scholarly journals IHP-PING—generating integrated human protein–protein interaction networks on-the-fly

2020 ◽  
Author(s):  
Gaston K Mazandu ◽  
Christopher Hooper ◽  
Kenneth Opap ◽  
Funmilayo Makinde ◽  
Victoria Nembaware ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
High Throughput Sequencing ◽  
Current Knowledge ◽  
Interaction Network ◽  
Human Protein ◽  
Online Resources ◽  
Ppi Network ◽  
Genomic Context ◽  
Protein Protein Interaction

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 As with many biological datasets, such regular updates continuously render older PPI datasets potentially outdated.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.

scholarly journals A Novel Method for Identifying Essential Proteins Based on Non-negative Matrix Tri-Factorization

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhihong Zhang ◽  
Meiping Jiang ◽  
Dongjie Wu ◽  
Wang Zhang ◽  
Wei Yan ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Negative Impact ◽  
False Negative ◽  
Interaction Network ◽  
Biological Information ◽  
Ppi Network ◽  
Essential Proteins ◽  
Protein Protein Interaction ◽  
Ppi Networks

Identification of essential proteins is very important for understanding the basic requirements to sustain a living organism. In recent years, there has been an increasing interest in using computational methods to predict essential proteins based on protein–protein interaction (PPI) networks or fusing multiple biological information. However, it has been observed that existing PPI data have false-negative and false-positive data. The fusion of multiple biological information can reduce the influence of false data in PPI, but inevitably more noise data will be produced at the same time. In this article, we proposed a novel non-negative matrix tri-factorization (NMTF)-based model (NTMEP) to predict essential proteins. Firstly, a weighted PPI network is established only using the topology features of the network, so as to avoid more noise. To reduce the influence of false data (existing in PPI network) on performance of identify essential proteins, the NMTF technique, as a widely used recommendation algorithm, is performed to reconstruct a most optimized PPI network with more potential protein–protein interactions. Then, we use the PageRank algorithm to compute the final ranking score of each protein, in which subcellular localization and homologous information of proteins were used to calculate the initial scores. In addition, extensive experiments are performed on the publicly available datasets and the results indicate that our NTMEP model has better performance in predicting essential proteins against the start-of-the-art method. In this investigation, we demonstrated that the introduction of non-negative matrix tri-factorization technology can effectively improve the condition of the protein–protein interaction network, so as to reduce the negative impact of noise on the prediction. At the same time, this finding provides a more novel angle of view for other applications based on protein–protein interaction networks.

scholarly journals Statistical Approaches for the Construction and Interpretation of Human Protein-Protein Interaction Network

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Yang Hu ◽  
Ying Zhang ◽  
Jun Ren ◽  
Yadong Wang ◽  
Zhenzhen Wang ◽  
...  
Keyword(s):  
Experimental Data ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Latent Variable ◽  
Interaction Network ◽  
Human Protein ◽  
Ppi Network ◽  
Confidence Measure ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

The overall goal is to establish a reliable human protein-protein interaction network and develop computational tools to characterize a protein-protein interaction (PPI) network and the role of individual proteins in the context of the network topology and their expression status. A novel and unique feature of our approach is that we assigned confidence measure to each derived interacting pair and account for the confidence in our network analysis. We integrated experimental data to infer human PPI network. Our model treated the true interacting status (yes versus no) for any given pair of human proteins as a latent variable whose value was not observed. The experimental data were the manifestation of interacting status, which provided evidence as to the likelihood of the interaction. The confidence of interactions would depend on the strength and consistency of the evidence.

Predicting disease-related genes by topological similarity in human protein-protein interaction network

Open Physics ◽  
2010 ◽  
Vol 8 (4) ◽  
Author(s):  
Lei Zhang ◽  
Ke Hu ◽  
Yi Tang
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Human Protein ◽  
Human Diseases ◽  
Topological Similarity ◽  
Protein Protein Interaction ◽  
Disease Related Genes ◽  
Protein Protein Interaction Network

AbstractPredicting genes likely to be involved in human diseases is an important task in bioinformatics field. Nowadays, the accumulation of human protein-protein interactions (PPIs) data provides us an unprecedented opportunity to gain insight into human diseases. In this paper, we adopt the topological similarity in human protein-protein interaction network to predict disease-related genes. As a computational algorithm to speed up the identification of disease-related genes, the topological similarity has substantial advantages over previous topology-based algorithms. First of all, it provides a global measurement of similarity between two vertices. Secondly, quantity which can measure new topological feature has been integrated into the notion of topological similarity. Our method is specially designed for predicting disease-related genes of single disease-gene family. The proposed method is applied to human protein-protein interaction and hepatocellular carcinoma (HCC) data. The results show a significant enrichment of disease-related genes that are characterized by higher topological similarity than other genes.

scholarly journals Decoding the molecular mechanism of parthenocarpy in Musa spp. through protein–protein interaction network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Suthanthiram Backiyarani ◽  
Rajendran Sasikala ◽  
Simeon Sharmiladevi ◽  
Subbaraya Uma
Keyword(s):  
Candidate Genes ◽  
Protein Interaction ◽  
Target Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Auxin Signaling ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
The Difference

AbstractBanana, one of the most important staple fruit among global consumers is highly sterile owing to natural parthenocarpy. Identification of genetic factors responsible for parthenocarpy would facilitate the conventional breeders to improve the seeded accessions. We have constructed Protein–protein interaction (PPI) network through mining differentially expressed genes and the genes used for transgenic studies with respect to parthenocarpy. Based on the topological and pathway enrichment analysis of proteins in PPI network, 12 candidate genes were shortlisted. By further validating these candidate genes in seeded and seedless accession of Musa spp. we put forward MaAGL8, MaMADS16, MaGH3.8, MaMADS29, MaRGA1, MaEXPA1, MaGID1C, MaHK2 and MaBAM1 as possible target genes in the study of natural parthenocarpy. In contrary, expression profile of MaACLB-2 and MaZEP is anticipated to highlight the difference in artificially induced and natural parthenocarpy. By exploring the PPI of validated genes from the network, we postulated a putative pathway that bring insights into the significance of cytokinin mediated CLAVATA(CLV)–WUSHEL(WUS) signaling pathway in addition to gibberellin mediated auxin signaling in parthenocarpy. Our analysis is the first attempt to identify candidate genes and to hypothesize a putative mechanism that bridges the gaps in understanding natural parthenocarpy through PPI network.

scholarly journals Repurposing novel therapeutic candidate drugs for coronavirus disease-19 based on protein-protein interaction network analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Masoumeh Adhami ◽  
Balal Sadeghi ◽  
Ali Rezapour ◽  
Ali Akbar Haghdoost ◽  
Habib MotieGhader
Keyword(s):  
Network Analysis ◽  
Protein Interaction ◽  
Target Therapy ◽  
Interaction Network ◽  
Drug Repurposing ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Candidate Drug ◽  
Starting Point ◽  
Experimental Validations

Abstract Background The coronavirus disease-19 (COVID-19) emerged in Wuhan, China and rapidly spread worldwide. Researchers are trying to find a way to treat this disease as soon as possible. The present study aimed to identify the genes involved in COVID-19 and find a new drug target therapy. Currently, there are no effective drugs targeting SARS-CoV-2, and meanwhile, drug discovery approaches are time-consuming and costly. To address this challenge, this study utilized a network-based drug repurposing strategy to rapidly identify potential drugs targeting SARS-CoV-2. To this end, seven potential drugs were proposed for COVID-19 treatment using protein-protein interaction (PPI) network analysis. First, 524 proteins in humans that have interaction with the SARS-CoV-2 virus were collected, and then the PPI network was reconstructed for these collected proteins. Next, the target miRNAs of the mentioned module genes were separately obtained from the miRWalk 2.0 database because of the important role of miRNAs in biological processes and were reported as an important clue for future analysis. Finally, the list of the drugs targeting module genes was obtained from the DGIDb database, and the drug-gene network was separately reconstructed for the obtained protein modules. Results Based on the network analysis of the PPI network, seven clusters of proteins were specified as the complexes of proteins which are more associated with the SARS-CoV-2 virus. Moreover, seven therapeutic candidate drugs were identified to control gene regulation in COVID-19. PACLITAXEL, as the most potent therapeutic candidate drug and previously mentioned as a therapy for COVID-19, had four gene targets in two different modules. The other six candidate drugs, namely, BORTEZOMIB, CARBOPLATIN, CRIZOTINIB, CYTARABINE, DAUNORUBICIN, and VORINOSTAT, some of which were previously discovered to be efficient against COVID-19, had three gene targets in different modules. Eventually, CARBOPLATIN, CRIZOTINIB, and CYTARABINE drugs were found as novel potential drugs to be investigated as a therapy for COVID-19. Conclusions Our computational strategy for predicting repurposable candidate drugs against COVID-19 provides efficacious and rapid results for therapeutic purposes. However, further experimental analysis and testing such as clinical applicability, toxicity, and experimental validations are required to reach a more accurate and improved treatment. Our proposed complexes of proteins and associated miRNAs, along with discovered candidate drugs might be a starting point for further analysis by other researchers in this urgency of the COVID-19 pandemic.

A Comparison Analysis for Protein-Protein Interaction Network-Based Methods in Prioritizing Arabidopsis Functional Genes

2021 ◽  
Vol 16 ◽  
Author(s):  
Chun-Jing Si ◽  
Si-Min Deng ◽  
Yuan Quan ◽  
Hong-Yu Zhang
Keyword(s):  
Hybrid Model ◽  
Protein Interaction ◽  
High Throughput Sequencing ◽  
Functional Gene ◽  
Functional Genes ◽  
Systems Genetics ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Gene Enrichment ◽  
Comprehensive Score

Background: Connecting genes to phenotypes is still a great challenge in genetics. Research related to gene-phenotype associations has made remarkable progress recently due to high-throughput sequencing technology and genome-wide association study (GWAS). However, these genes, which are considered to be significantly associated with a target phenotype according to traditional GWAS, are less precise or subject to greater confounding. Objective: The present study is an attempt to prioritize functional genes for complex phenotypes employing protein-protein interaction (PPI) network-based systems genetics methods on available GWAS results. Method: In this paper, we calculated the functional gene enrichment ratios of the trait ontology of A. thaliana for three common systems genetics methods (i.e. GeneRank, K-shell and HotNet2). Then, comparison of gene enrichment ratios obtained by PPI network-based methods was performed. Finally, a hybrid model was proposed, integrating GeneRank, comprehensive score algorithm and HotNet diffusion-oriented subnetworks (HotNet2) to prioritize functional genes. Results: These PPI network-based systems genetics methods were indeed useful for prioritizing phenotype-associated genes. And functional gene enrichment ratios calculated from the top 20% of GeneRank-identified genes were higher than these ratios of K-shell and these ratios of HotNet2 for most phenotypes. However, the hybrid model can improve the efficiency of functional gene enrichment for A. thaliana (up to 40%). Conclusion: The present study provides a hybrid method integrating GeneRank, comprehensive score algorithm and HotNet2 to prioritize functional genes. The method will contribute to functional genomics in plants. The source data and codes are freely available at http://47.242.161.60/Plant/.

Extracting Biological Significant Subnetworks from Protein-Protein Interactions Induced by Differentially Expressed Genes of HIV-1 Vpr Variants

2015 ◽  
Vol 4 (4) ◽  
pp. 35-51 ◽  
Author(s):  
Bandana Barman ◽  
Anirban Mukhopadhyay
Keyword(s):  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Wild Type ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Hiv 1

Identification of protein interaction network is very important to find the cell signaling pathway for a particular disease. The authors have found the differentially expressed genes between two sample groups of HIV-1. Samples are wild type HIV-1 Vpr and HIV-1 mutant Vpr. They did statistical t-test and found false discovery rate (FDR) to identify the genes increased in expression (up-regulated) or decreased in expression (down-regulated). In the test, the authors have computed q-values of test to identify minimum FDR which occurs. As a result they found 172 differentially expressed genes between their sample wild type HIV-1 Vpr and HIV-1 mutant Vpr, R80A. They found 68 up-regulated genes and 104 down-regulated genes. From the 172 differentially expressed genes the authors found protein-protein interaction network with string-db and then clustered (subnetworks) the PPI networks with cytoscape3.0. Lastly, the authors studied significance of subnetworks with performing gene ontology and also studied the KEGG pathway of those subnetworks.

scholarly journals Protein–Protein Interaction Network Analysis Reveals Several Diseases Highly Associated with Polycystic Ovarian Syndrome

2019 ◽  
Vol 20 (12) ◽  
pp. 2959 ◽  
Author(s):  
Balqis Ramly ◽  
Nor Afiqah-Aleng ◽  
Zeti-Azura Mohamed-Hussein
Keyword(s):  
Network Analysis ◽  
Protein Interaction ◽  
Ribosome Biogenesis ◽  
Antigen Processing ◽  
Polycystic Ovarian Syndrome ◽  
Interaction Network ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ovarian Syndrome

Based on clinical observations, women with polycystic ovarian syndrome (PCOS) are prone to developing several other diseases, such as metabolic and cardiovascular diseases. However, the molecular association between PCOS and these diseases remains poorly understood. Recent studies showed that the information from protein–protein interaction (PPI) network analysis are useful in understanding the disease association in detail. This study utilized this approach to deepen the knowledge on the association between PCOS and other diseases. A PPI network for PCOS was constructed using PCOS-related proteins (PCOSrp) obtained from PCOSBase. MCODE was used to identify highly connected regions in the PCOS network, known as subnetworks. These subnetworks represent protein families, where their molecular information is used to explain the association between PCOS and other diseases. Fisher’s exact test and comorbidity data were used to identify PCOS–disease subnetworks. Pathway enrichment analysis was performed on the PCOS–disease subnetworks to identify significant pathways that are highly involved in the PCOS–disease associations. Migraine, schizophrenia, depressive disorder, obesity, and hypertension, along with twelve other diseases, were identified to be highly associated with PCOS. The identification of significant pathways, such as ribosome biogenesis, antigen processing and presentation, and mitophagy, suggest their involvement in the association between PCOS and migraine, schizophrenia, and hypertension.

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