Sharp Colin, Golubchik Tanya, Gregory William F, McNaughton Anna L, Gow Nicholas, Selvaratnam Mathyruban, Mirea Alina, Foster Dona, Andersson Monique, Klenerman Paul, Jeffery Katie, Matthews Philippa C
The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, Edinburgh, EH25 9RG, Scotland, UK.
Edinburgh Genomics, Ashworth Laboratories, University of Edinburgh, Edinburgh, EH9 3FL, Scotland, UK.
BMC Res Notes. 2018 Feb 9;11(1):120. doi: 10.1186/s13104-018-3234-8.
There is increasing interest in the use of metagenomic (next generation sequencing, NGS) approaches for diagnosis of infection. We undertook a pilot study to screen samples submitted to a diagnostic microbiology laboratory in a UK teaching hospital using Illumina HiSeq. In the short-term, this small dataset provides insights into the virome of human respiratory and cerebrospinal fluid (CSF) samples. In the longer term, assimilating metagenomic data sets of this nature can inform optimization of laboratory and bioinformatic methods, and develop foundations for the interpretation of results in a clinical context. The project underpins a larger ongoing effort to develop NGS pipelines for diagnostic use.
Our data comprise a complete metagenomic dataset from 20 independent samples (10 CSF and 10 respiratory) submitted to the clinical microbiology laboratory for a large UK teaching hospital (Oxford University Hospitals NHS Foundation Trust). Sequences have been uploaded to the European Nucleotide Archive and are also presented as Krona plots through which the data can be interactively visualized. In the longer term, further optimization is required to better define sensitivity and specificity of this approach to clinical samples.
使用宏基因组学(下一代测序,NGS)方法诊断感染越来越受到关注。我们进行了一项试点研究,使用Illumina HiSeq对提交给英国一家教学医院诊断微生物实验室的样本进行筛查。短期内,这个小数据集能让我们深入了解人类呼吸道和脑脊液(CSF)样本的病毒组。从长远来看,整合这类宏基因组数据集可为实验室和生物信息学方法的优化提供参考,并为在临床背景下解读结果奠定基础。该项目为正在进行的一项更大规模的开发用于诊断的NGS流程的工作提供了支持。
我们的数据包括来自一家大型英国教学医院(牛津大学医院国民保健服务信托基金)临床微生物实验室提交的20个独立样本(10个脑脊液样本和10个呼吸道样本)的完整宏基因组数据集。序列已上传至欧洲核苷酸档案馆,并且还以科罗纳图的形式呈现,通过该图可以交互式地可视化数据。从长远来看,需要进一步优化以更好地确定这种方法对临床样本的敏感性和特异性。