NetVA: an R package for network vulnerability and influence analysis

Abstract

In biological network analysis, identifying key molecules plays a decisive role in the development of potential diagnostic and therapeutic candidates. Among various approaches of network analysis, network vulnerability analysis is quite important, as it assesses significant associations between topological properties and the functional essentiality of a network. Similarly, some node centralities are also used to screen out key molecules. Among these node centralities, escape velocity centrality (EVC), and its extended version (EVC+) outperform others, viz., Degree, Betweenness, and Clustering coefficient. Keeping this in mind, we aimed to develop a first-of-its-kind R package named NetVA, which analyzes networks to identify key molecular players (individual proteins and protein pairs/triplets) through network vulnerability and EVC+-based approaches. To demonstrate the application and relevance of our package in network analysis, previously published and publicly available protein–protein interactions (PPIs) data of human breast cancer were analyzed. This resulted in identifying some most important proteins. These included essential proteins, non-essential proteins, hubs, and bottlenecks, which play vital roles in breast cancer development. Thus, the NetVA package, available at https://github.com/kr-swapnil/NetVA with a detailed tutorial to download and use, assists in predicting potential candidates for therapeutic and diagnostic purposes by exploring various topological features of a disease-specific PPIs network.

Communicated by Ramaswamy H. Sarma

Keywords:

• Protein–protein interactions
• network vulnerability analysis
• k-shell
• escape velocity centrality
• influential proteins
• sub-networks
• cliques

Acknowledgments

The authors would like to thank the Center for Modeling, Simulation & Design (CMSD), University of Hyderabad, for providing computational facilities. Further, VV would like to thank the Indian Council of Medical Research (ICMR), New Delhi (ISRM/12(72)/2020, ID: 2020-2951), Institution of Eminence (IoE)—University of Hyderabad (No. UoH/IoE/RC3-21-052), and Department of Biotechnology (DBT)—Government of India (GoI), New Delhi (No. BUILDER-DBT-BT/INF/22/SP41176/2020) for their financial support. SK also acknowledges the ICMR for Senior Research Fellowship (Grant No.: 3/2/2/113/2019/NCD-III, ID: 2019-6723), and IoE—UoH for Performance based publication incentives.

Author contributions

S.K. and V.V. conceived and designed the experimental approach of this study. S.K. and G.P. participated in code writing and package development. S.K. performed all analyses and data interpretation. S.K. wrote and edited the manuscript. VV supervised the study and reviewed the final version of the manuscript. All authors read and approved the final manuscript.

Data availability statement

The data produced in the current study is available as supplementary data with this article and also at https://github.com/kr-swapnil/NetVA. Furthermore, the R script and the input file (BRCA PPI data) used in the current analysis is available at https://github.com/kr-swapnil/NetVA/blob/master/Tutorial_Rscript and https://github.com/kr-swapnil/NetVA/blob/master/data/bca_ppi.rda, respectively.

Disclosure statement

The authors declare no conflict of interest.