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Abstract
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.
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.
Original language | English |
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Article number | vbad137 |
Pages (from-to) | 1-10 |
Number of pages | 10 |
Journal | Bioinformatics Advances |
Volume | 3 |
Issue number | 1 |
DOIs | |
Publication status | Published - 17 Oct 2023 |
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Dive into the research topics of 'BioNAR: an integrated biological network analysis package in bioconductor'. Together they form a unique fingerprint.Projects
- 1 Finished
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Human Brain Project Specific Grant Agreement 3
Armstrong, D. (Principal Investigator) & Storkey, A. (Co-investigator)
1/04/20 → 31/03/23
Project: Research
Research output
- 1 Other contribution
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BioNAR: An Integrated Biological Network Analysis Package in Bioconductor
McLean, C., Sorokin, A., Simpson, T. I., Armstrong, J. D. & Sorokina, O., 10 Feb 2023Research output: Other contribution
Open Access