Identification of active signaling pathways by integrating gene expression and protein interaction data

Signaling pathways are the key biological mechanisms that transduce extracellular signals to affect transcription factor mediated gene regulation within cells. A number of computational methods have been developed to identify the topological structure of a specific signaling pathway using protein-pr...

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Bibliographic Details
Published in:BMC systems biology 2018-12, Vol.12 (Suppl 9), p.120-120, Article 120
Main Authors: Kabir, Md Humayun, Patrick, Ralph, Ho, Joshua W K, O'Connor, Michael D
Format: Article
Language:English
Subjects:
Algorithms
Animals
Bioinformatics
Cell adhesion & migration
Cellular signal transduction
Computational Biology - methods
Computer applications
Consortia
Datasets
Deoxyribonucleic acid
DNA
Encyclopedias
Epithelial cells
Gene expression
Gene Ontology
Gene regulation
Gene Regulatory Networks
Genomes
Humans
Kinases
Lens, Crystalline - cytology
Lens, Crystalline - metabolism
Lenses
Ligands
Linear programming
Mice
Pluripotency
Protein interaction
Protein Interaction Maps
Protein structure
Protein-protein interactions
Proteins
Signal transduction
Signal Transduction - genetics
Signaling
Statistical methods
Stem cells
Teeth
Tooth - cytology
Tooth - metabolism
Transcription factors
Transcriptome
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Summary:Signaling pathways are the key biological mechanisms that transduce extracellular signals to affect transcription factor mediated gene regulation within cells. A number of computational methods have been developed to identify the topological structure of a specific signaling pathway using protein-protein interaction data, but they are not designed for identifying active signaling pathways in an unbiased manner. On the other hand, there are statistical methods based on gene sets or pathway data that can prioritize likely active signaling pathways, but they do not make full use of active pathway structure that link receptor, kinases and downstream transcription factors. Here, we present a method to simultaneously predict the set of active signaling pathways, together with their pathway structure, by integrating protein-protein interaction network and gene expression data. We evaluated the capacity for our method to predict active signaling pathways for dental epithelial cells, ocular lens epithelial cells, human pluripotent stem cell-derived lens epithelial cells, and lens fiber cells. This analysis showed our approach could identify all the known active pathways that are associated with tooth formation and lens development. The results suggest that SPAGI can be a useful approach to identify the potential active signaling pathways given a gene expression profile. Our method is implemented as an open source R package, available via https://github.com/VCCRI/SPAGI/ .
ISSN:1752-0509
1752-0509
DOI:10.1186/s12918-018-0655-x