ANGSD: Analysis of next generation Sequencing Data
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Contamination: Difference between revisions
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Angsd can estimate contamination, but only for chromosomes that exists in one genecopy (eg chrX for males). This method requires a list of polymorphic sites along with their frequency and we also recommend to discard regions with low mappability. | Angsd can estimate contamination, but only for chromosomes that exists in one genecopy (eg chrX for males). This method requires a list of polymorphic sites along with their frequency and we also recommend to discard regions with low mappability. | ||
We have included a mappability and HapMap files for chrX these are found in the '''RES''' subfolder of the angsd source package. | We have included a mappability and HapMap files for chrX these are found in the '''RES''' subfolder of the angsd source package (using hg19). | ||
So if you are working with humans, and your sample is a male then you can estimate the contamination with the follow two commands. | So if you are working with humans, and your sample is a male then you can estimate the contamination with the follow two commands. | ||
Revision as of 20:09, 28 June 2014
Angsd can estimate contamination, but only for chromosomes that exists in one genecopy (eg chrX for males). This method requires a list of polymorphic sites along with their frequency and we also recommend to discard regions with low mappability.
We have included a mappability and HapMap files for chrX these are found in the RES subfolder of the angsd source package (using hg19). So if you are working with humans, and your sample is a male then you can estimate the contamination with the follow two commands.
- First we generate a binary count file for chrX for a single BAM file (ANGSD cprogram)
- Then we do a Fisher's exact test for finding a p-value, and jackknife to get an estimate of contamination (Rprogram)
An example are found below:
#run angsd ./angsd -i my.bam -r X: -doCounts 1 -iCounts 1 -minMapQ 30 -minQ 20 #do jackKnife in R Rscript contamination.R mapFile="map100.chrX.bz2" hapFile="hapMapCeuXlift.map.bz2" countFile="angsdput.icnts.gz" mc.cores=24
The contamination.R program is found in the R/ subfolder, and the resource files are found in the RES folder. The jackknive procedure can be quite slow, so we allocate 24 cores for this analysis mc.cores=24.
Output
The output from the above command is shown below
Rscript ../R/contamination.R mapFile="map100.chrX.bz2" hapFile="hapMapCeuXlift.map.bz2" countFile="angsdput.icnts.gz" mc.cores=24 Loading required package: parallel ----------------------- Doing Fisher exact test for Method1: SNP site adjacent site minor base 616 3554 major base 198492 1589087 Fisher's Exact Test for Count Data data: mat p-value = 5.286e-13 alternative hypothesis: true odds ratio is not equal to 1 95 percent confidence interval: 1.271632 1.512213 sample estimates: odds ratio 1.387606 ----------------------- Doing Fisher exact test for Method2: SNP site adjacent site minor base 114 654 major base 37983 304122 Fisher's Exact Test for Count Data data: mat2 p-value = 0.001532 alternative hypothesis: true odds ratio is not equal to 1 95 percent confidence interval: 1.133367 1.705751 sample estimates: odds ratio 1.395672 ----------------------- major and minor bases - Method1: -4 -3 -2 -1 SNP site 1 2 3 4 minor base 427 417 475 437 616 486 439 427 446 major base 198651 198715 198656 198645 198492 198500 198681 198693 198546 ----------------------- major and minor bases - Method2: -4 -3 -2 -1 SNP site 1 2 3 4 minor base 75 76 96 73 114 86 79 80 89 major base 38022 38021 38001 38024 37983 38011 38018 38017 38008 ---------------------- Running jackknife for Method1 (could be slow) Running jackknife for Method2 (could be slow) $est Method1 Method2 Contamination 0.03837625 0.03380983 llh 1034.078 483.5145 SE 0.002630455 0.003900376
Interpretation of output
Both methods shows a highly significant pvalue, and estimate the level of contamination to be approx 3%.
Method
The method is described in the supplementary of Rasmussen2011