Bioinformatics Core

项目摘要-生物信息学核心 该生物信息学核心的目标是支持统计和生物信息学分析， 研究骨质疏松症合成代谢疗法（romosozumab，一种硬化素抑制剂，Scl- 在临床和临床前研究中， 合成代谢疗法已经报道了Scl-Ab的短合成代谢治疗窗口; 与具有较长合成代谢窗口的teriparlitazone（PTH）形成对比。该生物信息学核心将 作为研究项目的关键基础，通过分析来自 单细胞RNA-seq（scRNA-seq）和批量RNA-seq。 生物信息学核心的能力将通过利用现有的 成千上万的人类样本的全基因组测序数据集;骨细胞 来自独特小鼠敲除的基因表达特征和深层骨骼表型 来自我们合作者的数据。为了支持项目#1、项目#2以及试点项目， 生物信息学核心将提供统计设计，计算分析，数据质量控制 和管理支持，以及获得其他外部人类遗传学和 生物信息学数据和资源。具体而言，在目标1中，生物信息学核心旨在建立一个 云上的协同数据管理、数据分析和生物信息学管道 计算环境，允许调查人员通过数据收集进行互动， 数据分析、信息共享和数据解释。在目标2中，核心将提供 从临床产生scRNA-seq和bulk RNA-seq的统计学和生物信息学分析 和临床前研究，以确定成骨细胞前体细胞簇随着时间的推移， 或PTH治疗;成骨细胞谱系追踪;成骨祖细胞和骨细胞基因 表达模式;和随时间的共表达模式。此外，跨物种整合 将进行分析以鉴定人和人之间的同源和不同的细胞类型。 小鼠在目标3中，我们将优先考虑调节骨形成的基因，并确定分子生物学。 通过人类基因表达（骨活检）和遗传学影响治疗效果的特征 与骨表型（BMD、HR-PQCT衍生骨结构、骨折）相关的变异， 大规模的全基因组测序研究。在目标4中，进一步表征骨骼功能 和从项目#和项目#2转录组分析鉴定的基因的表型，我们 将利用现有的KO小鼠深度表型数据;并生成新的KO小鼠品系。 最终，这些合并的分析将产生有力的、新颖的假设 关于骨干细胞生物学和对骨合成代谢剂的反应。

Project Summary – Bioinformatics Core The objective of this Bioinformatics Core is to support statistical and bioinformatics analyses to study the effects of osteoporosis anabolic therapies (romosozumab, a sclerostin inhibitor, Scl- Ab) on osteoblast progenitors as well as osteocytes in clinical and pre-clinical studies with anabolic therapies. A short anabolic treatment window from Scl-Ab has been reported; in contrast to teriparatide (PTH) that has a longer anabolic window. This Bioinformatics Core will serve as the critical foundation for the research projects by analyzing cross-species data from single cell RNA-seq (scRNA-seq) and bulk RNA-seq using innovative techniques and approaches. The capabilities of the Bioinformatics Core will be enhanced by being able to leverage existing datasets of whole genome sequencing in tens thousands of human samples; and osteocyte gene expression signatures and deeply skeletal phenotyping from a unique mouse knockout dataset from our collaborators. To support Project #1, Project #2 as well as pilot projects, the Bioinformatics Core will provide statistical design, computational analysis, data quality control and management support as well as accessing to other external human genetics and bioinformatics data and resource. Specifically, in Aim 1, the Bioinformatics Core seeks to build a synergistic data management, data analytical and bioinformatics pipelines over cloud computing environment which allows interaction within investigators through data collection, data analyses, information sharing and data interpretation. In Aim 2, the Core will provide statistical and bioinformatics analyses for scRNA-seq and bulk RNA-seq generated from clinical and pre-clinical studies to identify osteoblast precursor cell clusters over time with either Scl-Ab or PTH treatments; osteoblast lineage tracing; osteoblast progenitors and osteocytes gene expression patterns; and co-expression pattern over time. In addition, cross-species integrating analysis will be conducted to identify homologous and divergent cell types between human and mice. In Aim 3, we will prioritize genes regulating bone formation and Identify molecular signatures affecting treatment effect via human gene expression (in bone biopsies) and genetic variation associated with bone phenotypes (BMD, HR-PQCT derived bone structure, fracture) in large-scale whole genome sequenced studies. In Aim 4, To further characterize skeletal function and phenotypes of the genes identified from project# and project#2 transcriptome analyses, we will utilize the existing KO mice deep phenotyping data; and generate a new KO mice strain. Ultimately, these merged analyses will generate powerfully informed, novel hypotheses regarding bone stem cell biology and responses to bone anabolic agents.