##Tomato Interns Bioinformatics Week

Scripts, outline, and course material for teaching tomato interns.

Meeting times and locations

Week of July 29, 2013

• Monday 2:00 - 5:00 LS3002
• Tuesday 2:00 - 5:00 LS3002
• Wednesday 2:00 - 5:00 LS2002
• Thursday 1:00 - 4:00 LS2002
• Friday 2:00 - 5:00 LS2002

##Outline

Day 1: Intro to Atmosphere and Unix (MFC)

• what is atmosphere?
• starting an instance
• connecting to your instance
  • vnc
  • ssh
• idrop
  • move fastq files and other files to play with
• Terminal
  • commands at the terminal
    • idea is to define some useful commands and then give them some tasks
    • be sure to introduce the commands that they will use in examining fastq files...
      • ls
      • cd
      • pwd
      • cp
      • mv
      • mv -i
      • mkdir
      • rm
      • rmdir
      • cat
      • head, tail
      • less
      • pico or equivalent
      • grep
        • possibly with some regex (simple!)
      • pipes
      • man
• Looking at fastq files (applying was learned)
  • use head, tail, etc to examine fastq files
    • examine fastq file: have students describe how they appear to be organized
  • exercises with grep and wc, e.g. number of reads
  • grep -c

Day 2: Processing fastq files (MFC)

• Questions/ starting problem set:
  • create a new directory in /mydata
  • navigate to directory in iRods with sequence files
  • how many files are in the directory?
  • download your desiganated file to the new directory in /mydata
  • What is the machine name that the sequences come from?
  • How many lines are in the file?
  • How many sequence records are in the file?
  • create a new file that contains 1,000,000 records from the file you downloaded
• What files do we actually get back from the sequencing facility
• QualityCheck
  • go through FastQC, what the differenet measures mean, etc
  • Phred scores
• BarcodeSplit
  • new QC
• Trimming / filtering
  • new QC
• Mapping to reference
  • BWA to ITAGs
  • tophat to genome
• Stats for mapping:
  • how many ready mapped?

Day 3: Visualization

• IGV (MFC)
  • can you find genes that appear up-regulated?
    • think about normalization
  • places where the data doesn't seem to match the annotation
• Sam2Counts.R (JNM)
• Histograms of read counts (R) (JNM)

Day 4: Differential Expression (JNM)

• normalization
• what does differential expression mean?
• the idea of a linear model
• 1 way comparison
• 2 way comparisons
• 2 way comparisons (with interactions!)

Day 5 (JNM)

• What do you do with these results?
• GO category enrichment
• plotting particular genes of interest
• what would the follow up experiments be?