clusterMaker: a multi-algorithm clustering plugin for Cytoscape

Morris, John H.; Apeltsin, Leonard; Newman, Aaron M.; Baumbach, Jan; Wittkop, Tobias; Su, Gang; Bader, Gary D.; Ferrin, Thomas E.

doi:10.1186/1471-2105-12-436

Cited by 536 publications

(462 citation statements)

References 65 publications

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“…To reveal the underlying properties of GCNs, a graph clustering algorithm in the form of a Markov cluster algorithm (MCL) was used to identify network modules (Enright et al, 2002;Morris et al, 2011). The result showed a shared pattern between the PA and SA networks that was distinct from the MS network (Supplemental Table S4).…”

Section: Pcc and Scc-built Gcn Exhibits Identical Topological And Funmentioning

confidence: 99%

Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data

et al. 2017

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ORCID IDs: 0000-0002-6182-800X (J.H.); 0000-0001-6612-3570 (S.V.); 0000-0002-9564-8146 (K.M.M.).With the emergence of massively parallel sequencing, genomewide expression data production has reached an unprecedented level. This abundance of data has greatly facilitated maize research, but may not be amenable to traditional analysis techniques that were optimized for other data types. Using publicly available data, a gene coexpression network (GCN) can be constructed and used for gene function prediction, candidate gene selection, and improving understanding of regulatory pathways. Several GCN studies have been done in maize (Zea mays), mostly using microarray datasets. To build an optimal GCN from plant materials RNA-Seq data, parameters for expression data normalization and network inference were evaluated. A comprehensive evaluation of these two parameters and a ranked aggregation strategy on network performance, using libraries from 1266 maize samples, were conducted. Three normalization methods and 10 inference methods, including six correlation and four mutual information methods, were tested. The three normalization methods had very similar performance. For network inference, correlation methods performed better than mutual information methods at some genes. Increasing sample size also had a positive effect on GCN. Aggregating single networks together resulted in improved performance compared to single networks.Maize (Zea mays) is the most widely produced crop in United States, and U.S. agriculture accounted for 36% of world maize production in 2015 (USDA, 2016). Maize has also been in the center of genetics research for more than 100 years, including McClintock's pioneering work with transposable elements (reviewed by McClintock, 1983;Fedoroff, 2012). Due to recent technological advances in nucleic acid sequencing and the availability of the maize genome sequence (Schnable et al., 2009), maize genomics research has been greatly expedited.RNA-sequencing (RNA-Seq) has become the favored technique for detecting genomewide expression patterns. RNA-Seq has some advantages over microarray analysis of gene expression, including single base-pair resolution, detection of novel transcripts, and the ability to analyze transcript abundance without existing genome information (reviewed by Wang et al., 2009;Han et al., 2015;Conesa et al., 2016). RNA-Seq data provides information about single nucleotide polymorphisms, which facilitates genomewide association studies (Fu et al., 2013;Li et al., 2013a;Lonsdale et al., 2013;Fadista et al., 2014). Because of its widespread adaptability, greater than 5000 Illumina platform Maize RNA-Seq libraries (Fig. 1A) are available in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database (Leinonen et al., 2010), adding to the body of data that can be used to study the maize genome.The maize genome is large and heterogeneous, and the genome annotation is still far from complete (Cigan et al., 2005;Ficklin and Feltus, 2011). Although recent work has...

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Section: Pcc and Scc-built Gcn Exhibits Identical Topological And Funmentioning

confidence: 99%

Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data

et al. 2017

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“…To identify novel subcomplexes among the LSU processome interactome, we used the Markov cluster algorithm (MCL) option in the clusterMaker plug-in for Cytoscape (Shannon et al 2003;Morris et al 2011). The MCL is an unsupervised, agglomerative algorithm designed to reveal natural groups within a highly connected graph (Enright et al 2002).…”

Section: Analysis Of the High-confidence Lsu Processome Interactome Mapmentioning

confidence: 99%

“…The high-confidence LSU processome interactome was imported into Cytoscape (Shannon et al 2003) and clustered using the MCL option in the Cytoscape plug-in clusterMaker (Morris et al 2011) with the following settings: granularity parameter = 2.5 and array sources = confidence score. The MCL advanced settings were weak edge weight pruning threshold = 1 × 10 −15 , maximum residual value = 0.001, and iterations = 16.…”

Section: Markov Clustering Analysismentioning

confidence: 99%

A protein interaction map of the LSU processome

et al. 2015

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Maturation of the large ribosomal subunit (LSU) in eukaryotes is a complex and highly coordinated process that requires the concerted action of a large, dynamic, ribonucleoprotein complex, the LSU processome. While we know that >80 ribosome biogenesis factors are required throughout the course of LSU assembly, little is known about how these factors interact with each other within the LSU processome. To interrogate its organization and architecture, we took a systems biology approach and performed a semi-high-throughput, array-based, directed yeast two-hybrid assay. Assaying 4800 protein-protein interactions, we identified 232 high-confidence, binary-interacting protein pairs, representing a fourfold increase from current knowledge. The resulting LSU processome interactome map has enhanced our understanding of the organization and function of the biogenesis factors within the LSU processome, revealing both novel and previously identified subcomplexes and hub proteins, including Nop4.[Keywords: LSU processome; RNA; ribosome; yeast two-hybrid] Supplemental material is available for this article. Eukaryotic ribosomes are complex cellular machines that are comprised of four different ribosomal RNAs (rRNAs) and >70 ribosomal proteins (r-proteins) (Woolford and Baserga 2013). Ribosome assembly begins in the nucleolus with the transcription of the polycistronic pre-rRNA, termed the 35S in Saccharomyces cerevisiae, by RNA polymerase I. The 35S pre-rRNA undergoes numerous cleavage and modification steps to generate the mature 18S, 5.8S, and 25S rRNAs that are assembled with the r-proteins to form the small and large ribosomal subunits. Accurate and efficient production of ribosomes requires the coordinated activity of >150 biogenesis factors, including a number of proteins with enzymatic activity (Fatica and Tollervey 2002; Fromont-Racine et al.

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“…The redundant GO classes were deleted to simplify the figure, and all GO classes for each treatment were included as Supplementary Tables S1 and S2 (Supplementary Data are available online at www.liebertpub.com/zeb). In addition, we conducted a complementary analysis with the ClusterMaker cytoscape plugin, 22 using the MCL algorithm to search proteinprotein interaction network modules derived from TAP/ MAS (tandem affinity purification/mass spectrometry). This approach clustered the network into modules based on the purification enrichment (PE) score to indicate the strength of the node association given a fixed set of genes with high protein-protein affinity (interactome cluster nodes).…”

Section: Microarray Analysesmentioning

confidence: 99%

The Involvement of Cholesterol in Sepsis and Tolerance to Lipopolysaccharide Highlighted by the Transcriptome Analysis of Zebrafish (Danio rerio)

Dios

Balseiro²,

Costa³

et al. 2014

Zebrafish

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Septic shock is the most common cause of death in intensive care units due to an aggressive inflammatory response that leads to multiple organ failure. However, a lipopolysaccharide (LPS) tolerance phenomenon (a nonreaction to LPS), is also often described. Neither the inflammatory response nor the tolerance is completely understood. In this work, both of these responses were analyzed using microarrays in zebrafish. Fish that were 4 or 6 days postfertilization (dpf) and received a lethal dose (LD) of LPS exhibited 100% mortality in a few days. Their transcriptome profile, even at 4 dpf, resembled the profile in humans with severe sepsis. Moreover, we selected 4-dpf fish to set up a tolerance protocol: fish treated with a nonlethal concentration of Escherichia coli LPS exhibited complete protection against the LD of LPS. Most of the main inflammatory molecules described in mammals were represented in the zebrafish microarray experiments. Additionally and focusing on this tolerance response, the use of cyclodextrins may mobilize cholesterol reservoirs to decrease mortality after a LD dose of LPS. Therefore, it is possible that the use of the whole animal could provide some clues to enhance the understanding of the inflammatory/tolerance response and to guide drug discovery.

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clusterMaker: a multi-algorithm clustering plugin for Cytoscape

Cited by 536 publications

References 65 publications

Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data

Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data

A protein interaction map of the LSU processome

The Involvement of Cholesterol in Sepsis and Tolerance to Lipopolysaccharide Highlighted by the Transcriptome Analysis of Zebrafish (Danio rerio)

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