SCALABLE CANCER GENOMICS VIA NANOCODING AND SEQUENCING


Year of Award:
2015
Award Type:
R33
Project Number:
CA182360
RFA Number:
RFA-CA-14-004
Technology Track:
Molecular & Cellular Analysis Technologies
PI/Project Leader:
SCHWARTZ, DAVID C.
Other PI or Project Leader:
MA, JIAN
Institution:
UNIVERSITY OF WISCONSIN-MADISON
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.