Training

Interested in learning more about computational biology and bioinformatics? We have compiled resources from various sources to help you learn more about RNAseq:

Quick start in R:

1. Download R & R Studio https://www.rstudio.com/products/rstudio/download/
2. Understand what Bioconductor is http://bioconductor.org/
3. Read this paper and keep it for future reference: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0881-8
4. Work through this RNAseq workflow and understand the steps in R studio https://www.bioconductor.org/help/workflows/RNAseq123/
5. Run Webgestalt on your DE gene list http://www.webgestalt.org/option.php to find enriched pathways and enriched TFs
6. Run ToppFun to find enriched gene sets and TFs https://toppgene.cchmc.org/enrichment.jsp
7. Find a GEO RNAseq dataset that is of interest biologically https://www.ncbi.nlm.nih.gov/geo/
8. Adapt the RNAseq workflow and steps 5 &6 to reanalyze the new GEO dataset (this is the hardest part)
9. Make conclusions about your analysis including pulling out the differentially expressed TFs and the enriched TFs that are differentially expressed

Additional Information:

Recommended reading:

• https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0881-8
• http://www.nature.com/nprot/journal/v8/n9/abs/nprot.2013.099.html

UC San Diego courses:

• https://healthsciences.ucsd.edu/som/dbmi/education/courses/Pages/MED263.aspx
• https://biom262.github.io/biom262-2016/

Coursera courses:

• https://www.coursera.org/specializations/genomic-data-science
• https://www.coursera.org/learn/r-programming-environment
• https://www.coursera.org/specializations/jhu-data-science 

Miscellaneous events and materials:

• http://bioinformatics.ucdavis.edu/training/events/
• https://www.bioconductor.org/help/course-materials/

CCBB’s blog and github:

• http://compbio.ucsd.edu/outreach/data-science-blog/
• https://github.com/ucsd-ccbb