ANGSD: Analysis of next generation Sequencing Data
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Heterozygosity: Difference between revisions
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Things to consider is: | Things to consider is: | ||
1. Add -C 50 -ref ref.fa -minQ 20 -minmapq 30 to the angsd parameters to weed out the worst reads and | 1. Add -C 50 -ref ref.fa -minQ 20 -minmapq 30 to the angsd parameters to weed out the worst reads and bases. | ||
2. The output file could be very big. One might argue that we just need a reasonable large subset of the genome to estimate the one samples SFS (is is only 2 free parameters). So you could limit the analysis to a single chromosome by adding -r chr1. to the angsd part. Or you could add -nSites to the ''realSFS'' function. | 2. The output file could be very big. One might argue that we just need a reasonable large subset of the genome to estimate the one samples SFS (is is only 2 free parameters). So you could limit the analysis to a single chromosome by adding -r chr1. to the angsd part. Or you could add -nSites to the ''realSFS'' function. | ||
3. if you work with ancient data. You can discard transition by adding -noTrans 1, to the angsd part of the code. | |||
=Local estimate= | =Local estimate= |
Revision as of 16:32, 10 January 2017
The heterozygosity is the proportion of heterozygous genotypes.
This can either be a global estimate or a local estimate.
For diploid single samples the hetereo zygosity is simply second value in the SFS/AFS. An important aspect with this approach is that we DO NOT require to fix the major and minor. By fixing the ancestral we loop over the 3 possible derived alleles, or we can use the reference as the ancestral and fold the spectrum.
Global estimate
This is simply the SFS Estimation for single samples. A short example is:
./angsd -i my.bam -anc ancestral.fa -dosaf 1 -gl 1 #OR ./angsd -i my.bam -anc ref.fa -dosaf 1 -fold 1 #followed by the actual estimation ./realSFS angsdput.saf.idx >est.ml
The heterozygosity is then:
#in R a<-scan("est.ml") a[2]/sum(a)
Things to consider is:
1. Add -C 50 -ref ref.fa -minQ 20 -minmapq 30 to the angsd parameters to weed out the worst reads and bases.
2. The output file could be very big. One might argue that we just need a reasonable large subset of the genome to estimate the one samples SFS (is is only 2 free parameters). So you could limit the analysis to a single chromosome by adding -r chr1. to the angsd part. Or you could add -nSites to the realSFS function.
3. if you work with ancient data. You can discard transition by adding -noTrans 1, to the angsd part of the code.