Computational Core

COMPUTATIONAL CORE: PROJECT SUMMARY The overarching goal of the Computational Core is to provide robust and reproducible analysis of high-throughput genomic (WES), epigenomic (ChIP-seq, ATAC-seq, single-cell ATAC-seq), chromatin conformation (Hi-C, Hi- ChIP and 4C) and transcriptomic (RNA-seq and single-cell RNA-seq) data using a variety of established computational workflows. As described in the Core Research Strategy, the data will be uniformly processed by well-established computational pipelines for standard analyses and advanced computational methods, including machine learning, published recently by our group. The Computational Core is designed to provide start-to-finish standardization of the analysis of sequencing datasets, rigorous data quality assessment, integration, and visualization, as well as statistical expertise. The results of the bioinformatics analyses conducted in the Core will be used by all three Projects, with the shareable workflows of our computational platform. The Computational Core will analyze all the sequencing data generated by the Genomics Core using established best practices and computational pipelines. Existing infrastructure at NYU Grossman School of Medicine will be used to ensure seamless hand-off of all the data via the storage and computing environment provided by the NYU High- Performance Computing Facility. This infrastructure has been in place for several years now and greatly facilitates data exchange between the Genome Technology Center (GTC) and the Applied Bioinformatics Laboratories (ABL) at NYU Grossman School of Medicine. Finally, the bioinformatics staff has set up and will maintain a data exchange portal for the data and analyses that is accessible to all members of the proposed study. This data exchange portal will also facilitate data sharing with the broader scientific community and the public when our studies are published.