Statistical Methods for RNA-seq Data Analysis

描述(由申请人提供)： 表达芯片产生的基因表达数据不仅极大地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，将基因表达签名纳入医疗实践的步伐相对较慢。这主要是由于 基因表达微阵列和跨组织或发育阶段基因表达的自然变异。本研究项目旨在通过对种系DNA多态和从RNA序列数据获得的等位基因特异性表达(ASE)的联合研究来克服这些限制。由于胚系DNA多态在不同组织和发育阶段都是稳定的，纳入DNA信息将有助于我们为患者的临床建立更可靠的生物标记物 关心。更具体地说，我们将研究ASE在正常组织和肿瘤组织中的遗传基础，剖析人类细胞系中ASE的遗传和亲本效应，并在小鼠正反交和人类细胞系中识别逃脱X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.