DPPIN is a collection of dynamic networks, which consists of twelve generated dynamic protein-protein interaction networks of yeast cells, stored in twelve folders.
In each folder (e.g. DPPIN-Krogan(LCMS)),
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"Dynamic_PPIN.txt" stores temporal connections of the generated dynamic network. To be specific, a node represents a gene coding protein, an edge represents a protein-protein interaction at a certain timestamp, and each edge is timestamped with the format (node_u, node_v, timestamp, weight).
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"Static_PPIN.txt" stores the required static network for generating the dynamic network, each edge is recorded as (node_u, node_v, weight).
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"Node_Features" records the temporal gene expression value of each protein node, and the format is "node_id, node_name, value_at_t_1, value_at_t_2, ..., value_at_t_36".
Moreover, "Node_Labels.xlsx" stores labels (i.e., types) of 6,738 protein nodes shared by twelve dynamic networks in DPPIN, which covers the label of each node from each dynamic network. The label of each protein is retrievaled from Saccharomyces Genome Database.
The statistics of the twelve generated dynamic networks are shown in Table 1.
Table 1. Statistics of DPPIN.
In brief, two inputs are required to construct a dynamic protein-protein interaction network. The first one is a static protein-protein interation network and the second one is the time-aware gene expression value series of each protein. Through the activity and co-expression analysis (as shown in Figure 1), a dynamic network is constructed.
Figure 1. Dynamic Protein Network Generation Process.
The static networks for building DPPIN are available at YeastNet. The yeast temporal gene expression value (GSE3431.txt) from "Tu et al., Logic of the Yeast Metabolic Cycle: Temporal Compartmentalization of Cellular Processes. Science 2005." is available at NCBI.
The dynamic network construction method is mainly adopted from "Zhang et al., A method for predicting protein complex in dynamic PPI networks. BMC Bioinformatics 2016.".
The dynamic networks have already been constructed and stored in each folder, users can directly use it. If you want to construct them again on your own and modify some parameters, just run main.py. The main.py program will analyze the GES3431 gene expression value and the static network in each folder to generate the corresponding dynamic network and store it in that folder.
The program is written under Python 3.7, and the prerequisites are listed below.
- numpy 1.20.1
- scipy 1.6.0
If you use the materials from this repositiory, please refer to our paper.
@article{DBLP:journals/corr/abs-2107-02168,
author = {Dongqi Fu and
Jingrui He},
title = {{DPPIN:} {A} Biological Dataset of Dynamic Protein-Protein Interaction
Networks},
journal = {CoRR},
volume = {abs/2107.02168},
year = {2021},
url = {https://arxiv.org/abs/2107.02168},
archivePrefix = {arXiv},
eprint = {2107.02168},
timestamp = {Wed, 07 Jul 2021 15:23:11 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2107-02168.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}