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 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.
- -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 (If you want to use it, it must be only when you have one genome in the outgroup, it won't work otherwise).
- -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
- -sizeFile [fileName]
1: specifies number of individuals in each population
- -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 -out perfectSampleCEU gunzip perfectSampleCEU.fa.gz samtools faidx perfectSampleCEU.fa
In this tutorial we perform the ABBABABA test on H1,H2,H3,H4 consisting respectively of 3,5,2,1 individuals. In this case we use a fasta file for the outgroup H4 (we use a bam file in next tutorial). We will apply error correction to groups H1 and H2 assuming one of the samples from H3 as high-quality reference one.
Generate files for the error correction
Assume population H1 consists of the first two genomes of our list, while population H2 consists of the genomes 3 to 7. We want to apply error correction to those genomes, because we know they have been subjected to contamination. We generate two files containing the pathnames of the genomes of H1 and H2 on which we want to apply error correction.
sed -n 1,2p bam.filelist > bamH1.filelist sed -n 3,7p bam.filelist > bamH2.filelist
and then we use "doAncError" to generate the intermediate files that we will use later to estimate the error rates for the two groups H1 and H2. "doAncError" apply 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 bamH1 -bam bamH1.filelist ./ANGSD -doAncError 1 -anc chimpHg19.fa -ref perfectSampleCEU.fa -out bamH2 -bam bamH2.filelist
4-population test
Now, we want to run the four population test using: H1: first 2 bam files H2: bam files from 3 to 7 H3: bam files from 8 to 10 H4: chimpHg19.fa file After running ANGSD we will call the R script who apply error correction to the ABBA and BABA allele combinations and produce the final output files.
./ANGSD -doAbbababa2 1 -bam bam.filelist -doCounts 1 -out bam.AllelePatterns -sizeH1 2 -sizeH2 5 -sizeH3 3 -anc chimpHg19.fa -minQ 20 -minMapQ 30
The output file is bam.AllelePatterns.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. This file is used as input for the R script estAvgError.R.
We run the R script specifying the intermediate error files for populations H1 and H2. We also want to study the effect of error correction if we add individually to each population an error rate between 0 and 0.005 with step 0.001 and involving transitions A->T. You can run more transitions separating the letters with a comma. It is also possible to specify the names of H1,H2,H3 to be seen on the plot (do not specify them to keep standard names). In this case we use the generic names CEU1,CEU2,CEU3. When at least an error file is given as input, the script will apply error correction.
Rscript RSCRIPT angsdFile="bam.AllelePatterns" out="result" file1="bamH1.ancError" file2="bamH2.ancError" addErr="0,0.005,0.001;A;T;CEU1,CEU2,CEU3"
The script will show the calculated D statistic along with Z-score, Pvalues, Standard deviation and other quantities.
--- Table of Results --- --------------------------------------------------------------------------------- Mode |Dstat |sd(Dstat) |Djack |Zscore |Pvalue --------------------------------------------------------------------------------- Observed |-6.323e-02 |6.985e-02 |-6.323e-02 |-0.905 |3.7e-01 --------------------------------------------------------------------------------- Err Corr |-6.430e-02 |7.226e-02 |-6.431e-02 |-0.890 |3.7e-01 --------------------------------------------------------------------------------- No Trans |-1.141e-02 |6.311e-02 |-1.141e-02 |-0.181 |8.6e-01 --------------------------------------------------------------------------------- Err Corr | | | | | and |-1.494e-02 |6.615e-02 |-1.496e-02 |-0.226 |8.2e-01 No Trans | | | | | --------------------------------------------------------------------------------- plots with effect of removed errors and D statistic files for all the removed errors are in folder result.errorDataFolder
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.
