Gephi: An Open Source Software for Exploring and Manipulating Networks

Bastian, Mathieu; Heymann, Sébastien; Jacomy, Mathieu

doi:10.1609/icwsm.v3i1.13937

Cited by 6,141 publications

(1,568 citation statements)

References 6 publications

Supporting

Mentioning

1,107

Contrasting

Unclassified

Order By: Relevance

“…Finally, seekr graph segmented the RNA sequences into different communities based on the similarity matrix described above and the following parameters: .13 -n 3 --louvain. The network graph of the semi-extractable RNAs was visualized using Gephi (v0.9) (50) with a Yifan Hu proportional layout.…”

Section: Methodsmentioning

confidence: 99%

Landscape of semi-extractable RNAs across five human cell lines

Zeng¹,

Chujo²,

Hirose³

et al. 2022

Preprint

View full text Add to dashboard Cite

Phase-separated membraneless organelles often contain RNAs that exhibit unusual semi-extractability upon the conventional RNA extraction method, and can be efficiently retrieved by needle shearing or heating during RNA extraction. Semi-extractable RNAs are promising resources for understanding RNA-centric phase separation. However, limited assessments have been performed to systematically identify and characterize semi-extractable RNAs. In this study, 1,325 semi-extractable RNAs were identified across five human cell lines, including NEAT1, TRIO, EXT1, ZCCHC7, and FTX, which exhibited stable semi-extractability. Semi-extractable RNAs tend to be distributed in the nucleolus but are dissociated from the chromatin. Long and repeat-containing semi-extractable RNAs act as hubs to provide global RNA-RNA interactions. Semi-extractable RNAs were divided into four groups based on their k-mer content. Consistently, the NEAT1 group preferred to interact with paraspeckle proteins, such as FUS and NONO, implying that RNAs in this group are potential candidates of architectural RNAs that constitute nuclear bodies.

show abstract

Section: Methodsmentioning

confidence: 99%

Landscape of semi-extractable RNAs across five human cell lines

Zeng¹,

Chujo²,

Hirose³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Subsequently, they were plotted and analyzed as a graph, using Gephi (Bastian, Heymann and Jacomy, 2009), applying Circle Pack Layout for representation with the variables 'predicted or annotated', experimental condition and node degree as hierarchy.…”

Section: Putative Cis-regulatory Elements Discoverymentioning

confidence: 99%

Mining novel cis-regulatory elements from the emergent host Rhodosporidium toruloides using transcriptomic data

Nora

Cassiano

Santana

et al. 2022

Preprint

View full text Add to dashboard Cite

The demand for robust microbial cell factories that can produce valuable biomaterials while being resistant to stresses imposed by current bioprocesses is rapidly growing. R. toruloides is an emerging host that presents desirable features for bioproduction, since it can grow in a wide range of substrates and tolerate a variety of toxic compounds. In order to explore R. toruloides suitability for application as a cell factory in biorefineries, we sought to understand the transcriptional responses of this yeast when growing under experimental settings that simulated those used in biofuels-related industries. Thus, we performed RNA sequencing of the oleaginous, carotenogenic yeast in different contexts. The first ones were stress-related: two conditions of high temperature (37 ˚C and 42 ˚C) and two ethanol concentrations (2% and 4%), while the other was using the inexpensive and abundant sugarcane juice as substrate. Using transcriptomic data, differential expression and functional analysis were implemented to select differentially expressed genes and enriched pathways from each set-up. A reproducible bioinformatics workflow was developed for mining new regulatory elements. We then predicted, for the first time in this yeast, binding motifs for several transcription factors, including HAC1, ARG80, RPN4, ADR1, and DAL81. Most of the putative transcription factors uncovered here were involved in stress responses and found in the yeast genome. Our method for motif discovery provides a new realm of possibilities in the study of gene regulatory networks, not only for the emerging host R. toruloides, but for other organisms of biotechnological importance.

show abstract

“…The matrix data was analysed using specialised open-source software Gephi [29] (Version 0.9.2). The Louvain community detection algorithm [30] for detecting modules was applied, and the resultant graphs displayed using the ForceAtlas2 algorithm [31].…”

Section: Adjacency Matrices Network Graphs and Community Detection Me...mentioning

confidence: 99%

Modular structures and the delivery of inpatient care in hospitals: a network science perspective on healthcare function and dysfunction

Ben-Tovim

Bajger

Bui

et al. 2022

Preprint

View full text Add to dashboard Cite

BackgroundReinforced by the COVID-19 pandemic, the capacity of health systems to cope with increasing healthcare demands has been an abiding concern of both governments and the public. Health systems are made up from non-identical humans and physical components interacting in diverse ways in varying locations. It is challenging to represent the function and dysfunction of such systems in a scientific manner. We describe a novel approach to that dilemma.General hospitals with large emergency caseloads are the resource intensive components of health systems. We propose that the care-delivery services in such entities are modular, and that their structure and function can be usefully analysed by contemporary network science. We explore that possibility in a study of Australian hospitals during 2019 and 2020.MethodsWe accessed monthly snapshots of whole of hospital administrative patient level data in two general hospitals during 2019 and 2020. We represented the organisation inpatient services as network graphs and explored their graph structural characteristics using the Louvain algorithm and other methods. We related graph topological features to aspects of observable function and dysfunction in the delivery of care.Results We constructed a series of whole of institution bipartite hospital graphs with clinical unit and labelled wards as nodes, and patients treated by units in particular wards as edges. Examples of the graphs are provided. Algorithmic identification of community structures confirmed the modular structure of the graphs. Their functional implications were readily identified by domain experts. Topological graph features could be related to functional and dysfunctional issues such as COVID-19 related service changes and levels of hospital congestion.Discussion and Conclusions Contemporary Network Science is one of the fastest growing areas of current scientific and technical advance. Network Science confirms the modular nature of healthcare service structures. It holds considerable promise for understanding function and dysfunction in healthcare systems, and for reconceptualising issues such as hospital capacity in new and interesting ways.

show abstract

Gephi: An Open Source Software for Exploring and Manipulating Networks

Cited by 6,141 publications

References 6 publications

Landscape of semi-extractable RNAs across five human cell lines

Landscape of semi-extractable RNAs across five human cell lines

Mining novel cis-regulatory elements from the emergent host Rhodosporidium toruloides using transcriptomic data

Modular structures and the delivery of inpatient care in hospitals: a network science perspective on healthcare function and dysfunction

Contact Info

Product

Resources

About