Whiteside Matthew D, Laing Chad R, Manji Akiff, Kruczkiewicz Peter, Taboada Eduardo N, Gannon Victor P J
National Microbiology Laboratory @ Lethbridge, Public Health Agency of Canada, Lethbridge, AB, T1J 3Z4, Canada.
BMC Microbiol. 2016 Apr 12;16:65. doi: 10.1186/s12866-016-0680-0.
Predictive genomics is the translation of raw genome sequence data into a phenotypic assessment of the organism. For bacterial pathogens, these phenotypes can range from environmental survivability, to the severity of human disease. Significant progress has been made in the development of generic tools for genomic analyses that are broadly applicable to all microorganisms; however, a fundamental missing component is the ability to analyze genomic data in the context of organism-specific phenotypic knowledge, which has been accumulated from decades of research and can provide a meaningful interpretation of genome sequence data.
In this study, we present SuperPhy, an online predictive genomics platform ( http://lfz.corefacility.ca/superphy/ ) for Escherichia coli. The platform integrates the analytical tools and genome sequence data for all publicly available E. coli genomes and facilitates the upload of new genome sequences from users under public or private settings. SuperPhy provides real-time analyses of thousands of genome sequences with results that are understandable and useful to a wide community, including those in the fields of clinical medicine, epidemiology, ecology, and evolution. SuperPhy includes identification of: 1) virulence and antimicrobial resistance determinants 2) statistical associations between genotypes, biomarkers, geospatial distribution, host, source, and phylogenetic clade; 3) the identification of biomarkers for groups of genomes on the based presence/absence of specific genomic regions and single-nucleotide polymorphisms and 4) in silico Shiga-toxin subtype.
SuperPhy is a predictive genomics platform that attempts to provide an essential link between the vast amounts of genome information currently being generated and phenotypic knowledge in an organism-specific context.
预测基因组学是将原始基因组序列数据转化为对生物体的表型评估。对于细菌病原体而言，这些表型范围涵盖从环境生存能力到人类疾病的严重程度。在开发广泛适用于所有微生物的通用基因组分析工具方面已取得重大进展；然而，一个基本缺失的要素是在特定生物体表型知识背景下分析基因组数据的能力，这种知识是经过数十年研究积累而来的，能够对基因组序列数据提供有意义的解读。
在本研究中，我们展示了SuperPhy，这是一个针对大肠杆菌的在线预测基因组学平台（http://lfz.corefacility.ca/superphy/ ）。该平台整合了所有公开可用的大肠杆菌基因组的分析工具和基因组序列数据，并便于用户在公共或私人设置下上传新的基因组序列。SuperPhy能对数千个基因组序列进行实时分析，其结果对于包括临床医学、流行病学、生态学和进化领域在内的广大群体而言是易于理解且有用的。SuperPhy包括以下内容的识别：1）毒力和抗菌抗性决定因素；2）基因型、生物标志物、地理空间分布、宿主、来源和系统发育分支之间的统计关联；3）基于特定基因组区域和单核苷酸多态性的存在与否来识别基因组群体的生物标志物；4）计算机模拟的志贺毒素亚型。
SuperPhy是一个预测基因组学平台，试图在当前产生的大量基因组信息与特定生物体背景下的表型知识之间提供关键联系。
