Plasmodium_falciparum

Reproducing methods from "Genomic analysis reveals independent evolution of Plasmodium falciparum populations in Ethiopia" Repo contains scripts, results, and report.

Downloading raw data

$ wget ftp://ngs.sanger.ac.uk/production/pf3k/release_5/BAM/*.bam

Creating index files

1.For reference genome

$gatk CreateSequenceDictionary -R Pfalciparum.genome.fasta
$samtools faidx Pfalciparum.genome.fasta

2.For bam files (samples)

#indexing of bam files
for file in *bam;
    do
    samtools index -b $file
done

HaplotypeCalling

#variant calling
 gatk --java-options "-Xmx4g" HaplotypeCaller  \
    -R Pfalciparum.genome.fasta \
    -I PA0007-C.bam \
    -O v7.g.vcf.gz \
    -ERC GVCF

Combining vcf samples

#combining the variants
gatk CombineGVCFs \
    -R Pfalciparum.genome.fasta \
     --variant v7.g.vcf.gz \
     --variant v8.g.vcf.gz \
    -O combined.g.vcf.gz

Genotyping

#genotyping variants
gatk --java-options "-Xmx96g -Xms96g" GenotypeGVCFs \
    -R Pfalciparum.genome.fasta \
    -V combined.g.vcf.gz   \
    -O genotyped.vcf.gz

Hard filtering

variant to table

gatk VariantsToTable -V genotyped.vcf -F QD -F FS -F SOR -F MQ -F DP -O QD.table

visulisition of tables in R.

library(ggplot2)
QD.plot <- ggplot(data = QD, aes(x=QD)) + geom_density(alpha=0.2)
QD.plot
generating MQ  PLOT
MQ.plot <- ggplot(data = QD, aes(x=MQ)) + geom_density(alpha=0.2)
MQ.plot
SOR.plot <- ggplot(data = QD, aes(x=SOR)) + geom_density(alpha=0.2)
SOR.plot
DP.plot <- ggplot(data = QD, aes(x=DP)) + geom_density(alpha=0.2)
DP.plot + scale_x_log10()

filtering

 gatk VariantFiltration \
   -R Pfalciparum.genome.fasta \
   -V genotyped.vcf.gz \
   -O filtered.vcf.gz\
   --filter-name "QD3"\
   --filter-expression "QD < 3.0" \
   --filter-name "DP10"\
   --filter-expression "DP < 10.0" \
   --filter-name "SOR5"\
   --filter-expression "SOR > 5.0" \
   --filter-name "MQ32"\
   --filter-expression "MQ < 32.0"\

selecting pass

gatk SelectVariants \
    -R Pfalciparum.genome.fasta \
    -V filtered.vcf \
    --select 'vc.isNotFiltered()' \
    -O selected.vcf

1.selecting snps and indels

 #snps
gatk SelectVariants \
    -V genotyped.vcf.gz \
    -select-type SNP \
    -O snps.vcf.gz
  #indels
gatk SelectVariants \
    -V genotyped.vcf.gz \
    -select-type INDEL \
    -O indels.vcf.gz

variants to table

#snps
gatk VariantsToTable -V snps.vcf.gz -F QD -F FS -F SOR -F MQ -F DP  -O SNPS.table
#indels
gatk VariantsToTable -V indels.vcf.gz -F QD -F FS -F SOR -F MQ -F DP  -O indels.table

visulisition of tables in R.

## ggplots for SNPS AND INDELS
library(ggplot2)
#SNPS
SNPQD.plot <- ggplot(data = SNPS, aes(x=QD)) + geom_density(alpha=0.2)
SNPQD.plot
#INDELS
INDELQD.plot <- ggplot(data = indels, aes(x=QD)) + geom_density(alpha=0.2)
INDELQD.plot
#generating MQ  PLOT
#SNPS
SNPMQ.plot <- ggplot(data = SNPS, aes(x=MQ)) + geom_density(alpha=0.2)
SNPMQ.plot
#INDELS
#INDELMQ.plot <- ggplot(data = indels, aes(x=MQ)) + geom_density(alpha=0.2)
#INDELMQ.plot
#SNPS
SNPSOR.plot <- ggplot(data = SNPS, aes(x=SOR)) + geom_density(alpha=0.2)
SNPSOR.plot
#INDELS
INDELSOR.plot <- ggplot(data = indels, aes(x=SOR)) + geom_density(alpha=0.2)
INDELSOR.plot
#SNPS
SNPDP.plot <- ggplot(data = SNPS, aes(x=DP)) + geom_density(alpha=0.2)
SNPDP.plot + scale_x_log10()
#INDELS
INDELDP.plot <- ggplot(data = indels, aes(x=DP)) + geom_density(alpha=0.2)
INDELDP.plot + scale_x_log10()
##FS
#SNPS
SNPFS.plot <- ggplot(data = SNPS, aes(x=FS)) + geom_density(alpha=0.2)
SNPFS.plot + scale_x_log10()
#INDELS
INDELFS.plot <- ggplot(data = indels, aes(x=FS)) + geom_density(alpha=0.2)
INDELFS.plot + scale_x_log10()

filtering

gatk VariantFiltration \
   -R Pfalciparum.genome.fasta \
   -V snps.vcf.gz\
   -O SNPfiltered.vcf.gz\
   --filter-name "QD3"\
   --filter-expression "QD < 3.0" \
   --filter-name "DP10"\
   --filter-expression "DP < 10.0" \
   --filter-name "SOR5"\
   --filter-expression "SOR > 5.0" \
   --filter-name "MQ32"\
   --filter-expression "MQ < 32.0"\
   --filter-name "FS1"\
   --filter-expression "FS < 1.0"\

Annotation using snpEff

java -Xmx8g -jar snpEff.jar GRCh37.75 /opt/data/bole/variant/filtered.vcf > Pf.ann.vcf

For snps

java -Xmx8g -jar snpEff.jar GRCh37.75 /opt/data/bole/variant/SNPfiltered.vcf.gz  > snp.ann.vcf

Running loop(working)

parallel 'gatk HaplotypeCaller -R Pfalciparum.genome.fasta  -I {} -O {}.hppc.g.vcf' ::: *.bam

challenge

1.viewing from HPC using IGV.

NEXT STEP

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