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==New 1K Genomes Reference== * 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: {| {{table}} | align="center" style="background:#f0f0f0;"|'''''' | align="center" style="background:#f0f0f0;"|'''Mean''' | align="center" style="background:#f0f0f0;"|'''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== * SNPs thresholded on 8x coverage and 30x quality ===Part 1=== "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=== "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=== "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: [[Image:Aug17_percent1k.jpgβ]] ** Lower left hand corner: [[Image:Aug17 percent1k closeup.jpg]] * ROC (each "stripe" is either a single quality or coverage threshold) [[Image:Aug17_roc.jpg]] [[Image:Aug17_roc2.jpg]] ===Other=== [[Image:Aug17_match1k.jpg]] ==Thoughts about classification== * 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
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