Pathway-specific model estimation for improved pathway annotation by network crosstalk

Abstract Pathway enrichment analysis is the most common approach for understanding which biological processes are affected by altered gene activities under specific conditions. However, it has been challenging to find a method that efficiently avoids false positives while keeping a high sensitivity. We here present a new network-based method ANUBIX based on sampling random gene sets against intact pathway. Benchmarking shows that ANUBIX is considerably more accurate than previous network crosstalk based methods, which have the drawback of modelling pathways as random gene sets. We demonstrate that ANUBIX does not have a bias for finding certain pathways, which previous methods do, and show that ANUBIX finds biologically relevant pathways that are missed by other methods.

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Systems Pharmacological Approach to Investigate the Mechanism of Acori Tatarinowii Rhizoma for Alzheimer’s Disease

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2018/5194016 ◽

2018 ◽

Vol 2018 ◽

pp. 1-20 ◽

Cited By ~ 5

Author(s):

Zhenyan Song ◽

Fang Yin ◽

Biao Xiang ◽

Bin Lan ◽

Shaowu Cheng

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Therapeutic Effects ◽

Biological Processes ◽

Pathway Enrichment ◽

Study Results ◽

Target Network ◽

Anti Inflammatory

In traditional Chinese medicine (TCM), Acori Tatarinowii Rhizoma (ATR) is widely used to treat memory and cognition dysfunction. This study aimed to confirm evidence regarding the potential therapeutic effect of ATR on Alzheimer’s disease (AD) using a system network level based in silico approach. Study results showed that the compounds in ATR are highly connected to AD-related signaling pathways, biological processes, and organs. These findings were confirmed by compound-target network, target-organ location network, gene ontology analysis, and KEGG pathway enrichment analysis. Most compounds in ATR have been reported to have antifibrillar amyloid plaques, anti-tau phosphorylation, and anti-inflammatory effects. Our results indicated that compounds in ATR interact with multiple targets in a synergetic way. Furthermore, the mRNA expressions of genes targeted by ATR are elevated significantly in heart, brain, and liver. Our results suggest that the anti-inflammatory and immune system enhancing effects of ATR might contribute to its major therapeutic effects on Alzheimer’s disease.

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Novel Biomarker MicroRNAs for Subtyping of Acute Coronary Syndrome: A Bioinformatics Approach

BioMed Research International ◽

10.1155/2016/4618323 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 7

Author(s):

Yujie Zhu ◽

Yuxin Lin ◽

Wenying Yan ◽

Zhandong Sun ◽

Zhi Jiang ◽

...

Keyword(s):

Gene Expression ◽

Acute Coronary Syndrome ◽

Target Genes ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Pathway Enrichment Analysis ◽

Biological Processes ◽

Pathway Enrichment ◽

Coronary Syndrome ◽

Life Threatening

Acute coronary syndrome (ACS) is a life-threatening disease that affects more than half a million people in United States. We currently lack molecular biomarkers to distinguish the unstable angina (UA) and acute myocardial infarction (AMI), which are the two subtypes of ACS. MicroRNAs play significant roles in biological processes and serve as good candidates for biomarkers. In this work, we collected microRNA datasets from the Gene Expression Omnibus database and identified specific microRNAs in different subtypes and universal microRNAs in all subtypes based on our novel network-based bioinformatics approach. These microRNAs were studied for ACS association by pathway enrichment analysis of their target genes. AMI and UA were associated with 27 and 26 microRNAs, respectively, nine of them were detected for both AMI and UA, and five from each subtype had been reported previously. The remaining 22 and 21 microRNAs are novel microRNA biomarkers for AMI and UA, respectively. The findings are then supported by pathway enrichment analysis of the targets of these microRNAs. These novel microRNAs deserve further validation and will be helpful for personalized ACS diagnosis.

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netGO: R-Shiny package for network-integrated pathway enrichment analysis

Bioinformatics ◽

10.1093/bioinformatics/btaa077 ◽

2020 ◽

Vol 36 (10) ◽

pp. 3283-3285

Author(s):

Jinhwan Kim ◽

Sora Yoon ◽

Dougu Nam

Keyword(s):

Target Genes ◽

Genome Wide Association Study ◽

Enrichment Analysis ◽

Supplementary Information ◽

Pathway Enrichment Analysis ◽

Pathway Enrichment ◽

Pathway Gene ◽

Gene Sets ◽

R Shiny ◽

Integrated Pathway

Abstract Summary We present an R-Shiny package, netGO, for novel network-integrated pathway enrichment analysis. The conventional Fisher’s exact test (FET) considers the extent of overlap between target genes and pathway gene-sets, while recent network-based analysis tools consider only network interactions between the two. netGO implements an intuitive framework to integrate both the overlap and networks into a single score, and adaptively resamples genes based on network degrees to assess the pathway enrichment. In benchmark tests for gene expression and genome-wide association study (GWAS) data, netGO captured the relevant gene-sets better than existing tools, especially when analyzing a small number of genes. Specifically, netGO provides user-interactive visualization of the target genes, enriched gene-set and their network interactions for both netGO and FET results for further analysis. For this visualization, we also developed a standalone R-Shiny package shinyCyJS to connect R-shiny and the JavaScript version of cytoscape. Availability and implementation netGO R-Shiny package is freely available from github, https://github.com/unistbig/netGO. Supplementary information Supplementary data are available at Bioinformatics online.

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Identification of 37 Heterogeneous Drug Candidates for Treatment of COVID-19 via a Rational Transcriptomics-Based Drug Repurposing Approach

Pharmaceuticals ◽

10.3390/ph14020087 ◽

2021 ◽

Vol 14 (2) ◽

pp. 87

Author(s):

Andrea Gelemanović ◽

Tinka Vidović ◽

Višnja Stepanić ◽

Katarina Trajković

Keyword(s):

Treatment Options ◽

Drug Repurposing ◽

Enrichment Analysis ◽

Current Treatment ◽

Host Cells ◽

Biological Knowledge ◽

Pathway Enrichment Analysis ◽

Biological Processes ◽

Pathway Enrichment ◽

Drug Candidates

A year after the initial outbreak, the COVID-19 pandemic caused by SARS-CoV-2 virus remains a serious threat to global health, while current treatment options are insufficient to bring major improvements. The aim of this study is to identify repurposable drug candidates with a potential to reverse transcriptomic alterations in the host cells infected by SARS-CoV-2. We have developed a rational computational pipeline to filter publicly available transcriptomic datasets of SARS-CoV-2-infected biosamples based on their responsiveness to the virus, to generate a list of relevant differentially expressed genes, and to identify drug candidates for repurposing using LINCS connectivity map. Pathway enrichment analysis was performed to place the results into biological context. We identified 37 structurally heterogeneous drug candidates and revealed several biological processes as druggable pathways. These pathways include metabolic and biosynthetic processes, cellular developmental processes, immune response and signaling pathways, with steroid metabolic process being targeted by half of the drug candidates. The pipeline developed in this study integrates biological knowledge with rational study design and can be adapted for future more comprehensive studies. Our findings support further investigations of some drugs currently in clinical trials, such as itraconazole and imatinib, and suggest 31 previously unexplored drugs as treatment options for COVID-19.

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Mechanism of Peony and Licorice and Aconite Decoction in the Treatment of Osteoarthritis Based on Network Pharmacology and Molecular Docking

10.21203/rs.3.rs-1057016/v1 ◽

2021 ◽

Author(s):

Zhiqiang li ◽

Luo Jun

Keyword(s):

Molecular Docking ◽

Chinese Medicine ◽

Protein Interaction ◽

Active Component ◽

Kegg Pathway ◽

Enrichment Analysis ◽

Network Pharmacology ◽

Pathway Enrichment Analysis ◽

Biological Processes ◽

Pathway Enrichment

Abstract Objective: To predict the key molecular mechanism of Shaoyao Liquorice Aconite Decoction in the treatment of osteoarthritis by using network pharmacology and molecular docking technology, and to provide a new target for the treatment of osteoarthritis. Methods: by means of traditional Chinese medicine database TCMSP screening peony licorice monkshood soup main active component of radix paeoniae alba, radix glycyrrhizae, and the corresponding targets, lateral root of aconite and retrieve OMIM, GeneCards, TDD, PharmGKB and Drugbank database related target for treatment of osteoarthritis, and then forecast drug targets and disease targets for intersection get peony licorice monkshood soup targets for the treatment of osteoarthritis.Then, STRING database and Cytoscape software were used to construct the "drug active component - action target" network and protein interaction network of Shaoyaogaofuzi Decoction in the treatment of osteoarthritis, and David database was used for GO function enrichment analysis and KEGG pathway enrichment analysis of shaoyaogaofuzi Decoction in the treatment of osteoarthritis.Finally, PyMOL, Chem3D, AutoDock, OpenBabel and other software were used to verify the molecular docking of the key active ingredients and key targets of Shaoyao Liquorice Aconite Decoction. Results: 162 active components were screened out.A total of 954 disease targets were collected, and a total of 72 disease targets were obtained after weight removal.Protein interaction analysis suggested that TNF, AKT1, IL6, IL1B and TP53 were the core targets of protein interaction network.Through GO enrichment analysis, 393 biological processes were obtained, and it was found that biological processes were mainly enriched in cell differentiation, migration, apoptosis, and cell stress response to organisms.A total of 116 Pathways were obtained through KEGG pathway enrichment analysis, mainly involving Pathways in cancer, TNF Signaling Pathway, Tuberculosis, Chagas disease, Hepatitis B, etc. Finally, the molecular docking of key active molecules and key targets was realized for verification.Conclusions: this study of compound Chinese medicine pharmacology, through the network of peony licorice monkshood soup ingredients with osteoarthritis, targets, pathway analysis, you can see that drugs in the treatment of osteoarthritis is not a simple single targeted therapy, but by many components, multi-channel, mutual communications between the multiple targets, on the treatment of osteoarthritis in the future to provide more advice.

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pathfindR: An R Package for Pathway Enrichment Analysis Utilizing Active Subnetworks

10.1101/272450 ◽

2018 ◽

Cited By ~ 5

Author(s):

Ege Ulgen ◽

Ozan Ozisik ◽

Osman Ugur Sezerman

Keyword(s):

Interaction Network ◽

Enrichment Analysis ◽

R Package ◽

Pathway Enrichment Analysis ◽

Pathway Enrichment ◽

Protein Protein Interaction ◽

Gene Sets ◽

User Data ◽

Pathway Diagrams ◽

Protein Protein Interaction Network

AbstractSummaryPathfindR is a tool for pathway enrichment analysis utilizing active subnetworks. It identifies gene sets that form active subnetworks in a protein-protein interaction network using a list of genes provided by the user. It then performs pathway enrichment analyses on the identified gene sets. Further, using the R package pathview, it maps the user data on the enriched pathways and renders pathway diagrams with the mapped genes. Because many of the enriched pathways are usually biologically related, pathfindR also offers functionality to cluster these pathways and identify representative pathways in the clusters. PathfindR is built as a stand-alone package but it can easily be integrated with other tools, such as differential expression/methylation analysis tools, for building fully automated pipelines. In this article, an overview of pathfindR is provided and an example application on a rheumatoid arthritis dataset is presented and discussed.AvailabilityThe package is freely available under MIT license at: https://github.com/egeulgen/pathfindR

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Functional Network Community Detection Can Disaggregate and Filter Multiple Underlying Pathways in Enrichment Analyses

10.1101/166207 ◽

2017 ◽

Author(s):

Lia X. Harrington ◽

Gregory P. Way ◽

Jennifer A. Doherty ◽

Casey S. Greene

Keyword(s):

Community Detection ◽

Simulation Study ◽

Enrichment Analysis ◽

False Positives ◽

Functional Networks ◽

Pathway Enrichment Analysis ◽

Expression Data ◽

Pathway Enrichment ◽

Gene Sets ◽

Overrepresentation Analysis

Differential expression experiments or other analyses often end in a list of genes. Pathway enrichment analysis is one method to discern important biological signals and patterns from noisy expression data. However, pathway enrichment analysis may perform suboptimally in situations where there are multiple implicated pathways – such as in the case of genes that define subtypes of complex diseases. Our simulation study shows that in this setting, standard overrepresentation analysis identifies many false positive pathways along with the true positives. These false positives hamper investigators’ attempts to glean biological insights from enrichment analysis. We develop and evaluate an approach that combines community detection over functional networks with pathway enrichment to reduce false positives. Our simulation study demonstrates that a large reduction in false positives can be obtained with a small decrease in power. Though we hypothesized that multiple communities might underlie previously described subtypes of high-grade serous ovarian cancer and applied this approach, our results do not support this hypothesis. In summary, applying community detection before enrichment analysis may ease interpretation for complex gene sets that represent multiple distinct pathways.

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CogNet: classification of gene expression data based on ranked active-subnetwork-oriented KEGG pathway enrichment analysis

PeerJ Computer Science ◽

10.7717/peerj-cs.336 ◽

2021 ◽

Vol 7 ◽

pp. e336

Author(s):

Malik Yousef ◽

Ege Ülgen ◽

Osman Uğur Sezerman

Keyword(s):

Gene Expression ◽

Gene Selection ◽

Kegg Pathway ◽

Enrichment Analysis ◽

Biological Knowledge ◽

Pathway Enrichment Analysis ◽

Biologically Relevant ◽

Pathway Enrichment ◽

Kegg Pathways

Most of the traditional gene selection approaches are borrowed from other fields such as statistics and computer science, However, they do not prioritize biologically relevant genes since the ultimate goal is to determine features that optimize model performance metrics not to build a biologically meaningful model. Therefore, there is an imminent need for new computational tools that integrate the biological knowledge about the data in the process of gene selection and machine learning. Integrative gene selection enables incorporation of biological domain knowledge from external biological resources. In this study, we propose a new computational approach named CogNet that is an integrative gene selection tool that exploits biological knowledge for grouping the genes for the computational modeling tasks of ranking and classification. In CogNet, the pathfindR serves as the biological grouping tool to allow the main algorithm to rank active-subnetwork-oriented KEGG pathway enrichment analysis results to build a biologically relevant model. CogNet provides a list of significant KEGG pathways that can classify the data with a very high accuracy. The list also provides the genes belonging to these pathways that are differentially expressed that are used as features in the classification problem. The list facilitates deep analysis and better interpretability of the role of KEGG pathways in classification of the data thus better establishing the biological relevance of these differentially expressed genes. Even though the main aim of our study is not to improve the accuracy of any existing tool, the performance of the CogNet outperforms a similar approach called maTE while obtaining similar performance compared to other similar tools including SVM-RCE. CogNet was tested on 13 gene expression datasets concerning a variety of diseases.

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