Clustering and Cross-talk in a Yeast Functional Interaction Network

2006 ◽  
Author(s):  
Jennifer Hallinan ◽  
Anil Wipat
Keyword(s):  
Cross Talk ◽  
Interaction Network ◽  
Functional Interaction ◽  
Functional Interaction Network

scholarly journals Visualization of drug target interactions in the contexts of pathways and networks with ReactomeFIViz

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 908 ◽  
Author(s):  
Aurora S. Blucher ◽  
Shannon K. McWeeney ◽  
Lincoln Stein ◽  
Guanming Wu
Keyword(s):  
Targeted Therapies ◽  
Biological Networks ◽  
Drug Target ◽  
Drug Targets ◽  
Interaction Network ◽  
Genetic Alterations ◽  
Patient Specific ◽  
Functional Interaction ◽  
Functional Interaction Network ◽  
A Genome

The precision medicine paradigm is centered on therapies targeted to particular molecular entities that will elicit an anticipated and controlled therapeutic response. However, genetic alterations in the drug targets themselves or in genes whose products interact with the targets can affect how well a drug actually works for an individual patient. To better understand the effects of targeted therapies in patients, we need software tools capable of simultaneously visualizing patient-specific variations and drug targets in their biological context. This context can be provided using pathways, which are process-oriented representations of biological reactions, or biological networks, which represent pathway-spanning interactions among genes, proteins, and other biological entities. To address this need, we have recently enhanced the Reactome Cytoscape app, ReactomeFIViz, to assist researchers in visualizing and modeling drug and target interactions. ReactomeFIViz integrates drug-target interaction information with high quality manually curated pathways and a genome-wide human functional interaction network. Both the pathways and the functional interaction network are provided by Reactome, the most comprehensive open source biological pathway knowledgebase. We describe several examples demonstrating the application of these new features to the visualization of drugs in the contexts of pathways and networks. Complementing previous features in ReactomeFIViz, these new features enable researchers to ask focused questions about targeted therapies, such as drug sensitivity for patients with different mutation profiles, using a pathway or network perspective.

Identification of candidate genes for reproductive traits in cattle using a functional interaction network approach

2020 ◽  
Vol 11 (3) ◽  
pp. 894-904
Author(s):  
Francisco Alejandro Paredes-Sánchez ◽  
Daniel Trejo-Martínez ◽  
Elsa Verónica Herrera-Mayorga ◽  
Williams Arellano-Vera ◽  
Felipe Rodríguez Almeida ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Interaction Network ◽  
Reproductive Traits ◽  
Network Approach ◽  
Functional Interaction ◽  
Functional Interaction Network

scholarly journals Genetic and functional interaction network analysis reveals global enrichment of regulatory T cell genes influencing basal cell carcinoma susceptibility

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Christelle Adolphe ◽  
Angli Xue ◽  
Atefeh Taherian Fard ◽  
Laura A. Genovesi ◽  
Jian Yang ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Network Analysis ◽  
Basal Cell Carcinoma ◽  
Regulatory Network ◽  
Interaction Network ◽  
Susceptibility Genes ◽  
Genome Wide Association ◽  
Functional Interaction ◽  
Functional Interaction Network ◽  
Genome Wide

Abstract Background Basal cell carcinoma (BCC) of the skin is the most common form of human cancer, with more than 90% of tumours presenting with clear genetic activation of the Hedgehog pathway. However, polygenic risk factors affecting mechanisms such as DNA repair and cell cycle checkpoints or which modulate the tumour microenvironment or host immune system play significant roles in determining whether genetic mutations culminate in BCC development. We set out to define background genetic factors that play a role in influencing BCC susceptibility via promoting or suppressing the effects of oncogenic drivers of BCC. Methods We performed genome-wide association studies (GWAS) on 17,416 cases and 375,455 controls. We subsequently performed statistical analysis by integrating data from population-based genetic studies of multi-omics data, including blood- and skin-specific expression quantitative trait loci and methylation quantitative trait loci, thereby defining a list of functionally relevant candidate BCC susceptibility genes from our GWAS loci. We also constructed a local GWAS functional interaction network (consisting of GWAS nearest genes) and another functional interaction network, consisting specifically of candidate BCC susceptibility genes. Results A total of 71 GWAS loci and 46 functional candidate BCC susceptibility genes were identified. Increased risk of BCC was associated with the decreased expression of 26 susceptibility genes and increased expression of 20 susceptibility genes. Pathway analysis of the functional candidate gene regulatory network revealed strong enrichment for cell cycle, cell death, and immune regulation processes, with a global enrichment of genes and proteins linked to TReg cell biology. Conclusions Our genome-wide association analyses and functional interaction network analysis reveal an enrichment of risk variants that function in an immunosuppressive regulatory network, likely hindering cancer immune surveillance and effective antitumour immunity.

scholarly journals A network-based method for predicting disease-associated enhancers

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260432
Author(s):  
Duc-Hau Le
Keyword(s):  
Random Walk ◽  
Heterogeneous Network ◽  
Target Genes ◽  
Interaction Network ◽  
Potential Method ◽  
Diffusion Method ◽  
Random Walk With Restart ◽  
Functional Interaction ◽  
Functional Interaction Network ◽  
Network Diffusion

Background Enhancers regulate transcription of target genes, causing a change in expression level. Thus, the aberrant activity of enhancers can lead to diseases. To date, a large number of enhancers have been identified, yet a small portion of them have been found to be associated with diseases. This raises a pressing need to develop computational methods to predict associations between diseases and enhancers. Results In this study, we assumed that enhancers sharing target genes could be associated with similar diseases to predict the association. Thus, we built an enhancer functional interaction network by connecting enhancers significantly sharing target genes, then developed a network diffusion method RWDisEnh, based on a random walk with restart algorithm, on networks of diseases and enhancers to globally measure the degree of the association between diseases and enhancers. RWDisEnh performed best when the disease similarities are integrated with the enhancer functional interaction network by known disease-enhancer associations in the form of a heterogeneous network of diseases and enhancers. It was also superior to another network diffusion method, i.e., PageRank with Priors, and a neighborhood-based one, i.e., MaxLink, which simply chooses the closest neighbors of known disease-associated enhancers. Finally, we showed that RWDisEnh could predict novel enhancers, which are either directly or indirectly associated with diseases. Conclusions Taken together, RWDisEnh could be a potential method for predicting disease-enhancer associations.

scholarly journals Generation and Analysis of Large-Scale Data-Driven Mycobacterium tuberculosis Functional Networks for Drug Target Identification

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Gaston K. Mazandu ◽  
Nicola J. Mulder
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Drug Targets ◽  
Large Scale ◽  
Global Analysis ◽  
Interaction Network ◽  
Interaction Networks ◽  
Microbial Pathogens ◽  
Potential Drug ◽  
Functional Interaction ◽  
Potential Drug Targets

Technological developments in large-scale biological experiments, coupled with bioinformatics tools, have opened the doors to computational approaches for the global analysis of whole genomes. This has provided the opportunity to look at genes within their context in the cell. The integration of vast amounts of data generated by these technologies provides a strategy for identifying potential drug targets within microbial pathogens, the causative agents of infectious diseases. As proteins are druggable targets, functional interaction networks between proteins are used to identify proteins essential to the survival, growth, and virulence of these microbial pathogens. Here we have integrated functional genomics data to generate functional interaction networks between Mycobacterium tuberculosis proteins and carried out computational analyses to dissect the functional interaction network produced for identifying drug targets using network topological properties. This study has provided the opportunity to expand the range of potential drug targets and to move towards optimal target-based strategies.

scholarly journals GeneMANIA: Fast gene network construction and function prediction for Cytoscape

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 153 ◽  
Author(s):  
Jason Montojo ◽  
Khalid Zuberi ◽  
Harold Rodriguez ◽  
Gary D. Bader ◽  
Quaid Morris
Keyword(s):  
Gene Network ◽  
Expression Profiles ◽  
Gene List ◽  
Interaction Network ◽  
Functional Interaction ◽  
Network Construction ◽  
Functional Annotations ◽  
C Elegans ◽  
Data Source ◽  
And Function

The GeneMANIA Cytoscape app enables users to construct a composite gene-gene functional interaction network from a gene list. The resulting network includes the genes most related to the original list, and functional annotations from Gene Ontology. The edges are annotated with details about the publication or data source the interactions were derived from. The app leverages GeneMANIA’s database of 1800+ networks, containing over 500 million interactions spanning 8 organisms: A. thaliana, C. elegans, D. melanogaster, D. rerio, H. sapiens, M. musculus, R. norvegicus, and S. cerevisiae. Users may also import their own organisms, networks, and expression profiles. The app is compatible with Cytoscape versions 2 and 3.

Sign in / Sign up

Export Citation Format

Share Document