Scalable Cancer Genomics via Nanocoding and Sequencing

 描述（由申请人提供）：癌症基因组在其结构方面为基因组学家提供了惊人的难题。试图组装成一个完整视图的数据“块”的大小既很大（细胞遗传学）又很小（序列数据），通常在规模上相差一千倍以上。再加上肿瘤内的基因组分散性和穿插重复序列中的断点，这个难题的解决变得更加困难。因此，本申请的目的是通过分层框架有效地无缝缩放数据“块”大小，通过新的算法和计算管道，其将利用通过对Nanocoding的重大进步构建的长距离物理图和序列数据来创建从核苷酸到染色体的癌症基因组的可缩放视图。这个多管齐下的项目将涉及协同进步：DNA标记，非常大的基因组DNA分子的呈现，单分子分析物的扫描仪和机器视觉-所有系统组件都将由先进的生物信息学分析技术提供信息，为单分子分析开发，以及设备内DNA构象的尖端计算机模拟，这将是大型数据集的铸造厂。这个高度集成的系统将旨在发现四个配对的多发性骨髓瘤/正常样本中的新结构变体，用于将以前无法检测到的事件制表，作为验证和进一步研究的候选者。由此产生的平台，包括新的单分子技术，融合了先进的生物信息学技术，预示着可扩展的，全面的，快速的基因组分析导航癌症基因组。

 DESCRIPTION (provided by applicant): Cancer genomes present amazing puzzles for genomicists to solve in terms of their structures. The size of the data "pieces" for attempting assembly into a complete view are both very large (cytogenetic) and very small (sequence data), often differing in scale by more than a thousand-fold. Add to this, genomic dispersity within a tumor and breakpoints within interspersed repeats, and the puzzle solution grows much more difficult. As such, the aims of this application are to effectively seamlessly scale data "piece" size by a hierarchical framework, through new algorithms and computational pipelines that will engage both long-range physical maps constructed by significant advancements to Nanocoding, and sequence data to create scalable views of cancer genomes that span from nucleotide-to-chromosome. This multipronged project will involve synergistic advancements to: DNA labeling, presentation of very large genomic DNA molecules, scanners for single molecule analytes, and machine vision-all system components that will be informed by advanced bioinformatic analysis techniques, developed for single molecule analysis, and cutting-edge computer simulations of DNA conformations within the devices that will be the foundry for large datasets. This highly integrated system will be aimed at the discovery of novel structural variants within four paired multiple myeloma / normal samples for tabulation of previously undetectable events as candidates for validation and further study. The resulting platform, comprising new single molecule technologies, melded with advanced bioinformatics techniques, portends scalable, comprehensive, fast genome analysis for navigating cancer genomes.