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BioPlexR and BioPlexPy: integrated data products for the analysis of human protein interactions
SUMMARY: The BioPlex project has created two proteome scale, cell-line-specific protein–protein interaction (PPI) networks: the first in 293T cells, including 120k interactions among 15k proteins; and the second in HCT116 cells, including 70k interactions between 10k proteins. Here, we describe prog...
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/PMC9978581/ https://www.ncbi.nlm.nih.gov/pubmed/36794911 http://dx.doi.org/10.1093/bioinformatics/btad091 |
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author | Geistlinger, Ludwig Vargas, Roger Lee, Tyrone Pan, Joshua Huttlin, Edward L Gentleman, Robert |
author_facet | Geistlinger, Ludwig Vargas, Roger Lee, Tyrone Pan, Joshua Huttlin, Edward L Gentleman, Robert |
author_sort | Geistlinger, Ludwig |
collection | PubMed |
description | SUMMARY: The BioPlex project has created two proteome scale, cell-line-specific protein–protein interaction (PPI) networks: the first in 293T cells, including 120k interactions among 15k proteins; and the second in HCT116 cells, including 70k interactions between 10k proteins. Here, we describe programmatic access to the BioPlex PPI networks and integration with related resources from within R and Python. Besides PPI networks for 293T and HCT116 cells, this includes access to CORUM protein complex data, PFAM protein domain data, PDB protein structures, and transcriptome and proteome data for the two cell lines. The implemented functionality serves as a basis for integrative downstream analysis of BioPlex PPI data with domain-specific R and Python packages, including efficient execution of maximum scoring sub-network analysis, protein domain–domain association analysis, mapping of PPIs onto 3D protein structures and analysis of BioPlex PPIs at the interface of transcriptomic and proteomic data. AVAILABILITY AND IMPLEMENTATION: The BioPlex R package is available from Bioconductor (bioconductor.org/packages/BioPlex), and the BioPlex Python package is available from PyPI (pypi.org/project/bioplexpy). Applications and downstream analyses are available from GitHub (github.com/ccb-hms/BioPlexAnalysis). |
format | Online Article Text |
id | pubmed-9978581 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-99785812023-03-03 BioPlexR and BioPlexPy: integrated data products for the analysis of human protein interactions Geistlinger, Ludwig Vargas, Roger Lee, Tyrone Pan, Joshua Huttlin, Edward L Gentleman, Robert Bioinformatics Applications Note SUMMARY: The BioPlex project has created two proteome scale, cell-line-specific protein–protein interaction (PPI) networks: the first in 293T cells, including 120k interactions among 15k proteins; and the second in HCT116 cells, including 70k interactions between 10k proteins. Here, we describe programmatic access to the BioPlex PPI networks and integration with related resources from within R and Python. Besides PPI networks for 293T and HCT116 cells, this includes access to CORUM protein complex data, PFAM protein domain data, PDB protein structures, and transcriptome and proteome data for the two cell lines. The implemented functionality serves as a basis for integrative downstream analysis of BioPlex PPI data with domain-specific R and Python packages, including efficient execution of maximum scoring sub-network analysis, protein domain–domain association analysis, mapping of PPIs onto 3D protein structures and analysis of BioPlex PPIs at the interface of transcriptomic and proteomic data. AVAILABILITY AND IMPLEMENTATION: The BioPlex R package is available from Bioconductor (bioconductor.org/packages/BioPlex), and the BioPlex Python package is available from PyPI (pypi.org/project/bioplexpy). Applications and downstream analyses are available from GitHub (github.com/ccb-hms/BioPlexAnalysis). Oxford University Press 2023-02-16 /pmc/articles/PMC9978581/ /pubmed/36794911 http://dx.doi.org/10.1093/bioinformatics/btad091 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 | Applications Note Geistlinger, Ludwig Vargas, Roger Lee, Tyrone Pan, Joshua Huttlin, Edward L Gentleman, Robert BioPlexR and BioPlexPy: integrated data products for the analysis of human protein interactions |
title | BioPlexR and BioPlexPy: integrated data products for the analysis of human protein interactions |
title_full | BioPlexR and BioPlexPy: integrated data products for the analysis of human protein interactions |
title_fullStr | BioPlexR and BioPlexPy: integrated data products for the analysis of human protein interactions |
title_full_unstemmed | BioPlexR and BioPlexPy: integrated data products for the analysis of human protein interactions |
title_short | BioPlexR and BioPlexPy: integrated data products for the analysis of human protein interactions |
title_sort | bioplexr and bioplexpy: integrated data products for the analysis of human protein interactions |
topic | Applications Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9978581/ https://www.ncbi.nlm.nih.gov/pubmed/36794911 http://dx.doi.org/10.1093/bioinformatics/btad091 |
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