DIBSI Nonmodel mRNASeq Workshop (2017)
These are the schedule and classroom materials for the
2017 DIBSI nonmodel mRNAseq workshop at UC Davis,
which will run from July 17th to July 21st, 2017.
This workshop runs under a Code of Conduct. Please
respect it and be excellent to each other!
If you’re not comfortable working on the command line, please work through some of this command-line bootcamp before the workshop.
Twitter hash tag: #ngs2017
Schedule and Location:
9am-3pm + M/Th evenings (6:30-8pm)
All sessions are in Valley Hall, unless otherwise noted.
Workshop materials
Monday, Day 1: Introduction and QC
- 11am: Introductions & RNA-Seq uses & pitfalls
- 1:15pm: All-hands gathering to introduce across all workshops
- 3pm: Intro & setup
- Hands-on:
- 6:30pm-8pm: student presentations and questions! (social)
Tuesday, Day 2: Assembly and Evaluation
Morning: 9am-12pm
- Lecture: Kmers, de bruijn graphs, diginorm, and assembly (C. Titus Brown)
- Hands-on: De novo RNAseq assembly (Tessa)
Afternoon: 1:15pm - 3pm.
Evening: free time
Wednesday, Day 3: Annotation and Quantification
Morning 9am-12pm
Afternoon: 1:15pm - 4pm
Evening: free time / social Wed Farmers’ market!. We’ll be over there starting at ~5:30pm.
Thursday, Day 4: Differential Expression and Downstream Assessment
Morning 9am-12pm
Afternoon: 1:15pm - 3pm.
- Lecture: RNA-Seq Study design
- Options:
Evening 6:30pm-8pm
Friday, Day 5: Wrap-Up (+ optional Github)
Morning 9am-12pm
9am: All-hands wrap-up
Since housing checkout is at 12, everything is optional after the hands-on meeting. We will run a brief intro to github, and then depending on interest, one or more of the instructors can stick around to teach additional automation tutorials or help you with your own data.
(Optional) Morning Hands-on:
Useful Further Resources:
Note that these are taken from the 2017 ANGUS workshop
- Introduction to automation
- Jupyter Notebook, R and Python for data science.
- Review and explore: Command line UNIX, and R/RStudio
- Pathway Analysis
- RMarkdown
- where do I find the data? NCBI, ENSEMBL, ENA; how to get FASTQ out of NCBI.
- Adrienne Roeder, Cornell - Reaching biological conclusions from RNA-seq: the good, the bad, and the ugly
- Michael I Love, UNC Chapel Hill - “Statistics and bias correction in RNAseq differential expression analysis”
- Robert Patro, Stony Brook University - “Don’t count on it: Pragmatic and theoretical concerns and best practices for mapping and quantifying RNA-seq data”
- C. Titus Brown, UC Davis - “Effectively infinite: next steps in Data Intensive Biology.”
- Assessing & assembling nanopore data (Lisa Cohen and Jon Badalamenti)