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Phylomark, a Tool To Identify Conserved Phylogenetic Markers from Whole-Genome Alignments

机译:Phylomark,一种从全基因组比对中识别保守的系统发生标记的工具

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The sequencing and analysis of multiple housekeeping genes has been routinely used to phylogenetically compare closely related bacterial isolates. Recent studies using whole-genome alignment (WGA) and phylogenetics from >100 Escherichia coli genomes has demonstrated that tree topologies from WGA and multilocus sequence typing (MLST) markers differ significantly. A nonrepresentative phylogeny can lead to incorrect conclusions regarding important evolutionary relationships. In this study, the Phylomark algorithm was developed to identify a minimal number of useful phylogenetic markers that recapitulate the WGA phylogeny. To test the algorithm, we used a set of diverse draft and complete E. coli genomes. The algorithm identified more than 100,000 potential markers of different fragment lengths (500 to 900 nucleotides). Three molecular markers were ultimately chosen to determine the phylogeny based on a low Robinson-Foulds (RF) distance compared to the WGA phylogeny. A phylogenetic analysis demonstrated that a more representative phylogeny was inferred for a concatenation of these markers compared to all other MLST schemes for E. coli . As a functional test of the algorithm, the three markers (genomic guided E. coli markers, or GIG-EM) were amplified and sequenced from a set of environmental E. coli strains (ECOR collection) and informatically extracted from a set of 78 diarrheagenic E. coli strains (DECA collection). In the instances of the 40-genome test set and the DECA collection, the GIG-EM system outperformed other E. coli MLST systems in terms of recapitulating the WGA phylogeny. This algorithm can be employed to determine the minimal marker set for any organism that has sufficient genome sequencing.
机译:多个管家基因的测序和分析已被常规用于系统地比较紧密相关的细菌分离株。最近使用全基因组比对(WGA)和来自超过100个大肠杆菌基因组的系统发育学的研究表明,来自WGA和多基因座序列分型(MLST)标记的树形拓扑显着不同。不具有代表性的系统发育可能会导致有关重要进化关系的错误结论。在这项研究中,开发了Phylomark算法以识别可概括WGA系统发育的最小数量的有用系统发育标记。为了测试该算法,我们使用了一组不同的草稿和完整的大肠杆菌基因组。该算法识别出超过100,000个具有不同片段长度(500至900个核苷酸)的潜在标记。最终选择了三种分子标记来确定与WGA系统发育相比较低的Robinson-Foulds(RF)距离,从而确定系统发育。系统发育分析表明,与所有其他大肠杆菌MLST方案相比,这些标记的级联推断出更具代表性的系统发育。作为算法的功能测试,从一组环境大肠杆菌菌株(ECOR集合)中扩增了三种标记(基因组指导的大肠杆菌标记或GIG-EM)并对其进行了测序,并从78例腹泻病原体中进行了信息提取大肠杆菌菌株(DECA收集)。在40个基因组测试集和DECA收集的实例中,就概括WGA系统发育而言,GIG-EM系统优于其他大肠杆菌MLST系统。该算法可用于确定具有足够的基因组测序的任何生物的最小标记集。

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