RRSelection: A new simple and efficient software to detect selection region analysis based Variant Call Format
RRSelection: A Llinkage disequilibrium method to detect selection region across population VCF
Method1 For linux/Unix and macOS
git clone https://github.com/BGI-shenzhen/RRSelection.git
cd RRSelection ;chmod 755 configure; ./configure;
make;
mv RRSelection bin/; # [rm *.o]
Note: If fail to link,try to re-install the libraries zlib
Method2 For linux/Unix and macOS
tar -zxvf RRSelectionXXX.tar.gz
cd RRSelectionXXX;
cd src;
make ; make clean # or [sh make.sh]
../bin/RRSelection
Note: If fail to link,try to re-install the libraries zlib
see more detailed Usage in the Documentation
-
- Calculate sliding windows mean RR for one or two population,and give out the selection region. also give out the whole genome RR plot figure.
# 1) For all samples in one population
./bin/RRSelection -InVCF SNP.vcf.gz -OutPut OutPrefix
# 2) For same samples in one population
./bin/RRSelection -InVCF SNP.vcf.gz -OutPut OutPrefix -SubGroup subgroup.list # subgroup.list is the sample name of this population
# 3) For Tow population
./bin/RRSelection -InVCF SNP.vcf.gz -OutPut OutPrefix -SubGroup subgroup.list # PopID : sample name list
-
- see the result [OutPrefix.winRR.gz OutPrefix.selection.gz] and [OutPrefix.png OutPrefix.pdf]. ALso Run the perl script to regain the beautiful picture
perl PlotRRSele.pl -inFile OutPrefix.winRR.gz -output OutPrefix
To detect the selection region is the most important and most common analysis in the population resequencing. Here we introduce a new software :RRSelection, a simple-efficient software to detect the selection region analysis based Variant Call Format. Sliding whole genome windows to calculate every region mean R^2 for one or two population, and pick out the high-chained region (one population) or the region with the greatest difference (two populations), which is regarded the selection region according to the top distribution of measure MeanRR(Z-test Pvalue).
- Parameter description
Usage: RRSelection -InVCF <in.vcf.gz> -OutPut <outPrefix>
-InVCF <str> Input SNP VCF Format
-OutPut <str> OutPut sliding stat mean r^2 Result
-SubGroup <str> one/two sub-group Sample List File,-h for more help
-Windows <int> Sliding windows bin (kb),MaxDis between two pairwise SNP[300]
-Step <float> Step ratio(0,1] of windows,1:NoOverlap [0.2]
-Masked <int> Masked windows when the SNP Number too low[10]
-MAF <float> Min minor allele frequency filter [0.05]
-Het <float> Max ratio of het allele filter [0.88]
-Miss <float> Max ratio of miss allele filter [0.25]
-Pvalue <float> T-test Pvalue to pick out selection region[0.005]
-KeepR Keep Rscript used to modify and plots
-help See more help [hewm2008 Beta v0.85]The following is the format of the result output file header . and the Figure is no showed here.
#Chr Start End Mean_r^2_cul Sum_r^2_cul Count_cul Mean_r^2_wild Sum_r^2_wild Count_wild MeanRRDiff(cul-wild) ZScore Pvalue ##Group[cul], MeanRR:0.245096 SD:0.0529981 Effective windows Count:30 ##Group[wild], MeanRR:0.247118 SD:0.0631814 Effective windows Count:30 ##Diff MeanRR[cul-wild], Mean:-0.00202159 SD:0.0276476 Effective windows Count:30 Tu2 0 300000 0.1779 68833.0295 386921 0.1034 60456.6791 584831 0.0745 2.77 0.002822 Tu2 60000 360000 0.0782 12577.9534 160803 0.0734 21013.7645 286430 0.0049 0.25 0.401158 Tu2 120000 420000 0.1223 26006.7348 212590 0.0877 31059.4803 354331 0.0347 1.33 0.092056 ...
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