Cytoscape: the network visualization tool for GenomeSpace workflows

Demchak, Barry; Hull, Tim; Reich, Michael; Liefeld, Ted; Smoot, Michael; Ideker, Trey; Mesirov, Jill P.

doi:10.12688/f1000research.4492.2

Cited by 161 publications

(102 citation statements)

References 11 publications

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“…Protein-coding genes in each module were retrieved for GO enrichment analysis, and the functions of circRNAs were thus inferred. The network result of each WGCNA module was visualized using Cytoscape (v2.8.3; Demchak et al, 2014). …”

Section: Methodsmentioning

confidence: 99%

Identification of Circular RNAs in Kiwifruit and Their Species-Specific Response to Bacterial Canker Pathogen Invasion

Wang¹,

Liu

et al. 2017

Front. Plant Sci.

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Research studies have recently focused on circle RNAs (circRNAs) in relation to their regulatory functions in animals. However, the systematic identification of circRNAs in plants, especially non-model plants, is limited. In addition, raw report on the prediction of the potential role of circRNAs in plant response to pathogen invasion is currently available. We conducted the systematic identification of circRNAs from four materials originating from three species belonging to genus Actinidia under different situations using ribosomal RNA (rRNA) depleted RNA-Seq data. A total of 3,582 circRNAs were identified in Actinidia, of which 64.01, 21.44, and 14.55% were intergenic circRNAs, exonic circRNAs, and intronic circRNAs, respectively. Tissue-specific expression of circRNAs was observed in kiwifruit, and a species-specific response was detected when infected with Pseudomonas syringae pv. actinidiae (Psa), which is the causative agent of kiwifruit bacterial canker disease. Furthermore, we found that both exonic and intronic circRNAs were significantly positively correlated to parent protein-coding genes, and intronic circRNAs are a class of highly remarkable regulators the parent genes comparing to that of exonic circRNAs. Expression and weighted gene co-expression network analysis (WGCNA) identified a set of circRNAs that were closely associated with plant defense response. The findings of the presents study suggest that circRNAs exhibit tissue- and species-specific expression, as well as play an important role in plant immune response.

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Section: Methodsmentioning

confidence: 99%

Identification of Circular RNAs in Kiwifruit and Their Species-Specific Response to Bacterial Canker Pathogen Invasion

Wang¹,

Liu

et al. 2017

Front. Plant Sci.

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“…The criterion for being a hub gene selection was degree ≥10. Further analysis was performed using the cBioPortal online platform (http://www.cbioportal.org) to build the network of the DEGs and co-expressing genes (14). The mutation rates of the hub genes were also measured with the cBioPortal platform (15).…”

Section: Methodsmentioning

confidence: 99%

Identification of lung adenocarcinoma biomarkers based on bioinformatic analysis and human samples

et al. 2020

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Lung adenocarcinoma is one of the most common malignant tumors worldwide. Although efforts have been made to clarify its pathology, the underlying molecular mechanisms of lung adenocarcinoma are still not clear. The microarray datasets GSE75037, GSE63459 and GSE32863 were downloaded from the Gene Expression Omnibus (GEO) database to identify biomarkers for effective lung adenocarcinoma diagnosis and therapy. The differentially expressed genes (DEGs) were identified by GEO2R, and function enrichment analyses were conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The STRING database and Cytoscape software were used to construct and analyze the protein-protein interaction network (PPI). We identified 376 DEGs, consisting of 83 upregulated genes and 293 downregulated genes. Functional and pathway enrichment showed that the DEGs were mainly focused on regulation of cell proliferation, the transforming growth factor β receptor signaling pathway, cell adhesion, biological adhesion, and responses to hormone stimulus. Sixteen hub genes were identified and biological process analysis showed that these 16 hub genes were mainly involved in the M phase, cell cycle phases, the mitotic cell cycle, and nuclear division. We further confirmed the two genes with the highest node degree, DNA topoisomerase IIα (TOP2A) and aurora kinase A (AURKA), in lung adenocarcinoma cell lines and human samples. Both these genes were upregulated and associated with larger tumor size. Upregulation of AURKA in particular, was associated with lymphatic metastasis. In summary, identification of the DEGs and hub genes in our research enables us to elaborate the molecular mechanisms underlying the genesis and progression of lung adenocarcinoma and identify potential targets for the diagnosis and treatment of lung adenocarcinoma.

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“…(4) Visualizing experimental factors: the nodes, edges, and widths of networks based on the correlation coefficient were transformed in GML format by using the Networkx package in Python. Lastly, the network figure was drawn by using Cytoscape [57].…”

Section: Noise Factor Analysis Methodsmentioning

confidence: 99%

Signal Deconvolution and Noise Factor Analysis Based on a Combination of Time–Frequency Analysis and Probabilistic Sparse Matrix Factorization

Yamada

Kurotani

Chikayama

et al. 2020

IJMS

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Nuclear magnetic resonance (NMR) spectroscopy is commonly used to characterize molecular complexity because it produces informative atomic-resolution data on the chemical structure and molecular mobility of samples non-invasively by means of various acquisition parameters and pulse programs. However, analyzing the accumulated NMR data of mixtures is challenging due to noise and signal overlap. Therefore, data-cleansing steps, such as quality checking, noise reduction, and signal deconvolution, are important processes before spectrum analysis. Here, we have developed an NMR measurement informatics tool for data cleansing that combines short-time Fourier transform (STFT; a time-frequency analytical method) and probabilistic sparse matrix factorization (PSMF) for signal deconvolution and noise factor analysis. Our tool can be applied to the original free induction decay (FID) signals of a one-dimensional NMR spectrum. We show that the signal deconvolution method reduces the noise of FID signals, increasing the signal-to-noise ratio (SNR) about tenfold, and its application to diffusion-edited spectra allows signals of macromolecules and unsuppressed small molecules to be separated by the length of the T 2 * relaxation time. Noise factor analysis of NMR datasets identified correlations between SNR and acquisition parameters, identifying major experimental factors that can lower SNR.

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Cytoscape: the network visualization tool for GenomeSpace workflows

Cited by 161 publications

References 11 publications

Identification of Circular RNAs in Kiwifruit and Their Species-Specific Response to Bacterial Canker Pathogen Invasion

Identification of Circular RNAs in Kiwifruit and Their Species-Specific Response to Bacterial Canker Pathogen Invasion

Identification of lung adenocarcinoma biomarkers based on bioinformatic analysis and human samples

Signal Deconvolution and Noise Factor Analysis Based on a Combination of Time–Frequency Analysis and Probabilistic Sparse Matrix Factorization

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