scholarly journals WEADE: A Workflow for Enrichment Analysis and Data Exploration

2018 ◽  
Author(s):  
Nils Trost ◽  
Eugen Rempel ◽  
Olga Ermakova ◽  
Srividya Tamirisa ◽  
Letiția Pârcălăbescu ◽  
...  
Keyword(s):  
Web Application ◽  
Graphical Representation ◽  
Enrichment Analysis ◽  
Interaction Networks ◽  
Expression Data ◽  
Functional Interaction ◽  
Output Data ◽  
Large Gene ◽  
Web Tools ◽  
Consistent Manner

ABSTRACTData analysis based on enrichment of Gene Ontology terms has become an important step in exploring large gene or protein expression datasets and several stand-alone or web tools exist for that purpose. However, a comprehensive and consistent analysis downstream of the enrichment calculation is missing so far. With WEADE we present a free web application that offers an integrated workflow for the exploration of genomic data combining enrichment analysis with a versatile set of tools to directly compare and intersect experiments or candidate gene lists of any size or origin including cross-species data. Lastly, WEADE supports the graphical representation of output data in the form of functional interaction networks based on prior knowledge, allowing users to go from plain expression data to functionally relevant candidate sub-lists in an interactive and consistent manner.

scholarly journals Cystic Fibrosis: Systems Biology Analysis from Homozygous p.Phe508del Variant Patients’ Samples Reveals Perturbations in Tissue-Specific Pathways

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Joice de Faria Poloni ◽  
Thaiane Rispoli ◽  
Maria Lucia Rossetti ◽  
Cristiano Trindade ◽  
José Eduardo Vargas
Keyword(s):  
Cystic Fibrosis ◽  
Enrichment Analysis ◽  
Autosomal Recessive Disorder ◽  
Bronchial Epithelium ◽  
Interaction Networks ◽  
Homozygous State ◽  
Functional Interaction ◽  
Tissue Specific ◽  
Ppi Networks ◽  
Cf Transmembrane Conductance Regulator

Cystic fibrosis (CF) is an autosomal recessive disorder, caused by diverse genetic variants for the CF transmembrane conductance regulator (CFTR) protein. Among these, p.Phe508del is the most prevalent variant. The effects of this variant on the physiology of each tissue remains unknown. This study is aimed at predicting cell signaling pathways present in different tissues of fibrocystic patients, homozygous for p.Phe508del. The study involved analysis of two microarray datasets, E-GEOD-15568 and E-MTAB-360 corresponding to the rectal and bronchial epithelium, respectively, obtained from the ArrayExpress repository. Particularly, differentially expressed genes (DEGs) were predicted, protein-protein interaction (PPI) networks were designed, and centrality and functional interaction networks were analyzed. The study reported that p.Phe508del-mutated CFTR-allele in homozygous state influenced the whole gene expression in each tissue differently. Interestingly, gene ontology (GO) term enrichment analysis revealed that only “neutrophil activation” was shared between both tissues; however, nonshared DEGs were grouped into the same GO term. For further verification, functional interaction networks were generated, wherein no shared nodes were reported between these tissues. These results suggested that the p.Phe508del-mutated CFTR-allele in homozygous state promoted tissue-specific pathways in fibrocystic patients. The generated data might further assist in prediction diagnosis to define biomarkers or devising therapeutic strategies.

scholarly journals MGEnrichment: a web application for microglia gene list enrichment analysis

2021 ◽  
Author(s):  
Justin Jao ◽  
Annie Vogel Ciernia
Keyword(s):  
Gene Expression ◽  
Web Application ◽  
Gene List ◽  
Enrichment Analysis ◽  
Autism Spectrum ◽  
Postmortem Brain ◽  
Expression Data ◽  
Human Postmortem Brain ◽  
Online Documentation ◽  
Microglial Development

Gene expression analysis is becoming increasingly utilized in neuro-immunology research, and there is a growing need for non-programming scientists to be able to analyze their own genomic data. MGEnrichment is a web application developed both to disseminate to the community our curated database of microglia-relevant gene lists, and to allow non-programming scientists to easily conduct statistical enrichment analysis on their gene expression data. Users can upload their own gene IDs to assess the relevance of their expression data against gene lists from other studies. We include example datasets of differentially expressed genes (DEGs) from human postmortem brain samples from Autism Spectrum Disorder (ASD) and matched controls.  We demonstrate how MGEnrichment can be used to expand the interpretations of these DEG lists in terms of regulation of microglial gene expression and provide novel insights into how ASD DEGs may be implicated specifically in microglial development, microbiome responses and relationships to other neuropsychiatric disorders. This tool will be particularly useful for those working in microglia, autism spectrum disorders, and neuro-immune activation research. MGEnrichment is available at https://ciernialab.shinyapps.io/MGEnrichmentApp/ and further online documentation and datasets can be found at https://github.com/ciernialab/MGEnrichmentApp . The app is released under the GNU GPLv3 open source license.

scholarly journals MGEnrichment: A web application for microglia gene list enrichment analysis

2021 ◽  
Vol 17 (11) ◽  
pp. e1009160
Author(s):  
Justin Jao ◽  
Annie Vogel Ciernia
Keyword(s):  
Gene Expression ◽  
Web Application ◽  
Gene List ◽  
Enrichment Analysis ◽  
Autism Spectrum ◽  
Postmortem Brain ◽  
Expression Data ◽  
Human Postmortem Brain ◽  
Link Type ◽  
Online Documentation

Gene expression analysis is becoming increasingly utilized in neuro-immunology research, and there is a growing need for non-programming scientists to be able to analyze their own genomic data. MGEnrichment is a web application developed both to disseminate to the community our curated database of microglia-relevant gene lists, and to allow non-programming scientists to easily conduct statistical enrichment analysis on their gene expression data. Users can upload their own gene IDs to assess the relevance of their expression data against gene lists from other studies. We include example datasets of differentially expressed genes (DEGs) from human postmortem brain samples from Autism Spectrum Disorder (ASD) and matched controls. We demonstrate how MGEnrichment can be used to expand the interpretations of these DEG lists in terms of regulation of microglial gene expression and provide novel insights into how ASD DEGs may be implicated specifically in microglial development, microbiome responses and relationships to other neuropsychiatric disorders. This tool will be particularly useful for those working in microglia, autism spectrum disorders, and neuro-immune activation research. MGEnrichment is available at https://ciernialab.shinyapps.io/MGEnrichmentApp/ and further online documentation and datasets can be found at https://github.com/ciernialab/MGEnrichmentApp. The app is released under the GNU GPLv3 open source license.

scholarly journals 14-3-3 phosphoprotein interaction networks – does isoform diversity present functional interaction specification?

2012 ◽  
Vol 3 ◽  
Author(s):  
Anna-Lisa Paul ◽  
Fiona C. Denison ◽  
Eric R. Schultz ◽  
Agata K. Zupanska ◽  
Robert J. Ferl
Keyword(s):  
Interaction Networks ◽  
Functional Interaction ◽  
Isoform Diversity

scholarly journals A web application for the unspecific detection of differentially expressed DNA regions in strand-specific expression data: Fig. 1.

2015 ◽  
Vol 31 (19) ◽  
pp. 3228-3230
Author(s):  
José M. Juanes ◽  
Ana Miguel ◽  
Lucas J. Morales ◽  
José E. Pérez-Ortín ◽  
Vicente Arnau
Keyword(s):  
Web Application ◽  
Differentially Expressed ◽  
Expression Data ◽  
Specific Expression

scholarly journals Integrating Multiple Interaction Networks for Gene Function Inference

Molecules ◽  
2018 ◽  
Vol 24 (1) ◽  
pp. 30 ◽  
Author(s):  
Jingpu Zhang ◽  
Lei Deng
Keyword(s):  
State Of The Art ◽  
Interaction Networks ◽  
Dimensional Representation ◽  
Functional Interaction ◽  
High Throughput Data ◽  
Multiple Networks ◽  
The Past ◽  
Proteomic Data ◽  
Multiple Interaction ◽  
Low Dimensional

In the past few decades, the number and variety of genomic and proteomic data available have increased dramatically. Molecular or functional interaction networks are usually constructed according to high-throughput data and the topological structure of these interaction networks provide a wealth of information for inferring the function of genes or proteins. It is a widely used way to mine functional information of genes or proteins by analyzing the association networks. However, it remains still an urgent but unresolved challenge how to combine multiple heterogeneous networks to achieve more accurate predictions. In this paper, we present a method named ReprsentConcat to improve function inference by integrating multiple interaction networks. The low-dimensional representation of each node in each network is extracted, then these representations from multiple networks are concatenated and fed to gcForest, which augment feature vectors by cascading and automatically determines the number of cascade levels. We experimentally compare ReprsentConcat with a state-of-the-art method, showing that it achieves competitive results on the datasets of yeast and human. Moreover, it is robust to the hyperparameters including the number of dimensions.

scholarly journals Sex-specific longitudinal association of DNA methylation with lung function

2021 ◽  
pp. 00127-2021
Author(s):  
Shadia Khan Sunny ◽  
Hongmei Zhang ◽  
Caroline L. Relton ◽  
Susan Ring ◽  
Latha Kadalayil ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Function ◽  
Mixed Models ◽  
Enrichment Analysis ◽  
Expression Data ◽  
Specific Effects ◽  
Parents And Children ◽  
Longitudinal Association ◽  
Males And Females ◽  
Avon Longitudinal Study

Investigating whether DNA-M at an earlier age is associated with lung function at a later age and whether this relationship differs by sex could enable prediction of future lung function deficit.A training/testing-based technique was used to screen 402 714 cytosine-phosphate-guanine dinucleotide sites (CpGs) to assess the longitudinal association of blood-based DNA-M at ages 10 and 18-years with lung function at 18 and 26-years, respectively, in the Isle of Wight birth cohort (IOWBC). Multivariable linear mixed models were applied to the CpGs that passed screening. To detect differentially methylated regions (DMRs), DMR enrichment analysis was conducted. Findings were further examined in the Avon Longitudinal Study of Parents and Children (ALSPAC). Biological relevance of the identified CpGs was assessed utilizing gene expression data.DNA-M at 8 CpGs (FEV1: 5 and FEV1/FVC: 3 CpGs) at an earlier age was associated with lung function at a later age regardless of sex, while at 13 CpGs (FVC: 5, FEV1:3, and FEV1/FVC: 5 CpGs), the associations were sex-specific (pFDR<0.05) in IOWBC with consistent directions of association in ALSPAC (IOWBC-ALSPAC consistent CpGs). cg16582803 (WNT10A) and cg14083603 (ZGPAT) were replicated in ALSPAC for main and sex-specific effects, respectively. Among IOWBC-ALSPAC consistent CpGs, DNA-M at cg01376079 (SSH3) and cg07557690 (TGFBR3) was associated with gene expression both longitudinally and cross-sectionally. In total, 57 and 170 DMRs were linked to lung function longitudinally in males and females, respectively.CpGs showing longitudinal associations with lung function have the potential to serve as candidate markers in future studies on lung function deficit prediction.

scholarly journals SHAMAN: a user-friendly website for metataxonomic analysis from raw reads to statistical analysis

2020 ◽  
Author(s):  
Stevenn Volant ◽  
Pierre Lechat ◽  
Perrine Woringer ◽  
Laurence Motreff ◽  
Christophe Malabat ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Data Processing ◽  
Web Application ◽  
Graphical Representation ◽  
Statistical Modelling ◽  
Metagenomic Data ◽  
Sequencing Data ◽  
Microbiome Research ◽  
Interactive Visualizations ◽  
User Friendly

Abstract BackgroundComparing the composition of microbial communities among groups of interest (e.g., patients vs healthy individuals) is a central aspect in microbiome research. It typically involves sequencing, data processing, statistical analysis and graphical representation of the detected signatures. Such an analysis is normally obtained by using a set of different applications that require specific expertise for installation, data processing and in some case, programming skills. ResultsHere, we present SHAMAN, an interactive web application we developed in order to facilitate the use of (i) a bioinformatic workflow for metataxonomic analysis, (ii) a reliable statistical modelling and (iii) to provide among the largest panels of interactive visualizations as compared to the other options that are currently available. SHAMAN is specifically designed for non-expert users who may benefit from using an integrated version of the different analytic steps underlying a proper metagenomic analysis. The application is freely accessible at http://shaman.pasteur.fr/, and may also work as a standalone application with a Docker container (aghozlane/shaman), conda and R. The source code is written in R and is available at https://github.com/aghozlane/shaman. Using two datasets (a mock community sequencing and published 16S rRNA metagenomic data), we illustrate the strengths of SHAMAN in quickly performing a complete metataxonomic analysis. ConclusionsWe aim with SHAMAN to provide the scientific community with a platform that simplifies reproducible quantitative analysis of metagenomic data.

scholarly journals Identification of Hub Genes Associated With Melanoma Development by Comprehensive Bioinformatics Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Jie Jiang ◽  
Chong Liu ◽  
Guoyong Xu ◽  
Tuo Liang ◽  
Chaojie Yu ◽  
...  
Keyword(s):  
Target Gene ◽  
Enrichment Analysis ◽  
High Expression ◽  
Expression Data ◽  
Hub Genes ◽  
Gene Therapies ◽  
Protein Levels ◽  
Melanoma Diagnosis ◽  
Melanoma Patients ◽  
Core Genes

IntroductionThis study aimed to identify important genes associated with melanoma to further develop new target gene therapies and analyze their significance concerning prognosis.Materials and methodsGene expression data for melanoma and normal tissue were downloaded from three databases. Differentially co-expressed genes were identified by WGCNA and DEGs analysis. These genes were subjected to GO, and KEGG enrichment analysis and construction of the PPI visualized with Cytoscape and screened for the top 10 Hub genes using CytoHubba. We validated the Hub gene’s protein levels with an immunohistochemical assay to confirm the accuracy of our analysis.ResultsA total of 435 differentially co-expressed genes were obtained. Survival curves showed that high expression of FOXM1,\ EXO1, KIF20A, TPX2, and CDC20 in melanoma patients with 5 of the top 10 hub genes was associated with reduced overall survival (OS). Immunohistochemistry showed that all five genes were expressed at higher protein levels in melanoma than in paracancerous tissues.ConclusionFOXM1, EXO1, KIF20A, TPX2, and CDC20 are prognosis-associated core genes of melanoma, and their high expression correlates with the low prognosis of melanoma patients and can be used as biomarkers for melanoma diagnosis, treatment, and prognosis prediction.

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