Kevin outlined the processes involving in assembling a genome.

• Clean up the Reads
• Clustering and Building Contigs
• Order and Orient Contigs

There are two separate and distinct parts of data clean up; error correction and contaminant removal.

• May not be necessary for all types of data (Sanger/454).
• Can be done before or after contig assembly.
  • Before: K-mer Counting
  • After: Map reads to consensus sequences from contigs.

• Count the number of occurrences of each K-mer in the reads.
• Remove reads or correct individual bases of K-mers with low counts.
• K-mer size must be large enough not to produce trivial counts, but small enough to fit memory constraints.

• Contamination can come from many sources:
  • Human (dust)
  • Bacterial
  • Viral (hard to remove)
• Use BLAST to remove sequences that are unexpected.
  • Expensive to run.
  • Blast contigs instead of individual reads.
  • Strategies:
    • Look for specific contaminants (Human, E. coli).
    • Examine ribosome to identify possible contaminants.
    • Look for things we would not expected to see (e.g. eukaryotic sequence in prokaryotes or vice versa).
  • Once a contig is identified as contaminant:
    • Remove the contig and reads that map to it.
    • Rebuild the contigs.
• We risk removing parts of the target genome that are very similar to the contaminant
  • Example from class: nitrogen fixing genes common to two bacterial strains.

• Build a graph for the reads
  • De Bruijn Graph
    • Typically used for small reads.
  • Overlap Consensus
    • Generally used for larger reads.
    • larger memory requirements.
• Reads that don't quite fit can be error corrected.
• Ideally use high quality data (Sanger,454). Recent trend is to use cheaper data.
• Result: Contigs

• Iterative process:
  • Use new data when available.
  • Map reads to draft to import draft version.
  • Leftover reads are sent back for clustering.
• Mate-pair data is useful for bridging contigs that are adjacent but do not overlap (due to missing data, repeat sequence, etc.)
• Result: Scaffolds

Learn about the Jellyfish tool for K-mer counting. Try running it on the Pyrobaculum data. Use different parameters and monitor its memory usage. Fill the the wiki page for Jellyfish.