IHP-PING-generating integrated human protein-protein interaction networks on-the-fly

Abstract

Advances in high-throughput sequencing technologies have resulted in an exponential growth of publicly accessible biological datasets. In the 'big data' driven 'post-genomic' context, much work is being done to explore human protein-protein interactions (PPIs) for a systems level based analysis to uncover useful signals and gain more insights to advance current knowledge and answer specific biological and health questions. These PPIs are experimentally or computationally predicted, stored in different online databases and some of PPI resources are updated regularly. As with many biological datasets, such regular updates continuously render older PPI datasets potentially outdated. Moreover, while many of these interactions are shared between these online resources, each resource includes its own identified PPIs and none of these databases exhaustively contains all existing human PPI maps. In this context, it is essential to enable the integration of or combining interaction datasets from different resources, to generate a PPI map with increased coverage and confidence. To allow researchers to produce an integrated human PPI datasets in real-time, we introduce the integrated human protein-protein interaction network generator (IHP-PING) tool. IHP-PING is a flexible python package which generates a human PPI network from freely available online resources. This tool extracts and integrates heterogeneous PPI datasets to generate a unified PPI network, which is stored locally for further applications.

Keywords: high-throughput technology; human proteome; network analysis; post-genomic analysis; protein–protein interaction.

Figure 1

Overall workflow of the IHP-PING tool. The scheme goes through three main steps from user input to a generated human PPI network: Input is parsed via a simple single command-line terminal, then the selected human PPI datasets are retrieved and network generated in tsv, csv or csv2 format.

Figure 2

The IHP-PING output structure. The first two columns are the two interacting proteins, following columns are interaction source confidence or reliability scores and the last column is the interaction combined score. Space between columns can be tabs, commas or semicolons depending on whether the user chooses tsv, csv or csv2.

Figure 3

Distribution of interactions obtained from different resources contributing to a unified human PPI network—All interactions per source.

Figure 4

Distribution of interactions obtained from different resources contributing to a unified human PPI network—All interactions per source in high-low-medium confidence level interaction frequencies.

Figure 5

Highlighting lower and upper score thresholds for low-medium-high confidence levels.

Figure 6

Venn diagrams showing the general distributions of shared PPIs between experimentally derived PPIs from online databases following IMEx curation guidelines (IntAct, MINT and DIP), as well as BioGRID, HPRD and MPPI-MIPS, and computationally predicted PPIs from STRING and Sequence.

Figure 7

The unified human PPI network topological property. Power-law property visualizing protein degree against connections frequency in the network.

Figure 8

The unified human PPI network topological property. Small-world property—the distribution of shortest path lengths within the interaction network.

Figure 9

Using igraph and tcltk R software libraries to plot the Folylpolyglutamate synthase, mitochondrial (Q05932) protein for illustration. Node or protein size is now proportional to the its degree in the protein–protein interaction network and each link is proportional to the number of its data sources provided in Table 2, together with final or unified confidence scores.