Abbababa2 - Revision history

Samuele2 at 09:34, 26 June 2018

2018-06-26T09:34:19Z

← Older revision		Revision as of 11:34, 26 June 2018
Line 2:		Line 2:
	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.		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.

	This method [http://www.g3journal.org/content/8/2/551 has a publication]. ~~[[Cite the method Cite the method]]~~		This method [http://www.g3journal.org/content/8/2/551 has a publication].

	; some of the options only works for the developmental version availeble from github		; some of the options only works for the developmental version availeble from github

Samuele2 at 09:34, 26 June 2018

2018-06-26T09:34:00Z

← Older revision		Revision as of 11:34, 26 June 2018
Line 2:		Line 2:
	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.		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.

	This method [http://www.g3journal.org/content/8/2/551 has a publication]. ~~Bibtex Citation~~		This method [http://www.g3journal.org/content/8/2/551 has a publication]. [[Cite the method Cite the method]]

	; some of the options only works for the developmental version availeble from github		; some of the options only works for the developmental version availeble from github

Samuele2 at 09:31, 26 June 2018

2018-06-26T09:31:25Z

@@ Line 2: / Line 2: @@
 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.
-This method [http://www.g3journal.org/content/8/2/551 has a publication]
+This method [http://www.g3journal.org/content/8/2/551 has a publication]. Bibtex Citation
 ; some of the options only works for the developmental version availeble from github
@@ Line 195: / Line 195: @@
 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, nBlocks=number of blocks with observed data, H*=the names of the four populations for the specific tree. Note that the number of patterns might not be integer because of how ANGSD treats multiple genomes per populations.

Samuele2 at 09:28, 26 June 2018

2018-06-26T09:28:24Z

← Older revision		Revision as of 11:28, 26 June 2018
Line 1:		Line 1:
	The ''ABBABABA (multipop)'' compute the abbababa test (aka D-statistic), that means testing for an ancient admixture (or wrong tree topology).		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.		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.

			This method [http://www.g3journal.org/content/8/2/551 has a publication]

	; some of the options only works for the developmental version availeble from github		; 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.		; if you use -rf to specify regions. These MUST appear in the same ordering as your fai file.

Samuele at 14:55, 7 March 2017

2017-03-07T14:55:47Z

← Older revision		Revision as of 16:55, 7 March 2017
Line 166:		Line 166:
	NA		NA
	</pre>		</pre>
	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 of steps 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		Create a file popNames.name with written
	<pre>		<pre>
	Population1		Population1
Line 177:		Line 177:
	Run the Rscript with the command		Run the Rscript with the command
	<pre>		<pre>
	Rscript DSTAT angsdFile="bam.Angsd" out="result" sizeFile=sizeFile.size errFile=errorList.error nameFile=popNames.name ~~addErr="-0.005,0.005,0.001;A;T"~~		Rscript DSTAT angsdFile="bam.Angsd" out="result" sizeFile=sizeFile.size errFile=errorList.error nameFile=popNames.name
	</pre>		</pre>
	The script will show the calculated D statistic along with Z-score, Pvalues, Standard deviation and other quantities for all 30 4-populations trees. Note: If error correction is not needed, it is sufficient to avoid specifying any error file. If no names need to be provided, the script will assign Population_* as standard name. If no size file is provided, the script assigns 1 to each population. At least one between the name file and the size file is needed. It is possible to recycle the size file used in ANGSD.		The script will show the calculated D statistic along with Z-score, Pvalues, Standard deviation and other quantities for all 30 4-populations trees. Note: If error correction is not needed, it is sufficient to avoid specifying any error file. If no names need to be provided, the script will assign Population_* as standard name. If no size file is provided, the script assigns 1 to each population. At least one between the name file and the size file is needed. It is possible to recycle the size file used in ANGSD.

	The plots of error rates for type specific errors and added errors are in the folder result.errorDataFolder as pdf files (only if you have provided an error rate file and added error rates in the options for the R script). The other text files are used by the Rscript for the necessary computations and do not contain useful information.		The D-statistics and other informations are contained in four distinct files depending on the application of error correction and ancient transition removal. The files are named as follow:
	The D-statistics are contained in four distinct files ~~for each combination~~ of ~~populations~~. ~~For example, for the tree (((Population1, Population2)PopWithError)Chimpanzee) the~~ files ~~containing the results~~ are:		;[[1)result.Observed.txt]]
	;[[1)result~~.Population1.Population2.PopWithError.Chimpanzee~~.Observed.txt]]
	D-statistic calculated WITHOUT Error Correction and WITHOUT Ancient Transition removal		D-statistic calculated WITHOUT Error Correction and WITHOUT Ancient Transition removal
	;[[2) result~~.Population1.Population2.PopWithError.Chimpanzee~~.ErrorCorr.txt]]		;[[2) result.ErrorCorr.txt]]
	D-statistic calculated WITH Error Correction and WITHOUT Ancient Transition removal		D-statistic calculated WITH Error Correction and WITHOUT Ancient Transition removal
	;[[3) result.~~Population1.Population2.PopWithError.Chimpanzee~~.TransRem~~.ErrorCorr~~.txt]]		;[[3) result.ErrorCorr.TransRem.txt]]
	D-statistic calculated WITH Error Correction and WITH Ancient Transition removal		D-statistic calculated WITH Error Correction and WITH Ancient Transition removal
	;[[4) result~~.Population1.Population2.PopWithError.Chimpanzee~~.TransRem.txt]]		;[[4) result.TransRem.txt]]
	D-statistic calculated WITHOUT Error Correction and WITH Ancient Transition removal		D-statistic calculated WITHOUT Error Correction and WITH Ancient Transition removal

	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, the names of the four populations. Note that the number of patterns might not be integer because of how ANGSD treats multiple genomes per populations.		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, nBlocks=number of blocks with observed data, H*=the names of the four populations for the specific tree. 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.Population1.Population2.PopWithError.Chimpanzee.pdf showing a barplot of the error rates~~
	~~;-the file plotAddErr.A2T.Population1.Population2.PopWithError.Chimpanzee.pdf showing the effect of error correction on transition A-->T~~

Samuele at 17:51, 28 February 2017

2017-02-28T17:51:27Z

← Older revision		Revision as of 19:51, 28 February 2017
Line 146:		Line 146:
	16:10000000-15000000		16:10000000-15000000
	</pre>		</pre>
			The output of ANGSD will show no data about chromosome 1. This happens when all blocks within that chromosome contained no data and therefore where not printed.

	After running ANGSD to count ABBA and BABA combinations, we will call the R script who applies error correction to the ABBA and BABA allele combinations and produces the final output files.		After running ANGSD to count ABBA and BABA combinations, we will call the R script who applies error correction to the ABBA and BABA allele combinations and produces the final output files.
	<pre>		<pre>

Samuele at 17:49, 28 February 2017

2017-02-28T17:49:40Z

← Older revision		Revision as of 19:49, 28 February 2017
Line 140:		Line 140:
	1		1
	</pre>		</pre>
	We decide to target ~~two~~ chromosomes, one of the ~~two~~ with loci between position 10Mb and 15Mb. Thus create a file called regions.txt in which is written		We decide to target three chromosomes, one of the three with loci between position 10Mb and 15Mb. Thus create a file called regions.txt in which is written
	<pre>		<pre>
	1:		1:
			5:
	16:10000000-15000000		16:10000000-15000000
	</pre>		</pre>
Line 176:		Line 177:
	Rscript DSTAT angsdFile="bam.Angsd" out="result" sizeFile=sizeFile.size errFile=errorList.error nameFile=popNames.name addErr="-0.005,0.005,0.001;A;T"		Rscript DSTAT angsdFile="bam.Angsd" out="result" sizeFile=sizeFile.size errFile=errorList.error nameFile=popNames.name addErr="-0.005,0.005,0.001;A;T"
	</pre>		</pre>
	The script will show the calculated D statistic along with Z-score, Pvalues, Standard deviation and other quantities for all 30 4-populations trees. Note: If error correction is not needed, it is sufficient to avoid specifying any error file. If no names need to be provided, the script will assign Population_* as standard name. If no size file is provided, the script assigns 1 to each population. At least one between the name file and the size file is needed.		The script will show the calculated D statistic along with Z-score, Pvalues, Standard deviation and other quantities for all 30 4-populations trees. Note: If error correction is not needed, it is sufficient to avoid specifying any error file. If no names need to be provided, the script will assign Population_* as standard name. If no size file is provided, the script assigns 1 to each population. At least one between the name file and the size file is needed. It is possible to recycle the size file used in ANGSD.

	The plots of error rates for type specific errors and added errors are in the folder result.errorDataFolder as pdf files (only if you have provided an error rate file and added error rates in the options for the R script). The other text files are used by the Rscript for the necessary computations and do not contain useful information.		The plots of error rates for type specific errors and added errors are in the folder result.errorDataFolder as pdf files (only if you have provided an error rate file and added error rates in the options for the R script). The other text files are used by the Rscript for the necessary computations and do not contain useful information.

Samuele at 17:09, 28 February 2017

2017-02-28T17:09:25Z

← Older revision		Revision as of 19:09, 28 February 2017
Line 111:		Line 111:
	gunzip chimpHg19.fa.gz		gunzip chimpHg19.fa.gz
	samtools faidx chimpHg19.fa #indexing the fasta file		samtools faidx chimpHg19.fa #indexing the fasta file
	~~rm chimpHg19.fa.gz~~
	</pre>		</pre>
	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.		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.
Line 132:		Line 131:

	==4-population test==		==4-population test==
	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, where the last population is fixed as outgroup (so that there are 30 possible combinations). The last population is represented by the fasta file defined with the option -anc, of which we enable the use as outgroup by the option -useLast 0. One can use the last population of .bam files as outgroup with the option -useLast 1. Create a file named sizeFile.size and write the size of each population (~~it is NOT necessary~~ to define the size of the -anc ~~fasta~~ outgroup):		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, where the last population is fixed as outgroup (so that there are 30 possible combinations). The last population is represented by the fasta file defined with the option -anc, of which we enable the use as outgroup by the option -useLast 0. One can use the last population of .bam files as outgroup with the option -useLast 1. Create a file named sizeFile.size and write the size of each population (IT IS NECESSARY to define the size of the -anc outgroup population, that is always 1):
	<pre>		<pre>
	1		1
Line 139:		Line 138:
	3		3
	2		2
			1
	</pre>		</pre>
	We decide to target ~~three~~ chromosomes with loci ~~in the range 10000000 to 15000000 for chromosomes 1,2~~ and 3. Thus create a file called regions.txt in which is written		We decide to target two chromosomes, one of the two with loci between position 10Mb and 15Mb. Thus create a file called regions.txt in which is written
	<pre>		<pre>
	1:~~10000000-15000000~~		1:
	~~2:10000000-15000000~~		16:10000000-15000000
	3:10000000-15000000
	</pre>		</pre>
	After running ANGSD to count ABBA and BABA combinations, we will call the R script who applies error correction to the ABBA and BABA allele combinations and produces the final output files.		After running ANGSD to count ABBA and BABA combinations, we will call the R script who applies error correction to the ABBA and BABA allele combinations and produces the final output files.

Samuele at 08:50, 24 February 2017

2017-02-24T08:50:28Z

← Older revision		Revision as of 10:50, 24 February 2017
Line 34:		Line 34:
	-enhance 0 only analyze sites where outgroup H4 is non poly		-enhance 0 only analyze sites where outgroup H4 is non poly
	-Aanc 0 set H4 outgroup allele as A in each site		-Aanc 0 set H4 outgroup allele as A in each site
	-useLast 0 ~~set fasta file defined by -anc~~ as outgroup ~~in the analysis~~		-useLast 0 1=use the last group of bam files as outgroup

	</pre>		</pre>
	This function will counts the number of ABBA and BABA sites of the ~~possible~~ 4-population trees that can be built from the data.		This function will counts the number of ABBA and BABA sites of the 4-population trees that can be built from the data, where the outgroup is fixed.
	=Output=		=Output=
	;1)*.abbbababa2 (used for the 4-population test)		;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 create from the data (with one population fixed as outgroup)~~		Each line represents a block with a chromsome name (Column 1), a start position (Column 2), an end postion (Column 3). Columns 4 and 5 are the numerator and denominator of the D-statistic for their specific block. Column 6 is the number of sites containing data in that block. The other 256 columns are the normalized counts of the 256 allele patterns between the 4 populations, 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 trees that are built.
	This file is used as input for the R script estAvgError.R.		This file is used as input for the R script estAvgError.R.

Line 53:		Line 53:
	Include an outgroup in fasta format.		Include an outgroup in fasta format.
	; -useLast [int]		; -useLast [int]
	1: use the ~~file defined by -anc~~ as outgroup of the D-stat analysys. Default: 0 (use ~~last population~~ as ~~a fixed~~ outgroup)		1: use the last group of bam files as outgroup for the D-stat analysys. Default: 0 (use the fasta file as outgroup)
	; -doCounts 1		; -doCounts 1
	use -doCounts 1 in order to count the bases at each sites after filters.		use -doCounts 1 in order to count the bases at each sites after filters.
Line 63:		Line 63:
	allows for a maximum depth in each site to avoid overflow of the ABBA BABA counts. Default 100.		allows for a maximum depth in each site to avoid overflow of the ABBA BABA counts. Default 100.
	; -sizeFile [fileName]		; -sizeFile [fileName]
	file that specifies number of individuals in each population (more than 4 populations can be defined)		file that specifies number of individuals in each population (more than 4 populations can be defined). If not provided, it is assumed that each population has only one individual.
	; -Aanc [int]		; -Aanc [int]
	1: H4 allele is A in each site.		1: H4 allele is A in each site.
Line 132:		Line 132:

	==4-population test==		==4-population test==
	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, where the last population is fixed as outgroup (so that there are 30 possible combinations). The last population is represented by the fasta file defined with the option -anc, of which we enable the use as outgroup by the option -useLast 1. One can use the last population of .bam files as outgroup with the option -useLast 0. Create a file named sizeFile.size and write the size of each population (it is NOT necessary to define the size of the -anc fasta outgroup):		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, where the last population is fixed as outgroup (so that there are 30 possible combinations). The last population is represented by the fasta file defined with the option -anc, of which we enable the use as outgroup by the option -useLast 0. One can use the last population of .bam files as outgroup with the option -useLast 1. Create a file named sizeFile.size and write the size of each population (it is NOT necessary to define the size of the -anc fasta outgroup):
	<pre>		<pre>
	1		1
Line 148:		Line 148:
	After running ANGSD to count ABBA and BABA combinations, we will call the R script who applies error correction to the ABBA and BABA allele combinations and produces the final output files.		After running ANGSD to count ABBA and BABA combinations, we will call the R script who applies error correction to the ABBA and BABA allele combinations and produces the final output files.
	<pre>		<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		./ANGSD -doAbbababa2 1 -bam bam.filelist -sizeFile sizeFile.size -doCounts 1 -out bam.Angsd -anc chimpHg19.fa -rf regions.txt -useLast 0 -minQ 20 -minMapQ 30 -p 1
	</pre>		</pre>
	[[The output file is]]		[[The output file is]]
	[[bam.Angsd.abbbababa2 (used for the 4-population test)]]		[[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 30 ~~combinations~~ (so each block is written on 30 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.		Each line represents a block with a chromsome name (Column 1) for one of the possible 30 trees (so each block is written on 30 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 in the block. The next 256 columns are the counted patterns of alleles in the tree, 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.		This file is used as input for the R script estAvgError.R.

Line 177:		Line 177:
	Rscript DSTAT angsdFile="bam.Angsd" out="result" sizeFile=sizeFile.size errFile=errorList.error nameFile=popNames.name addErr="-0.005,0.005,0.001;A;T"		Rscript DSTAT angsdFile="bam.Angsd" out="result" sizeFile=sizeFile.size errFile=errorList.error nameFile=popNames.name addErr="-0.005,0.005,0.001;A;T"
	</pre>		</pre>
	The script will show the calculated D statistic along with Z-score, Pvalues, Standard deviation and other quantities for all 30 4-populations trees.		The script will show the calculated D statistic along with Z-score, Pvalues, Standard deviation and other quantities for all 30 4-populations trees. Note: If error correction is not needed, it is sufficient to avoid specifying any error file. If no names need to be provided, the script will assign Population_* as standard name. If no size file is provided, the script assigns 1 to each population. At least one between the name file and the size file is needed.

	The plots of error rates for type specific errors and added errors are in the folder result.errorDataFolder as pdf files (only if you have provided an error rate file and added error rates in the options for the R script). The other text files are used by the Rscript for the necessary computations and do not contain useful information.		The plots of error rates for type specific errors and added errors are in the folder result.errorDataFolder as pdf files (only if you have provided an error rate file and added error rates in the options for the R script). The other text files are used by the Rscript for the necessary computations and do not contain useful information.
	The D-statistics are contained in four distinct files for each combination of populations. For example, for the tree (((Population1, Population2)PopWithError)Chimpanzee) the files containing the results are:		The D-statistics are contained in four distinct files for each combination of populations. For example, for the tree (((Population1, Population2)PopWithError)Chimpanzee) the files containing the results are:

Samuele at 11:03, 15 February 2017

2017-02-15T11:03:35Z

← Older revision		Revision as of 13:03, 15 February 2017
Line 34:		Line 34:
	-enhance 0 only analyze sites where outgroup H4 is non poly		-enhance 0 only analyze sites where outgroup H4 is non poly
	-Aanc 0 set H4 outgroup allele as A in each site		-Aanc 0 set H4 outgroup allele as A in each site
	-useLast 0 ~~include~~ fasta file defined by -anc in the analysis		-useLast 0 set fasta file defined by -anc as outgroup in the analysis

	</pre>		</pre>
	This function will counts the number of ABBA and BABA sites.		This function will counts the number of ABBA and BABA sites of the possible 4-population trees that can be built from the data.
	=Output=		=Output=
	;1)*.abbbababa2 (used for the 4-population test)		;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		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 create from the data (with one population fixed as outgroup)
	This file is used as input for the R script estAvgError.R.		This file is used as input for the R script estAvgError.R.

Line 53:		Line 53:
	Include an outgroup in fasta format.		Include an outgroup in fasta format.
	; -useLast [int]		; -useLast [int]
	1: ~~include~~ the file defined by -anc in the D-stat analysys. Default: 0		1: use the file defined by -anc as outgroup of the D-stat analysys. Default: 0 (use last population as a fixed outgroup)
	; -doCounts 1		; -doCounts 1
	use -doCounts 1 in order to count the bases at each sites after filters.		use -doCounts 1 in order to count the bases at each sites after filters.
Line 70:		Line 70:

	=Tutorial for the ABBABABA (Multipop) test=		=Tutorial for the ABBABABA (Multipop) test=
			This tutorial require having Samtools previously installed, and the library 'pracma' previously installed in R.
	== Prepare BAM and FASTA files ==		== Prepare BAM and FASTA files ==
	Download the latest version of angsd in your working folder from the github repository		Download the latest version of angsd in your working folder from the github repository
Line 104:		Line 105:
	bams/smallNA11840.mapped.ILLUMINA.bwa.CEU.low_coverage.20111114.bam		bams/smallNA11840.mapped.ILLUMINA.bwa.CEU.low_coverage.20111114.bam
	</pre>		</pre>
	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)~~.		Download a fasta file for the chimpanzee. This is going to be used as the outgroup for the 4-population test.
	<pre>		<pre>
	wget http://popgen.dk/software/download/angsd/hg19ancNoChr.fa.gz		wget http://popgen.dk/software/download/angsd/hg19ancNoChr.fa.gz
Line 131:		Line 132:

	==4-population test==		==4-population test==
	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~~):		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, where the last population is fixed as outgroup (so that there are 30 possible combinations). The last population is represented by the fasta file defined with the option -anc, of which we enable the use as outgroup by the option -useLast 1. One can use the last population of .bam files as outgroup with the option -useLast 0. Create a file named sizeFile.size and write the size of each population (it is NOT necessary to define the size of the -anc fasta outgroup):
	<pre>		<pre>
	1		1
Line 145:		Line 146:
	3:10000000-15000000		3:10000000-15000000
	</pre>		</pre>
	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.		After running ANGSD to count ABBA and BABA combinations, we will call the R script who applies error correction to the ABBA and BABA allele combinations and produces the final output files.
	<pre>		<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		./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
Line 151:		Line 152:
	[[The output file is]]		[[The output file is]]
	[[bam.Angsd.abbbababa2 (used for the 4-population test)]]		[[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.		Each line represents a block with a chromsome name (Column 1) for one of the possible 30 combinations (so each block is written on 30 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.		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		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 (including a line for the outgroup population). If a population has no error file, it is sufficient to write NA. Create a file called errorList.error with written
	<pre>		<pre>
	NA		NA
Line 163:		Line 164:
	NA		NA
	</pre>		</pre>
	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		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 of steps 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
	<pre>		<pre>
	Population1		Population1
Line 176:		Line 177:
	Rscript DSTAT angsdFile="bam.Angsd" out="result" sizeFile=sizeFile.size errFile=errorList.error nameFile=popNames.name addErr="-0.005,0.005,0.001;A;T"		Rscript DSTAT angsdFile="bam.Angsd" out="result" sizeFile=sizeFile.size errFile=errorList.error nameFile=popNames.name addErr="-0.005,0.005,0.001;A;T"
	</pre>		</pre>
	The script will show the calculated D statistic along with Z-score, Pvalues, Standard deviation and other quantities for all ~~combinations~~.		The script will show the calculated D statistic along with Z-score, Pvalues, Standard deviation and other quantities for all 30 4-populations trees.
	The plots of error rates for type specific errors and added errors are in the folder result.errorDataFolder as pdf files. The other files are used by the Rscript for the necessary computations and do not contain useful information.		The plots of error rates for type specific errors and added errors are in the folder result.errorDataFolder as pdf files (only if you have provided an error rate file and added error rates in the options for the R script). The other text files are used by the Rscript for the necessary computations and do not contain useful information.
	The D-statistics are contained in four distinct files for each combination of populations. For example, for the ~~populations~~ Population1, Chimpanzee~~, Population2, Population3~~ the files containing the results are:		The D-statistics are contained in four distinct files for each combination of populations. For example, for the tree (((Population1, Population2)PopWithError)Chimpanzee) the files containing the results are:
	;[[1)result.Population1.~~Chimpanzee~~.~~Population2~~.~~Population3~~.Observed.txt]]		;[[1)result.Population1.Population2.PopWithError.Chimpanzee.Observed.txt]]
	D-statistic calculated WITHOUT Error Correction and WITHOUT Ancient Transition removal		D-statistic calculated WITHOUT Error Correction and WITHOUT Ancient Transition removal
	;[[2) result.Population1.~~Chimpanzee~~.~~Population2~~.~~Population3~~.ErrorCorr.txt]]		;[[2) result.Population1.Population2.PopWithError.Chimpanzee.ErrorCorr.txt]]
	D-statistic calculated WITH Error Correction and WITHOUT Ancient Transition removal		D-statistic calculated WITH Error Correction and WITHOUT Ancient Transition removal
	;[[3) result.Population1.~~Chimpanzee~~.~~Population2~~.~~Population3~~.TransRem.ErrorCorr.txt]]		;[[3) result.Population1.Population2.PopWithError.Chimpanzee.TransRem.ErrorCorr.txt]]
	D-statistic calculated WITH Error Correction and WITH Ancient Transition removal		D-statistic calculated WITH Error Correction and WITH Ancient Transition removal
	;[[4) result.Population1.~~Chimpanzee~~.~~Population2~~.~~Population3~~.~~RemTrans~~.txt]]		;[[4) result.Population1.Population2.PopWithError.Chimpanzee.TransRem.txt]]
	D-statistic calculated WITHOUT Error Correction and WITH Ancient Transition removal		D-statistic calculated WITHOUT Error Correction and WITH Ancient Transition removal

Line 191:		Line 192:

	In case of error correction, the R script also creates the folder [[result.errorDataFolder]] containing:		In case of error correction, the R script also creates the folder [[result.errorDataFolder]] containing:
	;-the file barPlotErrors.Population1.~~Chimpanzee~~.~~Population2~~.~~Population3~~.pdf showing a barplot of the error rates		;-the file barPlotErrors.Population1.Population2.PopWithError.Chimpanzee.pdf showing a barplot of the error rates
	;-the file plotAddErr.A2T.Population1.~~Chimpanzee~~.~~Population2~~.~~Population3~~.pdf showing the effect of error correction on transition A-->T		;-the file plotAddErr.A2T.Population1.Population2.PopWithError.Chimpanzee.pdf showing the effect of error correction on transition A-->T