Cytoscape stringApp: Network analysis and visualization of proteomics data

Doncheva, Nadezhda T.; Morris, John H.; Gorodkin, Jan; Jensen, Lars Juhl

doi:10.1101/438192

Cited by 242 publications

(267 citation statements)

References 20 publications

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“…For this analysis we setting the minimum required interaction score 0.400, none max number of interactors and all interaction sources were selected. The Cytoscape software [63] was used to analyze protein-protein interaction (PPI) generated by STRING. In this study, we set as input the genes 10 DAP-biased fruit separately from genes of 40 DAP-biased fruit.…”

Section: Protein-protein Interaction (Ppi) Network Construction and Mmentioning

confidence: 99%

Transcriptome profiling of non-climacteric ‘yellow’ melon during ripening: insights on sugar metabolism

et al. 2020

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Background: The non-climacteric 'Yellow' melon (Cucumis melo, inodorus group) is an economically important crop and its quality is mainly determined by the sugar content. Thus, knowledge of sugar metabolism and its related pathways can contribute to the development of new field management and post-harvest practices, making it possible to deliver better quality fruits to consumers.Results: The RNA-seq associated with RT-qPCR analyses of four maturation stages were performed to identify important enzymes and pathways that are involved in the ripening profile of non-climacteric 'Yellow' melon fruit focusing on sugar metabolism. We identified 895 genes 10 days after pollination (DAP)-biased and 909 genes 40 DAP-biased. The KEGG pathway enrichment analysis of these differentially expressed (DE) genes revealed that 'hormone signal transduction', 'carbon metabolism', 'sucrose metabolism', 'protein processing in endoplasmic reticulum' and 'spliceosome' were the most differentially regulated processes occurring during melon development. In the sucrose metabolism, five DE genes are up-regulated and 12 are down-regulated during fruit ripening. Conclusions:The results demonstrated important enzymes in the sugar pathway that are responsible for the sucrose content and maturation profile in non-climacteric 'Yellow' melon. New DE genes were first detected for melon in this study such as invertase inhibitor LIKE 3 (CmINH3), trehalose phosphate phosphatase (CmTPP1) and trehalose phosphate synthases (CmTPS5, CmTPS7, CmTPS9). Furthermore, the results of the protein-protein network interaction demonstrated general characteristics of the transcriptome of young and full-ripe melon and provide new perspectives for the understanding of ripening.

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Section: Protein-protein Interaction (Ppi) Network Construction and Mmentioning

confidence: 99%

Transcriptome profiling of non-climacteric ‘yellow’ melon during ripening: insights on sugar metabolism

et al. 2020

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“…This connection information is stored in the network table, which is used to recreate the link when a session file is loaded. An Omics Visualizer table can be connected to several networks, but a network can only be connected to one Omics Visualizer Alternatively, Omics Visualizer can retrieve and automatically connect a network from the STRING database 6 using the Cytoscape stringApp 5 . The user can select any column with gene/protein identifiers, which will be used to first query STRING to retrieve a network and subsequently as the key for connecting the network to the Omics Visualizer table.…”

Section: Methodsmentioning

confidence: 99%

Visualize omics data on networks with Omics Visualizer, a Cytoscape App

et al. 2020

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Cytoscape is an open-source software used to analyze and visualize biological networks. In addition to being able to import networks from a variety of sources, Cytoscape allows users to import tabular node data and visualize it onto networks. Unfortunately, such data tables can only contain one row of data per node, whereas omics data often have multiple rows for the same gene or protein, representing different post-translational modification sites, peptides, splice isoforms, or conditions. Here, we present a new app, Omics Visualizer, that allows users to import data tables with several rows referring to the same node, connect them to one or more networks, and visualize the connected data onto networks. Omics Visualizer uses the Cytoscape enhancedGraphics app to show the data either in the nodes (pie visualization) or around the nodes (donut visualization), where the colors of the slices represent the imported values. If the user does not provide a network, the app can retrieve one from the STRING database using the Cytoscape stringApp. The Omics Visualizer app is freely available at https://apps.cytoscape.org/apps/omicsvisualizer.

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“…For this purpose "STRING App" in "Cytoscape 3.7.2 software" was utilized. The STRING App is one of the Cytoscape software apps related to the String database [31]. This database is utilized for investigating the protein-protein interaction (PPI).…”

Section: Methodsmentioning

confidence: 99%

An in-silico study of cartilage tissue engineering and its primary developmental growth factors in a systems biology approach

Beheshtizadeh¹

2020

Preprint

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Abstract Background Numerous growth factors work in consonance to regulate the development and homeostasis of engineered cartilage. In order to fabricate mature cartilages provided to implantation, developing biological processes should be known and employed. In this regard, a systems biology study could be considered as an inexpensive evaluation method. In this work, a systems biology study was performed on 11 protein-coding genes participating in cartilage development. Results Developing a protein-protein interaction (PPI) network resulted in a graph with 11 nodes and 28 edges. Additionally, a gene ontology (GO) analysis and a centrality analysis were performed based on the degree index. Four most crucial biological processes involved in cartilage engineering identified by the gene ontology analysis: cartilage development, cartilage condensation, regulation of cartilage development, and cartilage development involved in endochondral bone morphogenesis. Also, seven most interactive proteins were detected performing a degree-based centrality analysis: COL2A1, SOX9, CTGF, BMP7, TGFB1, CD44, and MAPK14. Conclusion Cartilage developmental proteins are efficient in forming and developing extracellular matrix and cartilage development, from its formation to the mature structure. However, this in-silico study recommends four most important developmental growth factors applicable to cartilage tissue engineering: COL2A1, SOX9, CTGF, and TGFB1.

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Cytoscape stringApp: Network analysis and visualization of proteomics data

Cited by 242 publications

References 20 publications

Transcriptome profiling of non-climacteric ‘yellow’ melon during ripening: insights on sugar metabolism

Transcriptome profiling of non-climacteric ‘yellow’ melon during ripening: insights on sugar metabolism

Visualize omics data on networks with Omics Visualizer, a Cytoscape App

An in-silico study of cartilage tissue engineering and its primary developmental growth factors in a systems biology approach

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