Core B: Computational Genomics Core

Core B: Computational Genomics Core. Core Director: Raul Rabadan The Computational Genomics Core will provide a centralized structure to collect, analyzed and integrate data from all three projects. Simultaneous genomic, transcriptomic and proteomic mapping of bone marrow tissue will provide a unique opportunity to map cell types, their states and pathway specific expression patterns in a spatially resolved manner. In addition to current already implemented computational pipelines, the Core will develop new techniques that will be applied across projects, implement methods for data integration and provide quantitative training and computational support. The Core leaders (Drs. Rabadan and Wang) and their groups have proven expertise in developing and successfully implementing mathematical and statistical computational methods for large scale genomic analysis and data integration. Their work will be integrated with that of Drs. Gaublomme and Greenblatt who have developed and are further developing spatial mapping technologies to profile in situ cell states in the BM niche and delineate their intercellular communication patterns. Datasets will be mined to chart the rewiring of both cell states in the bone marrow niche and their aberrant signaling during AML transformation. Additionally, Core B will include a partnership between quantitative and bench scientists that will develop new techniques in addition to refining and implementing locally existing techniques across all 3 projects. More specifically, the Core will: i) provide analysis of genomic data including single cell mutational profiling through implementation of public pipelines, as well as through internally developed approaches; ii) characterize cancer AML evolution from mutational profiling data using Genotyping of Transcriptomes (GoT) and the MissionBio Platform, including refinement of clonal trajectory inference; iii) analyze single cell transcriptomic and chromatin data and integrate approaches across projects; iv) implement and improve computational approaches for systematic analysis of spatially resolved multi-omic data; v) integrate data across technologies and projects, including the characterization of genetic, epigenetic and transcriptomic states.