The Experts below are selected from a list of 18348 Experts worldwide ranked by ideXlab platform
Wooyoung Kim - One of the best experts on this subject based on the ideXlab platform.
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web quatexelero web based efficient Network Motif detection tool
Bioinformatics and Biomedicine, 2020Co-Authors: Marko Lakic, Bazen Zenebe Nega, Wooyoung KimAbstract:A Network Motif is a over-occurring subgraph pattern in a given Network. Network Motifs in bioinformatics play an important role in discovering significant biological functions. Although various algorithms and tools are available to detect Network Motifs, most of them lack accessibility and usability. Here we present, Web-QuateXelero, an online graphical user interface (GUI) program to detect Network Motifs efficiently. Experimental results show that the program runs faster than other online tools or other standalone programs.
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BIBM - Web-QuateXelero: Web-based efficient Network Motif detection tool
2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2020Co-Authors: Marko Lakic, Bazen Zenebe Nega, Wooyoung KimAbstract:A Network Motif is a over-occurring subgraph pattern in a given Network. Network Motifs in bioinformatics play an important role in discovering significant biological functions. Although various algorithms and tools are available to detect Network Motifs, most of them lack accessibility and usability. Here we present, Web-QuateXelero, an online graphical user interface (GUI) program to detect Network Motifs efficiently. Experimental results show that the program runs faster than other online tools or other standalone programs.
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nemolib Network Motif libraries for Network Motif detection and analysis
International Symposium on Bioinformatics Research and Applications, 2020Co-Authors: Wooyoung Kim, Zachary Arthur BraderAbstract:Network Motifs are frequent and unique subgraph patterns located inside Networks, and have been applied to solve various biological problems. Due to the high computational costs of performing Network Motif analysis, various tools have been created to make the process more efficient. However, existing tools lack extensible functionality and provide limited output formats. This restricts the ability to use Network Motif analysis for extensive and exhaustive experiments in real problems. We provide NemoLib (Network Motif Libraries) as a general purpose tool for detection and analysis of Network Motifs. It is an easily adoptable and highly accessible tool with a focus on efficiency and extensibility.
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ISBRA - NemoLib: Network Motif Libraries for Network Motif Detection and Analysis
Bioinformatics Research and Applications, 2020Co-Authors: Wooyoung Kim, Zachary Arthur BraderAbstract:Network Motifs are frequent and unique subgraph patterns located inside Networks, and have been applied to solve various biological problems. Due to the high computational costs of performing Network Motif analysis, various tools have been created to make the process more efficient. However, existing tools lack extensible functionality and provide limited output formats. This restricts the ability to use Network Motif analysis for extensive and exhaustive experiments in real problems. We provide NemoLib (Network Motif Libraries) as a general purpose tool for detection and analysis of Network Motifs. It is an easily adoptable and highly accessible tool with a focus on efficiency and extensibility.
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nemomappy Motif centric Network Motif search on a web
Bioinformatics and Biomedicine, 2019Co-Authors: Preston Mar, Wooyoung KimAbstract:Networks Motifs, defined as statistically overrep-resented subgraph patterns in a Network, are important to study because they are generally related to specific functional modules in the biological Networks. Smaller sized Network Motifs are reasonably fast to detect with various programs. However, larger Network Motifs with more than 8 vertices, are difficult to study due to their heavy computational burden to detect and analyze. NemoMap, which is based on Grochow and Kellis' Motif-centric algorithm, has been developed to overcome the issue. To increase the usability, and provide easy accessibility to NemoMap, we developed NemoMapPy as a web application. This was accomplished by porting the existing NemoMap program to Python then creating an intuitive and easy to use front-end. The result is a fully web accessible Motif-centric Network Motif discovery tool on a web. NemoMapPy also reduces the runtime of NemoMap by on average 70% in sub-graphs larger than 10 nodes. Currently NemoMapPy is running as a part of NemoSuite which includes Network-centric, Motif-centric, and Network Motif visualization program.
Igor Zwir - One of the best experts on this subject based on the ideXlab platform.
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Identifying promoter features of co-regulated genes with similar Network Motifs.
BMC bioinformatics, 2009Co-Authors: Oscar Harari, Coral Del Val, Rocío Romero-zaliz, Dongwoo Shin, Henry Huang, Eduardo A Groisman, Igor ZwirAbstract:A large amount of computational and experimental work has been devoted to uncovering Network Motifs in gene regulatory Networks. The leading hypothesis is that evolutionary processes independently selected recurrent architectural relationships among regulators and target genes (Motifs) to produce characteristic expression patterns of its members. However, even with the same architecture, the genes may still be differentially expressed. Therefore, to define fully the expression of a group of genes, the strength of the connections in a Network Motif must be specified, and the cis-promoter features that participate in the regulation must be determined. We have developed a model-based approach to analyze proteobacterial genomes for promoter features that is specifically designed to account for the variability in sequence, location and topology intrinsic to differential gene expression. We provide methods for annotating regulatory regions by detecting their subjacent cis-features. This includes identifying binding sites for a transcriptional regulator, distinguishing between activation and repression sites, direct and reverse orientation, and among sequences that weakly reflect a particular pattern; binding sites for the RNA polymerase, characterizing different classes, and locations relative to the transcription factor binding sites; the presence of riboswitches in the 5'UTR, and for other transcription factors. We applied our approach to characterize Network Motifs controlled by the PhoP/PhoQ regulatory system of Escherichia coli and Salmonella enterica serovar Typhimurium. We identified key features that enable the PhoP protein to control its target genes, and distinct features may produce different expression patterns even within the same Network Motif. Global transcriptional regulators control multiple promoters by a variety of Network Motifs. This is clearly the case for the regulatory protein PhoP. In this work, we studied this regulatory protein and demonstrated that understanding gene expression does not only require identifying a set of connexions or Network Motif, but also the cis-acting elements participating in each of these connexions.
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Identifying promoter features of co-regulated genes with similar Network Motifs
BMC Bioinformatics, 2009Co-Authors: Oscar Harari, Coral Del Val, Rocío Romero-zaliz, Dongwoo Shin, Henry Huang, Eduardo A Groisman, Igor ZwirAbstract:Background A large amount of computational and experimental work has been devoted to uncovering Network Motifs in gene regulatory Networks. The leading hypothesis is that evolutionary processes independently selected recurrent architectural relationships among regulators and target genes (Motifs) to produce characteristic expression patterns of its members. However, even with the same architecture, the genes may still be differentially expressed. Therefore, to define fully the expression of a group of genes, the strength of the connections in a Network Motif must be specified, and the cis -promoter features that participate in the regulation must be determined. Results We have developed a model-based approach to analyze proteobacterial genomes for promoter features that is specifically designed to account for the variability in sequence, location and topology intrinsic to differential gene expression. We provide methods for annotating regulatory regions by detecting their subjacent cis -features. This includes identifying binding sites for a transcriptional regulator, distinguishing between activation and repression sites, direct and reverse orientation, and among sequences that weakly reflect a particular pattern; binding sites for the RNA polymerase, characterizing different classes, and locations relative to the transcription factor binding sites; the presence of riboswitches in the 5'UTR, and for other transcription factors. We applied our approach to characterize Network Motifs controlled by the PhoP/PhoQ regulatory system of Escherichia coli and Salmonella enterica serovar Typhimurium. We identified key features that enable the PhoP protein to control its target genes, and distinct features may produce different expression patterns even within the same Network Motif. Conclusion Global transcriptional regulators control multiple promoters by a variety of Network Motifs. This is clearly the case for the regulatory protein PhoP. In this work, we studied this regulatory protein and demonstrated that understanding gene expression does not only require identifying a set of connexions or Network Motif, but also the cis -acting elements participating in each of these connexions.
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Identifying promoter features of co-regulated genes with similar Network Motifs
BMC Bioinformatics, 2009Co-Authors: Oscar Harari, Coral Del Val, Rocío Romero-zaliz, Dongwoo Shin, Eduardo A Groisman, Henry V. Huang, Igor ZwirAbstract:Background A large amount of computational and experimental work has been devoted to uncovering Network Motifs in gene regulatory Networks. The leading hypothesis is that evolutionary processes independently selected recurrent architectural relationships among regulators and target genes (Motifs) to produce characteristic expression patterns of its members. However, even with the same architecture, the genes may still be differentially expressed. Therefore, to define fully the expression of a group of genes, the strength of the connections in a Network Motif must be specified, and the cis-promoter features that participate in the regulation must be determined.
Fengzhu Sun - One of the best experts on this subject based on the ideXlab platform.
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Network Motif identification in stochastic Networks.
Proceedings of the National Academy of Sciences of the United States of America, 2006Co-Authors: Rui Jiang, Ting Chen, Fengzhu SunAbstract:Network Motifs have been identified in a wide range of Networks across many scientific disciplines and are suggested to be the basic building blocks of most complex Networks. Nonetheless, many Networks come with intrinsic and/or experimental uncertainties and should be treated as stochastic Networks. The building blocks in these Networks thus may also have stochastic properties. In this article, we study stochastic Network Motifs derived from families of mutually similar but not necessarily identical patterns of interconnections. We establish a finite mixture model for stochastic Networks and develop an expectation-maximization algorithm for identifying stochastic Network Motifs. We apply this approach to the transcriptional regulatory Networks of Escherichia coli and Saccharomyces cerevisiae, as well as the protein-protein interaction Networks of seven species, and identify several stochastic Network Motifs that are consistent with current biological knowledge.
Sheil Kurmar - One of the best experts on this subject based on the ideXlab platform.
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sensible method for updating Motif instances in an increased biological Network
Methods, 2015Co-Authors: Wooyoung Kim, Sheil KurmarAbstract:Abstract A Network Motif is defined as an over-represented subgraph pattern in a Network. Network Motif based techniques have been widely applied in analyses of biological Networks such as transcription regulation Networks (TRNs), protein–protein interaction Networks (PPIs), and metabolic Networks. The detection of Network Motifs involves the computationally expensive enumeration of subgraphs, NP-complete graph isomorphism testing, and significance testing through the generation of many random graphs to determine the statistical uniqueness of a given subgraph. These computational obstacles make Network Motif analysis unfeasible for many real-world applications. We observe that the fast growth of biotechnology has led to the rapid accretion of molecules (vertices) and interactions (edges) to existing biological Network databases. Even with a small percentage of additions, revised Networks can have a large number of differing Motif instances. Currently, no existing algorithms recalculate Motif instances in ‘updated’ Networks in a practical manner. In this paper, we introduce a sensible method for efficiently recalculating Motif instances by performing Motif enumeration from only updated vertices and edges. Preliminary experimental results indicate that our method greatly reduces computational time by eliminating the repeated enumeration of overlapped subgraph instances detected in earlier versions of the Network. The software program implementing this algorithm, defined as SUNMI (Sensible Update of Network Motif Instances), is currently a stand-alone java program and we plan to upgrade it as a web-interactive program that will be available through http://faculty.washington.edu/kimw6/research.htm in near future. Meanwhile it is recommended to contact authors to obtain the stand-alone SUNMI program.
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efficient updates of Network Motif instances in the extended protein protein interaction Network
Bioinformatics and Biomedicine, 2014Co-Authors: Wooyoung Kim, Sheil KurmarAbstract:A Network Motif is defined as an over-represented subgraph pattern in a Network and has been widely applied in analyses of biological Networks. The detection of Network Motifs involves the computationally expensive enumeration of subgraphs, NP-complete graph isomorphism testing, and significance testing through the generation of many random graphs to determine the statistical uniqueness of a given subgraph. These computational obstacles make the analysis of Network Motifs unfeasible for many real-world applications. From some biological Network databases, we observe that the fast advancement of biotechnology has led to the rapid growth of existing biological Network databases. Even with a small percentage of additions, updated Networks will have a large number of differing Network Motif instances. Currently, no algorithms exist that can efficiently recalculate Motif instances in ‘updated’ Networks. In this paper, we introduce an algorithm to efficiently recalculate Motif instances by performing Motif enumeration from only updated vertices and edges. Preliminary experimental results show that it greatly reduces computational time by eliminating the regeneration of overlapped subgraph instances from earlier versions of the Network.
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BIBM - Efficient updates of Network Motif instances in the extended protein-protein interaction Network
2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2014Co-Authors: Wooyoung Kim, Sheil KurmarAbstract:A Network Motif is defined as an over-represented subgraph pattern in a Network and has been widely applied in analyses of biological Networks. The detection of Network Motifs involves the computationally expensive enumeration of subgraphs, NP-complete graph isomorphism testing, and significance testing through the generation of many random graphs to determine the statistical uniqueness of a given subgraph. These computational obstacles make the analysis of Network Motifs unfeasible for many real-world applications. From some biological Network databases, we observe that the fast advancement of biotechnology has led to the rapid growth of existing biological Network databases. Even with a small percentage of additions, updated Networks will have a large number of differing Network Motif instances. Currently, no algorithms exist that can efficiently recalculate Motif instances in ‘updated’ Networks. In this paper, we introduce an algorithm to efficiently recalculate Motif instances by performing Motif enumeration from only updated vertices and edges. Preliminary experimental results show that it greatly reduces computational time by eliminating the regeneration of overlapped subgraph instances from earlier versions of the Network.
Oscar Harari - One of the best experts on this subject based on the ideXlab platform.
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Identifying promoter features of co-regulated genes with similar Network Motifs.
BMC bioinformatics, 2009Co-Authors: Oscar Harari, Coral Del Val, Rocío Romero-zaliz, Dongwoo Shin, Henry Huang, Eduardo A Groisman, Igor ZwirAbstract:A large amount of computational and experimental work has been devoted to uncovering Network Motifs in gene regulatory Networks. The leading hypothesis is that evolutionary processes independently selected recurrent architectural relationships among regulators and target genes (Motifs) to produce characteristic expression patterns of its members. However, even with the same architecture, the genes may still be differentially expressed. Therefore, to define fully the expression of a group of genes, the strength of the connections in a Network Motif must be specified, and the cis-promoter features that participate in the regulation must be determined. We have developed a model-based approach to analyze proteobacterial genomes for promoter features that is specifically designed to account for the variability in sequence, location and topology intrinsic to differential gene expression. We provide methods for annotating regulatory regions by detecting their subjacent cis-features. This includes identifying binding sites for a transcriptional regulator, distinguishing between activation and repression sites, direct and reverse orientation, and among sequences that weakly reflect a particular pattern; binding sites for the RNA polymerase, characterizing different classes, and locations relative to the transcription factor binding sites; the presence of riboswitches in the 5'UTR, and for other transcription factors. We applied our approach to characterize Network Motifs controlled by the PhoP/PhoQ regulatory system of Escherichia coli and Salmonella enterica serovar Typhimurium. We identified key features that enable the PhoP protein to control its target genes, and distinct features may produce different expression patterns even within the same Network Motif. Global transcriptional regulators control multiple promoters by a variety of Network Motifs. This is clearly the case for the regulatory protein PhoP. In this work, we studied this regulatory protein and demonstrated that understanding gene expression does not only require identifying a set of connexions or Network Motif, but also the cis-acting elements participating in each of these connexions.
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Identifying promoter features of co-regulated genes with similar Network Motifs
BMC Bioinformatics, 2009Co-Authors: Oscar Harari, Coral Del Val, Rocío Romero-zaliz, Dongwoo Shin, Henry Huang, Eduardo A Groisman, Igor ZwirAbstract:Background A large amount of computational and experimental work has been devoted to uncovering Network Motifs in gene regulatory Networks. The leading hypothesis is that evolutionary processes independently selected recurrent architectural relationships among regulators and target genes (Motifs) to produce characteristic expression patterns of its members. However, even with the same architecture, the genes may still be differentially expressed. Therefore, to define fully the expression of a group of genes, the strength of the connections in a Network Motif must be specified, and the cis -promoter features that participate in the regulation must be determined. Results We have developed a model-based approach to analyze proteobacterial genomes for promoter features that is specifically designed to account for the variability in sequence, location and topology intrinsic to differential gene expression. We provide methods for annotating regulatory regions by detecting their subjacent cis -features. This includes identifying binding sites for a transcriptional regulator, distinguishing between activation and repression sites, direct and reverse orientation, and among sequences that weakly reflect a particular pattern; binding sites for the RNA polymerase, characterizing different classes, and locations relative to the transcription factor binding sites; the presence of riboswitches in the 5'UTR, and for other transcription factors. We applied our approach to characterize Network Motifs controlled by the PhoP/PhoQ regulatory system of Escherichia coli and Salmonella enterica serovar Typhimurium. We identified key features that enable the PhoP protein to control its target genes, and distinct features may produce different expression patterns even within the same Network Motif. Conclusion Global transcriptional regulators control multiple promoters by a variety of Network Motifs. This is clearly the case for the regulatory protein PhoP. In this work, we studied this regulatory protein and demonstrated that understanding gene expression does not only require identifying a set of connexions or Network Motif, but also the cis -acting elements participating in each of these connexions.
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Identifying promoter features of co-regulated genes with similar Network Motifs
BMC Bioinformatics, 2009Co-Authors: Oscar Harari, Coral Del Val, Rocío Romero-zaliz, Dongwoo Shin, Eduardo A Groisman, Henry V. Huang, Igor ZwirAbstract:Background A large amount of computational and experimental work has been devoted to uncovering Network Motifs in gene regulatory Networks. The leading hypothesis is that evolutionary processes independently selected recurrent architectural relationships among regulators and target genes (Motifs) to produce characteristic expression patterns of its members. However, even with the same architecture, the genes may still be differentially expressed. Therefore, to define fully the expression of a group of genes, the strength of the connections in a Network Motif must be specified, and the cis-promoter features that participate in the regulation must be determined.