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BioNAR: an integrated biological network analysis package in bioconductor
MOTIVATION: Biological function in protein complexes emerges from more than just the sum of their parts: molecules interact in a range of different sub-complexes and transfer signals/information around internal pathways. Modern proteomic techniques are excellent at producing a parts-list for such co...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10582516/ https://www.ncbi.nlm.nih.gov/pubmed/37860105 http://dx.doi.org/10.1093/bioadv/vbad137 |
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author | McLean, Colin Sorokin, Anatoly Simpson, Thomas Ian Armstrong, James Douglas Sorokina, Oksana |
author_facet | McLean, Colin Sorokin, Anatoly Simpson, Thomas Ian Armstrong, James Douglas Sorokina, Oksana |
author_sort | McLean, Colin |
collection | PubMed |
description | MOTIVATION: Biological function in protein complexes emerges from more than just the sum of their parts: molecules interact in a range of different sub-complexes and transfer signals/information around internal pathways. Modern proteomic techniques are excellent at producing a parts-list for such complexes, but more detailed analysis demands a network approach linking the molecules together and analysing the emergent architectural properties. Methods developed for the analysis of networks in social sciences have proven very useful for splitting biological networks into communities leading to the discovery of sub-complexes enriched with molecules associated with specific diseases or molecular functions that are not apparent from the constituent components alone. RESULTS: Here, we present the Bioconductor package BioNAR, which supports step-by-step analysis of biological/biomedical networks with the aim of quantifying and ranking each of the network’s vertices based on network topology and clustering. Examples demonstrate that while BioNAR is not restricted to proteomic networks, it can predict a protein’s impact within multiple complexes, and enables estimation of the co-occurrence of metadata, i.e. diseases and functions across the network, identifying the clusters whose components are likely to share common function and mechanisms. AVAILABILITY AND IMPLEMENTATION: The package is available from Bioconductor release 3.17: https://bioconductor.org/packages/release/bioc/html/BioNAR.html. |
format | Online Article Text |
id | pubmed-10582516 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-105825162023-10-19 BioNAR: an integrated biological network analysis package in bioconductor McLean, Colin Sorokin, Anatoly Simpson, Thomas Ian Armstrong, James Douglas Sorokina, Oksana Bioinform Adv Original Article MOTIVATION: Biological function in protein complexes emerges from more than just the sum of their parts: molecules interact in a range of different sub-complexes and transfer signals/information around internal pathways. Modern proteomic techniques are excellent at producing a parts-list for such complexes, but more detailed analysis demands a network approach linking the molecules together and analysing the emergent architectural properties. Methods developed for the analysis of networks in social sciences have proven very useful for splitting biological networks into communities leading to the discovery of sub-complexes enriched with molecules associated with specific diseases or molecular functions that are not apparent from the constituent components alone. RESULTS: Here, we present the Bioconductor package BioNAR, which supports step-by-step analysis of biological/biomedical networks with the aim of quantifying and ranking each of the network’s vertices based on network topology and clustering. Examples demonstrate that while BioNAR is not restricted to proteomic networks, it can predict a protein’s impact within multiple complexes, and enables estimation of the co-occurrence of metadata, i.e. diseases and functions across the network, identifying the clusters whose components are likely to share common function and mechanisms. AVAILABILITY AND IMPLEMENTATION: The package is available from Bioconductor release 3.17: https://bioconductor.org/packages/release/bioc/html/BioNAR.html. Oxford University Press 2023-09-29 /pmc/articles/PMC10582516/ /pubmed/37860105 http://dx.doi.org/10.1093/bioadv/vbad137 Text en © The Author(s) 2023. 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 | Original Article McLean, Colin Sorokin, Anatoly Simpson, Thomas Ian Armstrong, James Douglas Sorokina, Oksana BioNAR: an integrated biological network analysis package in bioconductor |
title | BioNAR: an integrated biological network analysis package in bioconductor |
title_full | BioNAR: an integrated biological network analysis package in bioconductor |
title_fullStr | BioNAR: an integrated biological network analysis package in bioconductor |
title_full_unstemmed | BioNAR: an integrated biological network analysis package in bioconductor |
title_short | BioNAR: an integrated biological network analysis package in bioconductor |
title_sort | bionar: an integrated biological network analysis package in bioconductor |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10582516/ https://www.ncbi.nlm.nih.gov/pubmed/37860105 http://dx.doi.org/10.1093/bioadv/vbad137 |
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