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VOLTA: adVanced mOLecular neTwork Analysis

MOTIVATION: 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 o...

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Autores principales: Pavel, Alisa, Federico, Antonio, del Giudice, Giusy, Serra, Angela, Greco, Dario
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8687180/
https://www.ncbi.nlm.nih.gov/pubmed/34498028
http://dx.doi.org/10.1093/bioinformatics/btab642
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author Pavel, Alisa
Federico, Antonio
del Giudice, Giusy
Serra, Angela
Greco, Dario
author_facet Pavel, Alisa
Federico, Antonio
del Giudice, Giusy
Serra, Angela
Greco, Dario
author_sort Pavel, Alisa
collection PubMed
description MOTIVATION: 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. AVAILABILITY AND IMPLEMENTATION: The package and used data are available at GitHub: https://github.com/fhaive/VOLTA and 10.5281/zenodo.5171719. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-86871802021-12-21 VOLTA: adVanced mOLecular neTwork Analysis Pavel, Alisa Federico, Antonio del Giudice, Giusy Serra, Angela Greco, Dario Bioinformatics Applications Notes MOTIVATION: 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. AVAILABILITY AND IMPLEMENTATION: The package and used data are available at GitHub: https://github.com/fhaive/VOLTA and 10.5281/zenodo.5171719. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2021-09-08 /pmc/articles/PMC8687180/ /pubmed/34498028 http://dx.doi.org/10.1093/bioinformatics/btab642 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Pavel, Alisa
Federico, Antonio
del Giudice, Giusy
Serra, Angela
Greco, Dario
VOLTA: adVanced mOLecular neTwork Analysis
title VOLTA: adVanced mOLecular neTwork Analysis
title_full VOLTA: adVanced mOLecular neTwork Analysis
title_fullStr VOLTA: adVanced mOLecular neTwork Analysis
title_full_unstemmed VOLTA: adVanced mOLecular neTwork Analysis
title_short VOLTA: adVanced mOLecular neTwork Analysis
title_sort volta: advanced molecular network analysis
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8687180/
https://www.ncbi.nlm.nih.gov/pubmed/34498028
http://dx.doi.org/10.1093/bioinformatics/btab642
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