Next Article in Journal
Cytogenetic Analysis Did Not Reveal Differentiated Sex Chromosomes in Ten Species of Boas and Pythons (Reptilia: Serpentes)
Next Article in Special Issue
Computational Inference of Gene Co-Expression Networks for the identification of Lung Carcinoma Biomarkers: An Ensemble Approach
Previous Article in Journal
Cell-Specific DNA Methylation Signatures in Asthma
Previous Article in Special Issue
Digital Immune Gene Expression Profiling Discriminates Allergic Rhinitis Responders from Non-Responders to Probiotic Supplementation
Open AccessArticle

Enriching Human Interactome with Functional Mutations to Detect High-Impact Network Modules Underlying Complex Diseases

1
Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA
2
Data Science Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA
3
Computer Science Department, Worcester Polytechnic Institute, Worcester, MA 01609, USA
*
Authors to whom correspondence should be addressed.
Genes 2019, 10(11), 933; https://doi.org/10.3390/genes10110933
Received: 29 September 2019 / Revised: 4 November 2019 / Accepted: 11 November 2019 / Published: 15 November 2019
(This article belongs to the Special Issue Current Advances in Network Biology for Disease Understanding)
Rapid progress in high-throughput -omics technologies moves us one step closer to the datacalypse in life sciences. In spite of the already generated volumes of data, our knowledge of the molecular mechanisms underlying complex genetic diseases remains limited. Increasing evidence shows that biological networks are essential, albeit not sufficient, for the better understanding of these mechanisms. The identification of disease-specific functional modules in the human interactome can provide a more focused insight into the mechanistic nature of the disease. However, carving a disease network module from the whole interactome is a difficult task. In this paper, we propose a computational framework, Discovering most IMpacted SUbnetworks in interactoMe (DIMSUM), which enables the integration of genome-wide association studies (GWAS) and functional effects of mutations into the protein–protein interaction (PPI) network to improve disease module detection. Specifically, our approach incorporates and propagates the functional impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on PPIs to implicate the genes that are most likely influenced by the disruptive mutations, and to identify the module with the greatest functional impact. Comparison against state-of-the-art seed-based module detection methods shows that our approach could yield modules that are biologically more relevant and have stronger association with the studied disease. We expect for our method to become a part of the common toolbox for the disease module analysis, facilitating the discovery of new disease markers. View Full-Text
Keywords: protein–protein interaction network; module detection; GWAS; network propagation; functional annotation; complex diseases protein–protein interaction network; module detection; GWAS; network propagation; functional annotation; complex diseases
Show Figures

Figure 1

MDPI and ACS Style

Cui, H.; Srinivasan, S.; Korkin, D. Enriching Human Interactome with Functional Mutations to Detect High-Impact Network Modules Underlying Complex Diseases. Genes 2019, 10, 933.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop