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==Bwa/Sam filtered variants== * Re-do heatmap with consensus quality instead of SNP quality (see yesterday's entry) bwaSam/NA12878_40bp_061109.sequence.pileup.variants.txt [[Image:Bwasam_dbSNP_q.jpg ]] ==Maq/Sam== * Make heatmap from: MaqSamTarget/snp.nonunique.dbSNP * Some SNPs in dbSNP have extremely high coverage, e.g. the highest coverage is at: chr6 160384279 G A/G 228 228 57 682 rs894817 A/G ** Set heatmap upper bounds to go to next highest coverage value, 396 [[Image:Maqsam_nu_dbSNP.jpg ]] * Try using duplicate-filtered SNP set for fewer funny numbers awk '{print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $8}' snp.chr.txt > snp.chr.qr.txt ../Scripts/matchCnsSnp2Ref_dbSNP.pl snp.chr.qr.txt > snp.chr.dbSNP.txt * False positives: [[Image:Maqsam_dbSNP.jpg ]] grep 'rs' snp.chr.dbSNP.txt | ../Scripts/matchCnsSnp2Ref_AG.pl>snp.compare.in_dbSNP.txt grep -v 'rs' snp.chr.dbSNP.txt | ../Scripts/matchCnsSnp2Ref_AG.pl>snp.compare.no_dbSNP.txt {| {{table}} | align="center" style="background:#f0f0f0;"|'''1K Genome''' | align="center" style="background:#f0f0f0;"|'''In dbSNP''' | align="center" style="background:#f0f0f0;"|'''Percent''' | align="center" style="background:#f0f0f0;"|'''Not in dbSNP''' | align="center" style="background:#f0f0f0;"|'''Percent''' |- | Match||7886||76.73%||216||1.69% |- | Miss||1137||11.06%||12506||97.66% |- | Mismatch||1255||12.21%||83||0.65% |- | Total||10277||||12805|| |- | |} * FP rate: '''48.04%''' [[Image:Maqsam_nodbSNP_rmdup.jpg ]] ==SNP Filtering== * Look into reducing false positives (not in dbSNP, not found by 1000 genome) ** Pattern classification: generate PDF in Matlab using 'ksdensity' ** Matlab's optimization toolbox ==Other== * Summarize FP and FN rates for each method
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