Protein-Protein interaction (PPI) analysis by String-db¶
STRING is a database of known and predicted protein-protein interactions. The interactions include direct (physical) and indirect (functional) associations; they stem from computational prediction, from knowledge transfer between organisms, and from interactions aggregated from other (primary) databases.
Here we produce a tutorial that use python to construct protein-protein interaction network
Colab_Reproducibility：https://colab.research.google.com/drive/1ReLCFA5cNNcem_WaMXYN9da7W0GN4gzl?usp=sharing
import omicverse as ov
ov.utils.ov_plot_set()
Prepare data¶
Here we use the example data of string-db to perform the analysis
FAA4 and its ten most confident interactors. FAA4 in yeast is a long chain fatty acyl-CoA synthetase; see it connected to other synthetases as well as regulators.
Saccharomyces cerevisiae NCBI taxonomy Id: 4932 Other names: ATCC 18824, Candida robusta, NRRL Y-12632, S. cerevisiae, Saccharomyces capensis, Saccharomyces italicus, Saccharomyces oviformis, Saccharomyces uvarum var. melibiosus, lager beer yeast, yeast
gene_list=['FAA4','POX1','FAT1','FAS2','FAS1','FAA1','OLE1','YJU3','TGL3','INA1','TGL5']
Besides, we also need to set the gene's type and color. Here, we randomly set the top 5 genes named Type1, other named Type2
gene_type_dict=dict(zip(gene_list,['Type1']*5+['Type2']*6))
gene_color_dict=dict(zip(gene_list,['#F7828A']*5+['#9CCCA4']*6))
STRING interaction analysis¶
The network API method also allows you to retrieve your STRING interaction network for one or multiple proteins in various text formats. It will tell you the combined score and all the channel specific scores for the set of proteins. You can also extend the network neighborhood by setting "add_nodes", which will add, to your network, new interaction partners in order of their confidence.
G_res=ov.bulk.string_interaction(gene_list,4932)
G_res.head()
| stringId_A | stringId_B | preferredName_A | preferredName_B | ncbiTaxonId | score | nscore | fscore | pscore | ascore | escore | dscore | tscore | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 4932.YBR041W | 4932.YKL182W | FAT1 | FAS1 | 4932 | 0.69 | 0 | 0 | 0 | 0 | 0 | 0 | 0.69 |
| 1 | 4932.YBR041W | 4932.YKL182W | FAT1 | FAS1 | 4932 | 0.69 | 0 | 0 | 0 | 0 | 0 | 0 | 0.69 |
| 2 | 4932.YBR041W | 4932.YPL231W | FAT1 | FAS2 | 4932 | 0.692 | 0 | 0 | 0 | 0 | 0 | 0 | 0.692 |
| 3 | 4932.YBR041W | 4932.YPL231W | FAT1 | FAS2 | 4932 | 0.692 | 0 | 0 | 0 | 0 | 0 | 0 | 0.692 |
| 4 | 4932.YBR041W | 4932.YOR081C | FAT1 | TGL5 | 4932 | 0.7 | 0 | 0 | 0 | 0 | 0 | 0 | 0.7 |
STRING PPI network¶
We also can use ov.bulk.pyPPI to get the PPI network of gene_list, we init it at first
ppi=ov.bulk.pyPPI(gene=gene_list,
gene_type_dict=gene_type_dict,
gene_color_dict=gene_color_dict,
species=4932)
<networkx.classes.graph.Graph at 0x1056abb80>
Then we connect to string-db to calculate the protein-protein interaction
ppi.interaction_analysis()
<networkx.classes.graph.Graph at 0x17aa86fd0>
We provided a very simple function to plot the network, you can refer the ov.utils.plot_network to find out the parameter
ppi.plot_network()
(<Figure size 320x320 with 1 Axes>, <AxesSubplot: >)
