Banana Slug Genomics

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  * Analyzing data from previously sequenced mollusk genomes
    * Broad institute /ftp/pub/assemblies/invertebrates/aplysia (seahare)
    * Need two files in this directory to analyze the data
    * LibStatsOverview.out
      * 5 libraries they're using in the genome assembly.
        * insert sizes of 2,000, 4,000, 4,000, 10,000 and 40,000
        * Coverage ranges betwens 0.04x and 60.83x
        * These coverage numbers are dubious, but the insert size are of interest
    * LibStatsOverview.out
      * Read size seen in this file are around 600 bases.
        * Indicates the sequencing was done mostly by 454.
    * Used an overlap consensus method (ARACHNE)
  * 13x coverage with 454 produced a publishable genome
  * Probably need alot more coverage with the shorter read length illumina reads.


  * Banana slug insert size estimation
    * soap de nova estimate for insert size: 135
      * size between sequencing reads
    * dna fragment length during library prep: 250-375
  * Trimming reads may increase assembly
  * First read may have higher quality than the second read

  * Estimating error rates
    * Use quality information directly from the sequencing machine.
    * Estimate error after mapping.
    * These two measures should be correlated to each other.

Panda Genome statistics
  * 38.5x coverage of the panda genome yielded an N50 contig size of 1,483 
    * Adding paired end distances yielded larger and larger contigs
  * 218 total lanes of illumina data
    * 73x raw coverage
    * 56x coverage after filtering reads
    * read lengths ranged from 35bp-71bp
  * Comparing Bacterial Artificial chromosome.
    * Made 9 BACs as a quality check.
    * Each BAC is covered by 90-99.9% by the short-read-assembled genome.
      * Indicates the genome is very patchy

  * We need about 50-75 basepairs for each read to assemble a genome from short reads according to panda paper.
  * How can we better utilized paired data for a de bruijn assembler?
    * We need to be able to subdivide reads into smaller subset of reads because shorter segments are easier to assemble.
      * Shorty utilizes a method similar to this.
      * Do some assembly to create short contigs.
      * Several reads can map to the same contig.
        * Take all paired reads that map to the short contig and map to paired clusters.
        * Take all reads that map to the original contig.
        * Assemble reads that fall within the same region.
        * Could be done with any size matepair library.
        * Danger of this method is the susceptibility to noise and randomly paired reads.
        * Another problem is the speed of this mapping
 
  * Soap de novo runs in about 7-9 hours for banana slug data to create initial contigs. Creates about 3 million contigs.
    * Use mapping software to map pieces onto contigs.
    * Parallelize assembly of many small chunks of reads.
    * For each of the 3 million contigs, build new contigs using paired reads.
    * How can you parallelize this algorithm such that it can be used for de novo assembly?
  * Soap de novo comes with a program that can use this called "Gap closer"
    * Is gap closer this program? If it is how, can we parallelize it.

  * Next slug to be sequenced should be photographed during dissection in order to identify the species.
Banana Slug Genomics

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