scholarly journals NETGE-PLUS: standard and network-based gene enrichment analysis in human and model organisms

2019 ◽  
Author(s):  
Samuele Bovo ◽  
Pier Luigi Martelli ◽  
Pietro Di Lena ◽  
Rita Casadio
Keyword(s):  
Complex Traits ◽  
Enrichment Analysis ◽  
Data Retrieval ◽  
Functional Enrichment ◽  
Model Organisms ◽  
Functional Interpretation ◽  
Kegg Pathways ◽  
Gene Enrichment Analysis ◽  
Gene Enrichment ◽  
Gene Sets

ABSTRACTOmics techniques provide a spectrum of information that needs to be disentangled to characterize complex traits at the molecular level. The gap between genotype and phenotype must be closed by reconciling the genome information with the set of molecular pathways and biological processes describing the phenotype. In dealing with this problem, gene enrichment analysis has become the most widely adopted strategy. Here, we present NETGE-PLUS, a web-server for standard and network-based functional interpretation of gene sets of human and of model organisms, including S. scrofa, S. cerevisiae, E. coli and A. thaliana. NETGE-PLUS enables the functional enrichment of both simple and ranked lists of genes, also introducing the possibility of exploring relationships among KEGG pathways. A web interface makes data retrieval complete and user-friendly. NETGE-PLUS is publicly available at http://net-ge2.biocomp.unibo.it

scholarly journals Positional gene enrichment analysis of gene sets for high-resolution identification of overrepresented chromosomal regions

2008 ◽  
Vol 36 (7) ◽  
pp. e43-e43 ◽  
Author(s):  
K. De Preter ◽  
R. Barriot ◽  
F. Speleman ◽  
J. Vandesompele ◽  
Y. Moreau
Keyword(s):  
High Resolution ◽  
Enrichment Analysis ◽  
Gene Enrichment Analysis ◽  
Gene Enrichment ◽  
Gene Sets

scholarly journals NETGE-PLUS: Standard and Network-Based Gene Enrichment Analysis in Human and Model Organisms

2020 ◽  
Vol 19 (7) ◽  
pp. 2873-2878 ◽  
Author(s):  
Samuele Bovo ◽  
Pier Luigi Martelli ◽  
Pietro Di Lena ◽  
Rita Casadio
Keyword(s):  
Enrichment Analysis ◽  
Model Organisms ◽  
Gene Enrichment Analysis ◽  
Gene Enrichment

scholarly journals A global analysis of CNVs in Chinese indigenous fine-wool sheep populations using whole-genome resequencing

2021 ◽  
Author(s):  
Chao Yuan ◽  
Zengkui Lu ◽  
Tingting Guo ◽  
Yaojing Yue ◽  
Xijun Wang ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Complex Traits ◽  
Phenotypic Diversity ◽  
Average Length ◽  
Global Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Nutrient Metabolism ◽  
Relaxin Family

Abstract Background Copy number variation (CNV) is an important source of genetic variation that has a significant influence on phenotypic diversity, economically important traits and the evolution of livestock species. In this study, the genome-wide CNV distribution characteristics of 32 fine-wool sheep from three breeds were analyzed using resequencing.Results A total of 1,747,604 CNVs were detected in this study, and 7,228 CNV regions (CNVR) were obtained after merging overlapping CNVs; these regions accounted for 2.17% of the sheep reference genome. The average length of the CNVRs was 4,307.17 bp. “Deletion” events took place more frequently than “duplication” or “both” events. The CNVRs obtained overlapped with previously reported sheep CNVRs to variable extents (4.39%–55.46%). Functional enrichment analysis showed that the CNVR-harboring genes were mainly involved in sensory perception systems, nutrient metabolism processes, and growth and development processes. Furthermore, 1,855 of the CNVRs were associated with 166 quantitative trait loci (QTL), including milk QTLs, carcass QTLs, and health-related QTLs, among others. In addition, the 32 fine-wool sheep were divided into horned and polled groups to analyze for the selective sweep of CNVRs, and it was found that the relaxin family peptide receptor 2 (RXFP2) gene was strongly influenced by selection.Conclusions In summary, we constructed a genomic CNV map for Chinese indigenous fine-wool sheep using resequencing, thereby providing a valuable genetic variation resource for sheep genome research, which will contribute to the study of complex traits in sheep.

scholarly journals Effect of flapless osteoperforation-assisted tooth movement on atrophic alveolar ridge: Histomorphometric and gene-enrichment analysis

2017 ◽  
Vol 88 (1) ◽  
pp. 82-90 ◽  
Author(s):  
Ji-Won Lee ◽  
Jung-Yul Cha ◽  
Ki-Ho Park ◽  
Yoon-Goo Kang ◽  
Su-Jung Kim
Keyword(s):  
Signaling Pathway ◽  
Tooth Movement ◽  
Osteoclast Differentiation ◽  
Wnt Signaling Pathway ◽  
Enrichment Analysis ◽  
Tissue Response ◽  
Alveolar Ridge ◽  
Mineral Density ◽  
Gene Enrichment Analysis ◽  
Gene Enrichment

ABSTRACT Objective: To investigate the effect of flapless osteoperforation on the tissue response of the atrophic alveolar ridge affected by orthodontic tooth movement (OTM). Materials and Methods: An atrophic alveolar ridge model was established in the mandibular quadrants of eight beagle dogs. As a split-mouth design, the quadrants were randomly divided into group C (OTM only) and group OP (OTM with flapless osteoperforation). The rate of OTM for 10 weeks was compared between groups, and micro-CT-based histomorphometric analysis and RNA-sequencing-based gene-enrichment analysis were performed targeting the atrophic ridge. Results: Group OP displayed more rapid tooth movement with lower bone mineral density and higher trabecular fraction in the atrophic ridge than did group C, showing no intergroup difference of total ridge volume. As contributing biological functional pathways in group OP, the genes related to osteoclast differentiation and TNF signaling pathway were up-regulated and those associated with Wnt signaling pathway and AMPK signaling pathway were down-regulated. Conclusions: Flapless osteoperforation facilitated the rate of OTM toward the atrophic ridge, maintaining low bone density, whereas it did not increase the volume of the atrophic ridge.

KEGG-PATH: Kyoto encyclopedia of genes and genomes-based pathway analysis using a path analysis model

2014 ◽  
Vol 10 (9) ◽  
pp. 2441-2447 ◽  
Author(s):  
Junli Du ◽  
Zhifa Yuan ◽  
Ziwei Ma ◽  
Jiuzhou Song ◽  
Xiaoli Xie ◽  
...  
Keyword(s):  
Time Course ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Analysis Model ◽  
Kegg Pathways ◽  
Regulation Mechanisms ◽  
Complex Regulation ◽  
Multiple Treatments ◽  
Path Analysis Model

The KEGG-PATH approach, a kind of data mining through functional enrichment analysis of time-course experiments or those involving multiple treatments, can uncover the complex regulation mechanisms of KEGG pathways through the subdivision of total effect.

scholarly journals A Network Pharmacology Approach to Uncover the Multiple Mechanisms of Hedyotis diffusa Willd. on Colorectal Cancer

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Xinkui Liu ◽  
Jiarui Wu ◽  
Dan Zhang ◽  
Kaihuan Wang ◽  
Xiaojiao Duan ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Network Analysis ◽  
Chinese Herb ◽  
Endocrine System ◽  
Target Prediction ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Gene Enrichment Analysis ◽  
Gene Enrichment ◽  
Hedyotis Diffusa

Background. As one of the most frequently diagnosed cancer diseases globally, colorectal cancer (CRC) remains an important cause of cancer-related death. Although the traditional Chinese herb Hedyotis diffusa Willd. (HDW) has been proven to be effective for treating CRC in clinical practice, its definite mechanisms have not been completely deciphered. Objective. The aim of our research is to systematically explore the multiple mechanisms of HDW on CRC. Methods. This study adopted the network pharmacology approach, which was mainly composed of active component gathering, target prediction, CRC gene collection, network analysis, and gene enrichment analysis. Results. The network analysis showed that 10 targets might be the therapeutic targets of HDW on CRC, namely, HRAS, PIK3CA, KRAS, TP53, APC, BRAF, GSK3B, CDK2, AKT1, and RAF1. The gene enrichment analysis implied that HDW probably benefits patients with CRC by modulating pathways related to cancers, infectious diseases, endocrine system, immune system, nervous system, signal transduction, cellular community, and cell motility. Conclusions. This study partially verified and predicted the pharmacological and molecular mechanism of HDW against CRC from a holistic perspective, which will also lay a foundation for the further experimental research and clinical rational application of HDW.

scholarly journals Improved cancer biomarkers identification using network-constrained infinite latent feature selection

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246668
Author(s):  
Lihua Cai ◽  
Honglong Wu ◽  
Ke Zhou
Keyword(s):  
Feature Selection ◽  
Expression Profiles ◽  
Biological Significance ◽  
Feature Selection Method ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Gene Sets ◽  
Significant Gene

Identifying biomarkers that are associated with different types of cancer is an important goal in the field of bioinformatics. Different researcher groups have analyzed the expression profiles of many genes and found some certain genetic patterns that can promote the improvement of targeted therapies, but the significance of some genes is still ambiguous. More reliable and effective biomarkers identification methods are then needed to detect candidate cancer-related genes. In this paper, we proposed a novel method that combines the infinite latent feature selection (ILFS) method with the functional interaction (FIs) network to rank the biomarkers. We applied the proposed method to the expression data of five cancer types. The experiments indicated that our network-constrained ILFS (NCILFS) provides an improved prediction of the diagnosis of the samples and locates many more known oncogenes than the original ILFS and some other existing methods. We also performed functional enrichment analysis by inspecting the over-represented gene ontology (GO) biological process (BP) terms and applying the gene set enrichment analysis (GSEA) method on selected biomarkers for each feature selection method. The enrichments analysis reports show that our network-constraint ILFS can produce more biologically significant gene sets than other methods. The results suggest that network-constrained ILFS can identify cancer-related genes with a higher discriminative power and biological significance.

scholarly journals Network pharmacology-based elucidation of the molecular mechanism underlying the anti-migraine effect of Asari Radix et Rhizoma

2021 ◽  
Vol 18 (10) ◽  
pp. 2067-2074
Author(s):  
Yun-Bin Jiang ◽  
Mei Zhong ◽  
Ting Huang ◽  
Zhong-Hua Dai ◽  
Xing-Bao Tao ◽  
...  
Keyword(s):  
Molecular Mechanism ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Scientific Basis ◽  
Network Pharmacology ◽  
Overlapping Genes ◽  
Hub Genes ◽  
Gene Enrichment Analysis ◽  
Gene Enrichment ◽  
Tnf Gene

Purpose: To determine the molecular mechanism involved in the anti-migraine effect of Asari Radix et Rhizoma (ARR) using network pharmacology. Methods: The compounds present in ARR were identified through information retrieval from literature and public databases, and were screened based on absorption, distribution, metabolism, excretion and toxicity. Target genes related to the selected compounds and migraine were identified or predicted from public databases. Hub genes in ARR against migraine were identified through analysis of interactions in overlapping genes between compounds and migraine target genes, based on STRING database. Gene enrichment analysis of overlapping genes was performed using Database for Annotation, Visualization and Integrated Discovery. Results: A total of 138 compounds were selected as potential bioactive compounds in ARR. Target genes related to the selected compounds (611 genes) and migraine (278 genes) were obtained, including 71 overlapping genes. The hub genes in the anti-migraine effect of ARR were BDNF, IL6, COMT, APP and TNF. Gene enrichment analysis showed the top 10 biological processes or pathways involved in the mechanism of anti-migraine action of ARR. The tissue source of the overlapping genes was not limited to the brain. The results from gene enrichment analysis revealed that the effect of ARR on migraine was holistic, which is characteristic of traditional Chinese medicines. Conclusion: Network pharmacology has been used to decipher the molecular mechanism involved in the action of ARR against migraine. The results provide a scientific basis for the clinical effect of ARR on migraine.

scholarly journals Graph analytics for phenome-genome associations inference

2019 ◽  
Author(s):  
Davide Cirillo ◽  
Dario Garcia-Gasulla ◽  
Ulises Cortés ◽  
Alfonso Valencia
Keyword(s):  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Specific Gene ◽  
Proteus Syndrome ◽  
Supporting Evidence ◽  
Human Phenotype ◽  
Biological Ontologies ◽  
Statistical Framework ◽  
Gene Sets

AbstractMotivationBiological ontologies, such as the Human Phenotype Ontology (HPO) and the Gene Ontology (GO), are extensively used in biomedical research to find enrichment in the annotations of specific gene sets. However, the interpretation of the encoded information would greatly benefit from methods that effectively interoperate between multiple ontologies providing molecular details of disease-related features.ResultsIn this work, we present a statistical framework based on graph theory to infer direct associations between HPO and GO terms that do not share co-annotated genes. The method enables to map genotypic features to phenotypic features thus providing a valid tool for bridging functional and pathological annotations. We validated the results by (a) supporting evidence of known drug-target associations (PanDrugs), protein-protein physical and functional interactions (BioGRID and STRING), and common pathways (Reactome); (b) comparing relationships inferred from early ontology releases with knowledge contained in the latest versions.ApplicationsWe applied our method to improve the interpretation of molecular processes involved in pathological conditions, illustrating the applicability of our predictions with a number of biological examples. In particular, we applied our method to expand the list of relevant genes from standard functional enrichment analysis of high-throughput experimental results in the context of comorbidities between Alzheimer’s disease, Lung Cancer and Glioblastoma. Moreover, we analyzed pathways linked to predicted phenotype-genotype associations getting insights into the molecular actors of cellular senescence in Proteus syndrome.Availabilityhttps://github.com/dariogarcia/phenotype-genotype_graph_characterization

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