scholarly journals GeNET: a web application to explore and share Gene Co-expression Network Analysis data

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3678 ◽  
Author(s):  
Amit P. Desai ◽  
Mehdi Razeghin ◽  
Oscar Meruvia-Pastor ◽  
Lourdes Peña-Castillo
Keyword(s):  
Network Analysis ◽  
Web Application ◽  
Gene Network ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Analysis Data ◽  
Published Data ◽  
Online Tool ◽  
The Public ◽  
Gene Sets

Gene Co-expression Network Analysis (GCNA) is a popular approach to analyze a collection of gene expression profiles. GCNA yields an assignment of genes to gene co-expression modules, a list of gene sets statistically over-represented in these modules, and a gene-to-gene network. There are several computer programs for gene-to-gene network visualization, but these programs have limitations in terms of integrating all the data generated by a GCNA and making these data available online. To facilitate sharing and study of GCNA data, we developed GeNET. For researchers interested in sharing their GCNA data, GeNET provides a convenient interface to upload their data and automatically make it accessible to the public through an online server. For researchers interested in exploring GCNA data published by others, GeNET provides an intuitive online tool to interactively explore GCNA data by genes, gene sets or modules. In addition, GeNET allows users to download all or part of the published data for further computational analysis. To demonstrate the applicability of GeNET, we imported three published GCNA datasets, the largest of which consists of roughly 17,000 genes and 200 conditions. GeNET is available at bengi.cs.mun.ca/genet.

scholarly journals A Supervised Network Analysis on Gene Expression Profiles of Breast Tumors Predicts a 41-Gene Prognostic Signature of the Transcription FactorMYBacross Molecular Subtypes

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Li-Yu D. Liu ◽  
Li-Yun Chang ◽  
Wen-Hung Kuo ◽  
Hsiao-Lin Hwa ◽  
King-Jen Chang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Essential Gene ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Prognostic Signature ◽  
Prognostic Features ◽  
Bioinformatic Tools ◽  
Prognostic Feature ◽  
Relevant Gene

Background. MYBis predicted to be a favorable prognostic predictor in a breast cancer population. We proposed to find the inferred mechanism(s) relevant to the prognostic features ofMYBvia a supervised network analysis.Methods. Both coefficient of intrinsic dependence (CID) and Galton Pierson’s correlation coefficient (GPCC) were combined and designated as CIDUGPCC. It is for the univariate network analysis. Multivariate CID is for the multivariate network analysis. Other analyses using bioinformatic tools and statistical methods are included.Results. ARNT2is predicted to be the essential gene partner ofMYB. We classified four prognostic relevant gene subpools in three breast cancer cohorts as feature types I–IV. Only the probes in feature type II are the potential prognostic feature ofMYB. Moreover, we further validated 41 prognosis relevant probes to be the favorable prognostic signature. Surprisingly, two additional family members ofMYBare elevated to promote poor prognosis when both levels ofMYBandARNT2decline. BothMYBL1andMYBL2may partially decrease the tumor suppressive activities that are predicted to be up-regulated byMYBandARNT2.Conclusions. The major prognostic feature ofMYBis predicted to be determined by theMYBsubnetwork (41 probes) that is relevant across subtypes.

scholarly journals RankerGUI: A Computational Framework to Compare Differential Gene Expression Profiles Using Rank Based Statistics

2019 ◽  
Vol 20 (23) ◽  
pp. 6098 ◽  
Author(s):  
Amarinder Singh Thind ◽  
Kumar Parijat Tripathi ◽  
Mario Rosario Guarracino
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Differential Gene Expression ◽  
Web Application ◽  
Cellular Response ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Experimental Conditions ◽  
Differential Gene

The comparison of high throughput gene expression datasets obtained from different experimental conditions is a challenging task. It provides an opportunity to explore the cellular response to various biological events such as disease, environmental conditions, and drugs. There is a need for tools that allow the integration and analysis of such data. We developed the “RankerGUI pipeline”, a user-friendly web application for the biological community. It allows users to use various rank based statistical approaches for the comparison of full differential gene expression profiles between the same or different biological states obtained from different sources. The pipeline modules are an integration of various open-source packages, a few of which are modified for extended functionality. The main modules include rank rank hypergeometric overlap, enriched rank rank hypergeometric overlap and distance calculations. Additionally, preprocessing steps such as merging differential expression profiles of multiple independent studies can be added before running the main modules. Output plots show the strength, pattern, and trends among complete differential expression profiles. In this paper, we describe the various modules and functionalities of the developed pipeline. We also present a case study that demonstrates how the pipeline can be used for the comparison of differential expression profiles obtained from multiple platforms’ data of the Gene Expression Omnibus. Using these comparisons, we investigate gene expression patterns in kidney and lung cancers.

EFFECTIVE MODELS FOR GENE NETWORKS AND THEIR APPLICATIONS

2012 ◽  
Vol 07 (01n02) ◽  
pp. 41-70 ◽  
Author(s):  
JASON SHULMAN ◽  
LARS SEEMANN ◽  
GREGG W. ROMAN ◽  
GEMUNU H. GUNARATNE
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Gene Network ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Stationary Solutions ◽  
The State ◽  
Effective Model ◽  
Knockout Mutant ◽  
Double Knockout

Networks are used to abstract large, highly-coupled sets of objects. Their analyses have included network classification into a few broad classes and selection of small substructures that perform simple yet common tasks. One issue that has received little attention is how the state of a network can be moved according to a pre-specified set of guidelines. In this paper, we address this question in the context of gene networks. In general, neither the full membership of the gene network associated with a biological process nor the precise form of interactions between nodes is known. What is available, through microarrays or sequencing, are gene expression profiles of an organism or its viable mutants. Our approach relies only on these expression profiles, and not on the availability of an accurate model for the network. The first step is to select a small set of core- or master- nodes, such as transcription factors or microRNAs, that can be used to alter the levels of many of the remaining genes in the network. We ask how the state — or solution — of the gene network changes as the levels of these master nodes are altered externally. The object of our study is, not the network, but the surface of these solutions. We argue that it can be approximated using gene expression profiles of the organism and single manipulation of master node activity. This is done through an "effective model." The effective model as well as error estimates for its predictions can be derived from experimental data. The method is validated using synthetic gene networks that have stationary solutions and those that are periodically driven, e.g., circadian networks. An effective model for the oxygen-deprivation network of E.coli is constructed using previously published gene expression profiles, and used to predict the expression levels in a double knockout mutant. Less that 30% of the predictions lie outside the 5% confidence level. We propose the use of the effective model methodology to compute how Drosophila melanogaster in the normal state can be genetically altered into a pre-defined sleep deprived-like state.

scholarly journals PCaDB - a comprehensive and interactive database for transcriptomes from prostate cancer population cohorts

2021 ◽  
Author(s):  
Ruidong Li ◽  
Zhenyu Jia
Keyword(s):  
Prostate Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
The Public ◽  
Interactive Analysis ◽  
Single Cell Rna Sequencing ◽  
Transcriptomics Data ◽  
Normal Human ◽  
User Friendly

Prostate cancer (PCa) is a heterogeneous disease with highly variable clinical outcomes which presents enormous challenges in the clinical management. A vast amount of transcriptomics data from large PCa cohorts have been generated, providing extraordinary opportunities for the comprehensive molecular characterization of the PCa disease and development of prognostic signatures to accurately predict the risk of PCa recurrence. The lack of an inclusive collection and standard processing of the public transcriptomics datasets constrains the extensive use of the valuable resources. In this study, we present a user-friendly database, PCaDB, for a comprehensive and interactive analysis and visualization of gene expression profiles from 50 public transcriptomics datasets with 7,231 samples. PCaDB also includes a single-cell RNA-sequencing (scRNAseq) dataset for normal human prostates and 30 published PCa prognostic signatures. The advanced analytical methods equipped in PCaDB would greatly facilitate data mining to understand the heterogeneity of PCa and to develop prognostic signatures and machine learning models for PCa prognosis. PCaDB is publicly available at http://bioinfo.jialab-ucr.org/PCaDB/.

scholarly journals Identification of IL10RA by Weighted Correlation Network Analysis and in vitro Validation of Its Association With Prognosis of Metastatic Melanoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Si Cheng ◽  
Zhe Li ◽  
Wenhao Zhang ◽  
Zhiqiang Sun ◽  
Zhigang Fan ◽  
...  
Keyword(s):  
Network Analysis ◽  
Metastatic Melanoma ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Migration And Invasion

Skin cutaneous melanoma (SKCM) is the major cause of death for skin cancer patients, its high metastasis often leads to poor prognosis of patients with malignant melanoma. However, the molecular mechanisms underlying metastatic melanoma remain to be elucidated. In this study we aim to identify and validate prognostic biomarkers associated with metastatic melanoma. We first construct a co-expression network using large-scale public gene expression profiles from GEO, from which candidate genes are screened out using weighted gene co-expression network analysis (WGCNA). A total of eight modules are established via the average linkage hierarchical clustering, and 111 hub genes are identified from the clinically significant modules. Next, two other datasets from GEO and TCGA are used for further screening of biomarker genes related to prognosis of metastatic melanoma, and identified 11 key genes via survival analysis. We find that IL10RA has the highest correlation with clinically important modules among all identified biomarker genes. Further in vitro biochemical experiments, including CCK8 assays, wound-healing assays and transwell assays, have verified that IL10RA can significantly inhibit the proliferation, migration and invasion of melanoma cells. Furthermore, gene set enrichment analysis shows that PI3K-AKT signaling pathway is significantly enriched in metastatic melanoma with highly expressed IL10RA, indicating that IL10RA mediates in metastatic melanoma via PI3K-AKT pathway.

scholarly journals Gene Expression Network Analysis Provides Potential Targets Against SARS-CoV-2

2020 ◽  
Author(s):  
Ana I. Hernández Cordero ◽  
Xuan Li ◽  
Chen Xi Yang ◽  
Stephen Milne ◽  
Yohan Bossé ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Host Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Tissue Samples ◽  
Drug Compounds ◽  
Bioinformatics Databases ◽  
Novel Coronavirus ◽  
Transmembrane Serine Protease

ABSTRACTBACKGROUNDCell entry of SARS-CoV-2, the novel coronavirus causing COVID-19, is facilitated by host cell angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2). We aimed to identify and characterize genes that are co-expressed with ACE2 and TMPRSS2, and to further explore their biological functions and potential as druggable targets.METHODSUsing the gene expression profiles of 1,038 lung tissue samples, we performed a weighted gene correlation network analysis (WGCNA) to identify modules of co-expressed genes. We explored the biology of co-expressed genes using bioinformatics databases, and identified known drug-gene interactions.RESULTSACE2 was in a module of 681 co-expressed genes; 12 genes with moderate-high correlation with ACE2 (r>0.3, FDR<0.05) had known interactions with existing drug compounds. TMPRSS2 was in a module of 1,086 co-expressed genes; 15 of these genes were enriched in the gene ontology biologic process ‘Entry into host cell’, and 53 TMPRSS2-correlated genes had known interactions with drug compounds.CONCLUSIONDozens of genes are co-expressed with ACE2 and TMPRSS2, many of which have plausible links to COVID-19 pathophysiology. Many of the co-expressed genes are potentially targetable with existing drugs, which may help to fast-track the development of COVID-19 therapeutics.

scholarly journals Frizzled class 7 receptor is differentially expressed in high-grade serous ovarian cancer and based on patient survival.

2020 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Ovarian Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Progression Free Survival ◽  
Global Gene Expression ◽  
Differentially Expressed ◽  
Published Data ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Free Survival

High-grade serous ovarian cancer (HGSC) is the most common type of the most lethal gynecologic malignancy (1). To identify genes whose expression was associated with survival outcomes in HGSC, we used published data from patients enrolled in the ICON7 trial to compare the global gene expression profiles of primary HGSC tumors from women with the best and worst progression-free survival (PFS) (2). We found that the Frizzled class 7 (Fzd7) receptor was among the genes most differentially expressed in HGSC tumors when comparing tumor transcriptomes based on superior or inferior PFS. In two independent datasets, Fzd7 was among the genes most differentially expressed in HGSC tumors when comparing primary tumor to the normal ovary (3, 4). Wnt pathway signaling through Fzd7 may be relevant to the biology of high-grade serous ovarian cancers.

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