A web-based craniofacial disease gene discovery tool

 描述（由申请人提供）：颅面（CF）异常占所有人类结构性出生缺陷的三分之一以上。为了确定其遗传病因，详细的分子 理解胚胎面部发育的协调运动和融合是必需的，因为这些形态发生事件的中断会导致诸如口面裂（OFC）之类的缺陷。NIH FaceBase计划是解决这一需求的重要一步，因为它旨在使用微阵列或下一代RNA测序（RNA-seq）在小鼠胚胎CF组织上生成全面的全基因组表达数据集。然而，全基因组分析鉴定了数千个“表达”基因，并且预测和优先选择对组织发育或发病机制至关重要的少数基因是一个艰巨的挑战。我们认为，虽然有丰富的基因组水平的数据，这种赤字仍然存在，因为一个适当的策略尚未应用到确定这些重要的候选CF基因。我们最近开发了一种创新的方法-称为计算机全胚体（WB）减法-以确定基于发育富集表达的重要基因。我们已经将这种新方法应用于约15%的FaceBase数据，并将这些知识组装为用户友好的基于Web的交互式工具SysFACE（基于颅面表达的基因发现系统工具，http：//bioinformatics.udel.edu/Research/SysFACE）。即使使用有限的数据集，与未处理的FaceBase数据集相比，SysFACE的测试版在从连锁和GWAS研究中识别与OFC相关的已知基因方面也显着更有效。为了处理所有现有的FaceBase数据集，我们将生成额外的特定于平台的WB参考数据集，并使用机器学习策略进一步评估这些数据集，以识别对CF发育重要的基因（目标1）。随后，我们的目标是通过实验验证这些组织丰富的基因表达谱，并组装这些知识-沿着一个新的循证功能基因调控网络（GRN），将允许所有的分子数据从CF发表文献的系统水平上表示-作为一个用户友好的基于网络的交互式资源（目标2），这也将通过FaceBase提供。如本申请中所述，SysFACE的开发将极大地改善候选CF基因的预测，为CF网络构建提供极好的资源，并将促进发育生物学家和临床医生的CF基因发现工作。

 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.