Jason Ernst | A Tutorial on Computational Methods for Modeling and Analyzing ... | CGSI 2022

Jason Ernst | A Tutorial on Computational Methods for Modeling and Analyzing Epigenomic Data

Related papers:

1. Vu, H., & Ernst, J. (2022). Universal annotation of the human genome through integration of over a thousand epigenomic datasets. Genome biology, 23(1), 9. https://doi.org/10.1186/s13059-021-02...

2. Kwon, S. B., & Ernst, J. (2021). Learning a genome-wide score of human-mouse conservation at the functional genomics level. Nature communications, 12(1), 2495. https://doi.org/10.1038/s41467-021-22...

3. Ernst, J., & Kellis, M. (2017). Chromatin-state discovery and genome annotation with ChromHMM. Nature protocols, 12(12), 2478–2492. https://doi.org/10.1038/nprot.2017.124

4. 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

5. Ernst, J., & Kellis, M. (2012). ChromHMM: automating chromatin-state discovery and characterization. Nature methods, 9(3), 215–216. https://doi.org/10.1038/nmeth.1906