Difference between revisions of "Luckau Protocols:Scoring"

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Revision as of 14:27, 29 May 2013

How I Score Frag Data

Tara K. Luckau's Home Page

Conservation Genetics Lab Notebook

Tara's Protocols

Step-by-Step Guidelines

Step 1 - Set up GeneMarker

  • refer to GeneMarker protocol (particularly the Panel Editor section)

Step 2 - Set up your score sheet in Excel

  • The best way to organize your score sheet is with samples in rows and markers in columns, with two columns for each marker (because we’re working with nuclear markers in diploid organisms), like so:
  • This layout is pretty standard and allows for easy import into the analysis software you’ll be using downstream.
  • I have one of these for each plate (as opposed to one of these for ALL several hundred of my samples), so each score sheet corresponds to one master mix, one cycler run, one frag ID. It makes it easier for me to troubleshoot if I need to.
  • My preference is to print out the blank score sheet and manually fill in the allele calls. I update the electronic score sheet when I’m all done, then archive the hard copy.

Step 3 - Score!

  • When I score my data, I actually look at it twice in different contexts. This allows me to identify any anomalies or problems with the data as well as double-check for any typos.

Context #1 = Marker-focused:

  • The first time I look at the data, I zoom in on one marker and score each sample (Marker 1 for all samples, then Marker 2 for all samples, etc.), entering the alleles into my score sheet as I go. Here, I’m focusing on accurate allele calls. Many times, the peaks will be low-intensity; when you zoom in on just the marker, it’s easier to ‘call’ the allele. Additionally, some of your markers may have unique peak shapes – so long as you’re calling them consistently, you’re golden. By focusing on one marker at a time, you can better remember the unique shape you have to deal with.

Context #2 = Sample-focused:

  • For the second time around, I make sure I can see all the markers at once for each sample. Here, I’m focusing on sample quality. If the sample is contaminated, I should see more than two peaks at multiple markers; I’ll know to re-extract, not re-PCR. If the sample is degraded, I should see strong peaks at the markers with shorter amplicons, and weaker peaks as the amplicon size increases; I’ll know to re-extract, not re-PCR. If the sample crapped out during extraction (e.g. the DNA got dumped during the alcohol washes, so you’re left with zero DNA), then you’ll see no amplification for any marker; I’d probably re-PCR first to make sure it wasn’t a PCR error, like not actually plating any DNA, or the sample evaporating during cycling, etc. These are all possibilities that you’ll be able to identify only if you take the whole sample into focus.

Step 4 - Quality Control

  • As I’m writing down the alleles, I’m also assessing the quality of the peaks – do they pass or fail?

High-Quality Peaks = PASS

  • I’m comfortable that the alleles I’m writing down are a true representation of what’s actually in that animal. Some characteristics of high-quality peaks:
- Peak height is distinct from background readings
- Peak has characteristic leading peak (this may not occur for every marker, but if it does, then it should be consistent for all samples within that marker)
- Peak has characteristic -a peak (again, this may not occur for every marker, but if it does, then it should be consistent for all samples within that marker)
- If heterozygous for a marker, then peaks follow slope rule

Low-Quality Peaks = FAIL

  • Something’s fishy about the peaks. Some characteristics of low-quality peaks:
- Peak height is too low with respect to background readings
- Peak has funny shape
- Peak is out of range from where most samples fall
- In heterozygotes, slope rule is violated

  • On my score sheet, low-quality peaks get an allele call (because usually, I have an idea of what it probably is, I’m just not ready to publish that result), and a highlighted cell. Highlight means “I don’t trust this allele call, but here’s what I think it might be for when I re-run this sample.”

Peak Height:

  • Peak height should be distinct from the background noise.
high and clean! PASS low, but above background, PASS Low, not distinct from background, FAIL
Scoring-PeakHeightHigh.png Scoring-PeakHeightMed.png Scoring-PeakHeightLow.png

Literature and Supporting Information