Statistical Methods for RNA-seq Data Analysis

描述（由申请人提供）： 从表达微阵列产生的基因表达数据不仅大大提高了我们对细胞生物学的理解，而且为指导人类疾病的诊断和治疗提供了宝贵的资源。然而，将基因表达标签纳入医疗实践的步伐相对缓慢。这主要是由于限制 基因表达微阵列和跨组织或发育阶段的基因表达的自然变异。该研究项目旨在通过联合研究生殖系DNA多态性和从RNA-seq数据获得的等位基因特异性表达（ASE）来克服这些局限性。由于生殖系DNA多态性在组织和发育阶段是稳定的，因此包含DNA信息将有助于我们为患者的临床诊断建立更可靠的生物标志物。 在乎更具体地说，我们将研究ASE在正常和肿瘤组织的遗传基础，解剖遗传和父母的起源对ASE在人类细胞系的影响，并确定逃避X在小鼠相互交叉和人类细胞系失活的基因。

DESCRIPTION (provided by applicant): Gene expression data produced from expression microarrays have not only greatly improved our understanding of cell biology, but also provided invaluable resources to guide the diagnosis and treatment of human diseases. However, the pace of incorporating gene expression signatures into medical practice has been relatively slow. This is mainly due to the limitations of gene expression microarrays and the natural variation of gene expression across tissues or developmental stages. This research project aims to overcome these limitations by joint study of germline DNA polymorphisms and allele-specific expression (ASE) obtained from RNA-seq data. Since germline DNA polymorphisms are stable across tissues and developmental stages, inclusion of DNA information will help us establish more reliable biomarkers for patients' clinical care. More specifically, we will study the genetic basis of ASE in both normal and tumor tissues, dissect genetic and parent-of-origin effects on ASE in human cell lines, and identify genes that escape X inactivation in both mouse reciprocal cross and human cell lines.