Cytoscape Web: an interactive web-based network browser

Lopes, Christian; Franz, Max; Kazi, Farzana; Donaldson, Sylva L.; Morris, Quaid; Bader, Gary D.

doi:10.1093/bioinformatics/btq430

Cited by 657 publications

(572 citation statements)

References 6 publications

Supporting

Mentioning

571

Contrasting

Unclassified

Order By: Relevance

“…S2, S3 and S4). The webserver is Cytoscape-web (Lopes et al, 2010) enabled and allows graphical display of clusters with node attributes set to highlight changes in the user provided transcriptome and genes with regulatory roles (e.g. TFs and kinases).…”

Section: Cc-by-nc-ndmentioning

confidence: 99%

RECoN: Rice Environment Coexpression Network for Systems Level Analysis of Abiotic-Stress Response

Krishnan

Gupta

Ambavaram

et al. 2017

Preprint

View full text Add to dashboard Cite

Author ContributionsAK, designed the network, conducted statistical analysis, drafted the manuscript; CG, contributed text, updated figures, and created the webserver; MA, conducted rice drought and gene expression experiments; AP, designed experiments and coordinated research; all authors contributed to writing the manuscript. AbstractTranscriptional profiling is a prevalent and powerful approach for capturing the response of crop plants to environmental stresses, e.g. response of rice to drought. However, functionally . CC-BY-NC-ND 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/166694 doi: bioRxiv preprint first posted online 2 interpreting the resulting genome-wide gene expression changes is severely hampered by the large gaps in our genomic knowledge about which genes work together in cellular pathways/processes in rice. Here, we present a new web resource -RECoN -that relies on a network-based approach to go beyond currently limited annotations in delineating functional and regulatory perturbations in new rice stress transcriptome datasets generated by a researcher. To build RECoN, we first enumerated 1,744 stress-specific gene modules covering 28,421 rice genes (>72% of the genes in the genome). Each module contains a group of genes tightly coexpressed across a large number of environmental conditions and, thus, is likely to be functionally coherent. When a user provides a new differential expression profile, RECoN identifies modules substantially perturbed in their experiment and further suggests deregulated functional and regulatory mechanisms based on the enrichment of current annotations within the predefined modules. We demonstrate the utility of this resource by analyzing new drought transcriptomes of rice in three developmental stages, which revealed large-scale insights into the cellular processes and regulatory mechanisms involved in common and stage-specific drought responses. RECoN enables biologists to functionally explore new data from all abiotic stresses on a genome-scale and to uncover gene candidates, including those that are currently functionally uncharacterized, for engineering stress tolerance.

show abstract

Section: Cc-by-nc-ndmentioning

confidence: 99%

RECoN: Rice Environment Coexpression Network for Systems Level Analysis of Abiotic-Stress Response

Krishnan

Gupta

Ambavaram

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…For the visualization of the network, the software Cytoscape (version 3.2.1) was used (Lopes et al , 2010; Smoot et al , 2011). A text file was compiled in which each row corresponds to one interaction.…”

Section: Methodsmentioning

confidence: 99%

From network to phenotype: the dynamic wiring of an Arabidopsis transcriptional network induced by osmotic stress

Broeck

Dubois

Vermeersch

et al. 2017

Molecular Systems Biology

View full text Add to dashboard Cite

Plants have established different mechanisms to cope with environmental fluctuations and accordingly fine‐tune their growth and development through the regulation of complex molecular networks. It is largely unknown how the network architectures change and what the key regulators in stress responses and plant growth are. Here, we investigated a complex, highly interconnected network of 20 Arabidopsis transcription factors (TFs) at the basis of leaf growth inhibition upon mild osmotic stress. We tracked the dynamic behavior of the stress‐responsive TFs over time, showing the rapid induction following stress treatment, specifically in growing leaves. The connections between the TFs were uncovered using inducible overexpression lines and were validated with transient expression assays. This study resulted in the identification of a core network, composed of ERF6, ERF8, ERF9, ERF59, and ERF98, which is responsible for most transcriptional connections. The analyses highlight the biological function of this core network in environmental adaptation and its redundancy. Finally, a phenotypic analysis of loss‐of‐function and gain‐of‐function lines of the transcription factors established multiple connections between the stress‐responsive network and leaf growth.

show abstract

“…To investigate node dependency relationships in response to various in silico knockouts (KOs), the connectivity of all nodes was determined using CellNetAnalyzer (3.1) [13], and visualised in Cytoscape (2.8) [14], and the three most highly connected nodes in the network chosen. Selected nodes and corresponding edges were deleted from the model to represent in vivo KO or mutation.…”

Section: Knock Out Analysis Of Highly Connected Nodesmentioning

confidence: 99%

“…Logical Steady State (LSS) analysis is a method for predictive input -output relationships in signalling networks [14]. A steady state is reached when the state of nodes is fully consistent with its Boolean function, and as such once a network has reached a LSS it will remain there over time.…”

Section: Application Of Logical Steady State Analysismentioning

confidence: 99%

“…Files listing proteins and interactions were generated for PMH260 in accordance with [14]. Four different scenarios were constructed which represent in vivo processes such as loss of p53 function due to mutation (mimicked in silico by removal of the p53 node) in the presence and absence of DNA damage ( Table 1).…”

Section: Application Of Logical Steady State Analysismentioning

confidence: 99%

See 1 more Smart Citation

The Expanded p53 Interactome as a Predictive Model for Cancer Therapy

Hussain

Tian

Mutti

et al. 2015

Genomics Comput Biol

View full text Add to dashboard Cite

The tumour suppressor gene TP53 is implicated in the majority of all human cancers, thus pivotal to genomic integrity. Even though over 72,000 PubMed publications are linked with the keyword p53 and this number is continuously increasing, due to the complexity of its interactions we are still far from fully elucidating p53's role in tumorigenesis. Computational methodologies are novel tools to depict and dissect complex disease networks. The Boolean PKT206 p53-DNA damage model has previously demonstrated good predictive capability for p53 wildtype and null tumours in various in silico knockouts. Here, we have expanded PKT206 to generate a more clinically robust representation of p53 dynamics. The new PMH260 model incorporates 260 nodes representing genes, with 980 interactions between them representing inhibitions and activations. Additional biological outputs, including angiogenesis, cell cycle arrest and DNA repair were also amalgamated into the model. Three in silico knockouts of highly connected nodes (p53, MDM2 and FGF2) were generated and logical steady state analysis and dependency relationships determined. 71 % of predictions were considered true from superimposition of human osteosarcoma and HCT116 microarray profiles. In silico knockout analysis revealed 98 potential novel predictions, of which 13 were validated by literature; 83 % of them were overlapping with PKT206. Thus the expanded Boolean PMH260 model offers a promising platform for clinical potential in targeted cancer therapeutics.

show abstract

Cytoscape Web: an interactive web-based network browser

Cited by 657 publications

References 6 publications

RECoN: Rice Environment Coexpression Network for Systems Level Analysis of Abiotic-Stress Response

RECoN: Rice Environment Coexpression Network for Systems Level Analysis of Abiotic-Stress Response

From network to phenotype: the dynamic wiring of an Arabidopsis transcriptional network induced by osmotic stress

The Expanded p53 Interactome as a Predictive Model for Cancer Therapy

Contact Info

Product

Resources

About