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Tuesday Aug 14

Tuesday Aug 14

Aug 14, 2012

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Lecture: Genome Assembly - Haibao Tang (9:00-10:15)

  • Assembly preparation – reads, libraries, etc.
  • Assembly – OLC assemblers vs. de Bruijn (K-mer) graph assemblers
  • Assembly QC - Identify data or assembly issues
  • Assembly curation - Further scaffolding, build chromosomes

Slides: https://docs.google.com/open?id=0Bx3KTjwwBb0rTm9kdm9XSDhidE0

Break (10:15-10:30)

Hands-On: Assemble a chromosome of baker's yeast (10:30-12:00)

Group Projects

  1. You are pre-assigned to one of 7 groups, each working on a different dataset
  2. We will discuss approaches and results in a group setting
  3. Present results as a group

de novo assembly hands-on guide: https://docs.google.com/document/d/1B1-kO41zGpVpHdGpHL_65T7STP9iSODf0Rt0f0OxZAc/edit

  1. Use three algorithms to assemble your data set (VELVET, ABYSS, SOAP)
    1. Know how to change K-mer in assembler option, and try different K-mers
    2. Available via OSU HPC and iPlant DE
  2. Identify which assembly has best metrics
    1. Length stats (N50, sum)
    2. Dot plot to a reference genome
  3. Group discussion: Best assembly for the fungal dataset (10 m)

Lunch 12:00-1:00

Big Data Lecture - Dan Stanzione: (1:00-1:45)

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Lecture: Read mapping and genetic variants - Haibao Tang (2:00-2:45)

  • Read mapping
  • Variant calling
  • Variant annotation
  • Visualization

Slides: https://docs.google.com/open?id=0Bx3KTjwwBb0rMGp3ZGdCM2VMWjA

Break (2:45-3:00)

Hands-On: Identify and annotate genetic variants of a mutant yeast 3:00-5:00

Genome mapping hands-on guide: https://docs.google.com/document/d/1du_RuAKvWRG03e-N6mskO9wa09zVL_ne8FzuyjiavXk/edi

  1. Compare two methods of finding SNPs - mapping assembly vs. mapping reads (preferred)
  2. Understand and run the SNP calling pipeline (BWA -> MPILEUP)
  3. Understand how to change criteria for read alignments and variant calling
  4. Visualize read alignments and variants in IGV genome browser (instructions on how to run IGV in manual)
  5. Determine possible effect of a few best quality SNPs
  6. Understand how to categorize the SNPs according to locations and effects
  7. Group discussion: Best approach and criteria to call SNPs; discuss the significance of the SNPs called

Wrap-up (5:00- )