scholarly journals Bayesian Functional Data Clustering for Temporal Microarray Data

2008 ◽  
Vol 2008 ◽  
pp. 1-4 ◽  
Author(s):  
Ping Ma ◽  
Wenxuan Zhong ◽  
Yang Feng ◽  
Jun S. Liu
Keyword(s):  
Clustering Algorithm ◽  
Enrichment Analysis ◽  
Information Criterion ◽  
Microarray Dataset ◽  
Gene Clusters ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Cluster Number ◽  
Mixed Effect ◽  
Temporal Gene Expression

We propose a Bayesian procedure to cluster temporal gene expression microarray profiles, based on a mixed-effect smoothing-spline model, and design a Gibbs sampler to sample from the desired posterior distribution. Our method can determine the cluster number automatically based on the Bayesian information criterion, and handle missing data easily. When applied to a microarray dataset on the budding yeast, our clustering algorithm provides biologically meaningful gene clusters according to a functional enrichment analysis.

scholarly journals Differential co-expression network analysis for identification of functional modules in HIV infection and tubercolosis-HIV co-infection

2016 ◽  
Vol 89 (2) ◽  
Author(s):  
Mohit Jha ◽  
Anvita Gupta ◽  
Sudha Singh ◽  
Khushhali Menaria Pandey
Keyword(s):  
Network Analysis ◽  
Hiv Infection ◽  
Enrichment Analysis ◽  
Gene Interaction ◽  
Gene Clusters ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Specific Gene ◽  
Diagnostic Strategies

Co-infection with tuberculosis (TB) is the preeminent cause of demise in human immunodeficiency virus (HIV) infected individuals. However, diagnosis of TB, particularly in the presence of an HIV co-infection, can be limiting owing to the high inaccuracy associated with conventional diagnostic strategies. Here we determine dysregulated pathways in TB-HIV co-infection and HIV infection utilizing coexpression networks. Primarily, we utilized preservation statistics to identify gene modules that exhibit a weak conservation of network topology within HIV infected and TB-HIV co-infected networks. Raw data was downloaded from Gene Expression Omnibus (GSE50834) and duly pre-processed. Co-expression networks for each condition (HIV infected and TB-HIV co-infected) were constructed independently. Preservation of HIV infected network edges was evaluated with respect to TB-HIV co-infected and vice versa using weighted correlation network analysis. Two out of the 22 modules were identified as exhibiting weak preservation in both conditions. Functional enrichment analysis identified that weakly preserved modules were pertinent to the condition under study. For instance, weakly preserved TBHIV co-infected module T1 enriched for genes associated with mitochondrion exhibited the highest fraction of gene interaction pairs exclusive to TB-HIV co-infection. Concisely, we illustrated the application of using preservation statistics to detect modules functionally linked with dysregulated pathways in disease, as exemplified by the mitochondrion module T1. Our analyses discovered gene clusters that are non-randomly linked with the disease. Highly specific gene pairs pointed to interactions between known markers of disease and favoured identification of possible markers that are likely to be associated with the disease.

scholarly journals Exploring the Functional Disorder and Corresponding Key Transcription Factors in Intraductal Papillary Mucinous Neoplasms Progression

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Guiying Bai ◽  
Chenxuan Wu ◽  
Yingtang Gao ◽  
Guiming Shu
Keyword(s):  
Transcription Factors ◽  
Ductal Carcinoma ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Microarray Dataset ◽  
Functional Enrichment Analysis ◽  
Mucinous Carcinoma ◽  
Functional Enrichment ◽  
Functional Disorder ◽  
Similar Expression Pattern

This study has analyzed the gene expression patterns of an IPMN microarray dataset including normal pancreatic ductal tissue (NT), intraductal papillary mucinous adenoma (IPMA), intraductal papillary mucinous carcinoma (IPMC), and invasive ductal carcinoma (IDC) samples. And eight clusters of differentially expressed genes (DEGs) with similar expression pattern were detected byk-means clustering. Then a survey map of functional disorder in IPMN progression was established by functional enrichment analysis of these clusters. In addition, transcription factors (TFs) enrichment analysis was used to detect the key TFs in each cluster of DEGs, and three TFs (FLI1, ERG, and ESR1) were found to significantly regulate DEGs in cluster 1, and expression of these three TFs was validated by qRT-PCR. All these results indicated that these three TFs might play key roles in the early stages of IPMN progression.

scholarly journals Transcriptional Regulations on the Low-Temperature-Induced Floral Transition in anOrchidaceaeSpecies,Dendrobium nobile: An Expressed Sequence Tags Analysis

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Shan Liang ◽  
Qing-Sheng Ye ◽  
Rui-Hong Li ◽  
Jia-Yi Leng ◽  
Mei-Ru Li ◽  
...  
Keyword(s):  
Expressed Sequence Tags ◽  
Genetic Basis ◽  
Protein Biosynthesis ◽  
Enrichment Analysis ◽  
Gene Clusters ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Floral Transition ◽  
Dendrobium Nobile ◽  
Expressed Sequence

Vernalization-induced flowering is a cold-relevant adaptation in many species, but little is known about the genetic basis behind inOrchidaceaespecies. Here, we reported a collection of 15017 expressed sequence tags (ESTs) from the vernalized axillary buds of anOrchidaceaespecies,Dendrobium nobile, which were assembled for 9616 unique gene clusters. Functional enrichment analysis showed that genes in relation to the responses to stresses, especially in the form of low temperatures, and those involving in protein biosynthesis and chromatin assembly were significantly overrepresented during 40 days of vernalization. Additionally, a total of 59 putative flowering-relevant genes were recognized, including those homologous to known key players in vernalization pathways in temperate cereals orArabidopsis, such as cerealVRN1,FT/VRN3, andArabidopsis AGL19. Results from this study suggest that the networks regulating vernalization-induced floral transition are conserved, but just in a part, inD. nobile, temperate cereals, andArabidopsis.

scholarly journals Integrated DNA Methylation and Gene Expression Analysis Identified S100A8 and S100A9 in the Pathogenesis of Obesity

2021 ◽  
Vol 8 ◽  
Author(s):  
Ningyuan Chen ◽  
Liu Miao ◽  
Wei Lin ◽  
Donghua Zou ◽  
Ling Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Mrna Expression ◽  
Regulatory Networks ◽  
Enrichment Analysis ◽  
Microarray Dataset ◽  
Functional Enrichment Analysis ◽  
Normal Subjects ◽  
Functional Enrichment ◽  
Differentially Methylated Genes

Background: To explore the association of DNA methylation and gene expression in the pathology of obesity.Methods: (1) Genomic DNA methylation and mRNA expression profile of visceral adipose tissue (VAT) were performed in a comprehensive database of gene expression in obese and normal subjects. (2) Functional enrichment analysis and construction of differential methylation gene regulatory networks were performed. (3) Validation of the two different methylation sites and corresponding gene expression was done in a separate microarray dataset. (4) Correlation analysis was performed on DNA methylation and mRNA expression data.Results: A total of 77 differentially expressed mRNAs matched with differentially methylated genes. Analysis revealed two different methylation sites corresponding to two unique genes—s100a8-cg09174555 and s100a9-cg03165378. Through the verification test of two interesting different expression positions [differentially methylated positions (DMPs)] and their corresponding gene expression, we found that methylation in these genes was negatively correlated to gene expression in the obesity group. Higher S100A8 and S100A9 expressions in obese subjects were validated in a separate microarray dataset.Conclusion: This study confirmed the relationship between DNA methylation and gene expression and emphasized the important role of S100A8 and S100A9 in the pathogenesis of obesity.

scholarly journals Gene Clusters Based on OLIG2 and CD276 Could Distinguish Molecular Profiling in Glioblastoma

2021 ◽  
Author(s):  
Minjie Fu ◽  
Jinsen Zhang ◽  
Weifeng Li ◽  
Shan He ◽  
Jingwen Zhang ◽  
...  
Keyword(s):  
Gene Cluster ◽  
Efficient Method ◽  
Enrichment Analysis ◽  
Gene Clusters ◽  
Functional Enrichment Analysis ◽  
Molecular Classification ◽  
Functional Enrichment ◽  
Protein Protein Interaction ◽  
Dna Elements

Abstract BackgroundThe molecular classification of glioblastoma (GBM) based on transcriptomic analysis could provide precise treatment and prognosis. However, current subtyping (Classic, Mesenchymal, Neural, Proneural) is a time-consuming and cost-intensive process, which hinders its clinical application. A simple and efficient method for classification was imperative.MethodsRandom forest algorithm was applied to conduct a gene cluster featured with hub genes, OLIG2 and CD276. Functional enrichment analysis and Protein-protein interaction were performed using the genes in this gene cluster. The classification efficiency of the gene cluster was validated by WGCNA and LASSO algorithms, and tested in GSE84010 and Gravandeel’s GBM datasets. ResultsThe gene cluster (n = 26) could distinguish mesenchymal and proneural excellently (AUC = 0.92), which could be validated by multiple algorithms (WGCNA, LASSO) and datasets (GSE84010 and Gravandeel’s GBM dataset). The gene cluster could be functionally enriched in DNA elements and T cell associated pathways. Additionally, five genes in the signature could predict the prognosis well (p = 0.0051 for training cohort, p = 0.065 for test cohort). ConclusionsThis study proved the accuracy and efficiency of random forest classifier for GBM subtyping and provided a convenient and efficient method for subtyping Proneural and Mesenchymal GBM.

scholarly journals Bioinformatics analysis of key biomarkers for retinoblastoma

2021 ◽  
Vol 49 (6) ◽  
pp. 030006052110222
Author(s):  
Xin-mei Zhao ◽  
Yuan-Bin Li ◽  
Peng Sun ◽  
Ya-di Pu ◽  
Meng-jie shan ◽  
...  
Keyword(s):  
Enrichment Analysis ◽  
Microarray Dataset ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Protein Protein Interaction ◽  
Bilateral Retinoblastoma ◽  
Endopeptidase Activity ◽  
Novel Biomarkers ◽  
Geo Database

Objective To identify key genes involved in occurrence and development of retinoblastoma. Methods The microarray dataset, GSE5222, was downloaded from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) between unilateral and bilateral retinoblastoma were identified and functional enrichment analysis performed. The protein–protein interaction (PPI) network was constructed and analysed by STRING and Cytoscape. Results DEGs were mainly associated with activation of cysteine-type endopeptidase activity involved in apoptotic process and small molecule catabolic process. Seven genes (WAS, GNB3, PTGER1, TACR1, GPR143, NPFF and CDKN2A) were identified as HUB genes. Conclusion Our research provides more understanding of the mechanisms of the disease at a molecular level and may help in the identification of novel biomarkers for retinoblastoma.

scholarly journals Gene clusters based on OLIG2 and CD276 could distinguish molecular profiling in glioblastoma

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Minjie Fu ◽  
Jinsen Zhang ◽  
Weifeng Li ◽  
Shan He ◽  
Jingwen Zhang ◽  
...  
Keyword(s):  
Random Forest ◽  
Gene Cluster ◽  
Efficient Method ◽  
Enrichment Analysis ◽  
Gene Clusters ◽  
Functional Enrichment Analysis ◽  
Molecular Profiling ◽  
Functional Enrichment ◽  
Protein Protein Interaction ◽  
Dna Elements

Abstract Background The molecular profiling of glioblastoma (GBM) based on transcriptomic analysis could provide precise treatment and prognosis. However, current subtyping (classic, mesenchymal, neural, proneural) is time-consuming and cost-intensive hindering its clinical application. A simple and efficient method for classification was imperative. Methods In this study, to simplify GBM subtyping more efficiently, we applied a random forest algorithm to conduct 26 genes as a cluster featured with hub genes, OLIG2 and CD276. Functional enrichment analysis and Protein–protein interaction were performed using the genes in this gene cluster. The classification efficiency of the gene cluster was validated by WGCNA and LASSO algorithms, and tested in GSE84010 and Gravandeel’s GBM datasets. Results The gene cluster (n = 26) could distinguish mesenchymal and proneural excellently (AUC = 0.92), which could be validated by multiple algorithms (WGCNA, LASSO) and datasets (GSE84010 and Gravandeel’s GBM dataset). The gene cluster could be functionally enriched in DNA elements and T cell associated pathways. Additionally, five genes in the signature could predict the prognosis well (p = 0.0051 for training cohort, p = 0.065 for test cohort). Conclusions Our study proved the accuracy and efficiency of random forest classifier for GBM subtyping, which could provide a convenient and efficient method for subtyping Proneural and Mesenchymal GBM.

Prediction of Potential Targets of an Emerging Zoonotic Paramyxovirus: An Integrated Bioinformatics Analysis

2021 ◽  
Vol 18 ◽  
Author(s):  
Minnikanti Venkata Satya Sai ◽  
Viswam Subeesh ◽  
Hema Sree G N S ◽  
Ganeshan Rajalakshmi Saraswathy ◽  
Nair Gouri
Keyword(s):  
Nipah Virus ◽  
Enrichment Analysis ◽  
Microarray Dataset ◽  
Functional Enrichment Analysis ◽  
Respiratory Illness ◽  
Functional Enrichment ◽  
Node Degree ◽  
Protein Protein Interaction ◽  
Search Tool ◽  
Infected Cells

Background: Nipah virus (NiV) is a zoonotic paramyxovirus that can cause severe respiratory illness and encephalitis in humans, with no effective targets and treatment. Objective: To investigate potential targets involved in the progression of NiV infection by bioinformatics studies. Methods: To identify the key gene involved in NiV infection, a microarray dataset (GSE32902) was downloaded from the National Centre of Biotechnology Information (NCBI). The differentially expressed genes were unraveled by using Geo2Enrichr and the functional enrichment analysis was identified by using Database for Annotation, Visualization, and Integrated Discovery (DAVID). Search Tool for the Retrieval of Interacting Genes (STRING) was used to construct the Protein-protein interaction (PPI) network and visualized by using Cytoscape. Results: A total of 500 genes (262 up-regulated and 238 down-regulated) were identified among NiV infected cells. 19 genes were found with a node degree of more than 10. All of them were upregulated genes. MX1, ISG15 and IFIT1 were found to have the highest node degree (degree = 20) followed by RSAD2 and IRF7 with node degree 18 and MX2 and IFIT3 with node degree 17. Conclusion: The above results explicitly demonstrate that the expressed genes attribute to a defensive response against the virus. Henceforth finding agonists for these genes would help in the effective management of Niv infection.

Systems Biology Approaches Reveal a Multi-stress Responsive WRKY Transcription Factor and Stress Associated Gene Co-expression Networks in Chickpea

2019 ◽  
Vol 14 (7) ◽  
pp. 591-601 ◽  
Author(s):  
Aravind K. Konda ◽  
Parasappa R. Sabale ◽  
Khela R. Soren ◽  
Shanmugavadivel P. Subramaniam ◽  
Pallavi Singh ◽  
...  
Keyword(s):  
Expression Pattern ◽  
Gene Networks ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Wrky Transcription Factor ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Callose Deposition ◽  
Significant Probability

Background: Chickpea is a nutritional rich premier pulse crop but its production encounters setbacks due to various stresses and understanding of molecular mechanisms can be ascribed foremost importance. Objective: The investigation was carried out to identify the differentially expressed WRKY TFs in chickpea in response to herbicide stress and decipher their interacting partners. Methods: For this purpose, transcriptome wide identification of WRKY TFs in chickpea was done. Behavior of the differentially expressed TFs was compared between other stress conditions. Orthology based cofunctional gene networks were derived from Arabidopsis. Gene ontology and functional enrichment analysis was performed using Blast2GO and STRING software. Gene Coexpression Network (GCN) was constructed in chickpea using publicly available transcriptome data. Expression pattern of the identified gene network was studied in chickpea-Fusarium interactions. Results: A unique WRKY TF (Ca_08086) was found to be significantly (q value = 0.02) upregulated not only under herbicide stress but also in other stresses. Co-functional network of 14 genes, namely Ca_08086, Ca_19657, Ca_01317, Ca_20172, Ca_12226, Ca_15326, Ca_04218, Ca_07256, Ca_14620, Ca_12474, Ca_11595, Ca_15291, Ca_11762 and Ca_03543 were identified. GCN revealed 95 hub genes based on the significant probability scores. Functional annotation indicated role in callose deposition and response to chitin. Interestingly, contrasting expression pattern of the 14 network genes was observed in wilt resistant and susceptible chickpea genotypes, infected with Fusarium. Conclusion: This is the first report of identification of a multi-stress responsive WRKY TF and its associated GCN in chickpea.

scholarly journals Structural variations in papaya genomes

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Zhenyang Liao ◽  
Xunxiao Zhang ◽  
Shengcheng Zhang ◽  
Zhicong Lin ◽  
Xingtan Zhang ◽  
...  
Keyword(s):  
Agronomic Traits ◽  
Enrichment Analysis ◽  
Copy Number Variant ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Structural Variations ◽  
Reproduction Traits ◽  
Depth Study ◽  
Environmental Adaptability ◽  
The Impact

Abstract Background Structural variations (SVs) are a type of mutations that have not been widely detected in plant genomes and studies in animals have shown their role in the process of domestication. An in-depth study of SVs will help us to further understand the impact of SVs on the phenotype and environmental adaptability during papaya domestication and provide genomic resources for the development of molecular markers. Results We detected a total of 8083 SVs, including 5260 deletions, 552 tandem duplications and 2271 insertions with deletion being the predominant, indicating the universality of deletion in the evolution of papaya genome. The distribution of these SVs is non-random in each chromosome. A total of 1794 genes overlaps with SV, of which 1350 genes are expressed in at least one tissue. The weighted correlation network analysis (WGCNA) of these expressed genes reveals co-expression relationship between SVs-genes and different tissues, and functional enrichment analysis shows their role in biological growth and environmental responses. We also identified some domesticated SVs genes related to environmental adaptability, sexual reproduction, and important agronomic traits during the domestication of papaya. Analysis of artificially selected copy number variant genes (CNV-genes) also revealed genes associated with plant growth and environmental stress. Conclusions SVs played an indispensable role in the process of papaya domestication, especially in the reproduction traits of hermaphrodite plants. The detection of genome-wide SVs and CNV-genes between cultivated gynodioecious populations and wild dioecious populations provides a reference for further understanding of the evolution process from male to hermaphrodite in papaya.

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