Topological analysis as a tool for detection of abnormalities in protein–protein interaction data

Abstract Motivation Protein–protein interaction datasets, which can be modeled as networks, constitute an essential layer in multi-omics approach to biomedical knowledge. This representation gives insight into molecular pathways, help to uncover novel potential drug targets or predict a therapy outc...

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Published inBioinformatics Vol. 38; no. 16; pp. 3968 - 3975
Main Authors Nowakowska, Alicja W, Kotulska, Malgorzata
Format Journal Article
LanguageEnglish
Published England Oxford University Press 10.08.2022
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Abstract Abstract Motivation Protein–protein interaction datasets, which can be modeled as networks, constitute an essential layer in multi-omics approach to biomedical knowledge. This representation gives insight into molecular pathways, help to uncover novel potential drug targets or predict a therapy outcome. Nevertheless, the data that constitute such systems are frequently incomplete, error-prone and biased by scientific trends. Implementation of methods for detection of such shortcomings could improve protein–protein interaction data analysis. Results We performed topological analysis of three protein–protein interaction networks (PPINs) from IntAct Molecular Database, regarding cancer, Parkinson’s disease (two most common subjects in PPINs analysis) and Human Reference Interactome. The data collections were shown to be often biased by scientific interests, which highly impact the networks structure. This may obscure correct systematic biological interpretation of the protein–protein interactions and limit their application potential. As a solution to this problem, we propose a set of topological methods for the bias detection, which performed in the first step provides more objective biological conclusions regarding protein–protein interactions and their multi-omics consequences. Availability and implementation A user-friendly tool Extensive Tool for Network Analysis (ETNA) is available on https://github.com/AlicjaNowakowska/ETNA. The software includes a graphical Colab notebook: https://githubtocolab.com/AlicjaNowakowska/ETNA/blob/main/ETNAColab.ipynb. Contact alicja.nowakowska@pwr.edu.pl or malgorzata.kotulska@pwr.edu.pl Supplementary information Supplementary data are available at Bioinformatics online.
AbstractList Abstract Motivation Protein–protein interaction datasets, which can be modeled as networks, constitute an essential layer in multi-omics approach to biomedical knowledge. This representation gives insight into molecular pathways, help to uncover novel potential drug targets or predict a therapy outcome. Nevertheless, the data that constitute such systems are frequently incomplete, error-prone and biased by scientific trends. Implementation of methods for detection of such shortcomings could improve protein–protein interaction data analysis. Results We performed topological analysis of three protein–protein interaction networks (PPINs) from IntAct Molecular Database, regarding cancer, Parkinson’s disease (two most common subjects in PPINs analysis) and Human Reference Interactome. The data collections were shown to be often biased by scientific interests, which highly impact the networks structure. This may obscure correct systematic biological interpretation of the protein–protein interactions and limit their application potential. As a solution to this problem, we propose a set of topological methods for the bias detection, which performed in the first step provides more objective biological conclusions regarding protein–protein interactions and their multi-omics consequences. Availability and implementation A user-friendly tool Extensive Tool for Network Analysis (ETNA) is available on https://github.com/AlicjaNowakowska/ETNA. The software includes a graphical Colab notebook: https://githubtocolab.com/AlicjaNowakowska/ETNA/blob/main/ETNAColab.ipynb. Contact alicja.nowakowska@pwr.edu.pl or malgorzata.kotulska@pwr.edu.pl Supplementary information Supplementary data are available at Bioinformatics online.
Protein-protein interaction datasets, which can be modeled as networks, constitute an essential layer in multi-omics approach to biomedical knowledge. This representation gives insight into molecular pathways, help to uncover novel potential drug targets or predict a therapy outcome. Nevertheless, the data that constitute such systems are frequently incomplete, error-prone and biased by scientific trends. Implementation of methods for detection of such shortcomings could improve protein-protein interaction data analysis.MOTIVATIONProtein-protein interaction datasets, which can be modeled as networks, constitute an essential layer in multi-omics approach to biomedical knowledge. This representation gives insight into molecular pathways, help to uncover novel potential drug targets or predict a therapy outcome. Nevertheless, the data that constitute such systems are frequently incomplete, error-prone and biased by scientific trends. Implementation of methods for detection of such shortcomings could improve protein-protein interaction data analysis.We performed topological analysis of three protein-protein interaction networks (PPINs) from IntAct Molecular Database, regarding cancer, Parkinson's disease (two most common subjects in PPINs analysis) and Human Reference Interactome. The data collections were shown to be often biased by scientific interests, which highly impact the networks structure. This may obscure correct systematic biological interpretation of the protein-protein interactions and limit their application potential. As a solution to this problem, we propose a set of topological methods for the bias detection, which performed in the first step provides more objective biological conclusions regarding protein-protein interactions and their multi-omics consequences.RESULTSWe performed topological analysis of three protein-protein interaction networks (PPINs) from IntAct Molecular Database, regarding cancer, Parkinson's disease (two most common subjects in PPINs analysis) and Human Reference Interactome. The data collections were shown to be often biased by scientific interests, which highly impact the networks structure. This may obscure correct systematic biological interpretation of the protein-protein interactions and limit their application potential. As a solution to this problem, we propose a set of topological methods for the bias detection, which performed in the first step provides more objective biological conclusions regarding protein-protein interactions and their multi-omics consequences.A user-friendly tool Extensive Tool for Network Analysis (ETNA) is available on https://github.com/AlicjaNowakowska/ETNA. The software includes a graphical Colab notebook: https://githubtocolab.com/AlicjaNowakowska/ETNA/blob/main/ETNAColab.ipynb.AVAILABILITY AND IMPLEMENTATIONA user-friendly tool Extensive Tool for Network Analysis (ETNA) is available on https://github.com/AlicjaNowakowska/ETNA. The software includes a graphical Colab notebook: https://githubtocolab.com/AlicjaNowakowska/ETNA/blob/main/ETNAColab.ipynb.alicja.nowakowska@pwr.edu.pl or malgorzata.kotulska@pwr.edu.pl.CONTACTalicja.nowakowska@pwr.edu.pl or malgorzata.kotulska@pwr.edu.pl.Supplementary data are available at Bioinformatics online.SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online.
Protein-protein interaction datasets, which can be modeled as networks, constitute an essential layer in multi-omics approach to biomedical knowledge. This representation gives insight into molecular pathways, help to uncover novel potential drug targets or predict a therapy outcome. Nevertheless, the data that constitute such systems are frequently incomplete, error-prone and biased by scientific trends. Implementation of methods for detection of such shortcomings could improve protein-protein interaction data analysis. We performed topological analysis of three protein-protein interaction networks (PPINs) from IntAct Molecular Database, regarding cancer, Parkinson's disease (two most common subjects in PPINs analysis) and Human Reference Interactome. The data collections were shown to be often biased by scientific interests, which highly impact the networks structure. This may obscure correct systematic biological interpretation of the protein-protein interactions and limit their application potential. As a solution to this problem, we propose a set of topological methods for the bias detection, which performed in the first step provides more objective biological conclusions regarding protein-protein interactions and their multi-omics consequences. A user-friendly tool Extensive Tool for Network Analysis (ETNA) is available on https://github.com/AlicjaNowakowska/ETNA. The software includes a graphical Colab notebook: https://githubtocolab.com/AlicjaNowakowska/ETNA/blob/main/ETNAColab.ipynb. alicja.nowakowska@pwr.edu.pl or malgorzata.kotulska@pwr.edu.pl. Supplementary data are available at Bioinformatics online.
Author Kotulska, Malgorzata
Nowakowska, Alicja W
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Cites_doi 10.1016/j.celrep.2020.108050
10.1186/1752-0509-7-32
10.1038/30918
10.1074/jbc.M113.537811
10.1038/s41586-020-2188-x
10.1002/pmic.200700131
10.3892/ijmm.2016.2577
10.1371/journal.pone.0059613
10.1186/gb-2010-11-5-r53
10.1371/journal.pone.0103047
10.1038/s41467-019-14224-9
10.1186/1471-2105-8-297
10.1038/nbt825
10.1073/pnas.061034498
10.1002/pmic.200300636
10.1093/brain/awv040
10.1038/35001009
10.1137/070710111
10.1371/journal.pcbi.1000140
10.1038/s41467-018-05692-6
10.1016/j.csbj.2019.05.007
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References Roberts (2023041408494047000_) 2015; 138
Ted (2023041408494047000_) 2013
Zotenko (2023041408494047000_) 2008; 4
Vazquez (2023041408494047000_) 2003; 21
Luck (2023041408494047000_) 2020; 580
Safari-Alighiarloo (2023041408494047000_) 2014; 7
Erdös (2023041408494047000_) 1961; 38
Ray (2023041408494047000_) 2014; 289
Barabási (2023041408494047000_) 2016
Berggård (2023041408494047000_) 2007; 7
Chen (2023041408494047000_) 2016; 37
Gillespie (2023041408494047000_) 2014
Yugandhar (2023041408494047000_) 2019; 17
Clauset (2023041408494047000_) 2009; 51
Friedel (2023041408494047000_) 2007; 8
Yook (2023041408494047000_) 2004; 4
Kennedy (2023041408494047000_) 2020; 11
Newman (2023041408494047000_) 2003; 67
Ran (2023041408494047000_) 2013; 7
Uetz (2023041408494047000_) 2000; 403
Wu (2023041408494047000_) 2010; 11
Iyer (2023041408494047000_) 2013; 8
Peixoto (2023041408494047000_) 2020
Adhikari (2023041408494047000_) 2018; 9
EMBL-EBI (2023041408494047000_) 2021
Haenig (2023041408494047000_) 2020; 32
Ito (2023041408494047000_) 2001; 98
Watts (2023041408494047000_) 1998; 393
Rakshit (2023041408494047000_) 2014; 9
36073107 - Bioinformatics. 2022 Oct 31;38(21):4997
References_xml – volume: 32
  start-page: 108050
  year: 2020
  ident: 2023041408494047000_
  article-title: Interactome mapping provides a network of neurodegenerative disease proteins and uncovers widespread protein aggregation in affected brains
  publication-title: Cell Rep
  doi: 10.1016/j.celrep.2020.108050
– volume: 7
  start-page: 32
  year: 2013
  ident: 2023041408494047000_
  article-title: Construction and analysis of the protein-protein interaction network related to essential hypertension
  publication-title: BMC Syst. Biol
  doi: 10.1186/1752-0509-7-32
– volume: 393
  start-page: 440
  year: 1998
  ident: 2023041408494047000_
  article-title: Collective dynamics of ‘small-world’ networks
  publication-title: Nature
  doi: 10.1038/30918
– start-page: 1
  year: 2014
  ident: 2023041408494047000_
  article-title: Fitting heavy tailed distributions: the poweRlaw package
– volume: 289
  start-page: 13042
  year: 2014
  ident: 2023041408494047000_
  article-title: The Parkinson disease-linked LRRK2 protein mutation i2020t stabilizes an active state conformation leading to increased kinase activity
  publication-title: J. Biol. Chem
  doi: 10.1074/jbc.M113.537811
– volume: 67
  start-page: 026126
  year: 2003
  ident: 2023041408494047000_
  article-title: Mixing patterns in networks
  publication-title: APS
– volume: 580
  start-page: 402
  year: 2020
  ident: 2023041408494047000_
  article-title: A reference map of the human binary protein interactome
  publication-title: Nature
  doi: 10.1038/s41586-020-2188-x
– volume: 7
  start-page: 2833
  year: 2007
  ident: 2023041408494047000_
  article-title: Methods for the detection and analysis of protein–protein interactions
  publication-title: Proteomics
  doi: 10.1002/pmic.200700131
– volume: 37
  start-page: 1576
  year: 2016
  ident: 2023041408494047000_
  article-title: Construction and analysis of protein-protein interaction networks based on proteomics data of prostate cancer
  publication-title: Int. J. Mol. Med
  doi: 10.3892/ijmm.2016.2577
– year: 2020
  ident: 2023041408494047000_
– volume: 8
  start-page: e59613
  year: 2013
  ident: 2023041408494047000_
  article-title: Attack robustness and centrality of complex networks
  publication-title: PLoS One
  doi: 10.1371/journal.pone.0059613
– volume: 11
  start-page: R53
  year: 2010
  ident: 2023041408494047000_
  article-title: A human functional protein interaction network and its application to cancer data analysis
  publication-title: Genome Biol
  doi: 10.1186/gb-2010-11-5-r53
– volume: 9
  start-page: e103047
  year: 2014
  ident: 2023041408494047000_
  article-title: Construction and analysis of the protein-protein interaction networks based on gene expression profiles of Parkinson’s disease
  publication-title: PLoS One
  doi: 10.1371/journal.pone.0103047
– volume: 7
  start-page: 17
  issue: 1
  year: 2014
  ident: 2023041408494047000_
  article-title: Protein-protein interaction networks (PPI) and complex diseases. Gastroenterol
  publication-title: Hepatol. Bed Bench.
– volume-title: Network Science: Theory and Applications
  year: 2013
  ident: 2023041408494047000_
– volume: 11
  start-page: 1
  year: 2020
  ident: 2023041408494047000_
  article-title: Extensive rewiring of the EGFR network in colorectal cancer cells expressing transforming levels of KRASG13d
  publication-title: Nat. Commun
  doi: 10.1038/s41467-019-14224-9
– volume: 8
  start-page: 297
  year: 2007
  ident: 2023041408494047000_
  article-title: Influence of degree correlations on network structure and stability in protein-protein interaction networks
  publication-title: BMC Bioinformatics
  doi: 10.1186/1471-2105-8-297
– volume: 21
  start-page: 697
  issue: 6
  year: 2003
  ident: 2023041408494047000_
  article-title: A Global protein function prediction in protein-protein interaction networks
  publication-title: Nature Biotechnology
  doi: 10.1038/nbt825
– volume: 98
  start-page: 4569
  year: 2001
  ident: 2023041408494047000_
  article-title: A comprehensive two-hybrid analysis to explore the yeast protein interactome
  publication-title: Proc. Natl. Acad. Sci. USA
  doi: 10.1073/pnas.061034498
– volume: 4
  start-page: 928
  year: 2004
  ident: 2023041408494047000_
  article-title: Functional and topological characterization of protein interaction networks
  publication-title: Proteomics
  doi: 10.1002/pmic.200300636
– volume-title: Network Science
  year: 2016
  ident: 2023041408494047000_
– year: 2021
  ident: 2023041408494047000_
– volume: 138
  start-page: 1642
  year: 2015
  ident: 2023041408494047000_
  article-title: Direct visualization of alpha-synuclein oligomers reveals previously undetected pathology in Parkinson’s disease brain
  publication-title: Brain
  doi: 10.1093/brain/awv040
– volume: 403
  start-page: 623
  year: 2000
  ident: 2023041408494047000_
  article-title: A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae
  publication-title: Nature
  doi: 10.1038/35001009
– volume: 51
  start-page: 661
  year: 2009
  ident: 2023041408494047000_
  article-title: Power-law distributions in empirical data
  publication-title: SIAM
  doi: 10.1137/070710111
– volume: 4
  start-page: e1000140
  year: 2008
  ident: 2023041408494047000_
  article-title: Why do hubs in the yeast protein interaction network tend to be essential: reexamining the connection between the network topology and essentiality
  publication-title: PLoS Comput. Biol
  doi: 10.1371/journal.pcbi.1000140
– volume: 9
  start-page: 3646
  year: 2018
  ident: 2023041408494047000_
  article-title: Interrogating the protein interactomes of RAS isoforms identifies PIP5k1a as a KRAS-specific vulnerability
  publication-title: Nat. Commun
  doi: 10.1038/s41467-018-05692-6
– volume: 17
  start-page: 805
  year: 2019
  ident: 2023041408494047000_
  article-title: Inferring protein-protein interaction networks from mass spectrometry-based proteomic approaches: a mini-review
  publication-title: Comput. Struct. Biotechnol. J
  doi: 10.1016/j.csbj.2019.05.007
– volume: 38
  start-page: 343
  year: 1961
  ident: 2023041408494047000_
  article-title: On the evolution of random graphs
  publication-title: Bull. Inst. Internat. Stat
– reference: 36073107 - Bioinformatics. 2022 Oct 31;38(21):4997
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Snippet Abstract Motivation Protein–protein interaction datasets, which can be modeled as networks, constitute an essential layer in multi-omics approach to biomedical...
Protein-protein interaction datasets, which can be modeled as networks, constitute an essential layer in multi-omics approach to biomedical knowledge. This...
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SubjectTerms Humans
Original Papers
Protein Interaction Maps
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Title Topological analysis as a tool for detection of abnormalities in protein–protein interaction data
URI https://www.ncbi.nlm.nih.gov/pubmed/35771625
https://www.proquest.com/docview/2682782941
https://pubmed.ncbi.nlm.nih.gov/PMC9746892
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