Jason Ernst | Computational Methods for Modeling and Analyzing Epigenomic Data | CGSI 2023

Related papers:

Ernst, J., & Kellis, M. (2010). Discovery and characterization of chromatin states for systematic annotation of the human genome. Nature Biotechnology, 28(8), 817–825. https://doi.org/10.1038/nbt.1662

Ernst, J., & Kellis, M. (2015). Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues. Nature Biotechnology, 33(4), 364–376. https://doi.org/10.1038/nbt.3157

Ernst, J., & Kellis, M. (2017). Chromatin-state discovery and genome annotation with ChromHMM. Nature Protocols, 12(12), Article 12. https://doi.org/10.1038/nprot.2017.124

Kwon, S. B., & Ernst, J. (2021). Learning a genome-wide score of human–mouse conservation at the functional genomics level. Nature Communications, 12(1), Article 1. https://doi.org/10.1038/s41467-021-22...

Vu, H., & Ernst, J. (2022). Universal annotation of the human genome through integration of over a thousand epigenomic datasets. Genome Biology, 23(1), Article 1. https://doi.org/10.1186/s13059-021-02...