利用虚拟杂交技术获得炭疽芽孢杆菌群的基因组指纹图谱。

Hueman Jaimes-Díaz, Violeta Larios-Serrato, Teresa Lloret-Sánchez, Gabriela Olguín-Ruiz, Carlos Sánchez-Vallejo, Luis Carreño-Durán, Rogelio Maldonado-Rodríguez, Alfonso Méndez-Tenorio
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引用次数: 3

摘要

在这项研究中,我们评估了虚拟杂交鉴定高度相关的细菌菌株的能力。通过虚拟杂交获得了8个炭疽芽孢杆菌基因组指纹图谱,并设计了一套15264个13核苷酸短探针,用于产生每种生物独特的基因组指纹图谱。从每个基因组指纹中获得的数据被用来获得模拟DNA微阵列的杂交模式。采用直接杂交和扩展杂交两种虚拟杂交方法来确定潜在杂交位点的数量,从而确定区分相似性为99.9%的基因组的最小灵敏度值。比较了两种方法的基因组指纹图谱,构建了系统基因组树,验证了最小检测值为0.000017。与其他分类方法相比,从基因组指纹中获得的结果表明,树木中的分布是正确的。研究还确定了每个基因组的特定虚拟杂交位点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

In Silico Genomic Fingerprints of the Bacillus anthracis Group Obtained by Virtual Hybridization.

In Silico Genomic Fingerprints of the Bacillus anthracis Group Obtained by Virtual Hybridization.

In Silico Genomic Fingerprints of the Bacillus anthracis Group Obtained by Virtual Hybridization.

In Silico Genomic Fingerprints of the Bacillus anthracis Group Obtained by Virtual Hybridization.

In this study we evaluate the capacity of Virtual Hybridization to identify between highly related bacterial strains. Eight genomic fingerprints were obtained by virtual hybridization for the Bacillus anthracis genome set, and a set of 15,264 13-nucleotide short probes designed to produce genomic fingerprints unique for each organism. The data obtained from each genomic fingerprint were used to obtain hybridization patterns simulating a DNA microarray. Two virtual hybridization methods were used: the Direct and the Extended method to identify the number of potential hybridization sites and thus determine the minimum sensitivity value to discriminate between genomes with 99.9% similarity. Genomic fingerprints were compared using both methods and phylogenomic trees were constructed to verify that the minimum detection value is 0.000017. Results obtained from the genomic fingerprints suggest that the distribution in the trees is correct, as compared to other taxonomic methods. Specific virtual hybridization sites for each of the genomes studied were also identified.

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来源期刊
自引率
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审稿时长
11 weeks
期刊介绍: High-Throughput (formerly Microarrays, ISSN 2076-3905) is a multidisciplinary peer-reviewed scientific journal that provides an advanced forum for the publication of studies reporting high-dimensional approaches and developments in Life Sciences, Chemistry and related fields. Our aim is to encourage scientists to publish their experimental and theoretical results based on high-throughput techniques as well as computational and statistical tools for data analysis and interpretation. The full experimental or methodological details must be provided so that the results can be reproduced. There is no restriction on the length of the papers. High-Throughput invites submissions covering several topics, including, but not limited to: Microarrays, DNA Sequencing, RNA Sequencing, Protein Identification and Quantification, Cell-based Approaches, Omics Technologies, Imaging, Bioinformatics, Computational Biology/Chemistry, Statistics, Integrative Omics, Drug Discovery and Development, Microfluidics, Lab-on-a-chip, Data Mining, Databases, Multiplex Assays.
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