Analysis of Protein–Protein Functional Associations by Using Gene Ontology and KEGG Pathway

Protein–protein interaction (PPI) plays an extremely remarkable role in the growth, reproduction, and metabolism of all lives. A thorough investigation of PPI can uncover the mechanism of how proteins express their functions. In this study, we used gene ontology (GO) terms and biological pathways to study an extended version of PPI (protein–protein functional associations) and subsequently identify some essential GO terms and pathways that can indicate the difference between two proteins with and without functional associations. The protein–protein functional associations validated by experiments were retrieved from STRING, a well-known database on collected associations between proteins from multiple sources, and they were termed as positive samples. The negative samples were constructed by randomly pairing two proteins. Each sample was represented by several features based on GO and KEGG pathway information of two proteins. Then, the mutual information was adopted to evaluate the importance of all features and some important ones could be accessed, from which a number of essential GO terms or KEGG pathways were identified. The final analysis of some important GO terms and one KEGG pathway can partly uncover the difference between proteins with and without functional associations.

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Gene Ontology and KEGG Enrichment Analyses of Genes Related to Age-Related Macular Degeneration

BioMed Research International ◽

10.1155/2014/450386 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 10

Author(s):

Jian Zhang ◽

ZhiHao Xing ◽

Mingming Ma ◽

Ning Wang ◽

Yu-Dong Cai ◽

...

Keyword(s):

Gene Ontology ◽

Feature Selection ◽

Macular Degeneration ◽

Classification System ◽

Underlying Disease ◽

Age Related Macular Degeneration ◽

Disease Genes ◽

Kegg Pathways ◽

Age Related ◽

Go Terms

Identifying disease genes is one of the most important topics in biomedicine and may facilitate studies on the mechanisms underlying disease. Age-related macular degeneration (AMD) is a serious eye disease; it typically affects older adults and results in a loss of vision due to retina damage. In this study, we attempt to develop an effective method for distinguishing AMD-related genes. Gene ontology and KEGG enrichment analyses of known AMD-related genes were performed, and a classification system was established. In detail, each gene was encoded into a vector by extracting enrichment scores of the gene set, including it and its direct neighbors in STRING, and gene ontology terms or KEGG pathways. Then certain feature-selection methods, including minimum redundancy maximum relevance and incremental feature selection, were adopted to extract key features for the classification system. As a result, 720 GO terms and 11 KEGG pathways were deemed the most important factors for predicting AMD-related genes.

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Transcriptome Analysis of Porcine PBMCs Reveals the Immune Cascade Response and Gene Ontology Terms Related to Cell Death and Fibrosis in the Progression of Liver Failure

Canadian Journal of Gastroenterology and Hepatology ◽

10.1155/2018/2101906 ◽

2018 ◽

Vol 2018 ◽

pp. 1-8

Author(s):

YiMin Zhang ◽

Li Shao ◽

Ning Zhou ◽

JianZhou Li ◽

Yu Chen ◽

...

Keyword(s):

Cell Death ◽

Gene Ontology ◽

Liver Failure ◽

Transcriptome Analysis ◽

Receptor Interaction ◽

Failure Group ◽

Kegg Pathways ◽

Gene Sets ◽

Baseline Group ◽

Go Terms

Background. The key gene sets involved in the progression of acute liver failure (ALF), which has a high mortality rate, remain unclear. This study aims to gain a deeper understanding of the transcriptional response of peripheral blood mononuclear cells (PBMCs) following ALF. Methods. ALF was induced by D-galactosamine (D-gal) in a porcine model. PBMCs were separated at time zero (baseline group), 36 h (failure group), and 60 h (dying group) after D-gal injection. Transcriptional profiling was performed using RNA sequencing and analysed using DAVID bioinformatics resources. Results. Compared with the baseline group, 816 and 1,845 differentially expressed genes (DEGs) were identified in the failure and dying groups, respectively. A total of five and two gene ontology (GO) term clusters were enriched in 107 GO terms in the failure group and 154 GO terms in the dying group. These GO clusters were primarily immune-related, including genes regulating the inflammasome complex and toll-like receptor signalling pathways. Specifically, GO terms related to cell death, including apoptosis, pyroptosis, and autophagy, and those related to fibrosis, coagulation dysfunction, and hepatic encephalopathy were enriched. Seven Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, cytokine-cytokine receptor interaction, hematopoietic cell lineage, lysosome, rheumatoid arthritis, malaria, and phagosome and pertussis pathways were mapped for DEGs in the failure group. All of these seven KEGG pathways were involved in the 19 KEGG pathways mapped in the dying group. Conclusion. We found that the dramatic PBMC transcriptome changes triggered by ALF progression was predominantly related to immune responses. The enriched GO terms related to cell death, fibrosis, and so on, as indicated by PBMC transcriptome analysis, seem to be useful in elucidating potential key gene sets in the progression of ALF. A better understanding of these gene sets might be of preventive or therapeutic interest.

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Analysis of Four Types of Leukemia Using Gene Ontology Term and Kyoto Encyclopedia of Genes and Genomes Pathway Enrichment Scores

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207322666181231151900 ◽

2020 ◽

Vol 23 (4) ◽

pp. 295-303

Author(s):

Jing Lu ◽

YuHang Zhang ◽

ShaoPeng Wang ◽

Yi Bi ◽

Tao Huang ◽

...

Keyword(s):

Gene Ontology ◽

Lymphoblastic Leukemia ◽

Feature Selection Method ◽

Lymphocytic Leukemia ◽

Myelogenous Leukemia ◽

Ontology Term ◽

Kegg Pathways ◽

Different Types ◽

First Time ◽

Go Terms

Aim and Objective: Leukemia is the second common blood cancer after lymphoma, and its incidence rate has an increasing trend in recent years. Leukemia can be classified into four types: acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), and chronic myelogenous leukemia (CML). More than forty drugs are applicable to different types of leukemia based on the discrepant pathogenesis. Therefore, the identification of specific drug-targeted biological processes and pathways is helpful to determinate the underlying pathogenesis among such four types of leukemia. Methods: In this study, the gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that were highly related to drugs for leukemia were investigated for the first time. The enrichment scores for associated GO terms and KEGG pathways were calculated to evaluate the drugs and leukemia. The feature selection method, minimum redundancy maximum relevance (mRMR), was used to analyze and identify important GO terms and KEGG pathways. Results: Twenty Go terms and two KEGG pathways with high scores have all been confirmed to effectively distinguish four types of leukemia. Conclusion: This analysis may provide a useful tool for the discrepant pathogenesis and drug design of different types of leukemia.

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A Topology-Based Metric for Measuring Term Similarity in the Gene Ontology

Advances in Bioinformatics ◽

10.1155/2012/975783 ◽

2012 ◽

Vol 2012 ◽

pp. 1-17 ◽

Cited By ~ 27

Author(s):

Gaston K. Mazandu ◽

Nicola J. Mulder

Keyword(s):

Gene Ontology ◽

Protein Function ◽

Protein Function Prediction ◽

Similarity Measures ◽

Biological Knowledge ◽

Online Tool ◽

Protein Protein Interaction ◽

Or Groups ◽

Term Similarity ◽

Go Terms

The wide coverage and biological relevance of the Gene Ontology (GO), confirmed through its successful use in protein function prediction, have led to the growth in its popularity. In order to exploit the extent of biological knowledge that GO offers in describing genes or groups of genes, there is a need for an efficient, scalable similarity measure for GO terms and GO-annotated proteins. While several GO similarity measures exist, none adequately addresses all issues surrounding the design and usage of the ontology. We introduce a new metric for measuring the distance between two GO terms using the intrinsic topology of the GO-DAG, thus enabling the measurement of functional similarities between proteins based on their GO annotations. We assess the performance of this metric using a ROC analysis on human protein-protein interaction datasets and correlation coefficient analysis on the selected set of protein pairs from the CESSM online tool. This metric achieves good performance compared to the existing annotation-based GO measures. We used this new metric to assess functional similarity between orthologues, and show that it is effective at determining whether orthologues are annotated with similar functions and identifying cases where annotation is inconsistent between orthologues.

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Prediction and Analysis of Retinoblastoma Related Genes through Gene Ontology and KEGG

BioMed Research International ◽

10.1155/2013/304029 ◽

2013 ◽

Vol 2013 ◽

pp. 1-8 ◽

Cited By ~ 3

Author(s):

Zhen Li ◽

Bi-Qing Li ◽

Min Jiang ◽

Lei Chen ◽

Jian Zhang ◽

...

Keyword(s):

Gene Ontology ◽

Disease Free Survival ◽

Computational Method ◽

The Other ◽

Free Survival ◽

Kegg Pathways ◽

Incremental Feature Selection ◽

Optimal Feature ◽

Go Terms ◽

Disease Free

One of the most important and challenging problems in biomedicine is how to predict the cancer related genes. Retinoblastoma (RB) is the most common primary intraocular malignancy usually occurring in childhood. Early detection of RB could reduce the morbidity and promote the probability of disease-free survival. Therefore, it is of great importance to identify RB genes. In this study, we developed a computational method to predict RB related genes based on Dagging, with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). 119 RB genes were compiled from two previous RB related studies, while 5,500 non-RB genes were randomly selected from Ensemble genes. Ten datasets were constructed based on all these RB and non-RB genes. Each gene was encoded with a 13,126-dimensional vector including 12,887 Gene Ontology enrichment scores and 239 KEGG enrichment scores. Finally, an optimal feature set including 1061 GO terms and 8 KEGG pathways was obtained. Analysis showed that these features were closely related to RB. It is anticipated that the method can be applied to predict the other cancer related genes as well.

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Analysis of Important Gene Ontology Terms and Biological Pathways Related to Pancreatic Cancer

BioMed Research International ◽

10.1155/2016/7861274 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 5

Author(s):

Hang Yin ◽

ShaoPeng Wang ◽

Yu-Hang Zhang ◽

Yu-Dong Cai ◽

Hailin Liu

Keyword(s):

Pancreatic Cancer ◽

Gene Ontology ◽

Feature Selection Method ◽

Selection Method ◽

Biological Processes ◽

Kegg Pathways ◽

Extensive Analysis ◽

The World ◽

Serious Disease ◽

Go Terms

Pancreatic cancer is a serious disease that results in more than thirty thousand deaths around the world per year. To design effective treatments, many investigators have devoted themselves to the study of biological processes and mechanisms underlying this disease. However, it is far from complete. In this study, we tried to extract important gene ontology (GO) terms and KEGG pathways for pancreatic cancer by adopting some existing computational methods. Genes that have been validated to be related to pancreatic cancer and have not been validated were represented by features derived from GO terms and KEGG pathways using the enrichment theory. A popular feature selection method, minimum redundancy maximum relevance, was employed to analyze these features and extract important GO terms and KEGG pathways. An extensive analysis of the obtained GO terms and KEGG pathways was provided to confirm the correlations between them and pancreatic cancer.

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Machine learning-based predictions of dietary restriction associations across ageing-related genes

BMC Bioinformatics ◽

10.1186/s12859-021-04523-8 ◽

2022 ◽

Vol 23 (1) ◽

Author(s):

Gustavo Daniel Vega Magdaleno ◽

Vladislav Bespalov ◽

Yalin Zheng ◽

Alex A. Freitas ◽

Joao Pedro de Magalhaes

Keyword(s):

Machine Learning ◽

Dietary Restriction ◽

Protein Interactions ◽

Predictive Power ◽

Tissue Expression ◽

Biological Pathways ◽

Related Factor ◽

Promising Candidate ◽

Kegg Pathways ◽

Go Terms

Abstract Background Dietary restriction (DR) is the most studied pro-longevity intervention; however, a complete understanding of its underlying mechanisms remains elusive, and new research directions may emerge from the identification of novel DR-related genes and DR-related genetic features. Results This work used a Machine Learning (ML) approach to classify ageing-related genes as DR-related or NotDR-related using 9 different types of predictive features: PathDIP pathways, two types of features based on KEGG pathways, two types of Protein–Protein Interactions (PPI) features, Gene Ontology (GO) terms, Genotype Tissue Expression (GTEx) expression features, GeneFriends co-expression features and protein sequence descriptors. Our findings suggested that features biased towards curated knowledge (i.e. GO terms and biological pathways), had the greatest predictive power, while unbiased features (mainly gene expression and co-expression data) have the least predictive power. Moreover, a combination of all the feature types diminished the predictive power compared to predictions based on curated knowledge. Feature importance analysis on the two most predictive classifiers mostly corroborated existing knowledge and supported recent findings linking DR to the Nuclear Factor Erythroid 2-Related Factor 2 (NRF2) signalling pathway and G protein-coupled receptors (GPCR). We then used the two strongest combinations of feature type and ML algorithm to predict DR-relatedness among ageing-related genes currently lacking DR-related annotations in the data, resulting in a set of promising candidate DR-related genes (GOT2, GOT1, TSC1, CTH, GCLM, IRS2 and SESN2) whose predicted DR-relatedness remain to be validated in future wet-lab experiments. Conclusions This work demonstrated the strong potential of ML-based techniques to identify DR-associated features as our findings are consistent with literature and recent discoveries. Although the inference of new DR-related mechanistic findings based solely on GO terms and biological pathways was limited due to their knowledge-driven nature, the predictive power of these two features types remained useful as it allowed inferring new promising candidate DR-related genes.

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Exploring the Mechanism of Laminaria for the Treatment of Alzheimer's Disease based on Network Pharmacology

10.21203/rs.3.rs-250110/v1 ◽

2021 ◽

Author(s):

Le Yu ◽

Shuchen Pei ◽

Kangyao Yuan ◽

Jian Zhang ◽

Jingya Zhao ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Laminaria Japonica ◽

Network Pharmacology ◽

Ppi Network ◽

Active Components ◽

Protein Protein Interaction ◽

Kegg Pathways ◽

Response To Stress ◽

Go Terms

Abstract Background：Laminaria japonica has also been reported to have a therapeutic effect on AD, but the mechanism is not entirely clear. To explore the mechanism of Laminaria for the treatment of Alzheimer's disease (AD), the “active components-targets” network and the protein-protein interaction (PPI) network were constructed for analyzing targets’ functions and pathways. Methods：The main active components of Laminaria were extracted using the TCMSP database and were predicted and screened by GeneCards. Cytoscape was used to construct the “drug-components-targets-disease” network. STRING and Cytoscape were applied to map the PPI network. The Gene Ontology (GO) terms and KEGG pathways of targets were analyzed by Metascape. Results: Seven active components involving 23 active targets were obtained. The network analysis elucidated that Laminaria was mainly involved in cell process, metabolic process, response to stress and other biological processes. CASP3, PPARG, RELA, CCND1 and CASP9 played a key role in treating AD by regulating two small cell lung cancer and Toxoplasmosis. Conclusion: This study demonstrated that Laminaria could prevent and treat AD with advantages of multi-components, multi-targets and multi-pathways, which explored a new way for further research on the mechanism of Laminaria in the treatment of AD.

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Aberrantly hydroxymethylated differentially expressed genes and the associated protein pathways in osteoarthritis

PeerJ ◽

10.7717/peerj.6425 ◽

2019 ◽

Vol 7 ◽

pp. e6425 ◽

Cited By ~ 13

Author(s):

Yang Fang ◽

Pingping Wang ◽

Lin Xia ◽

Suwen Bai ◽

Yonggang Shen ◽

...

Keyword(s):

Gene Expression ◽

Gene Ontology ◽

Differentially Expressed Genes ◽

Kegg Pathway ◽

Interaction Network ◽

Differentially Expressed ◽

Hub Genes ◽

Protein Protein Interaction ◽

Highly Expressed Genes ◽

Protein Protein Interaction Network

Background The elderly population is at risk of osteoarthritis (OA), a common, multifactorial, degenerative joint disease. Environmental, genetic, and epigenetic (such as DNA hydroxymethylation) factors may be involved in the etiology, development, and pathogenesis of OA. Here, comprehensive bioinformatic analyses were used to identify aberrantly hydroxymethylated differentially expressed genes and pathways in osteoarthritis to determine the underlying molecular mechanisms of osteoarthritis and susceptibility-related genes for osteoarthritis inheritance. Methods Gene expression microarray data, mRNA expression profile data, and a whole genome 5hmC dataset were obtained from the Gene Expression Omnibus repository. Differentially expressed genes with abnormal hydroxymethylation were identified by MATCH function. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the genes differentially expressed in OA were performed using Metascape and the KOBAS online tool, respectively. The protein–protein interaction network was built using STRING and visualized in Cytoscape, and the modular analysis of the network was performed using the Molecular Complex Detection app. Results In total, 104 hyperhydroxymethylated highly expressed genes and 14 hypohydroxymethylated genes with low expression were identified. Gene ontology analyses indicated that the biological functions of hyperhydroxymethylated highly expressed genes included skeletal system development, ossification, and bone development; KEGG pathway analysis showed enrichment in protein digestion and absorption, extracellular matrix–receptor interaction, and focal adhesion. The top 10 hub genes in the protein–protein interaction network were COL1A1, COL1A2, COL2A1, COL3A1, COL5A1, COL5A2, COL6A1, COL8A1, COL11A1, and COL24A1. All the aforementioned results are consistent with changes observed in OA. Conclusion After comprehensive bioinformatics analysis, we found aberrantly hydroxymethylated differentially expressed genes and pathways in OA. The top 10 hub genes may be useful hydroxymethylation analysis biomarkers to provide more accurate OA diagnoses and target genes for treatment of OA.

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CyKEGGParser: tailoring KEGG pathways to fit into systems biology analysis workflows

F1000Research ◽

10.12688/f1000research.4410.2 ◽

2014 ◽

Vol 3 ◽

pp. 145 ◽

Cited By ~ 11

Author(s):

Lilit Nersisyan ◽

Ruben Samsonyan ◽

Arsen Arakelyan

Keyword(s):

Systems Biology ◽

Protein Interaction ◽

Kegg Pathway ◽

Computer Assisted ◽

Pathway Database ◽

Protein Protein Interaction ◽

Kegg Pathways ◽

Kegg Pathway Database ◽

Context Specific ◽

Biological Context

The KEGG pathway database is a widely accepted source for biomolecular pathway maps. In this paper we present the CyKEGGParser app (http://apps.cytoscape.org/apps/cykeggparser) for Cytoscape 3 that allows manipulation with KEGG pathway maps. Along with basic functionalities for pathway retrieval, visualization and export in KGML and BioPAX formats, the app provides unique features for computer-assisted adjustment of inconsistencies in KEGG pathway KGML files and generation of tissue- and protein-protein interaction specific pathways. We demonstrate that using biological context-specific KEGG pathways created with CyKEGGParser makes systems biology analysis more sensitive and appropriate compared to original pathways.

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