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

摘要：在过去的4年里，Ramage Oral Biofilm Group通过BBSRC CASE学生（Christopher Delaney先生）与GSK合作，开发了专注于微生物组、转录组和代谢组分析平台的生物信息学工具，并开发了适当的数据整合管道。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可用。最近的单中心研究表明，有可能预测与龋齿和牙周病相关的不同微生物群，并在这些研究中确定稳定种群和益生菌不良种群之间的差异（Zaura等，2017）。有了这种方法，就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，并使用生物信息学工具来整合特定疾病的微生物组。事实上，这种方法最近在胃肠道微生物组的背景下得到了证明（Duvallet等，2017）。因此，除了适当的患者元数据之外，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项现实的成就。然而，这依赖于数据存储和处理的能力，以及未来的验证，以实现预期数据集的存储。研究目的：我们的目标是在众多患者队列中辨别口腔疾病患者微生物组的显著特征。我们将调查目前发表的数据，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有力的理解。简而言之，我们提出以下步骤。从以前所有口腔微生物组研究中检索所有原始数据，并将其存放在一个位置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）创建所有样本中所有队列信息的元表。我们将围绕上述已发表的研究构建我们的管道。总之，在数据收集之后，之前没有被解复用的样本将被解复用。序列将被过滤。在可能的情况下，考虑到一些较旧的研究和使用以前的测序技术，所有的reads将被修剪到相同的长度（Roche）。操作分类单位（otu）将通过使用USEARCH进行聚类来确定。然后将使用RDP分类器为这些otu分配分类。具有低丰度读取和OTUS的样本将从数据集中丢弃。5. 一旦所有数据都被处理到类似的标准，它们将被编译并进行统计分析。6. 我们将根据不同研究中相似的标准（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。这将使我们能够在更大的人群中比较微生物组。7. 将进行多变量分析和微生物群落分析，以寻找微生物组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).