Education and Training

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Education and Training

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The Center for Epigenetics Research is committed to programs in outreach and education which facilitate studies of the epigenome for affiliated labs as well as the broader research community. Best practices approaches to epigenomics begin with experimental design and carry over to downstream data analysis, and our goal is to teach both conceptual and practical materials which will provide students, fellows, and staff with the foundation for future self-guided learning. To date, the CER has worked with MSK’s Office of Scientific Education and Training and also partners with the Gerstner Graduate School of Biomedical Sciences to provide first-year PhD students with a background in quantitative and computational biology.

Courses

Epigenomic Analysis
MSK’s Office of Scientific Education and Training

This course is aimed at postdoctoral fellows and advanced graduate students who want to learn to process and analyze their own ChIP/ATAC-seq data. As such, this course offers a practical guide to best practices in the field and covers the conceptual background but focuses on hands-on experience with relevant software and simple scripting.

Quantitative and Computational Biology
Gerstner Graduate School of Biomedical Sciences

The goal of this course is to provide first-year PhD students with the foundation for future self-guided learning and skill acquisition in computational biology. They begin with an introduction to the Unix shell and the R programming language and learn to apply quantitative exploratory data analysis techniques to different forms of experimental data.

Primers

For students beginning bioinformatics training:

Unix and Perl Primer for Biologists 
http://korflab.ucdavis.edu/Unix_and_Perl/

YaRrr! The Pirate’s Guide to R
Slides: https://bookdown.org/ndphillips/YaRrr/
Video: https://www.youtube.com/playlist?list=PL9tt3I41HFS9gmeZFEuNrnu_7V_NFngfJ

Publications

Fostering bioinformatics education through skill development of professors: Big Genomic Data Skills Training for Professors. PLoS Comput Biol. 2019 Jun 13;15(6):e1007026.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6563947