Topology-driven protein-protein interaction network analysis detects genetic sub-networks regulating reproductive capacity
Tarun Kumar1, Leo Blondel2 & Cassandra G. Extavour1,2
1Department of Organismic and Evolutionary Biology & 2Department of Molecular and Cellular Biology, Harvard University, Cambridge MA-02138, USA
This repository holds all the code and data for the paper Topology-driven analysis of protein-protein interaction networks detects functional genetic modules regulating reproductive capacity
Understanding the genetic regulation of organ structure is a fundamental problem in developmental biology. Here, we use egg-producing structures of insect ovaries, called ovarioles, to deduce systems-level gene regulatory relationships from quantitative functional genetic analysis. We previously showed that Hippo signalling, a conserved regulator of animal organ size, regulates ovariole number in Drosophila melanogaster. To comprehensively determine how Hippo signalling interacts with other pathways in this regulation, we screened all known signalling pathway genes, and identified Hpo-dependent and Hpo-independent signalling requirements. Network analysis of known protein-protein interactions among screen results identified independent gene regulatory sub-networks regulating one or both of ovariole number and egg laying. These sub-networks predict involvement of previously uncharacterised genes with higher accuracy than the original candidate screen. This shows that network analysis combining functional genetic and large-scale interaction data can predict function of novel genes regulating development.
All the analysis is done in Python 3 and is written in ipython notebooks. The code is to be executed in the order indicated in the filename (1 -> 2 -> 3 etc).
Dependencies:
- networkx 2.3
- pandas 0.20.3
- numpy 1.11.3
- seaborn 0.9.0
- matplotlib 3.0.0
- scipy 1.1.0
- progressbar 3.38.0
├── Data
│ ├── GeneName.csv: Table of gene names and symboles for all the genes tested and considered in this study.
│ ├── PPIs/: All data regarding the PPI datasets.
│ │ ├── Download/: Dowloaded DroID tabular screen files.
│ │ └── PPI_2018_08.graphml: Compiled PPI graph from all the DroID single screen datasets.
│ ├── Screen/: All data regarding the different measurements performed in the screens. If _pred in the name, this corresponds to the measurements done on the predicted connectors.
│ │ ├── Raw/: Raw datasets as csv files for the three screens.
│ ├── ScreenPPI_[Metric].csv: Tabular file reccording the calculated centrality metrics for each genes in the PPI.
│ ├── fbgn_annotation_ID_fb_2018_06.tsv.gz: Flybase annotation dump of at 06/2018.
│ └── signaling.csv: Table matching the genes in this study with the signaling pathway they participate in.
├── Notebooks/: Ipython notebook of all the scripts used in this study.
└── Results:/ Folder containing all the results outputed by the scripts in the Notebooks folder.
├── Candidate_Above_Z_Threshold.csv: List of all the screen genes that passed the Z threshold.
├── ConnectorGeneList_2018_08.csv: List of all the predicted connectors.
├── Core_module_2018_08.graphml: Graph of the predicted core module.
├── EggL_module_2018_08.graphml: Graph of the predicted Egg Laying module.
├── Hpo_EggL_module_2018_08.graphml: Graph of the predicted HippoRNAi Egg Laying module.
├── Ova_module_2018_08.graphml: Graph of the predicted Ovariole number module.
├── Meta_module_2018_08.graphml: Graph of the Meta Module.
├── MasterTable.csv: Master Table summarizing all the key data generated in this study (Published as Table S1)
├── MetaNetworkData_2018_08.csv: Table data summarizing the meta module.
├── Modules_Table_2018_08.csv: Table summarizing the genes in each of the predicted modules.