Embedding next generation sequencing protocols in public health laboratories

临床微生物学实验室进行数千项测试，以确定临床样本中存在哪些病原体。在寻找细菌时，通常是在选择性培养基上培养这些细菌，并向临床医生报告检测结果是阳性还是阴性。然而，对于许多细菌，以及一些存在几种细菌的样品，要么没有可用的培养液，要么由于样品的混合性质，结果令人困惑和难以解释。此外，根据正在检测的微生物，检测可能需要3至4天，然后临床医生才会得知结果，然后决定如何进行治疗。此外，在报告阳性结果之前，可能需要进行几种不同的测试。检测DNA信号的基于分子的方法的应用已经在临床微生物学实验室中使用了多年，以识别病毒的存在。最近，这种基于DNA的方法已开始被用于检测腹泻患者粪便样本中的细菌病原体。然而，这些测试是专门针对已知的病原体的，为了确定存在什么病原体，在检测到阳性信号之前必须进行几次测试。下一代测序平台可以为这些问题提供解决方案，它们可以允许高通量筛选样本来检测病原体，同时不需要预先知道需要检测哪些病原体。下一代测序技术可以为临床微生物学实验室提供一站式的解决方案，以识别临床标本中的病原体。这项翻转提案旨在利用这项技术，这项技术目前正被用于了解微生物燃料电池的工作原理(进一步开发)，以便它可以应用于临床环境。计算机科学家安·史密斯博士将应用下一代测序知识，信息学管道将开发一个简单的用户友好界面来分析这些数据集，并为临床微生物学家提供这项使能技术。

Clinical microbiology laboratories undertake thousands of tests in order to identify what pathogen is present in a clinical sample. When looking for bacteria, it is common to grow these organisms on selective media and report back to the clinician as to whether the test is positive or negative. However, for many bacteria, and for some samples in which there are several bacteria present, there is either no culture media available or due to the mixed nature of the sample, the results are confusing and difficult to interpret. Furthermore, depending on the organism which is being detected, the test can take 3 to 4 days before the clinician will be informed of the results and then make a decision on how to proceed with treatment. Additionally, several different tests may need to be undertaken before a positive result is reported. The application of molecular-based methods, which detect a DNA signal, have been used for many years in clinical microbiology laboratories to identify the presence of viruses. More recently, such DNA-based approaches have started to be adopted for the detection of bacterial pathogens in stool samples from patients with diarrhoea. However, these tests are specifically targeted at known pathogens, and in order to identify what pathogen is present, several tests have to be undertaken before a positive signal is detected. Next-generation sequencing platforms can provide a solution to these problems, they can allow for high throughput screening samples to detect pathogens and at the same time, no pre-knowledge is required as to what pathogen needs to be detected. The next generation sequencing technology can provide clinical microbiology laboratories with a one-stop solution to identifying pathogens in clinical specimens. This FLIP proposal aims to take this technology, which is currently being used for understanding how microbial fuel cells work (on developing it further), so it can be applied in a clinical setting. The interchange, Dr Ann Smith who is a computer scientist, will apply the knowledge of next-generation sequencing, and by Informatics pipelines will develop a simple user-friendly interface for analysing these datasets, and provide clinical microbiologists with access to this enabling technology.