Banana Slug Genomics

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======De novo Assembly II======
**Guest lecturer: Stefan Prost, stefan.prost@berkley.edu** 

=====Illumina Paired-end Sequencing Libraries=====
  * MiSeq has 300 bp reads
  * Paired ends read from both directions, so you get one read for each end (may or may not overlap depending on molecule and read size)

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		           end_2 <=====

  * Problem: not really sufficient for repetitive regions
    * ends can't be very far apart because Illumina can't handle big molecules
    * not enough info for scaffolding

=====Illumina Mate-Pair Sequencing Libraries====
  * Idea: get paired reads that are much farther away (for more scaffolding info)
  * Basically, the same idea as paired-ends, except the middle section is missing and the ends are oriented the opposite way. 

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		           end_2 =====>

  * Genomic DNA > Fragment (2-5 kb) > biotinylate ends > Circularize > Fragment (400-600 bp) > Enrich biotinylated fragments > Ligate adaptors > …
    * Cut DNA, attach a biotin tag to both ends of the target molecule
    * Circularize target molecule
      * This step is hard
    * Circularized molecule is fragmented, fragment with the biotin tag (a.k.a the ends of the original target stuck to each other backwards, with the tags in between) is enriched
    * Then end repair, A-tailing, adapters added, amplification, sequencing 
  * Dependent on inferring insert size (can be tricky) 
  * Most companies can get you 8 kb inserts, with skill you can get up to 20 kb
  * Complicated process, weird stuff can happen in between 
  * Important difference between paired ends and mate-pairs: ends are oriented the opposite way. 


=====BAC (Bacterial Artificial Chromosome) and Fosmid Libraries=====
  * Uncommon and expensive, but the gold standard 
  * Bacterial F-plasmid takes< 40 kb insert size
  * Fragment query DNA > DNA into BAC > Transform E. coli with BAC > ~300 kb long reads
  * http://www.scq.ubc.ca/wp-content/plasmidtext.gif

=====Read Quality Assessment=====
  * Base quality: Phred scores reported by sequencer. 
  * Fastq files: fasta files, plus encoded phred scores
    * Need to know if your file has phred33 or phred64 encoding   
  * Quality for each individual base is not the whole story, the context matters to the signal processing too
  * Reads decrease in quality further down the read
    *  **Depending on the assembler, you might need to trim lower quality reads off the end. Others want untrimmed data**
  * Pacific Bio doesn’t have GC|AT bias

====Tools====
  * FastQC (Most popular tool to tell you about the read library) 
    * FastQC reported an issue with our data with kmer count (related to adapter content)
      * **This needs to be checked out and diagnosed!** 
  * Preqc
    * Estimates how difficult the assembly will be

=====Estimating Genome Size from Read Data=====
	G = (pn(1-k+1))/(λ_k)
	G = Genome size
	pn = proportion of correct reads
	k = kmer length
	λ_k= mode of the k-kmer count histogram
	Simpson 2013, arXiv

  * To estimate genome size need to know:i) total number of reads; ii) length of reads;  and iii) kmer distribution

=====Error Correction=====
  * High amount of small kmers are usually errors

**Simulated contig length in the k-de Brujin graph can estimate the best kmer to use for assembly. Based on contig length N50**
Banana Slug Genomics

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