GLay: community structure analysis of biological networks

Su, Gang; Kuchinsky, Allan; Morris, John H.; States, David J.; Meng, Fan

doi:10.1093/bioinformatics/btq596

Cited by 230 publications

(229 citation statements)

References 20 publications

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“…The maps include different gene properties and annotations, as well as alternative views that dissect the network hubs or genes with specific characteristics. We clustered the SL and SDL networks by applying the Girvan-Newman fast greedy algorithm as implemented by the GLay Cytoscape plug-in (Morris et al, 2011;Su et al, 2010) and performed gene annotation enrichment analysis for every network and every network cluster via DAVID (Huang et al, 2009).…”

Section: Network Availability and Visualizationmentioning

confidence: 99%

Predicting Cancer-Specific Vulnerability via Data-Driven Detection of Synthetic Lethality

Jerby‐Arnon

Pfetzer

Waldman

et al. 2014

Cell

248

286

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Synthetic lethality occurs when the inhibition of two genes is lethal while the inhibition of each single gene is not. It can be harnessed to selectively treat cancer by identifying inactive genes in a given cancer and targeting their synthetic lethal (SL) partners. We present a data-driven computational pipeline for the genome-wide identification of SL interactions in cancer by analyzing large volumes of cancer genomic data. First, we show that the approach successfully captures known SL partners of tumor suppressors and oncogenes. We then validate SL predictions obtained for the tumor suppressor VHL. Next, we construct a genome-wide network of SL interactions in cancer and demonstrate its value in predicting gene essentiality and clinical prognosis. Finally, we identify synthetic lethality arising from gene overactivation and use it to predict drug efficacy. These results form a computational basis for exploiting synthetic lethality to uncover cancer-specific susceptibilities.

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Section: Network Availability and Visualizationmentioning

confidence: 99%

Predicting Cancer-Specific Vulnerability via Data-Driven Detection of Synthetic Lethality

Jerby‐Arnon

Pfetzer

Waldman

et al. 2014

Cell

248

286

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“…Many recent studies have used network based studies to investigate various biological problems Lee and Lee 2013). In this work, we identified 5 functional modules or communities in the interaction network using fast greedy algorithm implemented as GLAY (Su et al 2010) plugin for Cytoscape. Furthermore, the functional enrichment of only 3 modules (based on the criteria described in the methods section) was explored by using functional annotation tool DAVID.…”

Section: Discussionmentioning

confidence: 99%

“…The modules with functional property were determined by applying greedy community-structure detection algorithm via GLay [http://brainarray.mbni.med.umich.edu/sugang/ glay] (Su et al 2010) plugin in Cytoscape. For identifying the over-represented biological functions within each cluster, the clusters were subjected to a functional enrichment analysis by focusing only on communities with at least 10 nodes.…”

Section: Community Analysismentioning

confidence: 99%

Identification of hub genes and their SNP analysis in West Nile virus infection for designing therapeutic methodologies using RNA-Seq data

et al. 2015

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The West Nile virus (WNV) infections are generally asymptomatic and are considered as immediate concerns of biodefense due to the lack of any therapeutic remedies. In this work, we created an interaction network of 1159 differentially expressed genes to detect potential hub genes from WNV infected primary human macrophages. We go on to explore the genetic variations that can alter the expression and function of identified hub genes (HCLS1, SLC15A3, HCK, and LY96) using the PROVEAN Protein Batch tool and PolyPhen-2. Community analysis of the network revealed that these clusters were enriched in GO terms such as inflammatory response and regulation of proliferation. Analysis of hub genes can aid in determining their degree of conservation and may help us in understanding their functional roles in biological systems. The nsSNPs proposed in this work may be further targeted through experimental methods for improving treatment towards the infection of WNV.

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“…GLay offers Cytoscape users a diverse group of community structure algorithms and graph layout functions for network clustering and structured visualization [29]. To identify the overrepresented biological functions within each cluster, the detected clusters were subjected to functional enrichment analyses by DAVID functional analysis tool.…”

Section: Methodsmentioning

confidence: 99%

Comparative Analysis of Glycogene Expression in Different Mouse Tissues Using RNA-Seq Data

Firoz

Malik

Singh

et al. 2014

International Journal of Genomics

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Glycogenes regulate a wide array of biological processes in the development of organisms as well as different diseases such as cancer, primary open-angle glaucoma, and renal dysfunction. The objective of this study was to explore the role of differentially expressed glycogenes (DEGGs) in three major tissues such as brain, muscle, and liver using mouse RNA-seq data, and we identified 579, 501, and 442 DEGGs for brain versus liver (BvL579), brain versus muscle (BvM501), and liver versus muscle (LvM442) groups. DAVID functional analysis suggested inflammatory response, glycosaminoglycan metabolic process, and protein maturation as the enriched biological processes in BvL579, BvM501, and LvM442, respectively. These DEGGs were then used to construct three interaction networks by using GeneMANIA, from which we detected potential hub genes such as PEMT and HPXN (BvL579), IGF2 and NID2 (BvM501), and STAT6 and FLT1 (LvM442), having the highest degree. Additionally, our community analysis results suggest that the significance of immune system related processes in liver, glycosphingolipid metabolic processes in the development of brain, and the processes such as cell proliferation, adhesion, and growth are important for muscle development. Further studies are required to confirm the role of predicted hub genes as well as the significance of biological processes.

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GLay: community structure analysis of biological networks

Cited by 230 publications

References 20 publications

Predicting Cancer-Specific Vulnerability via Data-Driven Detection of Synthetic Lethality

Predicting Cancer-Specific Vulnerability via Data-Driven Detection of Synthetic Lethality

Identification of hub genes and their SNP analysis in West Nile virus infection for designing therapeutic methodologies using RNA-Seq data

Comparative Analysis of Glycogene Expression in Different Mouse Tissues Using RNA-Seq Data

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