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Welcome to the Berlin 2018 Trinity Workshop Wiki!

Student AWS Instances Table

Agenda

Day	Time	Activities
Monday, June 11	morning	Workshop Introduction -slides-
		Exploring the Computational Infrastructure -slides-
	afternoon	Unix command-line review
		Data overview and setup
		Using FASTQC and Trimmomatic -slides-
Tuesday, June 12	morning	Trinity de novo transcriptome assembly -slides-
	afternoon	Uploading own data or identifying and downloading SRA studies of interest -slides-
		Running Trinity on your own data
Wednesday, June 13	morning	Expression quantification -slides-
		Quality assessment for assembly -slides-
	afternoon	QC samples and replicates
Thursday, June 14	morning	Statistical methods for differential expression analysis -slides- -extra slides (Charlotte Soneson)-
	afternoon	Transcript clustering and expression profiling
		Methods for functional annotation -slides-
		Trinotate and TrinotateWeb
	7pm	Dinner @ Golgatha, Katzbachstraße, Berlin
Friday, June 15	morning	Special Topics: Gene Set Enrichment Analysis and Inference in Biological Networks -GSEA slides- -Network slides-
		Independent work on personal project data
	afternoon	Continued independent work on personal project data
		Participant presentations
		Downloading your data from the workshop cloud server
		Running the Workshop Software via Docker Locally

Suggested readings and resources:

• Trinity protocol paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3875132/
• Trinity website and documentation: https://github.com/trinityrnaseq/trinityrnaseq/wiki
• Comparing differential expression methods: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-14-91
• Transcript vs. gene level expression DE: https://www.ncbi.nlm.nih.gov/pubmed/26925227
• Number of sample replicates needed for DE studies: https://www.ncbi.nlm.nih.gov/pubmed/27022035
• Specialty topic: Robust gene network inference: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3512113/
• Advanced RNA-Seq Data Analysis Course (EBI, Cambridge UK, 2018): http://52.18.118.183:3000/

Misc notes

For those with mixed samples, please explore methods for selecting or depleting sequences based on target alignments

Windows-users:

"Get that linux feeling ... on windows" cygwin: https://www.cygwin.com/