[Dedicated to Prof. T. Okada and Prof. T. Nishioka: data science in chemistry]DPClusOST: A Software Tool for General Purpose Graph Clustering

Karim, Mohammad Bozlul; Wakamatsu, Nobutaka; Altaf-Ul-Amin, Md.

doi:10.2751/jcac.18.76

Cited by 16 publications

(4 citation statements)

References 12 publications

(16 reference statements)

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“…A similar approach has been used to study various diseases, including inflammatory bowel disease [ 43 ] and polycystic ovarian syndrome (PCOS) [ 58 ], as well as to identify potential glucosinolate genes in Arabidopsis thaliana [ 41 , 59 , 60 , 61 ]. DPClusO algorithm creates overlapping clusters depending on the multifunctionality of a gene, fetching greater chances of occurrence in multiple clusters [ 40 , 41 , 62 ].…”

Section: Discussionmentioning

confidence: 99%

“…The overlapping clusters are described with several biological processes related to the hormone biosynthesis pathway. DPClusO constructs an undirected graph consisting of a finite set of nodes, N and a finite set of edges, E. Two key parameters, namely the density, d k and cluster property, cp nk , are applied in the DPClusO algorithm of the cluster network analysis [40,41]. The default cp value, 0.5 was used with reference to the previous studies [41][42][43].…”

Section: Network Clustering Analysismentioning

confidence: 99%

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Gene Co-Expression Network Analysis Reveals Key Regulatory Genes in Metisa plana Hormone Pathways

Vengatharajuloo

Goh

Hassan

et al. 2023

Insects

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Metisa plana Walker (Lepidoptera: Psychidae) is a major oil palm pest species distributed across Southeast Asia. M. plana outbreaks are regarded as serious ongoing threats to the oil palm industry due to their ability to significantly reduce fruit yield and subsequent productivity. Currently, conventional pesticide overuses may harm non-target organisms and severely pollute the environment. This study aims to identify key regulatory genes involved in hormone pathways during the third instar larvae stage of M. plana gene co-expression network analysis. A weighted gene co-expression network analysis (WGCNA) was conducted on the M. plana transcriptomes to construct a gene co-expression network. The transcriptome datasets were obtained from different development stages of M. plana, i.e., egg, third instar larvae, pupa, and adult. The network was clustered using the DPClusO algorithm and validated using Fisher’s exact test and receiver operating characteristic (ROC) analysis. The clustering analysis was performed on the network and 20 potential regulatory genes (such as MTA1-like, Nub, Grn, and Usp) were identified from ten top-most significant clusters. Pathway enrichment analysis was performed to identify hormone signalling pathways and these pathways were identified, i.e., hormone-mediated signalling, steroid hormone-mediated signalling, and intracellular steroid hormone receptor signalling as well as six regulatory genes Hnf4, Hr4, MED14, Usp, Tai, and Trr. These key regulatory genes have a potential as important targets in future upstream applications and validation studies in the development of biorational pesticides against M. plana and the RNA interference (RNAi) gene silencing method.

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Section: Discussionmentioning

confidence: 99%

Section: Network Clustering Analysismentioning

confidence: 99%

Gene Co-Expression Network Analysis Reveals Key Regulatory Genes in Metisa plana Hormone Pathways

Vengatharajuloo

Goh

Hassan

et al. 2023

Insects

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“…A similar approach was used in analysing protein-protein interaction network in inflammatory bowel disease (IBD) (Eguchi et al, 2018) and polycystic ovarian syndrome (PCOS) (Afiqah-Aleng et al, 2020). DPClusOST creates overlapping clusters depending on a gene's multifunctionality, resulting in a high probability of a gene being present in multiple clusters (Altaf-Ul-Amin, Wada & Kanaya, 2012;Bozlul Karim, Wakamatsu & Altaf-Ul-Amin, 2017). The DPClusOST algorithm extracts highly interconnected region that perform similar biological process.…”

Section: Discussionmentioning

confidence: 99%

Potential Arabidopsis thaliana glucosinolate genes identified from the co-expression modules using graph clustering approach

Harun

Afiqah‐Aleng

Karim

et al. 2021

PeerJ

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Background Glucosinolates (GSLs) are plant secondary metabolites that contain nitrogen-containing compounds. They are important in the plant defense system and known to provide protection against cancer in humans. Currently, increasing the amount of data generated from various omics technologies serves as a hotspot for new gene discovery. However, sometimes sequence similarity searching approach is not sufficiently effective to find these genes; hence, we adapted a network clustering approach to search for potential GSLs genes from the Arabidopsis thaliana co-expression dataset. Methods We used known GSL genes to construct a comprehensive GSL co-expression network. This network was analyzed with the DPClusOST algorithm using a density of 0.5. 0.6. 0.7, 0.8, and 0.9. Generating clusters were evaluated using Fisher’s exact test to identify GSL gene co-expression clusters. A significance score (SScore) was calculated for each gene based on the generated p-value of Fisher’s exact test. SScore was used to perform a receiver operating characteristic (ROC) study to classify possible GSL genes using the ROCR package. ROCR was used in determining the AUC that measured the suitable density value of the cluster for further analysis. Finally, pathway enrichment analysis was conducted using ClueGO to identify significant pathways associated with the GSL clusters. Results The density value of 0.8 showed the highest area under the curve (AUC) leading to the selection of thirteen potential GSL genes from the top six significant clusters that include IMDH3, MVP1, T19K24.17, MRSA2, SIR, ASP4, MTO1, At1g21440, HMT3, At3g47420, PS1, SAL1, and At3g14220. A total of Four potential genes (MTO1, SIR, SAL1, and IMDH3) were identified from the pathway enrichment analysis on the significant clusters. These genes are directly related to GSL-associated pathways such as sulfur metabolism and valine, leucine, and isoleucine biosynthesis. This approach demonstrates the ability of the network clustering approach in identifying potential GSL genes which cannot be found from the standard similarity search.

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“…In the first step, we created the HSCR disease-relevant PPI network by selecting data from the Human Integrated Protein-Protein Interaction reference (HIPPIE) database [38] and determined high-density clusters in the PPI network according to DPClusO algorithm by using nine different and independent density values from 0.1 to 0.9 [49][50][51][52][53][54].…”

Section: Identification Of Novel Candidate Disease Genes and Disease-...mentioning

confidence: 99%

Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease

Lucena-Padros,

Bravo-Gil,

Tous

et al. 2024

Biomolecules

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Hirschsprung’s disease (HSCR) is a rare developmental disorder in which enteric ganglia are missing along a portion of the intestine. HSCR has a complex inheritance, with RET as the major disease-causing gene. However, the pathogenesis of HSCR is still not completely understood. Therefore, we applied a computational approach based on multi-omics network characterization and clustering analysis for HSCR-related gene/miRNA identification and biomarker discovery. Protein–protein interaction (PPI) and miRNA–target interaction (MTI) networks were analyzed by DPClusO and BiClusO, respectively, and finally, the biomarker potential of miRNAs was computationally screened by miRNA-BD. In this study, a total of 55 significant gene–disease modules were identified, allowing us to propose 178 new HSCR candidate genes and two biological pathways. Moreover, we identified 12 key miRNAs with biomarker potential among 137 predicted HSCR-associated miRNAs. Functional analysis of new candidates showed that enrichment terms related to gene ontology (GO) and pathways were associated with HSCR. In conclusion, this approach has allowed us to decipher new clues of the etiopathogenesis of HSCR, although molecular experiments are further needed for clinical validations.

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[Dedicated to Prof. T. Okada and Prof. T. Nishioka: data science in chemistry]DPClusOST: A Software Tool for General Purpose Graph Clustering

Cited by 16 publications

References 12 publications

Gene Co-Expression Network Analysis Reveals Key Regulatory Genes in Metisa plana Hormone Pathways

Gene Co-Expression Network Analysis Reveals Key Regulatory Genes in Metisa plana Hormone Pathways

Potential Arabidopsis thaliana glucosinolate genes identified from the co-expression modules using graph clustering approach

Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease

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