IHP-PING: Integrated Human Protein-Protein Interaction Network Generator

Maintainer: Gaston K. Mazandu^1,2,3 <gaston.mazandu at uct.ac.za>, Bioinformatics and Genomics Informatics Tools

(1) Computational Biology (CBIO) Group, Department of integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town (UCT), Observatory 7925, South Africa

(2) African Institute for Mathematical Sciences (AIMS), Melrose Road, Muizenberg 7945, South Africa

(3) Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town (UCT), Observatory 7925, South Africa

The IHP-PING Interface

IHP-PING provides a flexible python package, which extracts and integrates heterogeneous PPI datasets to generate a unified PPI network on-the-fly, which is stored locally for further potential user applications through three main steps starting from user input as shown in the figure (Part A) below, with basic statistics and properties: power-law and small-world (Part B).

Input is parsed via a simple single command-line terminal, then the selected human PPI datasets are retrieved and network generated. Each user-requested database is downloaded from specific uniform resource locations (URLs). Eight different resources used are shown in Figure above, including protein sequence information to predict further interactions with scores computed using an information theory-based scheme

The IHP-PING Documentation

To view the documentation for the version of the IHP-PING package downloaded in your system, go to the ihp-ping directory and type the following command line:

python setup.py --long-description

Refer to the PDF version of the documentation below for more information on the IHP-PING package administration and usage, as well as information on different human protein-protein interaction datasets used in this package.

TXT	Show Library Source	IHP-PING
PDF		Reference Manual
Code	ihp-ping-tool.tar.gz	Python Source Code

The IHP-PING package can also be retrieved from the github public platform using git clone command line as follows.

git clone https://github.com/gkm-software-dev/post-analysis-tools.git

IHP-PING Requirement Details

Description	A user-friendly and accessible Python package, easing integration of PPI datasets from multiple sources into a unified PPI network on-the-fly.
Version	2.4.1
Depends	Python (≥ 2.7.x)
License	GPL (>= 3)
Requires	Depend on PPI datasets being retrieved: [local ncbi-blast, python-selenium, chromium-browser and chromium-chromedriver]
SystemRequirements	OS Independent, but tested only on Linux (Ubuntu)
Release date	Friday 23^rd Aprily, 2020 −12:00

Functions and tools included»

The main functions and tools in the IHP-PING Interface are:

ihpping and sequencescore python modules in the IHP-PING folder.
IHP-PING integrates existing human protein-protein interaction datasets from the databases shown in the left-side figure to produce a unified human protein-protein interaction network on-the-fly.

Important notes:

Currently, IHP-PING can include eight different sources, providing users with power of combining datasets of their choices.
It computes a combined confidence score of each interaction retrieved and standardizes outputs, easing human protein-protein interaction retrieval process.

Package Archives»

Follow IHP-PING administration and usage instructions in the PDF and txt documentation files to optimally use this python package.

Tool Source Code	ihp-ping-tool.tar.gz
Browse other tools	PySML library
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Tool website visitors»

Statistics since April 2020

Mailing Lists»

An appropriate IHP-PING forum is still to be implemented, for now, any problem accounted when using this tool or comments should be forwarded by email to the IHP-PING team:

Acknowledgements

Any work dependent on open-source software owes debt to those who developed these tools. The authors thank everyone involved with free software, from the core developers to those who contributed to the documentation. Many thanks to the authors of the freely available libraries for making this work possible. This study is supported by the National Institutes of Health (NIH), USA, under Common Fund under H3ABioNet (U24HG006941) and SADaCC (1U01HG007459-01).