2015 / March / mRNAseq on semi-model organisms

This workshop was given on March 4 and 5, 2015, by C. Titus Brown, Isabelle Laforest-Lapointe, and Tamer Mansour. See the workshop organization page for more information.

What are semi-model organisms? One with ok genomes but bad transcriptomes!

Tutorials:

• Welcome!
  • 1. Learning goals
  • 2. Safe space and code of conduct
  • 3. Instructor introductions
  • 4. Amazon and cloud computing - why?!
  • 5. Sticky notes and how they work... + Minute Cards
  • 6. Warning:
• Semi-model organisms and RNAseq: an overview of the options
• Getting started with Amazon EC2
  • Start up an EC2 instance
  • Logging into your new instance “in the cloud” (Windows version)
  • Logging into your new instance “in the cloud” (Mac version)
  • Amazon Web Services reference material
• Short read quality and trimming
  • Prepping the computer
  • Data source
  • 1. Copying in some data to work with.
  • 1. Copying in some data to work with.
  • 2. FastQC
  • 3. Trimmomatic
  • 4. FastQC again
  • 5. Subset and trim the rest of the sequences
• Building a new reference transcriptome
  • Install TopHat and Cufflinks
  • Grab the genome
  • Map all the reads to the genome with TopHat
  • Evaluating the mapping
  • Build a new transcriptome (“ab initio”) from the combined reads using Cufflinks
  • Merge the new transcriptome with the existing reference transcriptome
  • Extracting your new transcriptome sequences
  • Checking out your new transcriptome
• Mapping reads to the transcriptome with TopHat
  • Mapping reads
  • Viewing the mapped reads percentage
  • Making gene counts
• Processing another sample with TopHat and HTSeq
• Setting up more scripts
• Data analysis & differential expression
  • Running edgeR on a data subset
  • Transferring “official” gene names from the official transcriptome
  • Working with DAVID
• Tips and Tricks for working with Remote Computers
  • Use screen to run things that take a long time.
  • Use CyberDuck to transfer files
  • Subsetting data
• Miscellaneous advice
  • Sequencing depth and number of samples
  • Downloading your data
  • Developing your own pipeline
• More resources
  • Informational resources
  • Places to share data, scripts, and results files

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