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KIAT_SNP.Rmd
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KIAT_SNP.Rmd
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---
title: "KIAT_SNP"
author: "Ruijuan Li"
date: "11/2/2016"
output:
html_document:
keep_md: yes
---
# vcfR is another tool to work with VCF file.
# SNP calling freebayes
```{r}
# 1) uniquely mapped reads, remove PCR duplicate, add read group & freebayes
# /Network/Servers/avalanche.plb.ucdavis.edu/Volumes/Mammoth/Users/ruijuanli/SNP_parent/analysis/freebayes/freebayes_SNP_calling.sh
# 2) call SNPs (found that to filter based on RPL & RPR, I need to call SNPs from Da-Ae & Da-Ol-1 seperately...)
# /Network/Servers/avalanche.plb.ucdavis.edu/Volumes/Mammoth/Users/ruijuanli/SNP_parent/analysis/freebayes/run_freebayes.sh
# 3) process raw vcf file
# /Network/Servers/avalanche.plb.ucdavis.edu/Volumes/Mammoth/Users/ruijuanli/SNP_parent/analysis/freebayes/process_vcf.sh
# 4) extract only SNPs
# 5) get only biallelic SNPs
# extract_biallelic.sh input.vcf output.vcf
# data location: /Network/Servers/avalanche.plb.ucdavis.edu/Volumes/Mammoth/Users/ruijuanli/SNP_parent/result/freebayes/Ae_Ol_modified_SNPonly_biallelic.recode.ann.vcf
```
# work on Da-Ae & Da-Ol-1 seperately...
```{r}
source("/Users/ruijuanli/Desktop/Brassica_project/KIAT_RNA_seq/analysis/function_BnRNAseq.R")
# import
vcf.freebayes.Ae <- read.table("~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/data/freebayes/as_diploid/no_bolting_unique_mapped/Ae_SNP_biallelic.recode.vcf",as.is=T,na.strings = ".:.:.:.:.:.:.")
dim(vcf.freebayes.Ae) # 1019803 10
# save(vcf.freebayes.Ae, file ="~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/data/freebayes/vcf.freebayes.Ae.Rdata")
vcf.freebayes.Ol <- read.table("~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/data/freebayes/as_diploid/no_bolting_unique_mapped/Ol_SNP_biallelic.recode.vcf",as.is=T,na.strings = ".:.:.:.:.:.:.")
dim(vcf.freebayes.Ol) # 997209 10
save(vcf.freebayes.Ol, file ="~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/data/freebayes/vcf.freebayes.Ol.Rdata")
# reformat
load("~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/data/freebayes/vcf.freebayes.Ae.Rdata")
load("~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/data/freebayes/vcf.freebayes.Ol.Rdata")
# Da-Ae
# get header
vcf.header.freebayes.Ae <- system("grep '#C' ~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/data/freebayes/as_diploid/no_bolting_unique_mapped/Ae_SNP_biallelic.recode.vcf",intern = TRUE)
vcf.header.freebayes.Ae <- sub("#","",vcf.header.freebayes.Ae) #get rid of the pound sign
vcf.header.freebayes.Ae <- unlist(strsplit(vcf.header.freebayes.Ae,split="\t"))
# use function to reformat vcf file
vcf.freebayes.reform.Ae <- SNP.freebayes.reformat.Ae(vcf.freebayes.Ae, vcf.header.freebayes.Ae)
dim(vcf.freebayes.reform.Ae) # 1019803 17
table(vcf.freebayes.reform.Ae$Ae_gt)
# 0/0 0/1 1/1 NA
# 423018 531949 522721 201104
save(vcf.freebayes.reform.Ae, file = "~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/output/freebayes/vcf.freebayes.reform.Ae.Rdata")
load("~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/output/freebayes/vcf.freebayes.reform.Ae.Rdata")
# this way no 0/0.... but still this can be used to understand RPL & RLR...
# filter based on QUAL
vcf.freebayes.HQ.Ae <- vcf.freebayes.reform.Ae[vcf.freebayes.reform.Ae$QUAL>40,] # QUAL score of 20, which means less than 0.01 probability that it isn't polymorphic
dim(vcf.freebayes.HQ.Ae) # 798423 17
nrow(vcf.freebayes.HQ.Ae) / nrow(vcf.freebayes.reform.Ae) # 0.7829189 of SNPs were retained with QUAL > 40
min(vcf.freebayes.HQ.Ae[vcf.freebayes.HQ.Ae$Ae_gt=="0/1",]$QUAL)
min(vcf.freebayes.HQ.Ae[vcf.freebayes.HQ.Ae$Ae_gt=="1/1",]$QUAL)
# count the number
table(vcf.freebayes.HQ.Ae$Ae_gt)
# 0/1 1/1
# 285815 512608
mean(vcf.freebayes.HQ.Ae$Ae_tot.depth, na.rm=T) # 146
# distribution of depth
png("~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/output/figure/read.depth.png", width=4, height=3, units="in", res=300)
hist(log10(vcf.freebayes.HQ.Ae$Ae_tot.depth), xlab = "log10(read depth)", ylab="Number of SNPs", main="read depth")
abline(v=c(1,3), col="red")
dev.off()
save(vcf.freebayes.HQ.Ae, file = "~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/data/freebayes/vcf.freebayes.HQ.Ae.Rdata")
```
```{r}
load("~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/data/freebayes/vcf.freebayes.HQ.Ae.Rdata")
# use 1000 as the max, 10 as min
vcf.freebayes.HQ.1.Ae <- vcf.freebayes.HQ.Ae[(!is.na(vcf.freebayes.HQ.Ae$Ae_tot.depth) &
vcf.freebayes.HQ.Ae$Ae_tot.depth > 10 &
vcf.freebayes.HQ.Ae$Ae_tot.depth < 1000),]
dim(vcf.freebayes.HQ.1.Ae) # 441950 17
table(vcf.freebayes.HQ.1.Ae$Ae_gt)
# 0/1 1/1
# 224945 217005
# # get genotype quality score, Freebayes has its own way to calculate GQ, it uses population info as the prior, different from GATK.
# head(vcf.freebayes.HQ.1.Ae)
# gen.lik <- matrix(
# unlist(strsplit(vcf.freebayes.HQ.1.Ae$Ae_gen.lik,split = ",")),
# nrow=nrow(vcf.freebayes.HQ.1.Ae),
# byrow=TRUE
# )
#
# head(gen.lik)
#
# colnames(gen.lik) <- c("ref.lik","het.lik","alt.lik")
#
# head(gen.lik)
# gen.lik <- as.data.frame(gen.lik)
# colnames(gen.lik)
#
# vcf.freebayes.HQ.1.Ae <- cbind(vcf.freebayes.HQ.1.Ae,gen.lik,stringsAsFactors=FALSE)
# head(vcf.freebayes.HQ.1.Ae)
# dim(vcf.freebayes.HQ.1.Ae) # 441950 20
#
# for (i in 1:nrow(vcf.freebayes.HQ.1.Ae)){
# if (median(c(vcf.freebayes.HQ.1.Ae[i,]$ref.lik, vcf.freebayes.HQ.1.Ae[i,]$het.lik, vcf.freebayes.HQ.1.Ae[i,]$alt.lik)) * (-10) >= 99) {
# vcf.freebayes.HQ.1.Ae[i,]$Ae_GQ = 99
# } else {
# vcf.freebayes.HQ.1.Ae[i,]$Ae_GQ = median(c(vcf.freebayes.HQ.1.Ae[i,]$ref.lik, vcf.freebayes.HQ.1.Ae[i,]$het.lik, vcf.freebayes.HQ.1.Ae[i,]$alt.lik) * (-10))
# }
# }
#
# vcf.freebayes.HQ.1.Ae$alt.lik[1]
#####
tmp.list <- strsplit(vcf.freebayes.HQ.1.Ae$INFO,split = ";")
length(tmp.list) # 493019
length(tmp.list[[1]])
for (i in 1:length(tmp.list)){
tmp.list[[i]] <- tmp.list[[i]][c(1:41)]
}
# 1) seperate INFO column
head(vcf.freebayes.HQ.1.Ae)
info <- matrix(
unlist(tmp.list),
nrow=nrow(vcf.freebayes.HQ.1.Ae),
byrow=TRUE
)
head(info)
colnames(info) <- c("AB","ABP","AC","AF","AN","AO","CIGAR","DP","DPB","DPRA","EPP","EPPR","GTI","LEN","MEANALT",
"MQM","MQMR","NS","NUMALT","ODDS","PAIRED","PAIREDR","PAO","PQA","PQR","PRO","QA","QR","RO",
"RPL","RPP","RPPR","RPR","RUN","SAF","SAP","SAR","SRF","SRP","SRR","TYPE")
head(info)
info <- as.data.frame(info)
colnames(info)
for (i in 1:ncol(info)){
info[,i] <- gsub("([[:print:]]+)(=)([[:print:]])","\\3",info[,i])
}
head(info)
vcf.freebayes.HQ.1.Ae <- cbind(vcf.freebayes.HQ.1.Ae,info,stringsAsFactors=FALSE)
head(vcf.freebayes.HQ.1.Ae)
dim(vcf.freebayes.HQ.1.Ae) # 493019 67
vcf.freebayes.HQ.1.reform.Ae <- vcf.freebayes.HQ.1.Ae
vcf.freebayes.HQ.1.reform.Ae$MAF_Ae <- round(pmin(vcf.freebayes.HQ.1.reform.Ae$Ae_alt.depth, vcf.freebayes.HQ.1.reform.Ae$Ae_ref.depth)/vcf.freebayes.HQ.1.reform.Ae$Ae_tot.depth, digits = 2)
hist(vcf.freebayes.HQ.1.reform.Ae$MAF_Ae)
vcf.freebayes.HQ.1.reform.Ae$RPR <- as.numeric(vcf.freebayes.HQ.1.reform.Ae$RPR)
vcf.freebayes.HQ.1.reform.Ae$RPL <- as.numeric(vcf.freebayes.HQ.1.reform.Ae$RPL)
## tail bias
hist(log10(vcf.freebayes.HQ.1.reform.Ae$RPR))
hist(log10(vcf.freebayes.HQ.1.reform.Ae$RPL))
min(vcf.freebayes.HQ.1.reform.Ae$RPR)
min(vcf.freebayes.HQ.1.reform.Ae$RPL)
max(vcf.freebayes.HQ.1.reform.Ae$RPR + vcf.freebayes.HQ.1.reform.Ae$RPL)
vcf.freebayes.HQ.1.reform.Ae$MBR <- round(pmin(vcf.freebayes.HQ.1.reform.Ae$RPR, vcf.freebayes.HQ.1.reform.Ae$RPL)/(vcf.freebayes.HQ.1.reform.Ae$RPR + vcf.freebayes.HQ.1.reform.Ae$RPL), digits = 2)
hist(vcf.freebayes.HQ.1.reform.Ae$MBR, breaks = 50)
png("~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/output/figure/MBR_Ae.png", width=4, height=3, units="in", res=300)
hist(vcf.freebayes.HQ.1.reform.Ae$MBR, breaks = 50, xlab = "minor reads balance ratio (MBR)", ylab="number of SNPs", main="")
abline(v=c(0.01), col="red")
dev.off()
# cor(vcf.freebayes.HQ.1.reform.Ae$MBR, vcf.freebayes.HQ.1.reform.Ae$MAF_Ae)
# vcf.freebayes.HQ.1.reform.Ae.het <- vcf.freebayes.HQ.1.reform.Ae[vcf.freebayes.HQ.1.reform.Ae$Ae_gt=="1/1",]
#
# cor(vcf.freebayes.HQ.1.reform.Ae.het$MBR, vcf.freebayes.HQ.1.reform.Ae.het$MAF_Ae)
vcf.freebayes.HQ.2.tmp.Ae <- vcf.freebayes.HQ.1.reform.Ae
vcf.freebayes.HQ.2.test1.Ae <-
vcf.freebayes.HQ.2.tmp.Ae[(vcf.freebayes.HQ.2.tmp.Ae$MBR>0.01),]
# vcf.freebayes.HQ.2.test2 <-
# vcf.freebayes.HQ.2.tmp[(vcf.freebayes.HQ.2.tmp$RPR>=0 &
# vcf.freebayes.HQ.2.tmp$RPL>0),]
min(as.numeric(vcf.freebayes.HQ.2.test1.Ae$RPL))
min(as.numeric(vcf.freebayes.HQ.2.test1.Ae$RPR))
table(vcf.freebayes.HQ.2.test1.Ae$Ae_gt)
# 0/1 1/1
# 127827 187049
head(vcf.freebayes.HQ.1.reform.Ae)
# save(vcf.freebayes.HQ.1.reform.Ae, file = "~/SNP_parent/result/freebayes/from_cabernet_call_separately/output/vcf.freebayes.HQ.1.reform.Ae.Rdata")
### het problem
### compare QUAL, read depth, between 0/1 and 1/1
head(vcf.freebayes.HQ.1.Ae)
min(vcf.freebayes.HQ.1.Ae[vcf.freebayes.reform.Ae$Ae_gt=="0/1",]$QUAL)
min(vcf.freebayes.HQ.1.Ae[vcf.freebayes.HQ.1.Ae$Ae_gt=="1/1",]$QUAL)
min(vcf.freebayes.HQ.1.Ae[vcf.freebayes.HQ.1.Ae$Ae_gt=="0/1",]$QUAL)
min(vcf.freebayes.HQ.1.Ae[vcf.freebayes.HQ.1.Ae$Ae_gt=="1/1",]$QUAL)
min(vcf.freebayes.HQ.1.Ae[vcf.freebayes.HQ.1.Ol$Ol_gt=="0/1",]$QUAL)
min(vcf.freebayes.HQ.1.Ae[vcf.freebayes.HQ.1.Ol$Ol_gt=="1/1",]$QUAL)
min(vcf.freebayes.HQ.2.test1.Ol[vcf.freebayes.HQ.2.test1.Ol$Ol_gt=="0/1",]$QUAL)
min(vcf.freebayes.HQ.2.test1.Ol[vcf.freebayes.HQ.2.test1.Ol$Ol_gt=="1/1",]$QUAL)
library(ggplot2)
p.QUAL.Ol <- ggplot(data = vcf.freebayes.HQ.2.test1.Ol)
p.QUAL.Ol <- p.QUAL.Ol + geom_histogram(aes(log10(QUAL), fill=Ol_gt))
p.QUAL.Ol <- p.QUAL.Ol + facet_wrap(~Ol_gt, ncol = 1)
p.QUAL.Ol <- p.QUAL.Ol +labs(list(x="log10(QUAL)", y="Number of SNPs"))
p.QUAL.Ol <- p.QUAL.Ol + theme(legend.position = "none")
p.QUAL.Ol
# plot QUAL by Ae_gt
library(ggplot2)
p.QUAL.Ae <- ggplot(data = vcf.freebayes.HQ.2.test1.Ae)
p.QUAL.Ae <- p.QUAL.Ae + geom_histogram(aes(log10(QUAL), fill=Ae_gt))
p.QUAL.Ae <- p.QUAL.Ae + facet_wrap(~Ae_gt, ncol = 1)
p.QUAL.Ae <- p.QUAL.Ae +labs(list(x="log10(QUAL)", y="Number of SNPs"))
p.QUAL.Ae <- p.QUAL.Ae + theme(legend.position = "none")
p.QUAL.Ae
ggsave(p.QUAL.Ae, filename = "~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/output/figure/p.QUAL.Ae.png", height = 4, width = 6)
# plot total depth by Ae_gt
p.tot.depth.Ae <- ggplot(data = vcf.freebayes.HQ.2.test1.Ae)
p.tot.depth.Ae <- p.tot.depth.Ae + geom_histogram(aes(Ae_tot.depth, fill=Ae_gt)) + scale_x_log10()
p.tot.depth.Ae <- p.tot.depth.Ae + facet_wrap(~Ae_gt, ncol = 1)
p.tot.depth.Ae
# plot MBR
p.MBR.Ae <- ggplot(data = vcf.freebayes.HQ.2.test1.Ae)
p.MBR.Ae <- p.MBR.Ae + geom_histogram(aes(MBR, fill=Ae_gt))
p.MBR.Ae <- p.MBR.Ae + facet_wrap(~Ae_gt, ncol = 1)
p.MBR.Ae
ggsave(p.MBR.Ae, filename = "~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/output/figure/p.MBR.Ae.png", height = 4, width = 6)
# plot GQ
p.GQ.Ae <- ggplot(data = vcf.freebayes.HQ.1.reform.Ae)
p.GQ.Ae <- p.GQ.Ae + geom_histogram(aes(Ae_gt.qual, fill=Ae_gt))
p.GQ.Ae <- p.GQ.Ae + facet_wrap(~Ae_gt, ncol = 1)
p.GQ.Ae
### check GQ
head(vcf.freebayes.HQ.1.reform.Ae[vcf.freebayes.HQ.1.reform.Ae$Ae_gt.qual<10,])[,c("Ae_gt.qual","Ae_gen.lik")]
max(vcf.freebayes.HQ.1.reform.Ae$Ae_gt.qual)
# alt/ref allele ratio
vcf.freebayes.HQ.2.test1.Ae.het <-
vcf.freebayes.HQ.2.test1.Ae[vcf.freebayes.HQ.2.test1.Ae$Ae_gt=="0/1",]
vcf.freebayes.HQ.2.test1.Ae.het$allele_ratio <- round(vcf.freebayes.HQ.2.test1.Ae.het$Ae_alt.depth/vcf.freebayes.HQ.2.test1.Ae.het$Ae_ref.depth, digits = 2)
hist(log10(vcf.freebayes.HQ.2.test1.Ae.het$allele_ratio), breaks= 150)
nrow(vcf.freebayes.HQ.2.test1.Ae.het[vcf.freebayes.HQ.2.test1.Ae.het$allele_ratio>0,])
nrow(vcf.freebayes.HQ.2.test1.Ae.het)
pl <- ggplot(data = vcf.freebayes.HQ.2.test1.Ae.het)
pl <- pl + geom_histogram(aes(allele_ratio))
pl <- pl +labs(list(x="alt.depth/ref.depth", y="nunber of SNPs"))
pl
# didn't see correlation between MAF & MBR...
cor.test(vcf.freebayes.HQ.2.test1.Ae.het)
cor.test(vcf.freebayes.HQ.2.test1.Ae.het$MAF_Ae, vcf.freebayes.HQ.2.test1.Ae.het$MBR)
cor.test(vcf.freebayes.HQ.2.test1.Ae$MAF_Ae, vcf.freebayes.HQ.2.test1.Ae$MBR)
cor.test(vcf.freebayes.HQ.2.test1.Ae.het$MAF_Ae, vcf.freebayes.HQ.2.test1.Ae.het$QUAL)
cor.test(vcf.freebayes.HQ.2.test1.Ae$MAF_Ae, vcf.freebayes.HQ.2.test1.Ae$QUAL)
hist(vcf.freebayes.HQ.2.test1.Ae.het$Ae_alt.depth/vcf.freebayes.HQ.2.test1.Ae.het$Ae_ref.depth, breaks = 50)
####
# Da-Ol-1
load("~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/data/freebayes/vcf.freebayes.Ol.Rdata")
# get header
vcf.header.freebayes.Ol <- system("grep '#C' ~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/data/freebayes/as_diploid/no_bolting_unique_mapped/Ol_SNP_biallelic.recode.vcf",intern = TRUE)
vcf.header.freebayes.Ol <- sub("#","",vcf.header.freebayes.Ol) #get rid of the pound sign
vcf.header.freebayes.Ol <- unlist(strsplit(vcf.header.freebayes.Ol,split="\t"))
# use function to reformat vcf file
vcf.freebayes.reform.Ol <- SNP.freebayes.reformat.Ol(vcf.freebayes.Ol, vcf.header.freebayes.Ol)
dim(vcf.freebayes.reform.Ol) # 1019803 17
table(vcf.freebayes.reform.Ol$Ol_gt)
# 0/0 0/1 1/1 NA
# 423018 531949 522721 201104
save(vcf.freebayes.reform.Ol, file = "~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/output/freebayes/vcf.freebayes.reform.Ol.Rdata")
# filter based on QUAL
load("~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/output/freebayes/vcf.freebayes.reform.Ol.Rdata")
vcf.freebayes.HQ.Ol <- vcf.freebayes.reform.Ol[vcf.freebayes.reform.Ol$QUAL>40,] # QUAL score of 20, which means less than 0.01 probability that it isn't polymorphic
dim(vcf.freebayes.HQ.Ol) # 777458 17
nrow(vcf.freebayes.HQ.Ol) / nrow(vcf.freebayes.reform.Ol) # 70% of SNPs were retained with QUAL > 40
# count the number
table(vcf.freebayes.HQ.Ol$Ol_gt)
mean(vcf.freebayes.HQ.Ol$Ol_tot.depth, na.rm=T) # 146
# distribution of depth
hist(log10(vcf.freebayes.HQ.Ol$Ol_tot.depth))
# use 1000 as the max, 10 as min
vcf.freebayes.HQ.1.Ol <- vcf.freebayes.HQ.Ol[(!is.na(vcf.freebayes.HQ.Ol$Ol_tot.depth) &
vcf.freebayes.HQ.Ol$Ol_tot.depth > 10 &
vcf.freebayes.HQ.Ol$Ol_tot.depth < 1000),]
dim(vcf.freebayes.HQ.1.Ol) # 441950 17
table(vcf.freebayes.HQ.1.Ol$Ol_gt)
#### compare 0/0 & 0/1
# plot QUAL by Ol_gt
p.QUAL.Ol <- ggplot(data = vcf.freebayes.HQ.1.Ol)
p.QUAL.Ol <- p.QUAL.Ol + geom_histogram(aes(QUAL, fill=Ol_gt)) + scale_x_log10()
p.QUAL.Ol <- p.QUAL.Ol + facet_wrap(~Ol_gt, ncol = 1)
p.QUAL.Ol
# plot total depth by Ol_gt
p.tot.depth.Ol <- ggplot(data = vcf.freebayes.HQ.1.Ol)
p.tot.depth.Ol <- p.tot.depth.Ol + geom_histogram(aes(Ol_tot.depth, fill=Ol_gt)) + scale_x_log10()
p.tot.depth.Ol <- p.tot.depth.Ol + facet_wrap(~Ol_gt, ncol = 1)
p.tot.depth.Ol
######
tmp.list <- strsplit(vcf.freebayes.HQ.1.Ol$INFO,split = ";")
length(tmp.list) # 493019
length(tmp.list[[1]])
for (i in 1:length(tmp.list)){
tmp.list[[i]] <- tmp.list[[i]][c(1:41)]
}
# 1) seperate INFO column
head(vcf.freebayes.HQ.1.Ol)
info <- matrix(
unlist(tmp.list),
nrow=nrow(vcf.freebayes.HQ.1.Ol),
byrow=TRUE
)
head(info)
colnames(info) <- c("AB","ABP","AC","AF","AN","AO","CIGAR","DP","DPB","DPRA","EPP","EPPR","GTI","LEN","MEANALT",
"MQM","MQMR","NS","NUMALT","ODDS","PAIRED","PAIREDR","PAO","PQA","PQR","PRO","QA","QR","RO",
"RPL","RPP","RPPR","RPR","RUN","SAF","SAP","SAR","SRF","SRP","SRR","TYPE")
head(info)
info <- as.data.frame(info)
colnames(info)
for (i in 1:ncol(info)){
info[,i] <- gsub("([[:print:]]+)(=)([[:print:]])","\\3",info[,i])
}
head(info)
vcf.freebayes.HQ.1.Ol <- cbind(vcf.freebayes.HQ.1.Ol,info,stringsAsFactors=FALSE)
head(vcf.freebayes.HQ.1.Ol)
dim(vcf.freebayes.HQ.1.Ol) # 493019 67
vcf.freebayes.HQ.1.reform.Ol <- vcf.freebayes.HQ.1.Ol
vcf.freebayes.HQ.1.reform.Ol$MAF_Ol <- round(pmin(vcf.freebayes.HQ.1.reform.Ol$Ol_alt.depth, vcf.freebayes.HQ.1.reform.Ol$Ol_ref.depth)/vcf.freebayes.HQ.1.reform.Ol$Ol_tot.depth, digits = 2)
hist(vcf.freebayes.HQ.1.reform.Ol$MAF_Ol)
vcf.freebayes.HQ.1.reform.Ol$RPR <- as.numeric(vcf.freebayes.HQ.1.reform.Ol$RPR)
vcf.freebayes.HQ.1.reform.Ol$RPL <- as.numeric(vcf.freebayes.HQ.1.reform.Ol$RPL)
## tail bias
hist(log10(vcf.freebayes.HQ.1.reform.Ol$RPR))
hist(log10(vcf.freebayes.HQ.1.reform.Ol$RPL))
min(vcf.freebayes.HQ.1.reform.Ol$RPR)
min(vcf.freebayes.HQ.1.reform.Ol$RPL)
vcf.freebayes.HQ.1.reform.Ol$MBR <- round(pmin(vcf.freebayes.HQ.1.reform.Ol$RPR, vcf.freebayes.HQ.1.reform.Ol$RPL)/(vcf.freebayes.HQ.1.reform.Ol$RPR + vcf.freebayes.HQ.1.reform.Ol$RPL), digits = 2)
hist(vcf.freebayes.HQ.1.reform.Ol$MBR, xlim = c(0, 0.1), breaks = 50)
save(vcf.freebayes.HQ.1.reform.Ol, file = "~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/output/vcf.freebayes.HQ.1.reform.Ol.Rdata")
# cor(vcf.freebayes.HQ.1.reform.Ae$MBR, vcf.freebayes.HQ.1.reform.Ae$MAF_Ae)
# vcf.freebayes.HQ.1.reform.Ae.het <- vcf.freebayes.HQ.1.reform.Ae[vcf.freebayes.HQ.1.reform.Ae$Ae_gt=="1/1",]
#
# cor(vcf.freebayes.HQ.1.reform.Ae.het$MBR, vcf.freebayes.HQ.1.reform.Ae.het$MAF_Ae)
load("~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/output/vcf.freebayes.HQ.1.reform.Ol.Rdata")
vcf.freebayes.HQ.2.tmp.Ol <- vcf.freebayes.HQ.1.reform.Ol
vcf.freebayes.HQ.2.test1.Ol <-
vcf.freebayes.HQ.2.tmp.Ol[(vcf.freebayes.HQ.2.tmp.Ol$MBR>0.01),]
min(as.numeric(vcf.freebayes.HQ.2.test1.Ol$RPL))
min(as.numeric(vcf.freebayes.HQ.2.test1.Ol$RPR))
table(vcf.freebayes.reform.Ol$Ol_gt)
table(vcf.freebayes.HQ.2.test1.Ol$Ol_gt)
# 0/0 0/1 1/1
# 96721 112112 145987
save(vcf.freebayes.HQ.1.reform.Ol, file = "~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/data/freebayes/vcf.freebayes.HQ.1.reform.Ol.Rdata")
save(vcf.freebayes.HQ.2.test1.Ol, file = "~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/data/freebayes/vcf.freebayes.HQ.2.test1.Ol")
save(vcf.freebayes.HQ.2.test1.Ae, file = "~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/data/freebayes/vcf.freebayes.HQ.2.test1.Ae")
head(vcf.freebayes.HQ.1.reform.Ae[,c("CHROM", "POS", "RPL", "RPR")])
######## visualize in IGV ######################################
vcf.freebayes.HQ.2.test3.Ae <-
vcf.freebayes.HQ.2.tmp.Ae[(vcf.freebayes.HQ.2.tmp.Ae$RPL>0),]
# find filtered SNPs because of tail bias, and visulize them in IGV
test1 <- vcf.freebayes.HQ.2.tmp.Ae[,c("CHROM", "POS")]
test2 <- vcf.freebayes.HQ.2.test3.Ae[,c("CHROM", "POS")]
library(dplyr)
filtered_out_snp_tail_bias <- anti_join(test1, test2)
head(filtered_out_snp_tail_bias[filtered_out_snp_tail_bias$CHROM=="chrA01",], 50)
```
# combine Da-Ae & Da-Ol data
```{r}
load("~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/data/freebayes/vcf.freebayes.HQ.2.test1.Ae")
load("~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/data/freebayes/vcf.freebayes.HQ.2.test1.Ol")
dim(vcf.freebayes.HQ.2.test1.Ae)
dim(vcf.freebayes.HQ.2.test1.Ol)
table(vcf.freebayes.HQ.2.test1.Ae$Ae_gt)
table(vcf.freebayes.HQ.2.test1.Ol$Ol_gt)
# double check QUAL, depth, and MBR
min(vcf.freebayes.HQ.2.test1.Ae$QUAL)
min(vcf.freebayes.HQ.2.test1.Ae$Ae_tot.depth)
max(vcf.freebayes.HQ.2.test1.Ae$Ae_tot.depth)
min(vcf.freebayes.HQ.2.test1.Ae$MBR)
min(vcf.freebayes.HQ.2.test1.Ol$QUAL)
min(vcf.freebayes.HQ.2.test1.Ol$Ol_tot.depth)
max(vcf.freebayes.HQ.2.test1.Ol$Ol_tot.depth)
min(vcf.freebayes.HQ.2.test1.Ol$MBR)
# combine
colnames(vcf.freebayes.HQ.2.test1.Ol)
Ae <- vcf.freebayes.HQ.2.test1.Ae[,c("CHROM", "POS", "REF", "ALT", "Ae_gt")]
Ol <- vcf.freebayes.HQ.2.test1.Ol[,c("CHROM", "POS", "REF", "ALT", "Ol_gt")]
head(Ae)
Ae$feature <- paste(Ae$CHROM, Ae$POS, Ae$REF, Ae$ALT, sep = "_")
Ol$feature <- paste(Ol$CHROM, Ol$POS, Ol$REF, Ol$ALT, sep = "_")
freebayes_result <- merge(Ae, Ol, by="feature", all=T)
?merge
dim(freebayes_result)
head(freebayes_result)
freebayes_result_reform <- freebayes_result[,c("feature", "Ae_gt", "Ol_gt")]
head(freebayes_result_reform)
freebayes_result_reform$Ae_gt <- gsub("NA", "1/1", freebayes_result_reform$Ae_gt)
freebayes_result_reform[is.na(freebayes_result_reform$Ae_gt)] <- "0/0"
freebayes_result_reform[which(is.na(freebayes_result_reform$Ae_gt)),]$Ae_gt <- "0/0"
freebayes_result_reform[which(is.na(freebayes_result_reform$Ol_gt)),]$Ol_gt <- "0/0"
head(freebayes_result_reform)
freebayes_result_1 <- freebayes_result_reform[freebayes_result_reform$Ae_gt!=freebayes_result_reform$Ol_gt & freebayes_result_reform$Ae_gt!="0/1" & freebayes_result_reform$Ol_gt!="0/1",]
dim(freebayes_result_1) # 215702
head(freebayes_result_1, 100)
table(freebayes_result_1$Ae_gt)
table(freebayes_result_1$Ol_gt)
save(freebayes_result_1, file = "~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/data/freebayes/freebayes_result_1.Rdata")
###### save each step afterwards...
```
# test correlation between MAF & MBR (whether skewed distribution of hetergyzougs is due to realignment...)
```{r}
cor.test(vcf.freebayes.HQ.1.reform.Ol$MBR, vcf.freebayes.HQ.1.reform.Ol$MAF_Ol)
cor.test(vcf.freebayes.HQ.1.reform.Ae$MBR, vcf.freebayes.HQ.1.reform.Ae$MAF_Ae)
vcf.freebayes.HQ.1.reform.Ae.het <- vcf.freebayes.HQ.1.reform.Ae[vcf.freebayes.HQ.1.reform.Ae$Ae_gt=="0/1",]
vcf.freebayes.HQ.1.reform.Ae.homo <- vcf.freebayes.HQ.1.reform.Ae[vcf.freebayes.HQ.1.reform.Ae$Ae_gt!="0/1",]
head(vcf.freebayes.HQ.1.reform.Ae.het[,c("CHROM", "POS")],100)
hist(vcf.freebayes.HQ.1.reform.Ae.het$MBR)
hist(vcf.freebayes.HQ.1.reform.Ae.homo$MBR, ylim = c(1, 1e+5))
### great, seems like most of the het calls are due to realignment problem
# but there are still some het calls, why? allele specific expression? still need to test that ...
```
### analyze combined Da-Ae & Da-Ol-1 data
```{r}
# source("/Users/ruijuanli/Desktop/Brassica_project/KIAT_RNA_seq/analysis/function_BnRNAseq.R")
# vcf.freebayes.no.ANN <- read.table("~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/data/freebayes/as_diploid/no_bolting_unique_mapped/Ae_Ol_modified_SNPonly_biallelic.recode.vcf",as.is=T,na.strings = ".:.:.:.:.:.:.")
# dim(vcf.freebayes.no.ANN) # 1678792 11
# # save(vcf.freebayes, file ="~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/data/freebayes/vcf.freebayes.no.ANN.Rdata")
# load("~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/data/freebayes/vcf.freebayes.Rdata")
#
# vcf.header.freebayes <- system("grep '#C' ~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/data/freebayes/as_diploid/no_bolting_unique_mapped/Ae_Ol_modified_SNPonly_biallelic.recode.vcf",intern = TRUE)
# vcf.header.freebayes <- sub("#","",vcf.header.freebayes) #get rid of the pound sign
# vcf.header.freebayes <- unlist(strsplit(vcf.header.freebayes,split="\t"))
#
# # use function to reformat vcf file
# vcf.freebayes.no.ANN.reform <- SNP.freebayes.reformat(vcf.freebayes.no.ANN, vcf.header.freebayes)
# dim(vcf.freebayes.no.ANN.reform) # 1678792 25
# table(vcf.freebayes.no.ANN.reform$Ae_gt)
# # 0/0 0/1 1/1 NA
# # 423018 531949 522721 201104
#
# table(vcf.freebayes.no.ANN.reform$Ol_gt)
# # 0/0 0/1 1/1 NA
# # 459651 517580 518072 183489
#
# # 1) seperate INFO column
# info <- matrix(
# unlist(strsplit(vcf.freebayes.no.ANN.reform$INFO,split = ";")),
# nrow=nrow(vcf.freebayes.no.ANN.reform),
# byrow=TRUE
# )
#
# head(info)
#
# colnames(info) <- c("AB","ABP","AC","AF","AN","AO","CIGAR","DP","DPB","DPRA","EPP","EPPR","GTI","LEN","MEANALT",
# "MQM","MQMR","NS","NUMALT","ODDS","PAIRED","PAIREDR","PAO","PQA","PQR","PRO","QA","QR","RO",
# "RPL","RPP","RPPR","RPR","RUN","SAF","SAP","SAR","SRF","SRP","SRR","TYPE")
#
# info <- as.data.frame(info)
# colnames(info)
#
# for (i in 1:ncol(info)){
# info[,i] <- gsub("([[:print:]]+)(=)([[:print:]])","\\3",info[,i])
# }
#
# vcf.freebayes.no.ANN.reform <- cbind(vcf.freebayes.no.ANN,info,stringsAsFactors=FALSE)
# unique(vcf.freebayes.no.ANN.reform$CIGAR)
#
# save(vcf.freebayes.no.ANN.reform, file = "~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/output/freebayes/vcf.freebayes.no.ANN.reform.Rdata")
# It turns out SNPs all get CIGAR score of "1X".
```
### Freebayes w/ reported
```{r}
# data location: /Network/Servers/avalanche.plb.ucdavis.edu/Volumes/Mammoth/Users/ruijuanli/SNP_parent/result/freebayes/from_cabernet_call_separately
```
# work on Da-Ae & Da-Ol-1 seperately...
```{r}
source("~/KIAT/function_BnRNAseq.R")
# import
vcf.freebayes.Ae <- read.table("~/SNP_parent/result/freebayes/from_cabernet_call_separately/Ae_SNP_bi.recode.vcf",as.is=T,na.strings = ".")
dim(vcf.freebayes.Ae) # 1019617 10
# save(vcf.freebayes.Ae, file ="~/SNP_parent/result/freebayes/from_cabernet_call_separately/output/vcf.freebayes.Ae.Rdata")
vcf.freebayes.Ol <- read.table("~/SNP_parent/result/freebayes/from_cabernet_call_separately/Ol_SNP_bi.recode.vcf",as.is=T,na.strings = ".")
dim(vcf.freebayes.Ol) # 997357 10
# save(vcf.freebayes.Ol, file ="~/SNP_parent/result/freebayes/from_cabernet_call_separately/output/vcf.freebayes.Ol.Rdata")
# reformat
# load("~/SNP_parent/result/freebayes/from_cabernet_call_separately/output/vcf.freebayes.Ae.Rdata")
# load("~/SNP_parent/result/freebayes/from_cabernet_call_separately/output//vcf.freebayes.Ol.Rdata")
# Da-Ae
# get header
vcf.header.freebayes.Ae <- system("grep '#C' ~/SNP_parent/result/freebayes/from_cabernet_call_separately/Ae_SNP_bi.recode.vcf",intern = TRUE)
vcf.header.freebayes.Ae <- sub("#","",vcf.header.freebayes.Ae) #get rid of the pound sign
vcf.header.freebayes.Ae <- unlist(strsplit(vcf.header.freebayes.Ae,split="\t"))
# use function to reformat vcf file
vcf.freebayes.reform.Ae <- SNP.freebayes.reformat.Ae.GQ(vcf.freebayes.Ae, vcf.header.freebayes.Ae)
dim(vcf.freebayes.reform.Ae) # 1019617 19
table(vcf.freebayes.reform.Ae$Ae_gt)
# 0/0 0/1 1/1 NA
# 423018 531949 522721 201104
save(vcf.freebayes.reform.Ae, file = "~/SNP_parent/result/freebayes/from_cabernet_call_separately/output/vcf.freebayes.reform.Ae.Rdata")
load("~/SNP_parent/result/freebayes/from_cabernet_call_separately/output/vcf.freebayes.reform.Ae.Rdata")
# this way no 0/0.... but still this can be used to understand RPL & RLR...
# filter based on QUAL
vcf.freebayes.HQ.Ae <- vcf.freebayes.reform.Ae[vcf.freebayes.reform.Ae$QUAL>40,] # QUAL score of 20, which means less than 0.01 probability that it isn't polymorphic
dim(vcf.freebayes.HQ.Ae) # 798730 19
nrow(vcf.freebayes.HQ.Ae) / nrow(vcf.freebayes.reform.Ae) # 0.7829189 of SNPs were retained with QUAL > 40
min(vcf.freebayes.HQ.Ae[vcf.freebayes.HQ.Ae$Ae_gt=="0/1",]$QUAL)
min(vcf.freebayes.HQ.Ae[vcf.freebayes.HQ.Ae$Ae_gt=="1/1",]$QUAL)
# count the number
table(vcf.freebayes.HQ.Ae$Ae_gt)
# 0/1 1/1
# 285815 512608
mean(vcf.freebayes.HQ.Ae$Ae_tot.depth, na.rm=T) # 146
# distribution of depth
png("~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/output/figure/read.depth.png", width=4, height=3, units="in", res=300)
hist(log10(vcf.freebayes.HQ.Ae$Ae_tot.depth), xlab = "log10(read depth)", ylab="Number of SNPs", main="read depth")
abline(v=c(1,3), col="red")
dev.off()
save(vcf.freebayes.HQ.Ae, file = "~/SNP_parent/result/freebayes/from_cabernet_call_separately/output/vcf.freebayes.HQ.Ae.Rdata")
```
```{r}
load("~/SNP_parent/result/freebayes/from_cabernet_call_separately/output/vcf.freebayes.HQ.Ae.Rdata")
# use 1000 as the max, 10 as min
vcf.freebayes.HQ.1.Ae <- vcf.freebayes.HQ.Ae[(!is.na(vcf.freebayes.HQ.Ae$Ae_tot.depth) &
vcf.freebayes.HQ.Ae$Ae_tot.depth > 10 &
vcf.freebayes.HQ.Ae$Ae_tot.depth < 1000),]
dim(vcf.freebayes.HQ.1.Ae) # 442050 19
table(vcf.freebayes.HQ.1.Ae$Ae_gt)
# 0/1 1/1
# 224945 217005
# # get genotype quality score, Freebayes has its own way to calculate GQ, it uses population info as the prior, different from GATK.
# head(vcf.freebayes.HQ.1.Ae)
# gen.lik <- matrix(
# unlist(strsplit(vcf.freebayes.HQ.1.Ae$Ae_gen.lik,split = ",")),
# nrow=nrow(vcf.freebayes.HQ.1.Ae),
# byrow=TRUE
# )
#
# head(gen.lik)
#
# colnames(gen.lik) <- c("ref.lik","het.lik","alt.lik")
#
# head(gen.lik)
# gen.lik <- as.data.frame(gen.lik)
# colnames(gen.lik)
#
# vcf.freebayes.HQ.1.Ae <- cbind(vcf.freebayes.HQ.1.Ae,gen.lik,stringsAsFactors=FALSE)
# head(vcf.freebayes.HQ.1.Ae)
# dim(vcf.freebayes.HQ.1.Ae) # 441950 20
#
# for (i in 1:nrow(vcf.freebayes.HQ.1.Ae)){
# if (median(c(vcf.freebayes.HQ.1.Ae[i,]$ref.lik, vcf.freebayes.HQ.1.Ae[i,]$het.lik, vcf.freebayes.HQ.1.Ae[i,]$alt.lik)) * (-10) >= 99) {
# vcf.freebayes.HQ.1.Ae[i,]$Ae_GQ = 99
# } else {
# vcf.freebayes.HQ.1.Ae[i,]$Ae_GQ = median(c(vcf.freebayes.HQ.1.Ae[i,]$ref.lik, vcf.freebayes.HQ.1.Ae[i,]$het.lik, vcf.freebayes.HQ.1.Ae[i,]$alt.lik) * (-10))
# }
# }
#
# vcf.freebayes.HQ.1.Ae$alt.lik[1]
#####
tmp.list <- strsplit(vcf.freebayes.HQ.1.Ae$INFO,split = ";")
length(tmp.list) # 442050
length(tmp.list[[1]])
for (i in 1:length(tmp.list)){
tmp.list[[i]] <- tmp.list[[i]][c(1:41)]
}
# 1) seperate INFO column
head(vcf.freebayes.HQ.1.Ae)
info <- matrix(
unlist(tmp.list),
nrow=nrow(vcf.freebayes.HQ.1.Ae),
byrow=TRUE
)
head(info)
colnames(info) <- c("AB","ABP","AC","AF","AN","AO","CIGAR","DP","DPB","DPRA","EPP","EPPR","GTI","LEN","MEANALT",
"MQM","MQMR","NS","NUMALT","ODDS","PAIRED","PAIREDR","PAO","PQA","PQR","PRO","QA","QR","RO",
"RPL","RPP","RPPR","RPR","RUN","SAF","SAP","SAR","SRF","SRP","SRR","TYPE")
head(info)
info <- as.data.frame(info)
colnames(info)
for (i in 1:ncol(info)){
info[,i] <- gsub("([[:print:]]+)(=)([[:print:]])","\\3",info[,i])
}
head(info)
vcf.freebayes.HQ.1.Ae <- cbind(vcf.freebayes.HQ.1.Ae,info,stringsAsFactors=FALSE)
head(vcf.freebayes.HQ.1.Ae)
dim(vcf.freebayes.HQ.1.Ae) # 442050 60
vcf.freebayes.HQ.1.reform.Ae <- vcf.freebayes.HQ.1.Ae
vcf.freebayes.HQ.1.reform.Ae$MAF_Ae <- round(pmin(vcf.freebayes.HQ.1.reform.Ae$Ae_alt.depth, vcf.freebayes.HQ.1.reform.Ae$Ae_ref.depth)/vcf.freebayes.HQ.1.reform.Ae$Ae_tot.depth, digits = 2)
hist(vcf.freebayes.HQ.1.reform.Ae$MAF_Ae)
vcf.freebayes.HQ.1.reform.Ae$RPR <- as.numeric(vcf.freebayes.HQ.1.reform.Ae$RPR)
vcf.freebayes.HQ.1.reform.Ae$RPL <- as.numeric(vcf.freebayes.HQ.1.reform.Ae$RPL)
## tail bias
hist(log10(vcf.freebayes.HQ.1.reform.Ae$RPR))
hist(log10(vcf.freebayes.HQ.1.reform.Ae$RPL))
min(vcf.freebayes.HQ.1.reform.Ae$RPR)
min(vcf.freebayes.HQ.1.reform.Ae$RPL)
max(vcf.freebayes.HQ.1.reform.Ae$RPR + vcf.freebayes.HQ.1.reform.Ae$RPL)
vcf.freebayes.HQ.1.reform.Ae$MBR <- round(pmin(vcf.freebayes.HQ.1.reform.Ae$RPR, vcf.freebayes.HQ.1.reform.Ae$RPL)/(vcf.freebayes.HQ.1.reform.Ae$RPR + vcf.freebayes.HQ.1.reform.Ae$RPL), digits = 2)
hist(vcf.freebayes.HQ.1.reform.Ae$MBR, breaks = 50)
png("~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/output/figure/MBR_Ae.png", width=4, height=3, units="in", res=300)
hist(vcf.freebayes.HQ.1.reform.Ae$MBR, breaks = 50, xlab = "minor reads balance ratio (MBR)", ylab="number of SNPs", main="")
abline(v=c(0.01), col="red")
dev.off()
# cor(vcf.freebayes.HQ.1.reform.Ae$MBR, vcf.freebayes.HQ.1.reform.Ae$MAF_Ae)
# vcf.freebayes.HQ.1.reform.Ae.het <- vcf.freebayes.HQ.1.reform.Ae[vcf.freebayes.HQ.1.reform.Ae$Ae_gt=="1/1",]
#
# cor(vcf.freebayes.HQ.1.reform.Ae.het$MBR, vcf.freebayes.HQ.1.reform.Ae.het$MAF_Ae)
vcf.freebayes.HQ.2.tmp.Ae <- vcf.freebayes.HQ.1.reform.Ae
vcf.freebayes.HQ.2.test1.Ae <-
vcf.freebayes.HQ.2.tmp.Ae[(vcf.freebayes.HQ.2.tmp.Ae$MBR>0.01),]
# vcf.freebayes.HQ.2.test2 <-
# vcf.freebayes.HQ.2.tmp[(vcf.freebayes.HQ.2.tmp$RPR>=0 &
# vcf.freebayes.HQ.2.tmp$RPL>0),]
min(as.numeric(vcf.freebayes.HQ.2.test1.Ae$RPL))
min(as.numeric(vcf.freebayes.HQ.2.test1.Ae$RPR))
table(vcf.freebayes.HQ.2.test1.Ae$Ae_gt)
# 0/1 1/1
# 127827 187049
head(vcf.freebayes.HQ.1.reform.Ae)
save(vcf.freebayes.HQ.1.reform.Ae, file = "~/SNP_parent/result/freebayes/from_cabernet_call_separately/output/vcf.freebayes.HQ.1.reform.Ae.Rdata")
### het problem
### compare QUAL, read depth, between 0/1 and 1/1
head(vcf.freebayes.HQ.1.Ae)
min(vcf.freebayes.HQ.1.Ae[vcf.freebayes.reform.Ae$Ae_gt=="0/1",]$QUAL)
min(vcf.freebayes.HQ.1.Ae[vcf.freebayes.HQ.1.Ae$Ae_gt=="1/1",]$QUAL)
min(vcf.freebayes.HQ.1.Ae[vcf.freebayes.HQ.1.Ae$Ae_gt=="0/1",]$QUAL)
min(vcf.freebayes.HQ.1.Ae[vcf.freebayes.HQ.1.Ae$Ae_gt=="1/1",]$QUAL)
min(vcf.freebayes.HQ.1.Ae[vcf.freebayes.HQ.1.Ol$Ol_gt=="0/1",]$QUAL)
min(vcf.freebayes.HQ.1.Ae[vcf.freebayes.HQ.1.Ol$Ol_gt=="1/1",]$QUAL)
min(vcf.freebayes.HQ.2.test1.Ol[vcf.freebayes.HQ.2.test1.Ol$Ol_gt=="0/1",]$QUAL)
min(vcf.freebayes.HQ.2.test1.Ol[vcf.freebayes.HQ.2.test1.Ol$Ol_gt=="1/1",]$QUAL)
library(ggplot2)
p.QUAL.Ol <- ggplot(data = vcf.freebayes.HQ.2.test1.Ol)
p.QUAL.Ol <- p.QUAL.Ol + geom_histogram(aes(log10(QUAL), fill=Ol_gt))
p.QUAL.Ol <- p.QUAL.Ol + facet_wrap(~Ol_gt, ncol = 1)
p.QUAL.Ol <- p.QUAL.Ol +labs(list(x="log10(QUAL)", y="Number of SNPs"))
p.QUAL.Ol <- p.QUAL.Ol + theme(legend.position = "none")
p.QUAL.Ol
# plot QUAL by Ae_gt
library(ggplot2)
p.QUAL.Ae <- ggplot(data = vcf.freebayes.HQ.Ae)
p.QUAL.Ae <- p.QUAL.Ae + geom_histogram(aes(log10(QUAL), fill=Ae_gt))
p.QUAL.Ae <- p.QUAL.Ae + facet_wrap(~Ae_gt, ncol = 1)
p.QUAL.Ae <- p.QUAL.Ae +labs(list(x="log10(QUAL)", y="Number of SNPs"))
p.QUAL.Ae <- p.QUAL.Ae + theme(legend.position = "none")
p.QUAL.Ae
ggsave(p.QUAL.Ae, filename = "~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/output/figure/p.QUAL.Ae.png", height = 4, width = 6)
# plot total depth by Ae_gt
p.tot.depth.Ae <- ggplot(data = vcf.freebayes.HQ.2.test1.Ae)
p.tot.depth.Ae <- p.tot.depth.Ae + geom_histogram(aes(Ae_tot.depth, fill=Ae_gt)) + scale_x_log10()
p.tot.depth.Ae <- p.tot.depth.Ae + facet_wrap(~Ae_gt, ncol = 1)
p.tot.depth.Ae
# plot MBR
p.MBR.Ae <- ggplot(data = vcf.freebayes.HQ.1.reform.Ae)
p.MBR.Ae <- p.MBR.Ae + geom_histogram(aes(MBR, fill=Ae_gt))
p.MBR.Ae <- p.MBR.Ae + facet_wrap(~Ae_gt, ncol = 1)
p.MBR.Ae
ggsave(p.MBR.Ae, filename = "~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/output/figure/p.MBR.Ae.png", height = 4, width = 6)
# alt/ref allele ratio
vcf.freebayes.HQ.2.test1.Ae.het <-
vcf.freebayes.HQ.2.test1.Ae[vcf.freebayes.HQ.2.test1.Ae$Ae_gt=="0/1",]
vcf.freebayes.HQ.2.test1.Ae.het$allele_ratio <- round(vcf.freebayes.HQ.2.test1.Ae.het$Ae_alt.depth/vcf.freebayes.HQ.2.test1.Ae.het$Ae_ref.depth, digits = 2)
hist(log10(vcf.freebayes.HQ.2.test1.Ae.het$allele_ratio), breaks= 150)
nrow(vcf.freebayes.HQ.2.test1.Ae.het[vcf.freebayes.HQ.2.test1.Ae.het$allele_ratio>0,])
nrow(vcf.freebayes.HQ.2.test1.Ae.het)
pl <- ggplot(data = vcf.freebayes.HQ.2.test1.Ae.het)
pl <- pl + geom_histogram(aes(allele_ratio))
pl <- pl +labs(list(x="alt.depth/ref.depth", y="nunber of SNPs"))
pl
# didn't see correlation between MAF & MBR...
cor.test(vcf.freebayes.HQ.2.test1.Ae.het)
cor.test(vcf.freebayes.HQ.2.test1.Ae.het$MAF_Ae, vcf.freebayes.HQ.2.test1.Ae.het$MBR)
cor.test(vcf.freebayes.HQ.2.test1.Ae$MAF_Ae, vcf.freebayes.HQ.2.test1.Ae$MBR)
cor.test(vcf.freebayes.HQ.2.test1.Ae.het$MAF_Ae, vcf.freebayes.HQ.2.test1.Ae.het$QUAL)
cor.test(vcf.freebayes.HQ.2.test1.Ae$MAF_Ae, vcf.freebayes.HQ.2.test1.Ae$QUAL)
hist(vcf.freebayes.HQ.2.test1.Ae.het$Ae_alt.depth/vcf.freebayes.HQ.2.test1.Ae.het$Ae_ref.depth, breaks = 50)
```
# using freebayes to call SNP (now pretty sure is from no-bolting uniquely mapped result)
# import data
```{r}
source("/Users/ruijuanli/Desktop/Brassica_project/KIAT_RNA_seq/analysis/function_BnRNAseq.R")
vcf.freebayes <- read.table("~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/data/freebayes/as_diploid/no_bolting_unique_mapped/Ae_Ol_modified_SNPonly_biallelic.recode.ann.vcf",as.is=T,na.strings = ".:.:.:.:.:.:.")
dim(vcf.freebayes) # 1678792 11
vcf.freebayes$V11
# save(vcf.freebayes, file ="~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/data/freebayes/vcf.freebayes.Rdata")
```
# analysis
```{r}
load("~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/data/freebayes/vcf.freebayes.Rdata")
# get header
vcf.header.freebayes <- system("grep '#C' ~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/data/freebayes/as_diploid/no_bolting_unique_mapped/Ae_Ol_modified_SNPonly_biallelic.recode.ann.vcf",intern = TRUE)
vcf.header.freebayes <- sub("#","",vcf.header.freebayes) #get rid of the pound sign
vcf.header.freebayes <- unlist(strsplit(vcf.header.freebayes,split="\t"))
# use function to reformat vcf file
vcf.freebayes.reform <- SNP.freebayes.reformat(vcf.freebayes, vcf.header.freebayes)
dim(vcf.freebayes.reform) # 1678792 25
table(vcf.freebayes.reform$Ae_gt)
# 0/0 0/1 1/1 NA
# 423018 531949 522721 201104
table(vcf.freebayes.reform$Ol_gt)
# 0/0 0/1 1/1 NA
# 459651 517580 518072 183489
# save(vcf.freebayes.reform, file = "~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/output/freebayes/vcf.freebayes.reform.Rdata")
```
# 1st round of filtering
```{r}
#########
load("~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/output/freebayes/vcf.freebayes.reform.Rdata")
### 1) filter based on QUAl score
# subset the data to keep positions where the quality score is 40 or higher
vcf.freebayes.HQ <- vcf.freebayes.reform[vcf.freebayes.reform$QUAL>40,] # QUAL score of 20, which means less than 0.01 probability that it isn't polymorphic
dim(vcf.freebayes.HQ) # 1174345 25
nrow(vcf.freebayes.HQ) / nrow(vcf.freebayes.reform) # 70% of SNPs were retained with QUAL > 40
# count the number
table(vcf.freebayes.HQ$Ae_gt)
# 0/0 0/1 1/1 NA
# 229905 314228 473435 156777
table(vcf.freebayes.HQ$Ol_gt)
# 0/0 0/1 1/1 NA
# 253687 300596 474418 145644
# head(vcf.Ae.Ol.no.bolting.unique.HQ[which(vcf.Ae.Ol.no.bolting.unique.HQ$Ae_gt=="0/1"),], 20)
# tmp.unique <- ftable(vcf.Ae.Ol.no.bolting.unique.HQ[,c("Ae_gt","Ol_gt")])
# head(tmp.unique)
# write.table(vcf.Ae.Ol.no.bolting.unique.HQ, file = "~/Desktop/Brassica_project/KIAT_RNA_seq/SNP_calling/output/gt_Ae_Ol.vcf")
# save(vcf.freebayes.HQ, file = "~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/output/freebayes/vcf.freebayes.HQ.Rdata")
```
# check why there are so many heterozygous, plot by read depth & QUAL score
```{r}
load("~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/output/freebayes/vcf.freebayes.HQ.Rdata")
# 1) Julin suggested plot by mapping score, total depth, and QUAL score...
# 2) also visualize SNPs in igv to spot the realignment problem, SNP calls from end of reads & intron-exon insertion is bad, don’t believe…
# 3) 3, 4, and 5 visualize them in IGV…
# vcf.freebayes.HQ.Ae.het <- vcf.freebayes.HQ[vcf.freebayes.HQ$Ae_gt=="0/1",]
# vcf.freebayes.HQ
head(vcf.freebayes.HQ)
# plot QUAL by Ae_gt
library(ggplot2)
p.QUAL.Ae <- ggplot(data = vcf.freebayes.HQ)
p.QUAL.Ae <- p.QUAL.Ae + geom_histogram(aes(QUAL, fill=Ae_gt)) + scale_x_log10()
p.QUAL.Ae <- p.QUAL.Ae + facet_wrap(~Ae_gt, ncol = 1)
p.QUAL.Ae
# plot QUAL by Ol_gt
p.QUAL.Ol <- ggplot(data = vcf.freebayes.HQ)
p.QUAL.Ol <- p.QUAL.Ol + geom_histogram(aes(QUAL, fill=Ol_gt)) + scale_x_log10()
p.QUAL.Ol <- p.QUAL.Ol + facet_wrap(~Ol_gt, ncol = 1)
p.QUAL.Ol
# plot total depth by Ae_gt
p.tot.depth.Ae <- ggplot(data = vcf.freebayes.HQ)
p.tot.depth.Ae <- p.tot.depth.Ae + geom_histogram(aes(Ae_tot.depth, fill=Ae_gt)) + scale_x_log10()
p.tot.depth.Ae <- p.tot.depth.Ae + facet_wrap(~Ae_gt, ncol = 1)
p.tot.depth.Ae
# plot total depth by Ol_gt
p.tot.depth.Ol <- ggplot(data = vcf.freebayes.HQ)
p.tot.depth.Ol <- p.tot.depth.Ol + geom_histogram(aes(Ol_tot.depth, fill=Ol_gt)) + scale_x_log10()
p.tot.depth.Ol <- p.tot.depth.Ol + facet_wrap(~Ol_gt, ncol = 1)
p.tot.depth.Ol
# no difference in mapping score, because we used only mapping score of 255 for SNP calling. mapping score of 255 means unique mapping, check stats didn't tell me anything...
#### export vcf file to igv for visulization
# dim(vcf.Ae.Ol.no.bolting.unique.HQ) # 1502350 25
# vcf.Ae.Ol.no.bolting.unique.HQ.export <- vcf.Ae.Ol.no.bolting.unique.HQ[,c(1:11)]
#
# write.table(vcf.Ae.Ol.no.bolting.unique.HQ.export, file = "~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/output/vcf.Ae.Ol.no.bolting.unique.HQ.vcf", row.names = F)
# conclusion: 2,3,4,5 are caused by re-alignment problem, they are not SNPs
```
# continue explore why many 0/1 in my inbred lines (arround 30%), already filtered by QUAL
```{r}
load("~/Desktop/Brassica_project/KIAT_RNA_seq/parent_SNP/output/freebayes/vcf.freebayes.HQ.Rdata")
# 1) filter by max depth
mean(vcf.freebayes.HQ$Ae_tot.depth, na.rm=T) # 159
mean(vcf.freebayes.HQ$Ol_tot.depth, na.rm=T) # 156
# distribution of depth
?hist
hist(log10(vcf.freebayes.HQ$Ae_tot.depth))
hist(log10(vcf.freebayes.HQ$Ol_tot.depth))
# use 1000 as the max, 10 as min
vcf.freebayes.HQ.1 <- vcf.freebayes.HQ[(!is.na(vcf.freebayes.HQ$Ae_tot.depth) &
!is.na(vcf.freebayes.HQ$Ol_tot.depth) &
vcf.freebayes.HQ$Ae_tot.depth > 10 &
vcf.freebayes.HQ$Ol_tot.depth > 10 &
vcf.freebayes.HQ$Ae_tot.depth < 1000 &
vcf.freebayes.HQ$Ol_tot.depth < 1000),]
dim(vcf.freebayes.HQ.1) # 493019 25
head(vcf.freebayes.HQ.1)
#######
# 2) filter by genotype quality (figure genotype quality in freebayes vcf file)
# ... need to figure this out...
table(vcf.freebayes.HQ.1$Ae_gt)
table(vcf.freebayes.HQ.1$Ol_gt)
# function to draw plot
pl.allele.ratio.Ae <- function(vcf.subset){
vcf.subset$tmp <- with(vcf.subset,Ae_alt.depth/Ae_ref.depth)
pl <- ggplot(data = vcf.subset)
pl <- pl + geom_histogram(aes(x=tmp))
pl <- pl + geom_histogram(aes(Ae_alt.depth/Ae_ref.depth)) + scale_x_log10()
# pl <- pl +labs(list(x="log10(alt.depth/ref.depth)")) + xlim(-1,5)
return(pl)
}
pl.allele.ratio.Ol <- function(vcf.subset){
pl <- ggplot(data = vcf.subset)
pl <- pl + geom_histogram(aes(Ol_alt.depth/Ol_ref.depth)) + scale_x_log10()
pl <- pl +labs(list(x="log10(alt.depth/ref.depth)"))
return(pl)
}