Pilot of New Technologies to Increase the Genomic Diagnosis of Undiagnosed Disease Network (UDN) Patients

摘要： 尽管UDN项目的诊断成功，但仍有相当数量的患者 在外显子组测序、基因组测序和转录组分析后遗传学上未诊断的。我们 假设来自互补序列分析平台较长范围数据将增强诊断 通过使用传统的短- 读取测序平台。我们建议更长范围的测序技术，如Hi-C全基因组测序技术， 基因组测序（WGS）和长读段RNAseq将有助于等位基因特异性表征， 变体（单核苷酸、插入缺失和结构变体），允许转录物同种型分析， 顺式与反式变体的定相，提供有关表观遗传和核组学特征的信息， 结构域、环和区室，这将使所有形式的非编码 变体以提高诊断率。为此，我们将为UDN患者试验两种额外的数据类型 评估：Hi-C WGS和长读段RNAseq。这将补充UDN中已建立的技术 包括短读WGS和短RNAseq，以及增加我们目前对长读WGS的研究。

Abstract: Despite the diagnostic successes of the UDN program, a significant number of patients remain genetically undiagnosed after exome sequencing, genome sequencing, and transcriptome analyses. We hypothesize that longer-range data from complementary sequence analysis platforms will enhance diagnostic yield in the UDN by identifying structural variants with phasing that remain undetected using traditional short- read sequencing platforms. We propose that longer-range sequencing technologies, such as Hi-C Whole Genome Sequencing (WGS) and long-read RNAseq, will facilitate the allele-specific characterization of variants (single nucleotide, indel, and structural variants), allow transcript isoform analysis in addition to phasing of cis vs. trans variants, provide information about epigenetic and nucleomic features such as domains, loops and compartments, that will enable additional integrated analysis of all forms of noncoding variants to improve the diagnostic yield. To do this we will pilot two additional data types for UDN patient assessment: Hi-C WGS, and long-read RNAseq. This will complement established technologies in the UDN including short-read WGS and short-RNAseq, as well as add to our current investigations on long-read WGS.