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 in | Bioinformatics Vol. 38; no. 16; pp. 3968 - 3975 |
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Main Authors | , |
Format | Journal Article |
Language | English |
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. |
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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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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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Title | Topological analysis as a tool for detection of abnormalities in protein–protein interaction data |
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