A web-based craniofacial disease gene discovery tool

基于网络的颅面疾病基因发现工具

• 批准号: 9107846
• 负责人: Salil Lachke
• 金额: $ 19.25万
• 依托单位: UNIVERSITY OF DELAWARE
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9107846
• 关键词: Address; Affect; Bioinformatics; Candidate Disease Gene; Clinical; Communities; Complex; Computer Simulation; Congenital Abnormality; Craniofacial Abnormalities; Data; Data Set; Defect; Development; Disease; Effectiveness; Embryo; Event; Exhibits; Eye; Eye Development; Eye diseases; Face; FaceBase; Future; Gene Expression; Genes; Genetic Predisposition to Disease; Genomics; Health; Histocompatibility Testing; Human; In Situ Hybridization; Individual; Knowledge; Letters; Literature; Live Birth; Machine Learning; Medical; Methods; Molecular; Molecular Profiling; Movement; Mus; Online Systems; Pathogenesis; Patients; Persons; Play; Process; Productivity; Publishing; Regulator Genes; Research; Resources; Scientist; Series; Staging; Structural Congenital Anomalies; System; Tissues; United States National Institutes of Health; Visual Fields; base; cell type; cost effective; craniofacial; craniofacial development; data mining; evidence base; exome sequencing; gene discovery; gene interaction; genome browser; genome wide association study; genome-wide; improved; innovation; interactive tool; interest; learning strategy; lens; life time cost; microdeletion; novel; novel strategies; novel therapeutic intervention; orofacial cleft; rehabilitation service; success; tool; transcriptome sequencing; user-friendly; web based interface; whole genome

 DESCRIPTION (provided by applicant): Craniofacial (CF) abnormalities constitute more than a third of all human structural birth defects. To define their genetic etiology, detailed molecular understanding is required of coordinated movement and fusion of embryonic facial prominences - as disruption of these morphogenetic events cause defects such as orofacial clefts (OFC). The NIH FaceBase initiative is an important step to address this need, as it aims to generate comprehensive whole-genome expression datasets using microarrays or Next-Gen RNA-sequencing (RNA-seq) on mouse embryonic CF tissue. However, genome-wide profiling identifies several thousand "expressed" genes and it is a formidable challenge to predict and prioritize the select few genes that are critical to tissue development or pathogenesis. We posit that although there is a wealth of genomic-level data available, this deficit remains because an adequate strategy has not yet been applied to identify these important candidate CF genes. We recently developed an innovative approach - termed in silico whole embryo body (WB) subtraction - to identify such important genes based on developmentally-enriched expression. We have applied this novel approach to ~15% of FaceBase data and assembled this knowledge as a user-friendly web-based interactive tool SysFACE (Systems tool for craniofacial expression-based gene discovery, http://bioinformatics.udel.edu/Research/SysFACE). Even with limited datasets, the beta version of SysFACE is significantly more effective, compared with unprocessed FaceBase datasets, in identification of known genes associated with OFCs from both linkage and GWAS studies. To process all existing FaceBase datasets, we will generate additional platform-specific WB reference datasets and evaluate these further with machine learning strategies to identify genes important to CF development (Aim 1). Subsequently, we aim to experimentally validate these tissue-enriched gene expression profiles, and to assemble this knowledge - along with a new evidence-based functional gene regulatory network (GRN) that will allow all molecular data from the CF published literature to be represented on systems level - as a user-friendly web-based interactive resource (Aim 2), which will also be made available through FaceBase. Development of SysFACE, as outlined in this application, will greatly improve prediction of candidate CF genes, provide an excellent resource for CF-network construction, and will facilitate CF gene discovery efforts by developmental biologists and clinicians.

 描述（由应用提供）：颅面（CF）异常占所有人类结构性先天缺陷的三分之一。为了定义其遗传病因，详细的分子 需要理解胚胎面部突出的协同运动和融合 - 这些形态发生事件的破坏会导致缺陷，例如口面裂口（OFC）。 NIH面对基础计划是解决这一需求的重要步骤，因为它旨在在小鼠胚胎CF组织上使用微阵列或下一代RNA-Sepering（RNA-Seq）生成全面的全基因组表达数据集。然而，全基因组谱分析鉴定了数千种“表达”基因，这是预测和优先级对组织发育或发病机理至关重要的少数基因的巨大挑战。我们肯定的是，尽管有大量的基因组级数据可用，但此防御仍然存在，因为尚未应用适当的策略来识别这些重要的候选CF基因。我们最近开发了一种创新的方法 - 称为硅整个胚胎体（WB）减去 - 以基于发育增强的表达来识别此类重要基因。我们已经将这种新颖的方法应用于〜15％的面库数据，并将这些知识汇总为基于用户友好的互动工具sysface（基于颅面表达的基因发现的系统工具，http://bioinformatics.udel.udel.edu/research/sysearch/sysface/sysface）。即使数据集有限，与未经处理的面键数据集相比，Sysface的Beta版本在识别与链接和GWAS研究中的OFC相关的已知基因方面也更加有效。为了处理所有现有的Facebase数据集，我们将生成其他平台特定的WB参考数据集，并通过机器学习策略进一步评估这些数据集，以识别对CF开发重要的基因（AIM 1）。随后，我们旨在实验验证这些富含组织的基因表达谱，并组装这些知识 - 以及新的基于证据的功能基因调控网络（GRN），该网络（GRN）将允许CF发布的所有分子数据在系统级别上 - 以用户友好的网络资源来代表，作为用户基于网络的交互资源（AIM 2），这也可以通过面对面来访问。如本应用程序中概述的那样，Sysface的开发将大力改进候选CF基因，为CF网络构建提供了极好的资源，并将促进开发生物学家和临床医生的CF基因发现工作。