028885; PMCID: PMC7006390.
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BMC Bioinformatics. 2020 Feb 06;21(1):46. doi: 10.1186/s12859-020-3371-7.

PANEV: an R package for a pathway-based network visualization.

BMC bioinformatics

Valentino Palombo, Marco Milanesi, Gabriella Sferra, Stefano Capomaccio, Sandy Sgorlon, Mariasilvia D'Andrea

Affiliations

  1. Dipartimento Agricoltura, Ambiente e Alimenti, Università degli Studi del Molise, 86100, Campobasso, Italy.
  2. Department of Support, Production and Animal Health, School of Veterinary Medicine, São Paulo State University, Araçatuba, São Paulo, 16050-680, Brazil.
  3. Istituto di Zootecnica, Università Cattolica del Sacro Cuore, 29122, Piacenza, Italy.
  4. Dipartimento di Bioscienze e Territorio, Università degli Studi del Molise, 86090, Pesche, IS, Italy.
  5. Dipartimento di Medicina Veterinaria, Università di Perugia, 06126, Perugia, Italy.
  6. Dipartimento di Scienze Agrarie ed Ambientali, Università degli Studi di Udine, 33100, Udine, Italy.
  7. Dipartimento Agricoltura, Ambiente e Alimenti, Università degli Studi del Molise, 86100, Campobasso, Italy. [email protected].

PMID: 32028885 PMCID: PMC7006390 DOI: 10.1186/s12859-020-3371-7

Abstract

BACKGROUND: During the last decade, with the aim to solve the challenge of post-genomic and transcriptomic data mining, a plethora of tools have been developed to create, edit and analyze metabolic pathways. In particular, when a complex phenomenon is considered, the creation of a network of multiple interconnected pathways of interest could be useful to investigate the underlying biology and ultimately identify functional candidate genes affecting the trait under investigation.

RESULTS: PANEV (PAthway NEtwork Visualizer) is an R package set for gene/pathway-based network visualization. Based on information available on KEGG, it visualizes genes within a network of multiple levels (from 1 to n) of interconnected upstream and downstream pathways. The network graph visualization helps to interpret functional profiles of a cluster of genes.

CONCLUSIONS: The suite has no species constraints and it is ready to analyze genomic or transcriptomic outcomes. Users need to supply the list of candidate genes, specify the target pathway(s) and the number of interconnected downstream and upstream pathways (levels) required for the investigation. The package is available at https://github.com/vpalombo/PANEV.

Keywords: Data mining; Genomic and transcriptomic analysis; KEGG; Molecular pathways; Pathway visualization

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