Because of its wide application, the subgraph matching problem has been studied extensively during the past decade. However, most existing solutions assume that a data graph is a vertex/edge-labeled graph （i.e., each...Because of its wide application, the subgraph matching problem has been studied extensively during the past decade. However, most existing solutions assume that a data graph is a vertex/edge-labeled graph （i.e., each vertex/edge has a simple label）. These solutions build structural indices by considering the vertex labels. However, some real graphs contain rich-content vertices such as user profiles in social networks and HTML pages on the World Wide Web. In this study, we consider the problem of subgraph matching using a more general scenario. We build a structural index that does not depend on any vertex content. Based on the index, we design a holistic subgraph matching algorithm that considers the query graph as a whole and finds one match at a time. In order to further improve efficiency, we propose a ＂partial evaluation and assembly＂ framework to find sub- graph matches over large graphs. Last but not least, our index has light maintenance overhead. Therefore, our method can work well on dynamic graphs. Extensive experiments on real graphs show that our method outperforms the state-of-the-art algorithms.展开更多
With the rapid development of sequencing technologies,especially the maturity of third-generation sequencing technologies,there has been a significant increase in the number and quality of published genome assemblies....With the rapid development of sequencing technologies,especially the maturity of third-generation sequencing technologies,there has been a significant increase in the number and quality of published genome assemblies.The emergence of these high-quality genomes has raised higher requirements for genome evaluation.Although numerous computational methods have been developed to evaluate assembly quality from various perspectives,the selective use of these evaluation methods can be arbitrary and inconvenient for fairly comparing the assembly quality.To address this issue,we have developed the Genome Assembly Evaluating Pipeline(GAEP),which provides a comprehensive assessment pipeline for evaluating genome quality from multiple perspectives,including continuity,completeness,and correctness.Additionally,GAEP includes new functions for detecting misassemblies and evaluating the assembly redundancy,which performs well in our testing.GAEP is publicly available at https://github.com/zyoptimistic/GAEP under the GPL3.0 License.With GAEP,users can quickly obtain accurate and reliable evaluation results,facilitating the comparison and selection of high-quality genome assemblies.展开更多
基金This work was partially supported by the National Key Research and Development Program of China （2016YFB1000603）, Fundamental Research Funds for the Central Universities, the National Natural Science Foundation of China （Grant Nos. 61622201, 61472131, and 61272546）, and Science and Technology Key Projects of Hunan Province （2015 TP1004）.
文摘Because of its wide application, the subgraph matching problem has been studied extensively during the past decade. However, most existing solutions assume that a data graph is a vertex/edge-labeled graph （i.e., each vertex/edge has a simple label）. These solutions build structural indices by considering the vertex labels. However, some real graphs contain rich-content vertices such as user profiles in social networks and HTML pages on the World Wide Web. In this study, we consider the problem of subgraph matching using a more general scenario. We build a structural index that does not depend on any vertex content. Based on the index, we design a holistic subgraph matching algorithm that considers the query graph as a whole and finds one match at a time. In order to further improve efficiency, we propose a ＂partial evaluation and assembly＂ framework to find sub- graph matches over large graphs. Last but not least, our index has light maintenance overhead. Therefore, our method can work well on dynamic graphs. Extensive experiments on real graphs show that our method outperforms the state-of-the-art algorithms.
基金supported by the National Key Research and Development Project Program of China(2022YFC3400300,2019YFE0109600)the China Postdoctoral Science Foundation(2021M701584).
文摘With the rapid development of sequencing technologies,especially the maturity of third-generation sequencing technologies,there has been a significant increase in the number and quality of published genome assemblies.The emergence of these high-quality genomes has raised higher requirements for genome evaluation.Although numerous computational methods have been developed to evaluate assembly quality from various perspectives,the selective use of these evaluation methods can be arbitrary and inconvenient for fairly comparing the assembly quality.To address this issue,we have developed the Genome Assembly Evaluating Pipeline(GAEP),which provides a comprehensive assessment pipeline for evaluating genome quality from multiple perspectives,including continuity,completeness,and correctness.Additionally,GAEP includes new functions for detecting misassemblies and evaluating the assembly redundancy,which performs well in our testing.GAEP is publicly available at https://github.com/zyoptimistic/GAEP under the GPL3.0 License.With GAEP,users can quickly obtain accurate and reliable evaluation results,facilitating the comparison and selection of high-quality genome assemblies.
