Construction and Analysis of Protein-Protein Interaction Network

Understanding the mechanisms of a disease is highly complicated due to the complex pathways involved in the disease progression. Despite several decades of research, the occurrence and prognosis of the diseases is not completely understood even with high throughput experiments like DNA microarray and next-generation sequencing. This is due to challenges in analysis of huge data sets. Systems biology is one of the major divisions of bioinformatics and has laid cutting edge techniques for the better understanding of these pathways. Construction of protein-protein interaction network (PPIN) guides the modern scientists to identify vital proteins through protein-protein interaction network, which facilitates the identification of new drug target and associated proteins. The chapter is focused on PPI databases, construction of PPINs, and its analysis.

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Construction and analysis of an interologous protein–protein interaction network of Camellia sinensis leaf (TeaLIPIN) from RNA–Seq data sets

Plant Cell Reports ◽

10.1007/s00299-019-02440-y ◽

2019 ◽

Vol 38 (10) ◽

pp. 1249-1262 ◽

Cited By ~ 4

Author(s):

Gagandeep Singh ◽

Vikram Singh ◽

Vikram Singh

Keyword(s):

Camellia Sinensis ◽

Protein Interaction ◽

Protein Interaction Network ◽

Interaction Network ◽

Data Sets ◽

Rna Seq ◽

Protein Protein Interaction ◽

Protein Protein Interaction Network

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A Computational Framework to Identify Cross Association between Complex Disorders by Protein-Protein Interaction Network Analysis

Current Bioinformatics ◽

10.2174/1574893615999200724145434 ◽

2020 ◽

Vol 15 ◽

Author(s):

Nikhila T Suresh ◽

Vimina E R ◽

U. Krishnakumar

Keyword(s):

Blood Pressure ◽

Signaling Pathway ◽

Protein Interaction ◽

Protein Interaction Network ◽

Interaction Network ◽

Complex Disorders ◽

Protein Protein Interaction ◽

Associated Proteins ◽

Key Genes ◽

Protein Protein Interaction Network

Objective: It is a known fact that numerous complex disorders do not happen in isolation indicating the plausible set of shared causes common to several different sicknesses. Hence, analysis of comorbidity can be utilized to explore association between several disorders. In this study, we have proposed a network-based computational approach, in which genes are organized based on the topological characteristics of the constructed Protein-Protein Interaction Network (PPIN) followed by a network prioritization scheme, to identify distinctive key genes and biological pathways shared among diseases. Methods: The proposed approach is initiated from constructed PPIN of any randomly chosen disease genes in order to infer its associations with other diseases in terms of shared pathways, co-expression, co-occurrence etc. For this, initially proteins associated to any disease based on random choice were identified. Secondly, PPIN is organized through topological analysis to define hub genes. Finally, using a prioritization algorithm a ranked list of newly predicted multimorbidity-associated proteins is generated. Using Gene Ontology (GO), cellular pathways involved in multimorbidity-associated proteins are mined. Result and Conclusion: The proposed methodology is tested using three disorders namely Diabetes, Obesity and blood pressure at an atomic level and the results suggest the comorbidity of other complex diseases that have associations with the proteins included in disease of present study through shared proteins and pathways. For diabetes, we have obtained key genes like GAPDH, TNF, IL6, AKT1, ALB, TP53, IL10, MAPK3, TLR4 and EGF with key pathways like P53 pathway, VEGF signaling pathway, Ras Pathway, Interleukin signaling pathway, Endothelin signaling pathway, Huntington disease etc. Study on other disorders such as obesity and blood pressure also revealed promising results.

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