Ylaine/2009-8-17
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New 1K Genomes Reference[edit]
- BowtieMaq: Get 5 more matches and 5 fewer misses than when trio file used for comparison
- Should be more. Too many mismatches? Debugging code.
- Found bug: using reference instead of 1K call for comparison
../Scripts/hetOnly.pl no_dbSNP.new1K >no_dbSNP.new1K.het
- Quality and coverage alone are the best combination of parameters
- For n=50, p=0.3, f = 0.75:
' | Mean | Std |
FP | 25.26 | 7.32 |
MD | 15.20 | 6.17 |
Overall | 18.32 | 4.02 |
- Try with SOAP/SAM
../Scripts/compare1K.pl hiqual.no_dbSNP.het>hiqual.no_dbSNP.het.1K
- Place in separate folder
New Data[edit]
- SNPs thresholded on 8x coverage and 30x quality
Part 1[edit]
"What is the total size of exonic region that were covered by >=8x in this data set?"
WorkSpace/Exome/Solexa/NA12878/NA12878_061009_061109_080509_081309_40bp_sequence.bowtie.pileup
- Re-named 'orig.pileup'
genome-tech:Aug17Data ygerardin$ ../Scripts/threshold.pl orig.pileup 8 >cov8.temp& wc -l cov8.temp
- ANSWER: 23,830,845
Part 2[edit]
"How many variants found are within these regions and how many located outside of our target regions (non-specific capture)?"
- Use 'calculateSNPeff.pl', which outputs the number of probes covering the region as well as median efficiency of the probes
../Scripts/calculateSNPeff.pl orig.snp >snp.eff &
- ANSWER: 12412/20761 or 59.79% (for coverage >= 8 there are 10656/14708 or 72.45%)
- EDIT 8/18: Ran different script (snpTargeted.pl) and got much higher percentage.
Part 5[edit]
"Using 1KG data as reference, what is our false positive rate as the function of the filter we used? This could be plotted as a curve. Also what is the total variants we can call as the function of false-positive rate?"
- Compare to 1KG data
../Scripts/compare1K.pl orig.snp>snp.1k
- Separate into matches and misses. There are 74 heterozygous and 522 homozygous mismatches
grep 'match' snp.1k |grep 'het'>1k.match.het.txt grep 'match' snp.1k |grep 'homo'>1k.match.homo.txt grep 'miss' snp.1k |grep 'homo'>1k.miss.homo.txt grep 'miss' snp.1k |grep 'het'>1k.miss.het.txt
- EDIT 8/18: mismatches put into match file
- Use [pPresent, numPresent] output of Matlab script 'snpCurve.m'
- False positive rate as function of filter:
- Lower left hand corner:
File:Aug17 percent1k closeup.jpg
- ROC (each "stripe" is either a single quality or coverage threshold)
File:Aug17 roc.jpg File:Aug17 roc2.jpg
Other[edit]
Thoughts about classification[edit]
- We need a universal classification scheme that doesn't rely on knowing the classes of the training set.
- A more analogous problem is one of clustering