ANGSD: Analysis of next generation Sequencing Data
Latest tar.gz version is (0.938/0.939 on github), see Change_log for changes, and download it here.
Thetas,Tajima,Neutrality tests: Difference between revisions
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Obtain the maximum likelihood estimate of the SFS using the '''realSFS''' program found in the misc subfolder. (See more here [[realSFS]]) | Obtain the maximum likelihood estimate of the SFS using the '''realSFS''' program found in the misc subfolder. (See more here [[realSFS]]) | ||
<pre> | <pre> | ||
./misc/realSFS out.saf.idx-P 24 > out.sfs | ./misc/realSFS out.saf.idx -P 24 > out.sfs | ||
</pre> | </pre> | ||
Revision as of 17:52, 19 October 2015
This method will estimate different thetas (population scaled mutation rate) and can based on these thetas calculate Tajima's D and various other neutrality test statistics. Method is described in Korneliussen2013.
- NB Information on this website is for version 0.551 or higher.
- NB The Korneliussen2013 covers two methods,
- using an ML method
- using the emperical Bayes (EB) method. The information on this page relates to the EB method.
For performing the ML method, you should the use the SFS Estimation method and define the region af interest.
Example
Below is a chain of commands used for caculating statistics. These are based on the test files that can be dowloaded on the Quick Start page.
Its a 3 step procedure
- Estimate an site frequency spectrum. Output is out.sfs file. This is what is being used as the -pest argument in step2.
- Calculate per-site thetas. Output is a .thetas.gz file.
- Calculate neutrality tests statistics. Output is a .thetas.gz.pestPG file.
Full command list for below examples
Here is the chain of commands required to do estimate the thetas, and perform neutrality test statistics. These different commands are described in great detail in the following step 1,... step 3b sub sections.
./angsd -bam bam.filelist -doSaf 1 -anc chimpHg19.fa -GL 2 -P 24 -out out ./misc/realSFS out.saf.idx -P 24 > out.sfs ./angsd -bam bam.filelist -out out -doThetas 1 -doSaf 1 -pest out.sfs -anc chimpHg19.fa -GL 2 ./misc/thetaStat make_bed out.thetas.gz #Estimate for every Chromosome/scaffold ./misc/thetaStat do_stat out.thetas.gz -nChr 20 #Do a sliding window analysis based on the output from the make_bed command. ./misc/thetaStat do_stat out.thetas.gz -nChr 20 -win 50000 -step 10000 -outnames theta.thetasWindow.gz
Step 1: Finding a 'global estimate' of the SFS
First estimate the site allele frequency likelihood
./angsd -bam bam.filelist -doSaf 1 -anc chimpHg19.fa -GL 2 -P 24 -out out
-> Reading fasta: chimpHg19.fa
-> Parsing 10 number of samples
-> Printing at chr: 20 pos:14095817 chunknumber 3500
-> Done reading data waiting for calculations to finish
-> Calling destroy
-> Done waiting for threads
-> Output filenames:
->"out.arg"
->"out.saf"
->"out.saf.pos.gz"
-> Mon Jun 30 12:02:58 2014
-> Arguments and parameters for all analysis are located in .arg file
[ALL done] cpu-time used = 47.19 sec
[ALL done] walltime used = 43.00 sec
Obtain the maximum likelihood estimate of the SFS using the realSFS program found in the misc subfolder. (See more here realSFS)
./misc/realSFS out.saf.idx -P 24 > out.sfs
To plot the SFS in R :
s<-exp(scan('out.sfs'))
s<-s[-c(1,length(s))]
s<-s/sum(s)
barplot(s,names=1:length(s),main='SFS')
Step 2: Calculate the thetas for each site
./angsd -bam bam.filelist -out out -doThetas 1 -doSaf 1 -pest out.sfs -anc chimpHg19.fa -GL 2
Output of the .thetas.gz file
gunzip -c out.thetas.gz |head
#Chromo Pos Watterson Pairwise thetaSingleton thetaH thetaL 1 14000032 -9.457420 -10.372069 -8.319252 -13.025778 -10.997194 1 14000033 -9.463637 -10.379368 -8.324414 -13.035780 -11.004670 1 14000034 -9.463740 -10.379488 -8.324500 -13.035942 -11.004793 1 14000035 -9.463603 -10.379328 -8.324386 -13.035725 -11.004629 1 14000036 -9.323246 -10.218453 -8.204848 -12.826627 -10.840519 1 14000037 -9.179270 -10.048883 -8.086425 -12.596436 -10.666670 1 14000038 -9.004664 -9.845473 -7.941453 -12.328274 -10.458416 1 14000039 -9.327033 -10.222983 -8.207914 -12.833007 -10.845176 1 14000040 -9.621554 -10.557563 -8.461745 -13.262415 -11.185971
Step 3a: Estimate Tajimas D and other statistics
#create a binary version of thete.thetas.gz ./misc/thetaStat make_bed out.thetas.gz #calculate Tajimas D ./misc/thetaStat do_stat out.thetas.gz -nChr 20
cat out.thetas.gz.pestPG
## thetaStat VERSION: 0.01 build:(Jun 30 2014,12:06:12) #(indexStart,indexStop)(firstPos_withData,lastPos_withData)(WinStart,WinStop) Chr WinCenter tW tP tF tH tL Tajima fuf fud fayh zeng nSites (0,98316)(14000032,14100082)(0,14100082) 1 7050041 51.002623 46.171402 64.683834 51.290955 48.731178 -0.392892 -0.647071 -0.595302 -0.099654 -0.048444 98316 (0,98474)(13999910,14100060)(0,14100060) 2 7050030 92.689100 88.806005 101.768262 122.422498 105.614255 -0.174701 -0.252477 -0.220588 -0.360944 0.152373 98474 (0,93269)(14000529,14100095)(0,14100095) 3 7050047 70.757874 76.248087 75.447438 68.354514 72.301301 0.322902 0.020330 -0.148419 0.110921 0.023794 93269 (0,96339)(13999912,14100064)(0,14100064) 4 7050032 99.748624 107.898618 94.265208 130.283528 119.091076 0.340878 0.247030 0.123956 -0.223386 0.211971 96339 (0,99659)(13999926,14100063)(0,14100063) 5 7050031 120.941697 132.667821 86.726667 163.908351 148.288088 0.404945 0.688320 0.639821 -0.257254 0.247395 99659 (0,99541)(13999918,14100103)(0,14100103) 6 7050051 96.666344 112.146685 69.740992 143.403712 127.775201 0.667988 0.792499 0.627735 -0.321842 0.351730 99541 (0,99786)(13999926,14100047)(0,14100047) 7 7050023 93.164548 92.023886 92.742574 142.413716 117.218807 -0.051058 -0.013928 0.010201 -0.538288 0.282133 99786 (0,98759)(13999923,14100082)(0,14100082) 8 7050041 133.567125 177.157879 72.197498 204.069028 190.613463 1.363708 1.425567 1.040517 -0.200700 0.467490 98759 (0,97855)(14001686,14100094)(0,14100094) 9 7050047 88.777475 102.853333 64.660948 104.749694 103.801516 0.660983 0.776148 0.611265 -0.021256 0.184875 97855 (0,98031)(13999906,14100096)(0,14100096) 10 7050048 129.583334 134.877160 88.135115 213.231615 174.054390 0.170681 0.654145 0.724072 -0.602284 0.375595 98031 (0,99220)(13999900,14100060)(0,14100060) 11 7050030 66.349155 79.423643 60.194045 68.903312 74.163477 0.819589 0.520022 0.207421 0.157614 0.128409 99220 (0,99861)(13999913,14100078)(0,14100078) 12 7050039 86.461303 81.630083 96.156392 110.974922 96.302507 -0.232902 -0.302980 -0.252190 -0.337701 0.124323 99861 (0,98258)(13999943,14100097)(0,14100097) 16 7050048 83.191170 99.392421 77.561510 106.148748 102.770584 0.811500 0.472922 0.152079 -0.080798 0.257008 98258 (0,99428)(13999902,14100095)(0,14100095) 17 7050047 90.254620 99.816352 65.610351 113.328929 106.572638 0.441707 0.683942 0.614609 -0.148988 0.197530 99428 (0,97118)(13999898,14100071)(0,14100071) 18 7050035 79.843256 75.282296 86.844252 67.720321 71.501308 -0.237958 -0.260778 -0.196888 0.094212 -0.114062 97118 (0,93783)(13999895,14100089)(0,14100089) 19 7050044 54.311523 49.839190 64.913940 72.868913 61.354048 -0.341795 -0.495649 -0.434079 -0.421111 0.141133 93783 (0,98938)(13999916,14100091)(0,14100091) 20 7050045 68.148147 63.323800 78.463736 56.040370 59.682084 -0.294508 -0.398845 -0.338673 0.106250 -0.135474 98938
Remember that you will need to supply the ancestral state for the SFS Estimation, and you should try to remove the worst data by -minMapQ and -minQ.
Step 3b: Sliding Window example
We can easily do a sliding window analysis by adding -win/-step arguments to the last command. thetaStat
misc/thetaStat do_stat out.thetas.gz -nChr 20 -win 50000 -step 10000 -outnames theta.thetasWindow.gz
This will calculate the test statistic using a window size of 50kb and a step size of 10kb.
Example Output
- Output in the thetas.gz are the log scaled per site estimates of the thetas - Output in the pestPG file are the sum of the per site estimates for a region
.thetas.gz is
#Chromo Pos Watterson Pairwise thetaSingleton thetaH thetaL 1 14000032 -9.457420 -10.372069 -8.319252 -13.025778 -10.997194 1 14000033 -9.463637 -10.379368 -8.324414 -13.035780 -11.004670 1 14000034 -9.463740 -10.379488 -8.324500 -13.035942 -11.004793 1 14000035 -9.463603 -10.379328 -8.324386 -13.035725 -11.004629 1 14000036 -9.323246 -10.218453 -8.204848 -12.826627 -10.840519 1 14000037 -9.179270 -10.048883 -8.086425 -12.596436 -10.666670 1 14000038 -9.004664 -9.845473 -7.941453 -12.328274 -10.458416 1 14000039 -9.327033 -10.222983 -8.207914 -12.833007 -10.845176 1 14000040 -9.621554 -10.557563 -8.461745 -13.262415 -11.185971 1 14000041 -9.617449 -10.552869 -8.458225 -13.256257 -11.181185 1 14000042 -7.337841 -8.161756 -204.045433 -5.457443 -6.085818 1 14000043 -9.570405 -10.502160 -8.415195 -13.197596 -11.129976 1 14000044 -9.511097 -10.434558 -8.364249 -13.110037 -11.061100 1 14000045 -9.563664 -10.494371 -8.409489 -13.187203 -11.122022 1 14000046 -9.617690 -10.555402 -8.456395 -13.265004 -11.184107 1 14000047 -9.563722 -10.494438 -8.409538 -13.187292 -11.122090 1 14000048 -9.856578 -10.819096 -8.669691 -13.587898 -11.451396
- 1. chromosome
- 2. position
- 3. ThetaWatterson
- 4. ThetaD (nucleotide diversity)
- 5. Theta? (singleton category)
- 6. ThetaH
- 7. ThetaL
.thetas.gz.pestPG
The .pestPG file is a 14 column file (tab seperated). The first column contains information about the region. The second and third column is the reference name and the center of the window.
We then have 5 different estimators of theta, these are: Watterson, pairwise, FuLi, fayH, L. And we have 5 different neutrality test statistics: Tajima's D, Fu&Li F's, Fu&Li's D, Fay's H, Zeng's E. The final column is the effetive number of sites with data in the window.
## thetaStat VERSION: 0.01 build:(Jun 30 2014,12:06:12) #(indexStart,indexStop)(firstPos_withData,lastPos_withData)(WinStart,WinStop) Chr WinCenter tW tP tF tH tL Tajima fuf fud fayh zeng nSites (0,98316)(14000032,14100082)(0,14100082) 1 7050041 51.002623 46.171402 64.683834 51.290955 48.731178 -0.392892 -0.647071 -0.595302 -0.099654 -0.048444 98316 (0,98474)(13999910,14100060)(0,14100060) 2 7050030 92.689100 88.806005 101.768262 122.422498 105.614255 -0.174701 -0.252477 -0.220588 -0.360944 0.152373 98474 (0,93269)(14000529,14100095)(0,14100095) 3 7050047 70.757874 76.248087 75.447438 68.354514 72.301301 0.322902 0.020330 -0.148419 0.110921 0.023794 93269 (0,96339)(13999912,14100064)(0,14100064) 4 7050032 99.748624 107.898618 94.265208 130.283528 119.091076 0.340878 0.247030 0.123956 -0.223386 0.211971 96339 (0,99659)(13999926,14100063)(0,14100063) 5 7050031 120.941697 132.667821 86.726667 163.908351 148.288088 0.404945 0.688320 0.639821 -0.257254 0.247395 99659 (0,99541)(13999918,14100103)(0,14100103) 6 7050051 96.666344 112.146685 69.740992 143.403712 127.775201 0.667988 0.792499 0.627735 -0.321842 0.351730 99541 (0,99786)(13999926,14100047)(0,14100047) 7 7050023 93.164548 92.023886 92.742574 142.413716 117.218807 -0.051058 -0.013928 0.010201 -0.538288 0.282133 99786 (0,98759)(13999923,14100082)(0,14100082) 8 7050041 133.567125 177.157879 72.197498 204.069028 190.613463 1.363708 1.425567 1.040517 -0.200700 0.467490 98759
Format is:
(indexStart,indexStop)(posStart,posStop)(regStat,regStop) chrname wincenter tW tP tF tH tL tajD fulif fuliD fayH zengsE numSites
Most likely you are just interest in the wincenter (column 3) and the column 9 which is the Tajima's D statistic.
The first 3 columns relates to the region. The next 5 columns are 5 different estimators of theta, and the next 5 columns are neutrality test statistics. The final column is the number of sites with data in the region.
The first ()()() er mainly used for debugging the sliding window program. The interpretation is:
- The posStart and posStop is the first physical position, and last physical postion of sites included in the analysis.
- The regStat and regStop is the physical region for which the analysis is performed. Therefore the posStat and posStop is always included within the regStart and regStop
- The indexStart and IndexStop is the position within the internal array.
Unknown ancestral state (folded sfs)
- Below is for version 0.556 and above
If you don't have the ancestral states, you can still calculate the Watterson and Tajima theta, which means you can perform the Tajima's D neutrality test statistic. But this requires you to use the folded sfs. The output files will have the same format, but only the thetaW and thetaD, and tajimas D is meaningful.
Below is an example based on the earlier example where we now base our analysis on the folded spectrum. Notice the -fold 1 and that the second parameter to the realSFS is now 10 instead for 20.
First estimate the folded site allele frequency likelihood
./angsd -bam bam.filelist -doSaf 1 -anc hg19.fa -GL 2 -P 24 -out outFold -fold 1
Obtain the maximum likelihood estimate of the SFS
misc/realSFS outFold.saf.idx -P 24 > outFold.sfs
Calculate the thetas (remember to fold)
./angsd -bam bam.filelist -out outFold -doThetas 1 -doSaf 1 -pest outFold.sfs -anc hg19.fa -GL 2 -fold 1
Estimate Tajimas D
#create a binary version of thete.thetas.gz misc/thetaStat make_bed outFold.thetas.gz #calculate Tajimas D misc/thetaStat do_stat outFold.thetas.gz -nChr 10