Synaptome.db: a bioconductor package for synaptic proteomics data

Sorokina, Oksana; Sorokin, Anatoly; Armstrong, J. D.

doi:10.1093/bioadv/vbac086

Cited by 4 publications

(16 citation statements)

References 7 publications

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“…The plot of bridgeness, using the Louvain clustering algorithm, against semi-Local centrality is shown in Figure 3D. We highlighted 19 bridging proteins found most frequently in the presynaptic compartment (found in 15 and more presynaptic studies [41]). Among these, six were also observed for GRB2, HRAS, NRAS, which are recognised as local hubs participating in many signalling cascades (also known as party hubs [23]).…”

Section: Figure 2 Shown the Clustering Results For Case Study 1 (Masc...mentioning

confidence: 98%

“…Alternatively, a network can be imported from a standard graph file formats including gml, using the igraph's package builtin functionality. BioNAR also allows network import for specific synaptic protein set/synaptic compartments/brain region directly from the Synaptome.db package [41]. An example of this is shown for the presynaptic case study 2.…”

Section: Resultsmentioning

confidence: 99%

“…A scale up, in both size and complexity, from the MASC network in study 1 is the study of an entire subcellular compartment's protein-protein interaction network that integrates data from multiple experiments. For that we constructed a proteome network for the entire presynaptic compartment using the R package Synaptome.db [41]: 2304 vertices were extracted from published studies of this compartment, which were combined with protein-protein interactions from Synaptome.db to obtain a Largest Connected Component containing 1780 vertices connected by 6620 edges.…”

Section: Figure 2 Shown the Clustering Results For Case Study 1 (Masc...mentioning

confidence: 99%

“…Bioconductor package [41] by filtering out vertices from the 'neuroPPI' network, which were not found in DGN. We calculated the number of disease module interactions as the number of PPI edges found between genes annotated to the disorder class given in DGN (Fig 4 , C).…”

Section: Figure 2 Shown the Clustering Results For Case Study 1 (Masc...mentioning

confidence: 99%

“…BioNAR also allows network import for specific synaptic protein set/synaptic compartments/brain region directly from the Synaptome . db package [41]. An example of this is shown for the presynaptic case study 2.…”

Section: Resultsmentioning

confidence: 99%

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BioNAR: An Integrated Biological Network Analysis Package in Bioconductor

McLean

Sorokin

Simpson

et al. 2023

Preprint

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Biological function in protein complexes emerges from more than just the sum of their parts: molecules interact in a range of different subcomplexes 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 analyzing 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. 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 meta-data, i.e., diseases and functions across the network, identifying the clusters whose components are likely to share common function and mechanisms.

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Section: Figure 2 Shown the Clustering Results For Case Study 1 (Masc...mentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

Section: Figure 2 Shown the Clustering Results For Case Study 1 (Masc...mentioning

confidence: 99%

Section: Figure 2 Shown the Clustering Results For Case Study 1 (Masc...mentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

BioNAR: An Integrated Biological Network Analysis Package in Bioconductor

McLean

Sorokin

Simpson

et al. 2023

Preprint

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Computational Pipeline for Analysis of Biomedical Networks with BioNAR

McLean,

Sorokin,

Armstrong

et al. 2023

Current Protocols

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In a living cell, proteins interact to assemble both transient and constant molecular complexes, which transfer signals/information around internal pathways. Modern proteomic techniques can identify the constituent components of these complexes, but more detailed analysis demands a network approach linking the molecules together and analyzing the emergent architectural properties. The Bioconductor package BioNAR combines a selection of existing R protocols for network analysis with newly designed original methodological features to support step‐by‐step analysis of biological/biomedical . Critically, BioNAR supports a pipeline approach whereby many networks and iterative analyses can be performed. Here we present a network analysis pipeline that starts from initiating a network model from a list of components/proteins and their interactions through to identifying its functional components based solely on network topology. We demonstrate that BioNAR can help users achieve a number of network analysis goals that are difficult to achieve anywhere else. This includes how users can choose the optimal clustering algorithm from a range of options based on independent annotation enrichment, and predict a protein's influence within and across multiple subcomplexes in the network and estimate the co‐occurrence or linkage between metadata at the network level (e.g., diseases and functions across the network, identifying the clusters whose components are likely to share common function and mechanisms). The package is freely available in Bioconductor release 3.17: https://bioconductor.org/packages/3.17/bioc/html/BioNAR.html. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.Basic Protocol 1: Creating and annotating the networkSupport Protocol 1: Installing BioNAR from RStudioSupport Protocol 2: Building the sample network from synaptome.dbBasic Protocol 2: Network properties and centralityBasic Protocol 3: Network communitiesBasic protocol 4: Choosing the optimal clustering algorithm based on the enrichment with annotation termsBasic Protocol 5: Influencing network components and bridgenessBasic Protocol 6: Co‐occurrence of the annotations

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BioNAR: an integrated biological network analysis package in bioconductor

McLean,

Sorokin,

Simpson

et al. 2023

Bioinformatics Advances

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Motivation Biological function in protein complexes emerges from more than just the sum of their parts: molecules interact in a range of different subcomplexes 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 analyzing 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 meta-data, i.e., diseases and functions across the network, identifying the clusters whose components are likely to share common function and mechanisms. Availability The package is available from Bioconductor release 3.17: https://bioconductor.org/packages/release/bioc/html/BioNAR.html Supplementary information Supplementary data are available at Bioinformatics Advances online.

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Synaptome.db: a bioconductor package for synaptic proteomics data

Cited by 4 publications

References 7 publications

BioNAR: An Integrated Biological Network Analysis Package in Bioconductor

BioNAR: An Integrated Biological Network Analysis Package in Bioconductor

Computational Pipeline for Analysis of Biomedical Networks with BioNAR

BioNAR: an integrated biological network analysis package in bioconductor

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