Using Big Data to Model the Maintenance of Health in the Human Oral Cavity

学生奖学金战略优先领域：基础生物科学支撑健康关键词：口腔健康，生物信息学，生物膜摘要：Ramage口腔生物膜组在过去的4年中通过BBSRC CASE学生奖学金（Christopher Delaney先生）与GSK合作，开发生物信息学工具，重点是微生物组，转录组和代谢组的分析平台，并开发适当的数据集成管道。通过微生物组研究的爆炸式增长，许多描述良好的数据集已经公开。最近的单中心研究已经显示了预测与龋齿和牙周病相关的不同微生物群的潜力，并且在这些微生物群中识别稳定和微生态群之间的差异（Zaura等人，2017年）。通过这种方法，有机会使用适当设计的研究，其中生物微生物组数据集可以集中定位，生物信息学工具用于整合疾病特异性微生物组。事实上，这种方法最近已经在胃肠道微生物组的背景下得到证实（Duvallet等人，2017年）。因此，除了适当的患者元数据，数据挖掘的机会是无限的，创建口腔健康和疾病微生物组地图的可能性是一个现实的成就。然而，这取决于数据储存和处理能力，并需要将来进行校对，以便能够储存预期的数据集。研究目的：我们的目标是在众多患者队列中识别口腔疾病患者微生物组的显著特征。我们将询问目前发表的数据，以提供对不同口腔健康患者人口统计学中微生物变化的更可靠的理解。简而言之，我们提出以下步骤。1.收集所有先前口腔微生物组研究的所有原始数据，并将其存款在一个位置2。创建来自每个研究的所有样本的目录（包含、测序类型、解复用状态）3.创建所有样本的所有队列信息的元表。4.我们将围绕上述已发表的研究建立我们的管道。总之，数据收集后，之前未解复用的样本将被解复用。序列将被过滤。考虑到一些研究将是较旧的并且使用先前的测序技术（Roche），所有读段将尽可能被修剪至相同的长度。操作分类单元（OTU）将通过聚类进行鉴定，聚类将使用USEclassification进行。然后，将使用RDP分类器为这些OTU分配分类。具有低丰度读数和OTUS的样品将从数据集中丢弃。5.一旦所有数据都被处理到类似的标准，它们将被汇编并进行统计分析。6.我们将根据相似的标准（年龄、吸烟状况、疾病状况、严重程度等）对样本进行分组和分类。在不同的研究中。这将使我们能够在更大的队列中比较微生物组。7.将进行多变量分析和微生物群落分析，以寻找微生物群落中能够预测上述患者变量（龋齿、牙周病等）的变量。

Studentship strategic priority area:Basic Bioscience Underpinning HeathKeywords:Oral health, bioinformatics, biofilmAbstract: The Ramage Oral Biofilm Group have worked with GSK over the past 4 years through a BBSRC CASE studentship (Mr Christopher Delaney) to develop bioinformatic tools focussed on analysis platforms for microbiomes, transcriptomes and metabolomes, and developed appropriate pipelines for data integration. Through the explosion of microbiome studies, numerous well-described data sets have become publicly available. Recent single centre studies have shown the potential to predict different microbiomes associated with caries and periodontal disease, and within these identify differences between stable and dysbiotic populations (Zaura, et al., 2017). With this approach there lies an opportunity to use appropriately designed studies, where the biological microbiome data-sets can be centrally located, and bioinformatic tools used to integrate disease specific microbiomes. Indeed, this approach has recently been demonstrated in the context of gastrointestinal microbiomes (Duvallet, et al., 2017). Therefore, alongside the appropriate patient meta-data, the data-mining opportunities are boundless, and possibilities for creating a map of oral health and disease microbiomes is a realistic achievement. However, this relies on the capacity for data storage and processing, with future proofing to enable deposits of prospective data sets. Study objectives: We aim to discern distinguishing features in the microbiome of patients with oral diseases across numerous patient cohorts. We will interrogate the currently published data to provide a more robust understanding of the microbiological changes in different oral health patient demographics. In brief, we propose the following steps.1. Retrieve all the raw data from all previous oral microbiome studies and deposit them in a single location2. Create a catalog of all of the samples from each of the studies (Containing, sequencing type, demultiplexed status)3. Create meta-table of all cohort information across all samples.4. We will build our pipeline around the published study above. In summary, after data collection, samples that had not been previously demultiplexed will be demultiplexed. Sequences will be filtered. All reads will be trimmed to the same length where possible, taking into considerations some of the studies will be older and using previous sequencing technologies (Roche). Operational taxanomic units (OTUs) will be identified by clustering which will be performed using USEARCH. These OTUs will be then be assigned taxonomy using the RDP classifier. Samples with a low abundance of reads and OTUS will be discarded from the dataset. 5. Once the data has all been processed to a similar standard they will be compiled and undergo statistical analysis. 6. We will group and classify samples according to similar criteria (Age, Smoking status, Disease status, Severity etc.) across the different studies. This will allow for us to compare the microbiome across a much larger cohort. 7. Multivariate analysis and microbiome community analysis will be performed in order to look for variables in the microbiome that are able to predict patient variables as mentioned above (caries, periodontal disease, etc).