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Commit f1e542a4 authored by Kristian Ullrich's avatar Kristian Ullrich
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changed to recent ANGSD version with direct creation of FASTA files from... 

changed to recent ANGSD version with direct creation of FASTA files from mapped BAM files without intermediate steps for masking file generation and VCF file
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README.md
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Introgression patterns between house mouse subspecies and species reveal genomic windows of frequent exchange
Ullrich KK, Linnenbrink M, Tautz D
Ullrich KK, Linnenbrink M, McConnell E, Tautz D
## Data sources
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/>.
Genomic sequence data 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 the European Nucleotide Archive <https://www.ebi.ac.uk/ena> according to table 1 from the original publication ([Harr et al. 2016](http://www.nature.com/articles/sdata201675)) <https://www.nature.com/articles/sdata201675/tables/1> (see also <https://www.ebi.ac.uk/ena/data/view/PRJEB9450>, <https://www.ebi.ac.uk/ena/data/view/PRJEB11742>, <https://www.ebi.ac.uk/ena/data/view/PRJEB14167>, <https://www.ebi.ac.uk/ena/data/view/PRJEB2176>).
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>.
## Mapping
## Get masking regions for individual samples and natural populations
Genome sequencing reads were mapped against mm10 version of the mouse reference genome (GRCm38/mm10) with 'ngm' ([Sedlazeck et al. 2013](https://www.ncbi.nlm.nih.gov/pubmed/23975764)), followed by sorting, marking and removing duplicates with the picard software suite (<https://broadinstitute.github.io/picard/>).
For masking genomic regions in natural populations which showed low coverage based on the genomic mapping BAM files we only considered the stable chromosomes (chr1-chr19,chrX,chrY,chrM) from the reference GRCm38 _mm10_ <http://www.ncbi.nlm.nih.gov/projects/genome/assembly/grc/mouse/>.
The BAM files were processed with 'genomeCoverageBed' from the bedtools software suite ([Quinlan and Hall 2010](https://academic.oup.com/bioinformatics/article/26/6/841/244688/BEDTools-a-flexible-suite-of-utilities-for)) 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
#example for one Mus musculus musculus individual - AFG1 - 396
wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR142/005/ERR1425295/ERR1425295_1.fastq.gz
wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR142/006/ERR1425296/ERR1425296_1.fastq.gz
wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR142/007/ERR1425297/ERR1425297_1.fastq.gz
wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR142/008/ERR1425298/ERR1425298_1.fastq.gz
OUTPUT=Mmm_AFG.combined.bga
zcat ERR1425295_1.fastq.gz >> Mmm_AFG.396.1.fq
zcat ERR1425296_1.fastq.gz >> Mmm_AFG.396.1.fq
zcat ERR1425297_1.fastq.gz >> Mmm_AFG.396.1.fq
zcat ERR1425298_1.fastq.gz >> Mmm_AFG.396.1.fq
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
```
wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR142/005/ERR1425295/ERR1425295_2.fastq.gz
wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR142/006/ERR1425296/ERR1425296_2.fastq.gz
wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR142/007/ERR1425297/ERR1425297_2.fastq.gz
wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR142/008/ERR1425298/ERR1425298_2.fastq.gz
get masking region example for the population _Mus musculus musculus AFG_:
```
INPUT=Mmm_AFG.combined.bga
OUTPUT=Mmm_AFG.combined.bga.stcov5
zcat ERR1425295_2.fastq.gz >> Mmm_AFG.396.2.fq
zcat ERR1425296_2.fastq.gz >> Mmm_AFG.396.2.fq
zcat ERR1425297_2.fastq.gz >> Mmm_AFG.396.2.fq
zcat ERR1425298_2.fastq.gz >> Mmm_AFG.396.2.fq
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
```
FWD=Mmm_AFG.396.1.fq
REV=Mmm_AFG.396.2.fq
NGMOUTPUT=Mmm_AFG.396.mm10.sam
RGID=396
RGSM=396
United masked files generated can be obtained from:
ngm -1 $FWD -2 $REV -r mm10.fasta -o $NGMOUTPUT --no-unal --sensitive -t 24 --no-progress --rg-id $RGID --rg-sm $RGSM --rg-lb lib1 --rg-pl illumina --rg-pu unit1 -b
<http://wwwuser.gwdg.de/~evolbio/evolgen/wildmouse/introgression/masking/>
NGMSORTEDOUTPUT=Mmm_AFG.396.mm10.sorted.bam
##### Individual specific masking
java -jar picard.jar SortSam I=$NGMOUTPUT O=$NGMSORTEDOUTPUT SO=coordinate
For individuals regions with a coverage smaller than 5 were used as masking regions.
NODUPOUTPUT=Mmm_AFG.396.mm10.sorted.nodup.bam
DUPMETRICS=Mmm_AFG.396.mm10.sorted.duplicate.metrics
get masking region example for individual 396 of the population _Mus musculus musculus AFG_:
```
INPUT=AFG1_396.bam.bga
OUTPUT=AFG1_396.bam.bga.stcov5
java -jar picard.jar MarkDuplicates I=$NGMSORTEDOUTPUT O=$NODUPOUTPUT M=$DUPMETRICS REMOVE_DUPLICATES=true
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
```
_used software:_
+ bedtools v2.26.0 <http://bedtools.readthedocs.io/en/latest/>
+ awk
+ NextGenMap 0.5.3
+ picard.jar 2.9.2-SNAPSHOT
## SNP and INDEL calling
## Construction of consensus sequences for natural populations
For SNP and INDEL calling the BAM files were processed with 'samtools mpileup' and 'bcftools call' ([Li et al. 2011](https://academic.oup.com/bioinformatics/article/25/16/2078/204688/The-Sequence-Alignment-Map-format-and-SAMtools)) with relaxed quality options to retain information in CNV regions. Each chromosome was analyzed seperately.
To construct population specific consensus sequences, we used ANGSD and the 'doFasta 2' option.
samtools mpileup | bcftools call example for chromosome 1 for the population _Mus musculus musculus AFG_:
```
#example for population Mmm_AFG:
##### CONSENSUS FASTA
#used BAM files:
In addition to the 'doFasta 2' optin, which extracts the most common nucleotide, other filters like 'minQ' and coverage filters like 'setMinDepthInd' and 'setMaxDepthInd' were applied.
#AFG1_396.bam
#AFG2_413.bam
#AFG3_416.bam
#AFG4_424.bam
#AFG5_435.bam
#AFG6_444.bam
REFERENCE=mm10.fasta
PLOIDY=ploidy.txt
#chrX 1 171031299 M 1
#chrY 1 91744698 M 1
#chrY 1 91744698 F 0
#chrM 1 16299 F 1
#chrM 1 16299 M 1
#* * * M 2
#* * * F 2
SAMPLES=AFG_samples.txt
#396 M
#413 M
#416 M
#424 M
#435 F
#444 M
OUTPUT=mpileup.q0Q10.chr1.Mmm_AFG.bcfcall.mv.vcf
INPUT1=AFG1_396.bam
INPUT2=AFG2_413.bam
INPUT3=AFG3_416.bam
INPUT4=AFG4_424.bam
INPUT5=AFG5_435.bam
INPUT6=AFG6_444.bam
BAMLIST=Mmm_AFG.list
echo $INPUT1 >> $BAMLIST
echo $INPUT2 >> $BAMLIST
echo $INPUT3 >> $BAMLIST
echo $INPUT4 >> $BAMLIST
echo $INPUT5 >> $BAMLIST
echo $INPUT6 >> $BAMLIST
#example for chromosome 1
samtools mpileup -q 0 -Q 10 -A -d 99999 -t DP,AD,ADF,ADR -r chr1 -uf $REFERENCE -b $BAMLIST | bcftools call -O v -f GQ -m -v --ploidy-file $PLOIDY -S $SAMPLES > $OUTPUT
bgzip $OUTPUT
tabix $OUTPUT".gz"
CONSENSUS FASTA example for the population _Mus musculus musculus AFG_:
```
#example for populaton Mmm_AFG for chr1
_used software:_
+ samtools 1.3.1 (using htslib 1.3.1)
+ bcftools 1.3.1 (using htslib 1.3.1)
+ bgzip v1.3
+ tabix v1.3
All mpileup generated for each population can be obtained from:
<http://wwwuser.gwdg.de/~evolbio/evolgen/wildmouse/introgression/mpileup_pop_mv/vcf/mpileup/>
NOTE: For each population all analyzed chromosomes were merged into one file.
## K80 distance calculation
### Get population specific SNPs
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' ([Danecek et al. 2011](https://academic.oup.com/bioinformatics/article/27/15/2156/402296/The-variant-call-format-and-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").
#used BAM files:
vcftools example for chromosome 1 for the population _Mus musculus musculus AFG_:
#Mmm_AFG.396.mm10.sorted.nodup.bam
#Mmm_AFG.413.mm10.sorted.nodup.bam
#Mmm_AFG.416.mm10.sorted.nodup.bam
#Mmm_AFG.424.mm10.sorted.nodup.bam
#Mmm_AFG.435.mm10.sorted.nodup.bam
#Mmm_AFG.444.mm10.sorted.nodup.bam
echo Mmm_AFG.396.mm10.sorted.nodup.bam >> AFG.list
echo Mmm_AFG.413.mm10.sorted.nodup.bam >> AFG.list
echo Mmm_AFG.416.mm10.sorted.nodup.bam >> AFG.list
echo Mmm_AFG.424.mm10.sorted.nodup.bam >> AFG.list
echo Mmm_AFG.435.mm10.sorted.nodup.bam >> AFG.list
echo Mmm_AFG.444.mm10.sorted.nodup.bam >> AFG.list
angsd -doFasta 2 -doCounts 1 -minQ 13 -uniqueOnly -setMinDepthInd 5 -setMaxDepthInd 100 -b AFG.list -r chr1 -out AFG.ngm.minQ13.uniqueOnly.setMinDepthInd5.setMaxDepthInd100.chr1
```
#example for population Mmm_AFG:
#VCF IDs:
_used software:_
#396
#413
#416
#424
#435
#444
+ ANGSD v0.919 <http://www.popgen.dk/angsd/index.php/ANGSD>
POPIDS=AFG.vcf.ids
NOTE: Population consensus sequences were merged for each chromosome.
echo "396" >> $POPIDS
echo "413" >> $POPIDS
echo "416" >> $POPIDS
echo "424" >> $POPIDS
echo "435" >> $POPIDS
echo "444" >> $POPIDS
All CONSENSUS FASTA files can be obtained from:
GZVCF=mpileup.q0Q10.chr1.Mmm_AFG.bcfcall.mv.vcf.gz
OUTPUT=Mmm_AFG.mpileup.q0Q10.chr1.bcfcall.mv.remIndels
<http://wwwuser.gwdg.de/~evolbio/evolgen/wildmouse/introgression/ngm/fasta/pop/>
vcftools --gzvcf $GZVCF --remove-indels --recode --recode-INFO-all --non-ref-ac-any 1 --keep $POPIDS --out $OUTPUT
```
## Construction of individual sequences using IUPAC code
All re-coded vcf files can be obtained from:
To construct individual sequences, we used ANGSD and the 'doFasta 4' option. For this option, after all filters have been applied, all multi-allelic sites will be encoded as IUPAC code.
<http://wwwuser.gwdg.de/~evolbio/evolgen/wildmouse/introgression/mpileup_pop_mv/vcf/recode/>
##### INDIVIDUAL FASTA
NOTE: For each population all analyzed chromosomes were merged into one file.
In addition to the 'doFasta 4' optin, which extracts IUPAC code, other filters like 'minQ' and coverage filters like 'setMinDepthInd' and 'setMaxDepthInd' were applied.
vcfparser.py mvcf2consensus example for the population _Mus musculus musculus AFG_:
CONSENSUS FASTA example for the population _Mus musculus musculus AFG_:
```
#example for population Mmm_AFG:
#VCF IDs:
#example for one Mus musculus musculus individual - AFG1 - 396 for chr1
#396
#413
#416
#424
#435
#444
#used BAM file:
INPUT=Mmm_AFG.mpileup.q0Q10.chr1.bcfcall.mv.remIndels.recode.vcf
OUTPUT=Mmm_AFG.mpileup.q0Q10.chr1.bcfcall.mv.remIndels.recode.consensus
#Mmm_AFG.396.mm10.sorted.nodup.bam
python vcfparser.py mvcf2consensus -ivcf $INPUT -o $OUTPUT -cdp 11 -chr chr1 -samples 396,413,416,424,435,444 -id Mmm_AFG.mv
angsd -doFasta 4 -doCounts 1 -minQ 13 -uniqueOnly -setMinDepthInd 5 -setMaxDepthInd 100 -i Mmm_AFG.396.mm10.sorted.nodup.bam -r chr1 -out AFG.396.ngm.minQ13.uniqueOnly.setMinDepthInd5.setMaxDepthInd100.chr1
```
All CONSENSUS vcf files can be obtained from:
<http://wwwuser.gwdg.de/~evolbio/evolgen/wildmouse/introgression/mpileup_pop_mv/vcf/consensus/>
NOTE: For each population all analyzed chromosomes were merged into one file.
vcfparser.py vcf2fasta example for the population _Mus musculus musculus AFG_:
```
#example for population Mmm_AFG:
REFERENCE=mm10.fasta
INPUT=Mmm_AFG.mpileup.q0Q10.chr1.bcfcall.mv.remIndels.recode.consensus.vcf
OUTPUT=Mmm_AFG.mpileup.q0Q10.chr1.bcfcall.mv.remIndels.recode.consensus.chr1
MASKFILE=Mmm_AFG.combined.bga.stcov5
_used software:_
python vcfparser.py vcf2fasta -ivcf $INPUT -o $OUTPUT -R $REFERENCE -samples Mmm_AFG.mv -chr chr1 -ibga $MASKFILE -cov2N 4
```
+ ANGSD v0.919 <http://www.popgen.dk/angsd/index.php/ANGSD>
All pseudo-genome FASTA files can be obtained from:
NOTE: Individual sequences were merged for each chromosome.
<http://wwwuser.gwdg.de/~evolbio/evolgen/wildmouse/introgression/mpileup_pop_mv/fasta/consensus/>
All INDIVIDUAL FASTA files can be obtained from:
_used software:_
<http://wwwuser.gwdg.de/~evolbio/evolgen/wildmouse/introgression/ngm/fasta/ind/>
+ vcftools v0.1.15
+ vcfparser.py <https://gitlab.gwdg.de/evolgen/introgression/blob/master/scripts/vcfparser.py>
## K80 distance calculation
### Calculate dK80 distance between populations using the CONSENSUS pseudo-genome files
......@@ -271,9 +156,9 @@ TMP_DIR <- "/tmp"
popX.pos <- 2
popY.pos <- 3
popZ.pos <- 4
popO.pos <- 5
popO.pos <- 1
SEQ_FILE <- "http://wwwuser.gwdg.de/~evolbio/evolgen/wildmouse/introgression/mpileup_pop_mv/fasta/consensus/CAS_FRA_GER_IRA_AFG_SPRE.mpileup.q0Q10.chr1.bcfcall.mv.remIndels.recode.refmajorsample.ref.consensus.fasta"
SEQ_FILE <- "http://wwwuser.gwdg.de/~evolbio/evolgen/wildmouse/introgression/ngm/fasta/pop/chr1.ngm.minQ13.uniqueOnly.setMinDepthInd5.setMaxDepthInd100.fa"
chr <- "chr1"
OUT_FILE <- paste0(TMP_DIR,"/",popX,"_",popY,".",popZ,".",popO,".",chr,".tsv")
......@@ -281,11 +166,18 @@ WSIZE <- 25000
WJUMP <- 25000
DISTMODEL <- "K80"
#to run the script
nohup nice R CMD BATCH --vanilla get_dK80.r &
#result file will be in this case:
tail -f /tmp/Mmd_FRA_Mmd_GER.Mmd_IRA.Mmm_AFG.chr1.tsv
```
All dK80 files can be obtained from:
http://wwwuser.gwdg.de/~evolbio/evolgen/wildmouse/introgression/mpileup_pop_mv/browser_tracks/dK80/25kbp_sw/
http://wwwuser.gwdg.de/~evolbio/evolgen/wildmouse/introgression/ngm/dK80/
NOTE: For each quartet comparison all analyzed chromosomes were merged into one file.
......@@ -298,7 +190,11 @@ _used software:_
+ R package XVector_0.12.1
+ R package IRanges_2.6.1
+ R package BiocGenerics_0.18.0
+ get_dK80.r <https://gitlab.gwdg.de/evolgen/introgression/blob/master/scripts/get_dK80.r>
+ get_dK80.r <https://gitlab.gwdg.de/evolgen/introgression/raw/master/scripts/get_dK80.r>
## TWISST analysis
## Simulation
......@@ -338,7 +234,7 @@ python simdiv.py -i mm10_0.004725_DMC_0.002246_D_0.000262_FGI_0.000201_FG.fa -o
_used software:_
+ R version 3.4.1 (2017-06-30)
+ simdiv.py <https://gitlab.gwdg.de/evolgen/introgression/blob/master/scripts/simdiv.py>
+ simdiv.py <https://gitlab.gwdg.de/evolgen/introgression/raw/master/scripts/simdiv.py>
### Generate artificial illumina reads
......@@ -359,15 +255,14 @@ _used software:_
### Mapping and data post-processing
Subsequently, the artificial Illumina reads were mapped against the simulated reference with 'bwamem' ([Li et al. 2009](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2705234/)), followed by sorting, marking and removing duplicates with the picard software suite (<https://broadinstitute.github.io/picard/>) and an indel realignment step with 'GATK' ([McKenna et al. 2010](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2928508/)) as described in Harr et al. 2016 (<http://www.nature.com/articles/sdata201675>).
Subsequently, the artificial Illumina reads were mapped against the simulated reference with 'ngm' ([Sedlazeck et al. 2013](https://www.ncbi.nlm.nih.gov/pubmed/23975764)), followed by sorting, marking and removing duplicates with the picard software suite (<https://broadinstitute.github.io/picard/>).
_used software:_
+ bwa 0.7.12-r1039
+ NextGenMap 0.5.3
+ picard.jar 2.9.2-SNAPSHOT
+ GenomeAnalysisTK.jar v3.7
Masking, SNP calling, FASTA sequence construction and dK80 calculation was performed as described above.
FASTA sequence construction and dK80 calculation was performed as described above.
## Data visualization and availability
......
scripts/get_dK80.r 100644 → 100755
+ 3
− 6
View file @ f1e542a4
#author: Kristian K Ullrich
#date: July 2017
#date: November 2017
#email: ullrich@evolbio.mpg.de
#chr1 - IRA_AFG
#[X]: Mmd_FRA
#[Y]: Mmd_GER
#[Z]: Mmd_IRA
......@@ -18,9 +17,9 @@ TMP_DIR <- "/tmp"
popX.pos <- 2
popY.pos <- 3
popZ.pos <- 4
popO.pos <- 5
popO.pos <- 1
SEQ_FILE <- "http://wwwuser.gwdg.de/~evolbio/evolgen/wildmouse/introgression/mpileup_pop_mv/fasta/consensus/CAS_FRA_GER_IRA_AFG_SPRE.mpileup.q0Q10.chr1.bcfcall.mv.remIndels.recode.refmajorsample.ref.consensus.fasta"
SEQ_FILE <- "http://wwwuser.gwdg.de/~evolbio/evolgen/wildmouse/introgression/ngm/fasta/pop/chr1.ngm.minQ13.uniqueOnly.setMinDepthInd5.setMaxDepthInd100.fa"
chr <- "chr1"
OUT_FILE <- paste0(TMP_DIR,"/",popX,"_",popY,".",popZ,".",popO,".",chr,".tsv")
......@@ -116,7 +115,6 @@ cat("MISSING\tdK80.XZO\tdK80.YZO\tTREELENGTH\tTREE\tTREETOPOLOGY\n")
tmp.chr.seq<-readBStringSet(SEQ_FILE)
tmp.sw<-sliding_window_steps_generator(window=WSIZE,jump=WJUMP,start.by=1,end.by=unique(width(tmp.chr.seq)))
pb<-txtProgressBar(min=1,max=dim(tmp.sw)[2],style=3)
for(i in 1:dim(tmp.sw)[2]){
tmp.out<-subseq(tmp.chr.seq,tmp.sw[1,i],tmp.sw[2,i])
fourout<-calc_delta_consensus_four(tmp.out,popX.pos,popY.pos,popZ.pos,popO.pos,TMP_DIR,DISTMODEL)
......@@ -129,7 +127,6 @@ for(i in 1:dim(tmp.sw)[2]){
cat(unlist(fourout),sep="\t")
cat("\n")
}
close(pb)
sink(NULL)
......
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