The primary objective of the Computational Epigenetics Lab is to leverage next generation sequencing data to provide novel insights into cancer epigenetics, with the goal of deciphering both how cancers arise and how to treat them. To this end, we’ve established a group of experts in computational biology and bioinformatics who specialize in analyzing and integrating epigenomic data. Working together with other groups at MSK — including computational scientists in the Computational and Systems Biology Program, the Computational Oncology Service, and the Bioinformatics Core Facility — this team helps establishing methods for comprehensive genomic and epigenomic assessment of cancer cells.
Maximizing the impact of a dataset involves decisions made before it’s generated as well as specific choices during the analysis and how the data are later shared and presented. The Computational Epigenetics Lab works to optimize all stages in order to achieve a more comprehensive view of the epigenome in healthy and disease contexts, with an emphasis on the integration of gene expression, chromatin structure, 3D genome organization, and genetic variation. Our collaborative approach facilitates all of the above.
Optimize study design and assay type
In close collaboration with the Innovation Lab as well as project leaders, we work to determine (at the level of replicates, sequencing depth, assay type, controls, etc.) how best to address project-specific questions while also going broad enough to capture unanticipated trends in the data which are relevant to the questions at hand.
Utilizing best practices bioinformatics methods and developing customized analyses
We employ an integrative approach to assess corroborative changes in cellular state in all data types that may have otherwise gone undetected when considering one modality alone. While analyzing a single perturbation in one assay type is often straightforward, this integrative analysis is custom-tailored to each project and dataset.
Provide accessible analyses that allows for collaborative data exploration and interpretation by investigators
Our emphasis remains on biological relevance with the goal of interpreting past and current data and directing future experiments. This involves providing quality controlled and filtered data in the form of tables, plots, and customizable visualization tools for immediate use by project leaders and collaborators.