VOLTA: adVanced mOLecular neTwork Analysis

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dc.contributor.affiliationFaculty of Medicine and Health Technology, Tampere University, Tampere, Finland; BioMediTech Institute, Tampere University, Tampere, Finland; Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health Technology, Tampere University, Finland - Pavel, Alisa
dc.contributor.affiliationFaculty of Medicine and Health Technology, Tampere University, Tampere, Finland; BioMediTech Institute, Tampere University, Tampere, Finland; Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health Technology, Tampere University, Finland - Federico, Antonio
dc.contributor.affiliationFaculty of Medicine and Health Technology, Tampere University, Tampere, Finland; BioMediTech Institute, Tampere University, Tampere, Finland; Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health Technology, Tampere University, Finland - del Giudice, Giusy
dc.contributor.affiliationFaculty of Medicine and Health Technology, Tampere University, Tampere, Finland; BioMediTech Institute, Tampere University, Tampere, Finland; Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health Technology, Tampere University, Finland - Serra, Angela
dc.contributor.affiliationFaculty of Medicine and Health Technology, Tampere University, Tampere, Finland; BioMediTech Institute, Tampere University, Tampere, Finland; Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health Technology, Tampere University, Finland; Institute of Biotechnology, University of Helsinki, Helsinki, Finland - Greco, Dario
dc.contributor.authorPavel, Alisa
dc.contributor.authorFederico, Antonio
dc.contributor.authordel Giudice, Giusy
dc.contributor.authorSerra, Angela
dc.contributor.authorGreco, Dario
dc.date.accessioned2025-03-24T15:26:25Z
dc.date.issued2021-08-09
dc.date.issued2021-08-09
dc.descriptionMotivation: Network analysis is a powerful approach to investigate biological systems. It is often applied to study gene co-expression patterns derived from transcriptomics experiments. Even though co-expression analysis is widely used, there is still a lack of tools that are open and customizable on the basis of different network types and analysis scenarios (e.g. through function accessibility), but are also suitable for novice users by providing complete analysis pipelines. Results: We developed VOLTA, a Python package suited for complex co-expression network analysis. VOLTA is designed to allow users direct access to the individual functions, while they are also provided with complete analysis pipelines. Moreover, VOLTA offers when possible multiple algorithms applicable to each analytical step (e.g. multiple community detection or clustering algorithms are provided), hence providing the user with the possibility to perform analysis tailored to their needs. This makes VOLTA highly suitable for experienced users who wish to build their own analysis pipelines for a wide range of networks as well as for novice users for which a “plug and play” system is provided.
dc.identifierhttps://doi.org/10.5281/zenodo.5171719
dc.identifier.urihttps://hydatakatalogi-test-24.it.helsinki.fi/handle/123456789/11412
dc.rightsOpen
dc.rights.licensecc-by-4.0
dc.titleVOLTA: adVanced mOLecular neTwork Analysis
dc.typesoftware
dc.typesoftware

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