Dataset Information

ContigScape: a Cytoscape plugin facilitating microbial genome gap closing.

ABSTRACT: BACKGROUND: With the emergence of next-generation sequencing, the availability of prokaryotic genome sequences is expanding rapidly. A total of 5,276 genomes have been released since 2008, yet only 1,692 genomes were complete. The final phase of microbial genome sequencing, particularly gap closing, is frequently the rate-limiting step either because of complex genomic structures that cause sequence bias even with high genomic coverage, or the presence of repeat sequences that may cause gaps in assembly. RESULTS: We have developed a Cytoscape plugin to facilitate gap closing for high-throughput sequencing data from microbial genomes. This plugin is capable of interactively displaying the relationships among genomic contigs derived from various sequencing formats. The sequence contigs of plasmids and special repeats (IS elements, ribosomal RNAs, terminal repeats, etc.) can be displayed as well. CONCLUSIONS: Displaying relationships between contigs using graphs in Cytoscape rather than tables provides a more straightforward visual representation. This will facilitate a faster and more precise determination of the linkages among contigs and greatly improve the efficiency of gap closing.

SUBMITTER: Tang B

PROVIDER: S-EPMC3651407 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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ContigScape: a Cytoscape plugin facilitating microbial genome gap closing.

Tang Biao B Wang Qi Q Yang Minjun M Xie Feng F Zhu Yongqiang Y Zhuo Ying Y Wang Shengyue S Gao Hong H Ding Xiaoming X Zhang Lixin L Zhao Guoping G Zheng Huajun H

BMC genomics 20130430

<h4>Background</h4>With the emergence of next-generation sequencing, the availability of prokaryotic genome sequences is expanding rapidly. A total of 5,276 genomes have been released since 2008, yet only 1,692 genomes were complete. The final phase of microbial genome sequencing, particularly gap closing, is frequently the rate-limiting step either because of complex genomic structures that cause sequence bias even with high genomic coverage, or the presence of repeat sequences that may cause g ...[more]

PMID: 23627759

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Project description:One of the biggest challenges in the study of biological regulatory mechanisms is the integration, americanmodeling, and analysis of the complex interactions which take place in biological networks. Despite post transcriptional regulatory elements (i.e., miRNAs) are widely investigated in current research, their usage and visualization in biological networks is very limited. Regulatory networks are commonly limited to gene entities. To integrate networks with post transcriptional regulatory data, researchers are therefore forced to manually resort to specific third party databases. In this context, we introduce ReNE, a Cytoscape 3.x plugin designed to automatically enrich a standard gene-based regulatory network with more detailed transcriptional, post transcriptional, and translational data, resulting in an enhanced network that more precisely models the actual biological regulatory mechanisms. ReNE can automatically import a network layout from the Reactome or KEGG repositories, or work with custom pathways described using a standard OWL/XML data format that the Cytoscape import procedure accepts. Moreover, ReNE allows researchers to merge multiple pathways coming from different sources. The merged network structure is normalized to guarantee a consistent and uniform description of the network nodes and edges and to enrich all integrated data with additional annotations retrieved from genome-wide databases like NCBI, thus producing a pathway fully manageable through the Cytoscape environment. The normalized network is then analyzed to include missing transcription factors, miRNAs, and proteins. The resulting enhanced network is still a fully functional Cytoscape network where each regulatory element (transcription factor, miRNA, gene, protein) and regulatory mechanism (up-regulation/down-regulation) is clearly visually identifiable, thus enabling a better visual understanding of its role and the effect in the network behavior. The enhanced network produced by ReNE is exportable in multiple formats for further analysis via third party applications. ReNE can be freely installed from the Cytoscape App Store (http://apps.cytoscape.org/apps/rene) and the full source code is freely available for download through a SVN repository accessible at http://www.sysbio.polito.it/tools_svn/BioInformatics/Rene/releases/. ReNE enhances a network by only integrating data from public repositories, without any inference or prediction. The reliability of the introduced interactions only depends on the reliability of the source data, which is out of control of ReNe developers.

Project description:BackgroundIn the post-genomic era, the rapid increase in high-throughput data calls for computational tools capable of integrating data of diverse types and facilitating recognition of biologically meaningful patterns within them. For example, protein-protein interaction data sets have been clustered to identify stable complexes, but scientists lack easily accessible tools to facilitate combined analyses of multiple data sets from different types of experiments. Here we present clusterMaker, a Cytoscape plugin that implements several clustering algorithms and provides network, dendrogram, and heat map views of the results. The Cytoscape network is linked to all of the other views, so that a selection in one is immediately reflected in the others. clusterMaker is the first Cytoscape plugin to implement such a wide variety of clustering algorithms and visualizations, including the only implementations of hierarchical clustering, dendrogram plus heat map visualization (tree view), k-means, k-medoid, SCPS, AutoSOME, and native (Java) MCL.ResultsResults are presented in the form of three scenarios of use: analysis of protein expression data using a recently published mouse interactome and a mouse microarray data set of nearly one hundred diverse cell/tissue types; the identification of protein complexes in the yeast Saccharomyces cerevisiae; and the cluster analysis of the vicinal oxygen chelate (VOC) enzyme superfamily. For scenario one, we explore functionally enriched mouse interactomes specific to particular cellular phenotypes and apply fuzzy clustering. For scenario two, we explore the prefoldin complex in detail using both physical and genetic interaction clusters. For scenario three, we explore the possible annotation of a protein as a methylmalonyl-CoA epimerase within the VOC superfamily. Cytoscape session files for all three scenarios are provided in the Additional Files section.ConclusionsThe Cytoscape plugin clusterMaker provides a number of clustering algorithms and visualizations that can be used independently or in combination for analysis and visualization of biological data sets, and for confirming or generating hypotheses about biological function. Several of these visualizations and algorithms are only available to Cytoscape users through the clusterMaker plugin. clusterMaker is available via the Cytoscape plugin manager.

Dataset Information

ContigScape: a Cytoscape plugin facilitating microbial genome gap closing.

Publications

ContigScape: a Cytoscape plugin facilitating microbial genome gap closing.

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