• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Bioinformatics and Computational Biology >A GRAPH-BASED ALGORITHM FOR MINING MULTI-LEVEL PATTERNS IN GENOMIC DATA
【24h】

A GRAPH-BASED ALGORITHM FOR MINING MULTI-LEVEL PATTERNS IN GENOMIC DATA

机译：基于图形的遗传数据多层次挖掘算法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Comparative genomics is concerned with the study of genome structure and function of different species. It can provide useful information for the derivation of evolutionary and functional relationships between genomes. Previous work on genome comparison focuses mainly on comparing the entire genomes for visualization without further analysis. As many interesting patterns may exist between genomes and may lead to the discovering of functional gene segments (groups of genes), we propose an algorithm called Multi-Level Genome Comparison Algorithm (MGC) that can be used to facilitate the analysis of genomes at multi-levels during the comparison process to discover sequential and regional consistency in gene segments. Different genomes may have common sub-sequences that differ from each other due to mutations, lateral gene transfers, gene rearrangements, etc., and these sub-sequences are usually not easily identified. Not all the genes can have a perfect one-to-one matching with each other. It is quitepossible for one-to-many or many-to-many ambiguous relationships to exist between them. To perform the tasks effectively, MGC takes such ambiguity into consideration during genome comparison by representing genomes in a graph and then make use of a graph mining algorithm called the Multi-Level Attributed Graph Mining Algorithm (MAGMA) to build a hierarchical multi-level graph structure to facilitate genome comparison. To determine the effectiveness of these proposed algorithms, experiments were performed using intra- and inter-species of Microbial genomes. The results show that the proposed algorithms are able to discover multiple level matching patterns that show the similarities and dissimilarities among different genomes, in addition to confirming the specific role of the genes in the genomes.
机译：比较基因组学与研究不同物种的基因组结构和功能有关。它可以为推导基因组之间的进化和功能关系提供有用的信息。先前有关基因组比较的工作主要集中在比较整个基因组以进行可视化而无需进一步分析。由于基因组之间可能存在许多有趣的模式，并可能导致发现功能基因片段（基因组），因此我们提出了一种称为多级基因组比较算法（MGC）的算法，该算法可用于促进多基因组基因组分析。比较过程中的低水平，以发现基因片段的顺序和区域一致性。由于突变，横向基因转移，基因重排等原因，不同的基因组可能具有彼此不同的共同子序列，这些子序列通常不容易识别。并非所有的基因都可以具有完美的一对一匹配。它们之间存在一对多或多对多的模糊关系是很可能的。为了有效执行任务，MGC通过在图中表示基因组来比较基因组时考虑了这种歧义，然后利用称为多级属性图挖掘算法（MAGMA）的图挖掘算法来构建分层的多级图便于基因组比较的结构。为了确定这些提议算法的有效性，使用微生物基因组的种内和种间进行了实验。结果表明，所提出的算法除了证实基因在基因组中的特定作用外，还能够发现多个水平匹配模式，这些模式显示了不同基因组之间的相似性和异同性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号