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.
Abbababa2
The ABBABABA (multipop) compute the abbababa test (aka D-statistic), that means testing for an ancient admixture (or wrong tree topology). Differently from ABBABABA (D_stat) multiple individuals for each one of the groups are allowed. As all methods in ANGSD we require that the header of the BAM files are the same.
- some of the options only works for the developmental version availeble from github
- if you use -rf to specify regions. These MUST appear in the same ordering as your fai file.
<classdiagram type="dir:LR">
[*.bam and/or *.cram| NGS genome datasets{bg:orange}]->[Sequence data|All bases or Random bases]
[Sequence data]->[Elaborate multiple genomes per population] [Elaborate multiple genomes per population]->[*.abbababa2|weighted ABBA and BABA counts file {bg:blue}] </classdiagram>
<classdiagram type="dir:LR"> [*.abbababa2|weighted ABBA and BABA counts file {bg:blue}]->estAvgError.R[D stat and Z scores{bg:blue}] </classdiagram>
Method
Brief Overview
> ./angsd -doAbbababa2
--------------
abcDstat2.cpp:
-doAbbababa2 0 run the abbababa analysis
-rmTrans 0 remove transitions
-blockSize 5000000 size of each block in bases
-anc (null) fasta file with outgroup
-sample 0 sample a single base in each individual
-maxDepth 100 max depth of each site allowed
-sizeFile (null) file with sizes of the populations
-enhance 0 only analyze sites where outgroup H4 is non poly
-Aanc 0 set H4 outgroup allele as A in each site
-useLast 0 include fasta file defined by -anc in the analysis
This function will counts the number of ABBA and BABA sites.
Output
- 1)*.abbbababa2 (used for the 4-population test)
Each line represents a block with a chromsome name (Column 1), a start position (Column 2), an end postion (Column 3). The new columns are the counts of all 256 counted combination of alleles, starting from X0000=AAAA,X0001=AAAC,....,X3333=TTTT, with the correspondence 0=A,1=C,2=G,3=T. Every block is repeated a number of times corresponding to the combinations of 4 populations that are possible to do create from the data This file is used as input for the R script estAvgError.R.
Options
- -doAbbababa2 1
take all bases at each position.
- -rmTrans [int]
0; use all reads (default), 1 Remove ancient transitions (important for ancient DNA)
- -blockSize [int]
Size of each block. Choose a number that is higher than the LD in the populations. For human 5Mb (5000000) is usually used.
- -anc [fileName.fa]
Include an outgroup in fasta format.
- -useLast [int]
1: include the file defined by -anc in the D-stat analysys. Default: 0
- -doCounts 1
use -doCounts 1 in order to count the bases at each sites after filters.
- -enhance [int]
1: use only sites where the reads for the outgroup has the same base for all reads.
- -sample [int]
1: sample only one base at each position for every individual 0: all bases at each position are used for the ABBABABA test
- -maxDepth [int]
allows for a maximum depth in each site to avoid overflow of the ABBA BABA counts. Default 100.
- -sizeFile [fileName]
file that specifies number of individuals in each population (more than 4 populations can be defined)
- -Aanc [int]
1: H4 allele is A in each site.
In order to do fancy filtering of bases based on quality scores see the Allele counts options.
Tutorial for the ABBABABA (Multipop) test
Prepare BAM and FASTA files
Download the latest version of angsd in your working folder from the github repository
https://github.com/ANGSD/angsd.git
Create symbolic links to angsd and the necessary R script
ln -s ./angsd/angsd ANGSD ln -s ./angsd/R/estAvgError.R RSCRIPT
Get 10 example .bam datasets, position them in the folder ./bams/ and create a file bam.filelist listing the pathnames of those datasets
wget http://popgen.dk/software/download/angsd/bams.tar.gz tar xf bams.tar.gz for i in bams/*.bam;do samtools index $i;done #index bam files ls bams/*.bam > bam.filelist rm bams.tar.gz #remove zipped file
This is how the file bam.filelist looks like
cat bam.filelist
bams/smallNA06985.mapped.ILLUMINA.bwa.CEU.low_coverage.20111114.bam bams/smallNA06994.mapped.ILLUMINA.bwa.CEU.low_coverage.20111114.bam bams/smallNA07000.mapped.ILLUMINA.bwa.CEU.low_coverage.20111114.bam bams/smallNA07056.mapped.ILLUMINA.bwa.CEU.low_coverage.20111114.bam bams/smallNA07357.mapped.ILLUMINA.bwa.CEU.low_coverage.20111114.bam bams/smallNA11829.mapped.ILLUMINA.bwa.CEU.low_coverage.20111114.bam bams/smallNA11830.mapped.ILLUMINA.bwa.CEU.low_coverage.20111114.bam bams/smallNA11831.mapped.ILLUMINA.bwa.CEU.low_coverage.20111114.bam bams/smallNA11832.mapped.ILLUMINA.bwa.CEU.low_coverage.20111114.bam bams/smallNA11840.mapped.ILLUMINA.bwa.CEU.low_coverage.20111114.bam
Download a fasta file for the chimpanzee. This is going to be used as the outgroup for the 4-population test. One can use a bam file as well (see in one of the other examples after the tutorial how to do it).
wget http://popgen.dk/software/download/angsd/hg19ancNoChr.fa.gz mv hg19ancNoChr.fa.gz chimpHg19.fa.gz gunzip chimpHg19.fa.gz samtools faidx chimpHg19.fa #indexing the fasta file rm chimpHg19.fa.gz
Now, generate a fasta file for one of our 10 bam file. We assume such a genome has very high quality and we can use it as a reference for estimating error rates in others of our datasets.
./ANGSD -i bams/smallNA11840.mapped.ILLUMINA.bwa.CEU.low_coverage.20111114.bam -doFasta 1 -doCounts 1 -out perfectSampleCEU gunzip perfectSampleCEU.fa.gz samtools faidx perfectSampleCEU.fa
In this tutorial we perform the ABBABABA test on all the combinations of 4 populations amongst 6 populations of size 1,2,2,3,2,1 individuals (so that there are 360 possible combinations). The last population is represented by the fasta file defined with the option -anc, of which we enable the use by the option -useLast 1. Create a file named sizeFile.size and write the size of each population (skip the last referred to the -anc fasta file):
1 2 2 3 2
Generate files for the error correction
We will apply error correction to the group with 3 individuals, using "perfectSampleCEU" as high-quality reference genome. The population containing 3 individuals affected by transition error goes from line 6 to line 8 in the file bam.filelist. We select those individuals and write them in another file.
sed -n 6,8p bam.filelist > bamWithErrors.filelist
and then we use "doAncError" to generate the intermediate files that we will use later as input for the R script that calculates the D-statistic. "doAncError" applies the so called "perfect individual assumption", based on which error rates are estimate using a high quality genome (option -ref) and an outgroup (option -anc), both in fasta format. We have already prepared the two fasta files in our preparation phase.
./ANGSD -doAncError 1 -anc chimpHg19.fa -ref perfectSampleCEU.fa -out errorFile -bam bamWithErrors.filelist
4-population test
Now, we want to run the four population test using the 6 groups of 1,2,2,3,2,1 individuals (where the last one is the fasta file). We decide to target two regions between loci 10000000 and 15000000 for chromosomes 1 and 3. Thus create a file called regions.txt in which is written
1:10000000-15000000 2:10000000-15000000 3:10000000-15000000
After running ANGSD we will call the R script who applies error correction to the ABBA and BABA allele combinations and produces the final output files. <pre> ./ANGSD -doAbbababa2 1 -bam bam.filelist -sizeFile sizeFile.size -doCounts 1 -out bam.Angsd -anc chimpHg19.fa -rf regions.txt -useLast 1 -minQ 20 -minMapQ 30 -p 3
The output file is bam.Angsd.abbbababa2 (used for the 4-population test) Each line represents a block with a chromsome name (Column 1) for one of the possible 360 combinations (so each block is written on 360 lines), a start position (Column 2), an end postion (Column 3). Columns 4,5 and 6 are the numerator, denominator and number of sites analyzed. The next 256 columns are the counted patterns of alleles, starting from X0000=AAAA,X0001=AAAC,....,X3333=TTTT, with the correspondence 0=A,1=C,2=G,3=T. This file is used as input for the R script estAvgError.R.
We run the R script specifying the error files for the population with 3 individuals. This is done defining the error files in each populations inside a text file. If a population has no error file, it is sufficient to write NA. Create a file called errorList.error with written
NA NA NA ./errorFile.ancError NA NA
We also want to study the effect of error correction if we add individually to each population an error rate between -0.005 and 0.005 with step 0.001 and involving transition A->T. You can run more transitions separating the letters with a comma. We need to create a file with the names of the 6 populations. Create a file popNames.name with written
Population1 Population2 Population3 PopWithError Population4 Chimpanzee
Run the Rscript with the command <pre> Rscript DSTAT angsdFile="bam.Angsd" out="result" sizeFile=sizeFile.size errFile=errorList.error nameFile=popNames.name addErr="0,0.005,0.001;A;T"
The script will show the calculated D statistic along with Z-score, Pvalues, Standard deviation and other quantities for all combinations. The plots of error rates for type specific errors and added errors are in the folder result.errorDataFolder. The D-statistics are contained in four distinct files for each population.
Those results are also contained in four distinct files
D-statistic calculated WITHOUT Error Correction and WITHOUT Ancient Transition removal
mean(D) JK-D V(JK-D) Z pvalue nABBA nBABA nBBAA -0.063233 -0.063233 0.004878 -0.905320 0.365296 246.033565 279.248560 292.834879
D-statistic calculated WITH Error Correction and WITHOUT Ancient Transition removal
mean(D) JK-D V(JK-D) Z pvalue nABBA nBABA nBBAA -0.064295 -0.064309 0.005221 -0.889833 0.373555 238.242964 270.983960 293.326044
D-statistic calculated WITH Error Correction and WITH Ancient Transition removal
mean(D) JK-D V(JK-D) Z pvalue nABBA nBABA nBBAA -0.014939 -0.014959 0.004376 -0.225829 0.821335 81.636843 84.112983 293.326044
D-statistic calculated WITHOUT Error Correction and WITH Ancient Transition removal
mean(D) JK-D V(JK-D) Z pvalue nABBA nBABA nBBAA -0.011406 -0.011406 0.003983 -0.180730 0.856580 85.730478 87.708709 292.834879
Specifically, the values contained in the four files are: mean(D)=average D-stat, JK-D=jackknife estimate of the D-stat, V(JK-D)=variance of the D-stat, Z=Z score, pvalue=pvalue from the Z score, nABBA=number of ABBA patterns observed, nBABA=number of BABA patterns observed, nBBAA=all the other observed patterns. Note that the number of patterns might not be integer because of how ANGSD treats multiple genomes per populations.
In case of error correction, the R script also creates the folder result.errorDataFolder containing:
- -the file barPlotErrors.pdf showing a barplot of the error rates (plot shown below)
- -the file plotAddErr.A2T.pdf showing the effect of error correction on transition A-->T (plot shown below)
- -the file errorRates.txt showing in each line transition errors for each population, respectively
- -all the files related to the addition of error correction to H1,H2,H3, necessary to plot the files plotAddErr.A2T.pdf.
