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Genome mapping files for _Mus musculus domesticus GER_, _Mus musculus domesticus FRA_, _Mus musculus domesticus IRA_, _Mus musculus musculus AFG_, _Mus musculus castaneus CAS_ and _Mus spretus SPRE_ were obtained from <http://wwwuser.gwdg.de/~evolbio/evolgen/wildmouse/m_m_domesticus/genomes_bam/>, <http://wwwuser.gwdg.de/~evolbio/evolgen/wildmouse/m_m_musculus/genomes_bam/>, <http://wwwuser.gwdg.de/~evolbio/evolgen/wildmouse/m_m_castaneus/genomes_bam/>, <http://wwwuser.gwdg.de/~evolbio/evolgen/wildmouse/m_spretus/genomes_bam/>.
For mapping details please look into the original publication ([Harr et al. 2016](http://www.nature.com/articles/sdata201675)) <http://www.nature.com/article-assets/npg/sdata/2016/sdata201675/extref/sdata201675-s7.docx>.
## Get masking regions for individual samples and natural populations
For masking genomic regions in natural populations which showed low coverage based on the genomic mapping BAM files we only considered the stable chromosomes from the reference GRCm38 _mm10_ <http://www.ncbi.nlm.nih.gov/projects/genome/assembly/grc/mouse/>.
The BAM files were processed with 'genomeCoverageBed' to obtain site specific genome coverage and further united with 'unionBedGraphs'.
##### Population specific masking
The per population combined coverage was further processed to only retain regions with a coverage smaller than 5 resulting as the masking regions.
genomeCoverageBed example for the population _Mus musculus musculus AFG_:
```
#example for populaton Mmm_AFG:
#
#used BAM files:
#
#AFG1_396.bam
#AFG2_413.bam
#AFG3_416.bam
#AFG4_424.bam
#AFG5_435.bam
#AFG6_444.bam
$REFERENCE=mm10.fasta
for file in *.bam; do genomeCoverageBed -ibam $file -bga -g $REFFERENCE > $file".bga";done
```
unionBedGraphs example for the population _Mus musculus musculus AFG_:
```
INPUT1=AFG1_396.bam.bga
INPUT2=AFG2_413.bam.bga
INPUT3=AFG3_416.bam.bga
INPUT4=AFG4_424.bam.bga
INPUT5=AFG5_435.bam.bga
INPUT6=AFG6_444.bam.bga
OUTPUT=Mmm_AFG.combined.bga
unionBedGraphs -i $INPUT1 $INPUT2 $INPUT3 $INPUT4 $INPUT5 $INPUT6 | awk -v OFS='\t' 'BEGIN {sum=0} {for (i=4; i<=NF; i++) sum+=$i; print $1,$2,$3,sum; sum=0}' > $OUTPUT
```
get masking region example for the population _Mus musculus musculus AFG_:
```
INPUT=Mmm_AFG.combined.bga
OUTPUT=Mmm_AFG.combined.bga.stcov5
awk '{if($4<5) print $0}' $INPUT > $INPUT".stcov5"
bedtools merge -i $INPUT".stcov5" > $INPUT".stcov5.merge"
awk -v OFS='\t' '{print $1,$2,$3,4}' $INPUT".stcov5.merge" > $OUTPUT
```
##### Individual specific masking
For individuals regions with a coverage smaller than 5 were used as masking regions.
get masking region example for individual 396 of the population _Mus musculus musculus AFG_:
```
INPUT=AFG1_396.bam.bga
OUTPUT=AFG1_396.bam.bga.stcov5
awk '{if($4<5) print $0}' $INPUT > $INPUT".stcov5"
bedtools merge -i $INPUT".stcov5" > $INPUT".stcov5.merge"
awk -v OFS='\t' '{print $1,$2,$3,4}' $INPUT".stcov5.merge" > $OUTPUT
```
+ bedtools v2.24.0 <http://bedtools.readthedocs.io/en/latest/>
+ awk
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For SNP and INDEL calling the BAM files were processed with 'samtools mpileup' and 'bcftools call' with relaxed quality options to retain information in CNV regions.
Due to the amount of data first mpileup files were generated for each BAM file and merged with 'bcftools merge'. After merging mpileup files, 'bcftools call' was used to generate the final VCF file.
samtools mpileup | bcftools call example for the population _Mus musculus musculus AFG_:
```
#example for population Mmm_AFG:
#
#used BAM files:
#
#AFG1_396.bam
#AFG2_413.bam
#AFG3_416.bam
#AFG4_424.bam
#AFG5_435.bam
#AFG6_444.bam
#generating indiviual mpileup files for each BAM file
$REFERENCE=mm10.fasta
INPUT1=AFG1_396.bam
INPUT2=AFG2_413.bam
INPUT3=AFG3_416.bam
INPUT4=AFG4_424.bam
INPUT5=AFG5_435.bam
INPUT6=AFG6_444.bam
samtools mpileup -q 0 -Q 10 -A -d 99999 -t DP,AD,ADF,ADR -vf $REFERENCE -o $INPUT1".mpileup.q0Q10.vcf" $INPUT1
samtools mpileup -q 0 -Q 10 -A -d 99999 -t DP,AD,ADF,ADR -vf $REFERENCE -o $INPUT2".mpileup.q0Q10.vcf" $INPUT2
samtools mpileup -q 0 -Q 10 -A -d 99999 -t DP,AD,ADF,ADR -vf $REFERENCE -o $INPUT3".mpileup.q0Q10.vcf" $INPUT3
samtools mpileup -q 0 -Q 10 -A -d 99999 -t DP,AD,ADF,ADR -vf $REFERENCE -o $INPUT4".mpileup.q0Q10.vcf" $INPUT4
samtools mpileup -q 0 -Q 10 -A -d 99999 -t DP,AD,ADF,ADR -vf $REFERENCE -o $INPUT5".mpileup.q0Q10.vcf" $INPUT5
samtools mpileup -q 0 -Q 10 -A -d 99999 -t DP,AD,ADF,ADR -vf $REFERENCE -o $INPUT6".mpileup.q0Q10.vcf" $INPUT6
bgzip $INPUT1".mpileup.q0Q10.vcf"
bgzip $INPUT2".mpileup.q0Q10.vcf"
bgzip $INPUT3".mpileup.q0Q10.vcf"
bgzip $INPUT4".mpileup.q0Q10.vcf"
bgzip $INPUT5".mpileup.q0Q10.vcf"
bgzip $INPUT6".mpileup.q0Q10.vcf"
tabix $INPUT1".mpileup.q0Q10.vcf.gz"
tabix $INPUT2".mpileup.q0Q10.vcf.gz"
tabix $INPUT3".mpileup.q0Q10.vcf.gz"
tabix $INPUT4".mpileup.q0Q10.vcf.gz"
tabix $INPUT5".mpileup.q0Q10.vcf.gz"
tabix $INPUT6".mpileup.q0Q10.vcf.gz"
#merge individual mpileup files
MPILEUPLIST=AFG.mpileup.list
echo $INPUT1".mpileup.q0Q10.vcf.gz" >> $MPILEUPLIST
echo $INPUT2".mpileup.q0Q10.vcf.gz" >> $MPILEUPLIST
echo $INPUT3".mpileup.q0Q10.vcf.gz" >> $MPILEUPLIST
echo $INPUT4".mpileup.q0Q10.vcf.gz" >> $MPILEUPLIST
echo $INPUT5".mpileup.q0Q10.vcf.gz" >> $MPILEUPLIST
echo $INPUT6".mpileup.q0Q10.vcf.gz" >> $MPILEUPLIST
bcftools merge -m all -O z -o $MPILEUPOUTPUT -l $MPILEUPLIST
#call SNP and INDEL
OUTPUT=Mmm_AFG.mpileup.q0Q10.bcfcall.mv.vcf.gz
bcftools call -O z -f GQ -m -v -o $OUTPUT $MPILEUPOUTPUT
```
+ samtools 1.3.1-36-g613501f (using htslib 1.3.1-59-g0f2a88a)
+ bcftools 1.3.1-39-gd797e86 (using htslib 1.3.1-59-g0f2a88a)
+ bgzip v1.3
+ tabix v1.3
## K80 distance calculation
### Get population specific SNPs
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To get population specific CONSENSUS VCF files the VCF file produced with 'bcftools call' was first re-coded into population specific VCF files with 'vcftools'. Further the population specific VCF file containing multiple individuals was parsed with 'vcfparser.py mvcf2consensus' to obtain a CONSENSUS VCF file for each population. This CONSENSUS VCF files were used to generate pseudo-genomes files per natural population with 'vcfparser.py vcf2fasta' using also the masking regions (see "Get masking regions for individual samples and natural populations").
vcftools example for the population _Mus musculus musculus AFG_:
```
#example for population Mmm_AFG:
#
#VCF IDs:
#
#396
#413
#416
#424
#435
#444
POPIDS=AFG.vcf.ids
echo "396" >> $POPIDS
echo "413" >> $POPIDS
echo "416" >> $POPIDS
echo "424" >> $POPIDS
echo "435" >> $POPIDS
echo "444" >> $POPIDS
GZVCF=Mmm_AFG.mpileup.q0Q10.bcfcall.mv.vcf.gz
OUTPUT=Mmm_AFG.mpileup.q0Q10.bcfcall.mv.remIndels
vcftools --gzvcf $GZVCF --remove-indels --recode --recode-INFO-all --non-ref-ac-any 1 --keep $POPIDS --out $OUTPUT
```
vcfparser.py mvcf2consensus example for the population _Mus musculus musculus AFG_:
```
#example for population Mmm_AFG:
#
#VCF IDs:
#
#396
#413
#416
#424
#435
#444
INPUT=Mmm_AFG.mpileup.q0Q10.bcfcall.mv.remIndels.recode.vcf
OUTPUT=Mmm_AFG.mpileup.q0Q10.bcfcall.mv.remIndels.recode.consensus
python vcfparser.py mvcf2consensus -ivcf $INPUT -o $OUTPUT -cdp 11 -chr chr1,chr2,chr3,chr4,chr5,chr6,chr7,chr8,chr9,chr10,chr11,chr12,chr13,chr14,chr15,chr16,chr17,chr18,chr19,chrX,chrY -samples 396,413,416,424,435,444 -id Mmm_AFG.mv
```
vcfparser.py vcf2fasta example for the population _Mus musculus musculus AFG_:
```
#example for population Mmm_AFG:
#
REFERENCE=mm10.fasta
INPUT=Mmm_AFG.mpileup.q0Q10.bcfcall.mv.remIndels.recode.consensus.vcf
OUTPUT=Mmm_AFG.mpileup.q0Q10.chr1.bcfcall.mv.remIndels.recode.consensus.chr
MASKFILE=Mmm_AFG.combined.bga.stcov5
python vcfparser.py vcf2fasta -ivcf $INPUT -o $OUTPUT -R $REFERENCE -samples Mmm_AFG.mv -chr chr1,chr2,chr3,chr4,chr5,chr6,chr7,chr8,chr9,chr10,chr11,chr12,chr13,chr14,chr15,chr16,chr17,chr18,chr19,chrX,chrY -ibga $MASKFILE -cov2N 4
```
_used software:_
+ vcftools v0.1.15
+ vcfparser.py <https://gitlab.gwdg.de/evolgen/introgression/blob/master/scripts/vcfparser.py>
### Calculate K80 distance between populations using the CONSENSUS pseudo-genome files
## Nucleotide diversity calculations
### Get individual specific SNPs
To get individual specific VCF files the VCF file produced with 'bcftools call' was first re-coded for each individual with 'vcftools'. Further the individual specific VCF files were used to generate pseudo-genomes files per individual with 'vcfparser.py vcf2fasta' using individual masking regions (see "Get masking regions for individual samples and natural populations").
vcftools example for individual 396 of the population _Mus musculus musculus AFG_:
```
#example for individual 396 of the population Mmm_AFG:
#
#VCF IDs:
#
#396
GZVCF=Mmm_AFG.mpileup.q0Q10.bcfcall.mv.vcf.gz
OUTPUT=Mmm_AFG1.396.mpileup.q0Q10.bcfcall.mv.remIndels
vcftools --gzvcf $GZVCF --remove-indels --recode --recode-INFO-all --non-ref-ac-any 1 --indv 396 --out $OUTPUT
```
vcfparser.py vcf2fasta example for individual 396 of the population _Mus musculus musculus AFG_:
```
#example for individual 396 of the population Mmm_AFG:
#
REFERENCE=mm10.fasta
INPUT=Mmm_AFG1.396.mpileup.q0Q10.bcfcall.mv.remIndels.recode.consensus.vcf
OUTPUT=Mmm_AFG1.396.mpileup.q0Q10.chr1.bcfcall.mv.remIndels.recode.consensus.chr
MASKFILE=Mmm_AFG.combined.bga.stcov5
python vcfparser.py vcf2fasta -ivcf $INPUT -o $OUTPUT -R $REFERENCE -samples Mmm_AFG.mv -chr chr1,chr2,chr3,chr4,chr5,chr6,chr7,chr8,chr9,chr10,chr11,chr12,chr13,chr14,chr15,chr16,chr17,chr18,chr19,chrX,chrY -ibga $MASKFILE -cov2N 4
```
+ vcftools v0.1.15
+ vcfparser.py <https://gitlab.gwdg.de/evolgen/introgression/blob/master/scripts/vcfparser.py>
### Calculate nucleotide diversity within each population
## Dxy distance calculation
### Calculate Dxy distance between populations
### Calculate Dxy distance between individuals and populations