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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.
Afiliación
  • Geistlinger L; Center for Computational Biomedicine, Harvard Medical School, Boston, MA 02115, USA.
  • Vargas R; Center for Computational Biomedicine, Harvard Medical School, Boston, MA 02115, USA.
  • Lee T; Center for Computational Biomedicine, Harvard Medical School, Boston, MA 02115, USA.
  • Pan J; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA.
  • Huttlin EL; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA.
  • Gentleman R; Center for Computational Biomedicine, Harvard Medical School, Boston, MA 02115, USA.
Bioinformatics ; 39(3)2023 03 01.
Article en En | MEDLINE | ID: mdl-36794911
ABSTRACT

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).
Asunto(s)

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Programas Informáticos / Proteoma Límite: Humans Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Programas Informáticos / Proteoma Límite: Humans Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos