RCy3: Network biology using Cytoscape from within R

Gustavsen, Julia; Pai, Shraddha; Isserlin, Ruth; Demchak, Barry; Pico, Alexander R.

doi:10.12688/f1000research.20887.2

Cited by 126 publications

(53 citation statements)

References 12 publications

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“…Pathway data available in the KEGG database (https://www.kegg.jp/kegg/) was accessed via the KEGG API using KEGGREST (Tenenbaum, 2020) to supplement gprofiler data sources and build a custom data source in Gene Matrix Transposed file format (*.gmt) for subsequent visualization. Functional enrichment results were then output to a Generic Enrichment Map (GEM) for visualization using the Cytoscape EnrichmentMap app (Merico et al, 2010) and RCy3 (Gustavsen et al, 2019). Bar plots of differential gene expression values for genes of selected KEGG pathways were generated using ggplot2 (Wickham, 2016) and enriched KEGG pathways were visualized using KEGGprofile (Zhao S, Guo Y, 2020).…”

Section: Functional Enrichment Analysis Of Differentially Expressed Cmentioning

confidence: 99%

Common virulence gene expression in adult first-time infected malaria patients and severe cases

Wichers

Tonkin‐Hill

Thye

et al. 2021

eLife

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Sequestration of Plasmodium falciparum-infected erythrocytes to host endothelium through the parasite-derived PfEMP1 adhesion proteins is central to the development of malaria pathogenesis. PfEMP1 proteins have diversified and expanded to encompass many sequence variants conferring each parasite a similar array of human endothelial receptor binding phenotypes. Here, we analyzed RNA-seq profiles of parasites isolated from 32 P. falciparum infected adult travelers returning to Germany. Patients were categorized into either malaria naïve (n=15) or pre-exposed (n=17), and into severe (n=8) or non-severe (n=24) cases. For differential expression analysis of PfEMP1-encoding var gene transcripts were de novo assembled from RNA-seq data and, in parallel, var expressed sequence tags were analyzed and used to predict the encoded domain composition of the transcripts. Both approaches showed in concordance that severe malaria was associated with PfEMP1 containing the endothelial protein C receptor (EPCR)-binding CIDRα1 domain, whereas CD36-binding PfEMP1 was linked to non-severe malaria outcomes. First-time infected adults were more likely to develop severe symptoms and tended to be infected for a longer period. Thus, parasites with more pathogenic PfEMP1 variants are more common in patients with a naïve immune status and/or adverse inflammatory host responses to first infections favors growth of EPCR-binding parasites.

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Section: Functional Enrichment Analysis Of Differentially Expressed Cmentioning

confidence: 99%

Common virulence gene expression in adult first-time infected malaria patients and severe cases

Wichers

Tonkin‐Hill

Thye

et al. 2021

eLife

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“…We used the R package igraph (Csardi and Nepusz, 2006) to convert the CoRegNet object (coregulatory network) and the RA map SIF file into separate graphs. Then, we merged both networks using igraph, and imported the network into Cytoscape using the RCy3 R/ Bioconductor package (Gustavsen et al, 2019) forming the global RA-specific network.…”

Section: Global Ra Network Inferencementioning

confidence: 99%

Inference of an integrative, executable network for Rheumatoid Arthritis combining data-driven machine learning approaches and a state-of-the-art mechanistic disease map

Miagoux

Mezquita

Singh

et al. 2021

Preprint

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MotivationRheumatoid arthritis (RA) is a multifactorial autoimmune disease that causes chronic inflammation of the joints. RA is considered a complex disease as it involves various genetic, environmental, and epigenetic factors. Systems biology approaches provide the means to study complex diseases by integrating different layers of biological information. Combining multiple data types can help compensate for missing or conflicting information and limit the possibility of false positives. In this approach, we integrate three different biological layers (gene expression, signalling cascades, mutations), obtained by bottom-up and top-down methods to build an integrative, disease-specific network. The goal behind this endeavour is to see if we can unravel mechanisms governing the regulation of key genes identified as mutation carriers in RA and derive patient-specific models to gain more insights into the disease heterogeneity.ResultsIn this work, we combine biological data relevant to Rheumatoid Arthritis, in the form of a global, integrative network. We first make use of publicly available transcriptomic datasets (peripheral blood) relative to RA and machine learning to create an RA specific transcription factor (TF) co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using prior knowledge encoded in a state-of-the-art, RA-specific molecular map. Lastly, a list of RA specific variants highlights key genes associated with known disease mutations.AvailabilityDatasets used for the analysis are publicly available. All scripts used to generate results and the Shiny app will be freely accessible after peer-reviewed publication.

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“…To this end we used the list of genes of each group to interogate the g:Profiler web server https://biit.cs.ut.ee/gprofiler/gost for gene ontology term enrichment, using the gprofiler2 R package (Raudvere et al, 2019) and selected the top entry for biological process and cellular component in each case. To test various configurations for RECAP, we used the R igraph (Csardi & Nepusz, 2006) and RCy3 (Gustavsen et al, 2019) packages, together with visualization and network analysis in Cytoscape (Shannon et al, 2003).…”

Section: Methodsmentioning

confidence: 99%

Investigation of RNA metabolism through large-scale genetic interaction profiling in yeast

Decourty

Malabat

Frachon

et al. 2020

Preprint

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Gene deletion and gene expression alteration can lead to growth defects that are amplified or reduced when a second mutation is present in the same cells. We performed 154 genetic interaction mapping (GIM) screens with mutants related with RNA metabolism and measured growth rates of about 700 000 Saccharomyces cerevisiae double mutant strains. The screens used the gene deletion collection in addition to a set of 900 strains in which essential genes were affected by mRNA destabilization (DAmP). To analyze the results we developed RECAP, a strategy that validates genetic interaction profiles by comparison with gene co-citation frequency, and identified links between 1 471 genes and 117 biological processes. To validate specific results, we tested and confirmed a link between an inositol polyphosphate hydrolase complex and mRNA translation initiation. Altogether, the results and the newly developed analysis strategy should represent a useful resource for discovery of gene function in yeast.

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RCy3: Network biology using Cytoscape from within R

Cited by 126 publications

References 12 publications

Common virulence gene expression in adult first-time infected malaria patients and severe cases

Common virulence gene expression in adult first-time infected malaria patients and severe cases

Inference of an integrative, executable network for Rheumatoid Arthritis combining data-driven machine learning approaches and a state-of-the-art mechanistic disease map

Investigation of RNA metabolism through large-scale genetic interaction profiling in yeast

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