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lecture_notes:04-09-2010

Lecture Notes for April 09, 2010

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Notes before main lecture

Take fix mode script from /projects/compbio/bin/scripts and replace protein user group with BME 235 user group.

Next week will have a reference genome (Pyrobaculum oguniense, aka “Pog”) to use for testing the tools on. For the most part Pog is done; however, there are still some uncertainty with 8 SNPs left. It is definitely past the MIAMI standard at this point. (Pog assembly is down to only 8 SNPs & one potentially variable insert.)

Note about sequencing platform quality scores: most platforms are trying to use the Phred quality score[1], so the quality score is theoretically comparable between the platforms and runs (although calibration causes scores to vary between runs and instruments nonetheless).

It can be informative, once reads are mapped, to look at the quality scores for reads with observed errors.

Lior Pachter (from UC Berkeley) is vising on Monday, to speak about the Bowtie/TopHat/CuffLinks algorithms. (Bowtie: mapping; TopHat/Cufflinks: find splice junctions, predicted spliced transcripts. Bowtie is used in a lot of the assembly algorithms.)

Main lecture: Assembler graphs

Types of assembler graphs:

  • Overlap graph
  • de Bruijn graph (pronounced like “De Broin”)

Differences are “What are the nodes?”

  • Overlap: reads
  • de Bruijn: k-mers (usually fixed k, k < = length(read))

Overlap graphs

* read → * read (a directed graph)

        A
_______________
    | | | | | |
    __________________
            B

The problem with edges between contig nodes is in defining direction of the reads when aligning:

  • 4 different edge scenarios:
    • → → (A → B)
    • → ← (A → B' or B → A')
    • ← → (B' → A or A' → B)
    • ← ← (B → A)
  • 3 different edge types:
    • Same dir: A to B / B to A
    • Tail-to-tail (convergent): A' to B
    • Head-to-head (divergent): A to B'

Need to have some tolerance for error because the reads are noisy. When creating read overlaps, if you require 100% pairing, you’ll miss a lot of data. Also these include the read ends, where quality falls off, so you need a “overlap quality score”.

Can’t do all-vs-all searches (n2 algorithms not a good idea with billions of reads). So how do you search what to overlap? Most algorithms do a BLAST-like filter before trying to align edges (~nlogn).

Side Note: For transcriptome libraries, if done properly, reads should have known strandedness, so it can’t be run through algorithms which make strandedness arbitrary (story about problems with a prominent yeast microarray transcriptome analysis incorrectly finding a lot of “antisense” mRNAs due to library prep error).

de Bruijn graphs

* kmer → * kmer → * kmer → * kmer …

|----------|
 |-----------|
  |----------|
   |----------|
    …

No different than a count of k+1 mers.

Ways to handle representing the graph:

  • Direct addressing (becomes useless for assembly, need to map to unique places)
  • Hashing (key = size of kmer, have to go to 20…25 mer for the keys to start to be unique)

May run into problems with RAM on the computational nodes.

  • ~ 16 GB per core, was that 4 cores sharing 16 GB or each core with 16 GB?
  • /proc/meminfo, /proc/cpuinfo directories contains information about the node

With overlap graph:

A → B

A → C

A → D

|----------| A
    |----------| B
    |----------| C
       |----------| D

Don't know where to go / which copy of the repeat currently in.

In ideal situation for de Bruijn graph:

kmer -> kmer -> kmer -> kmer -> kmer (done!)

Realistically, there are issues:

End of contig boundaries

What if A→B and A→C and A→D BUT A→B and A→C are inconsistent with each other? … A becomes “end of contig”, because you aren’t sure where to go next. Also end of contig if there are no more edges from the node.

Spurs
kmer -> kmer -> kmer -> kmer -> kmer
    \-> kmer -> kmer -> kmer (off to nowhere)

Path diverges but does not reconverge, resulting in source/sink dead-ends (these are likely due to read errors).

Bubbles
    /-> kmer -> kmer -> kmer -\
kmer -> kmer -> kmer -> kmer -> kmer

The path splits due to a SNP but then converges. This can happen with real SNPs, read error SNPs, and real repeats which differ by a SNP or two.

Loop
kmer -> kmer -> kmer -> kmer -> kmer -> kmer -> kmer
                            \- kmers <-/

Tandem repeats will generate a circle, but have edges in and out; hard to disambiguate copy number though. If the data is really clean (i.e. in/out edges are ~10 read-depth with low SD, and inside circle has ~20 read-depth with low SD), we can guess that there might be 2 copies of the repeat, but this is not highly reliable.

Multiple paths
A                      B
 \                    /
  ------------------->
 /                    \
B                      A'

Which path to take?

If you have clean data, you can disambiguate some issues. Largest bias usually comes from PCR for amplification.

Assembly

Algorithms (both overlap and de Bruijn) need to collapse bubbles and trim spurs.
Spurs: Discard if their read count is low.
Bubbles: Tricky, because they can represent real, divergent paths.

References

Discussion

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, 2010/12/11 03:45

this is very good for you, ybg :)

, 2010/04/09 21:22

Hi All,

At today's lecture Sol mentioned a recent paper describing FASTQ, a new standard for including base quality info with sequence data. The paper is:

  P.J.A. Cock, C.J. Fields, N. Goto, M.L. Heuer and P.M. Rice.
  The Sanger FASTQ file format for sequences with quality scores,
  and the Solexa/Illumina FASTQ variants.
  Nucleic Acids Research. 38(6):1767-1771 (2010).
  doi:[[http://dx.doi.org/10.1093/nar/gkp1137]]

Essentially, PHRED quality scores are treated as indexes into the ASCII table so they can be represented as single characters (that align nicely with their bases).

Enjoy!

 Jonathan
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lecture_notes/04-09-2010.txt · Last modified: 2010/04/12 14:37 by cbrumbau