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PacBio2tree.R
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PacBio2tree.R
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# Creates a ggtree of all the PacBio samples. The script takes the path and will loop through
# all the final fastas to generate the tree.
# Currently this script does not root (until I figure out a way to automatically do that?)
list.of.packages <- c("treeio","ggtree","stringr","Biostrings","phylobase","pegas","tidyverse","lubridate","ape",
"plyr","phangorn","RColorBrewer","dplyr","optparse","data.table","tidyr", "BiocGenerics")
suppressMessages(invisible(lapply(list.of.packages,library,character.only=T)))
option_list <- list(make_option(c("-d", "--directory"), type="character", default=NULL, help="Specify working directory", metavar="character"),
make_option(c("-r", "--relativefreq"), type="character", default=NULL, help="relative frequency cutoff", metavar="character"),
make_option(c("-c", "--count"), type="character", default=NULL, help="count cutoff", metavar="character"),
make_option(c("-m", "--metadata"), type="character", default=NULL, help="Specify metadata", metavar="character"));
opt_parser <- OptionParser(option_list=option_list);
opt <- parse_args(opt_parser)
#path <- opt$directory
path <- "./"
if (! is.null(opt$relativefreq)) {
rf_cutoff = as.numeric(opt$relativefreq)
} else {
rf_cutoff = 0.2
}
#####
## To run this script manually in R, uncomment the following lines. You do not need to change the preceding lines of path and script.dir,
## but remember to recomment the lines if you want to run the script automatically in the pipeline.
## path refers to the folder your metadata.csv and sequencing files (.fastq) are.
#path <- "/Users/uwvirongs/Documents/Michelle/tprk_pipeline/AS_files_redo"
## This script can also be run from the command line.
## Usage: rscript \path\to\PacBio2tree.R -d [directory]
#####
# Grabs sample names and PacBio files from the metadata file
metadata <- read.table(opt$metadata, sep=',', header=TRUE)
PacBio_fns <- c(as.character(metadata$PacBio))
sample_names <- c(as.character(metadata$SampleName))
## Function to convert fasta to dataframe
BString2df=function(BString){
names = names(BString)
sequences = paste(BString)
df <- data.frame(names,sequences)
return(df)
}
## Function to convert fasta to dataframe. Assumes the two columns are [names,sequences].
df2BString=function(df){
BString <- BStringSet(df$sequences)
names(BString) = paste0(df$names)
return(BString)
}
## Function to remove translated ORFs with stop codons
removeTruncatedORF=function(AAFile){
AAFile[,2]$sequences<-as.character(AAFile[,2]$sequences)
for (num in c(1:length(nrow(allAA[,2])))){
if (!(regexpr("\\*", AAFile[num,2]) == nchar(AAFile[num,2]))){
AAFile[num,] <- NA}
}
return(AAFile)
}
## Function to remove translated ORFs with stop codons
removeFrameShift=function(AAFile){
conserved <- c("HGFKTTTDFKIVFPIVAKKD",
"RTREDGVQEYIKVELTGNST",
"VGAKVSMKLWGLCALAATDV",
"ADALLTLGYRWFSAGGYFAS",
"LETKGSDPDTSFLEGLDLGV",
"YFPVYGKVWGSYRHDMGEYG",
"WEQGKLQENSNVVIEKNVTE")
AAFile[,2]$sequences<-as.character(AAFile[,2]$sequences)
for (num in c(1:nrow(allAA[,2]))){
frameShift <- FALSE
for (i in c(1:length(conserved))){
if (agrepl(conserved[i],AAFile[num,2],max.distance=3,ignore.case=TRUE) == 0){
frameShift <- TRUE
}
}
if (frameShift){
AAFile[num,] <- NA
}
}
return(AAFile)
}
fasta_files <- list()
df_list <- list()
aa_list <- list()
df_aa_list <- list()
df_aa_filtered_list <- list()
allAA <- list()
allAAfilt <- list()
for (i in 1:length(PacBio_fns)) {
fastafile_name <- paste((substr(PacBio_fns[i],1,nchar(PacBio_fns[i])-9)),".noprimers.filtered.RAD.nolines.fix.fasta",sep="")
#fastafile_name <- PacBio_fns[i]
fastafile <- reverseComplement(readDNAStringSet(fastafile_name))
names(fastafile) <- paste(sample_names[i],"_",names(fastafile),sep="")
fasta_files <- c(fasta_files,fastafile)
df_list <- c(df_list,BString2df(fastafile))
amino_acids <- Biostrings::translate(fastafile, getGeneticCode("11"), no.init.codon=FALSE,
if.fuzzy.codon="error")
aa_list <- c(aa_list, amino_acids)
df_aa <- BString2df(amino_acids)
print(df_aa)
#df_aa <- mutate(df_aa,sample=sapply(strsplit(as.character(names),"_"),"[",1),
# count=as.numeric(sapply(strsplit(as.character(names),"_"),"[",3)))
df_aa <- mutate(df_aa,sample=sample_names[i],
count=as.numeric(sapply(strsplit(as.character(names),"_"),tail,1)))
##Combine same TprKs
df_aa <- data.table(df_aa)
df_aa <- df_aa[order(-df_aa$count),]
df_aa <- df_aa[,list(names,sample,count=sum(count)),by='sequences']
df_aa <- df_aa[!duplicated(df_aa$sequences),]
print("before df aa")
print(df_aa)
df_aa$names <- paste(df_aa$sample,sapply(strsplit(as.character(df_aa$names), split="_"), "[", 2),df_aa$count,sep="_")
print("after")
print(df_aa)
print("c after")
df_aa <- df_aa[,c(2,1,3,4)]
print(df_aa)
df_aa <- mutate(df_aa,percentage=round(count / sum(count)*100,3))
df_aa_filt <- filter(df_aa,percentage>= rf_cutoff)
df_aa_list[[i]] <- df_aa
df_aa_filtered_list[[i]] <- df_aa_filt
if (i == 1) {
allAA <- df_aa
allAAfilt <- df_aa_filt
} else {
allAA <- base::rbind(allAA, df_aa)
allAAfilt <- base::rbind(allAAfilt, df_aa_filt)
}
}
## Remove TprKs that are not complete ORFs
allAA_fullORFs_df <- drop_na(removeTruncatedORF(allAA))
allAAfilt_fullORFs_df <- drop_na(removeTruncatedORF(allAAfilt))
## Remove TprKs that have two frameshifts that put them
allAA_fullORFs_df <- drop_na(removeFrameShift(allAA_fullORFs_df))
allAAfilt_fullORFs_df <- drop_na(removeFrameShift(allAAfilt_fullORFs_df))
print("all aa filt full orfs df")
print(allAAfilt_fullORFs_df)
allAAfilt_fullORFs_df[which(duplicated(allAAfilt_fullORFs_df$sequences) == TRUE),]
print("now which duplicated")
print(allAAfilt_fullORFs_df)
print("end")
write.table(allAAfilt_fullORFs_df,"Table_allAAfilt_fullORFs.tsv",sep='\t',row.names=FALSE)
allAA_fullORFs_BString <- df2BString(allAA_fullORFs_df)
allAAfilt_fullORFs_BString <- df2BString(allAAfilt_fullORFs_df)
AAoutfile <- "Isolates_aa_fullORFs.fasta"
AAoutfilefilt <- "Isolates_aa_filt_fullORFs.fasta"
writeXStringSet(allAA_fullORFs_BString, AAoutfile, append=FALSE,format="fasta")
writeXStringSet(allAAfilt_fullORFs_BString, paste(AAoutfilefilt,sep=""), append=FALSE,format="fasta")
## Need to include our Illumina check right here! ###
AAaln_outfile <- paste0(substr(AAoutfile,1,nchar(AAoutfile)-5),"aln.fasta")
AAaln_outfile_filt <- paste0(substr(AAoutfilefilt,1,nchar(AAoutfilefilt)-5),"aln.fasta")
#mafft_command <- paste0("mafft --auto ",AAoutfile," > ",AAaln_outfile)
#system(mafft_command)
mafft_command <- paste0("/usr/local/miniconda/bin/mafft --auto ",AAoutfilefilt," > ",AAaln_outfile_filt)
system(mafft_command)
AAtree_outfile <-paste0(substr(AAoutfile,1,nchar(AAoutfile)-5),"aln.tree.nwk")
AAtree_outfile_filt <-paste0(substr(AAaln_outfile_filt,1,nchar(AAaln_outfile_filt)-5),"aln.tree.nwk")
#fasttree_command <- paste0("fasttree ",AAaln_outfile," > ",AAtree_outfile)
#system(fasttree_command)
fasttree_command <- paste0("/usr/local/miniconda/bin/fasttree ",AAaln_outfile_filt," > ",AAtree_outfile_filt)
system(fasttree_command)
##Read in FastTree newick file
tree <- read.newick(AAtree_outfile_filt)
mycolors <- colorRampPalette(brewer.pal(name="Accent", n = 8))(length(sample_names))
# mycolors <- colorRampPalette(brewer.pal(name="Dark2", n = 8))(length(sample_names)+4)
## Have to reorder manually!
# tree <- reorder(tree,"postorder")
# tree <- phangorn::midpoint(tree)
#tree <- root(tree, which(tree$tip.label == "148B_seqs86_220"))
#tree <- reorder(tree)
##Expects tip labels to contain the sample name followed by a "_" and will parse the sample name accordingly.
d2 = data.frame(taxa=tree$tip.label,sample=sapply(strsplit(tree$tip.label,"_"),"[",1),count=as.numeric(sapply(strsplit(tree$tip.label,"_"),"[",3)))
setDT(d2)[, percentage := round((count/sum(count))*100,3), by = sample]
ggtree <- ggtree(tree) %<+% d2 + geom_tippoint(aes(color=sample,size=percentage),alpha=0.8) +
theme(legend.position = "right",legend.title = element_blank(),legend.key.width=unit(0.2,"cm"),legend.text=element_text(size=10)) +
scale_colour_manual(values=mycolors) +
geom_treescale(x=0.001,y=600,fontsize=3,offset=3) + guides(colour = guide_legend(override.aes = list(size=3))) +
scale_size(name="Percentage", breaks=c(0.5,2,10,40))
# ggtree <- ggtree(tree) %<+% d2 + geom_tippoint(aes(color=sample,size=percentage),alpha=0.8,) +
# theme(legend.position = c(0.1,0.85),legend.title = element_blank(),legend.key.width=unit(0.2,"cm"),legend.text=element_text(size=10)) +
# scale_colour_manual(values=mycolors) +
# geom_treescale(x=0.005,y=150,fontsize=3.5,offset=2) + guides(colour = guide_legend(override.aes = list(size=3))) +
# scale_size(name="Percentage", breaks=c(0.2,0.5,2,5))
#ggsave(path="./",filename="PacBio_Tree_Filtered.pdf",height = 6, width=4)
ggsave(plot = ggtree, filename="PacBio_Tree_Filtered.pdf",height = 6, width=4)
save.image(file = "PacBio_Tree_Filtered.RData")
# ggtree(tree) %<+% d2 + geom_tippoint(aes(color=sample,size=count),alpha=0.8) +
# theme(legend.position = c(0.8,0.2),legend.title = element_blank(),legend.key.width=unit(0.2,"cm"),legend.text=element_text(size=10)) +
# scale_colour_brewer(palette="Dark2") + geom_treescale(fontsize=3.5,offset=5) + guides(colour = guide_legend(override.aes = list(size=3)))
# ggsave("Figure_PacBio_Tree.pdf",height = 6, width=4)
######### Sandbox below this line. Pay no attention!!
#
# install.packages("vegan")
# library(vegan)
# d2
#
# diversity(d2[d2$sample == "148B"]$percentage)
#
# diversity(d2[d2$sample == "148B",]$percentage)
# diversity(d2[d2$sample == "148B2",]$percentage)
#
# percentage_148B <- d2[d2$sample == "148B",]$percentage
# percentage_148B2 <- d2[d2$sample == "148B2",]$percentage
#
# View(percentage_148B)
# View(percentage_148B2)
#
# diversity(d2[d2$sample == "148B",]$percentage)/log10(specnumber(d2[d2$sample == "148B",]$percentage))
# diversity(d2[d2$sample == "148B2",]$percentage)/log10(specnumber(d2[d2$sample == "148B2",]$percentage))
# write.table(d2[d2$percentage >= 0.2,]$taxa,"myfastanames.txt",quote=FALSE,col.names=FALSE,row.names=FALSE)
# ?write.table
# length(d2$percentage)
# d2[d2$percentage >= 0.2,]
# write.table(paste0(d2[d2$percentage >= 0.2,]$taxa,"_freq_",d2[d2$percentage >= 0.2,]$percentage),"myfastanamesfreq.txt",quote=FALSE,col.names=FALSE,row.names=FALSE)
#
# d = data.frame(sample=sapply(strsplit(tree$tip.label,"_"),"[",1))
# d = data.frame(color=sample(c('red', 'green'), 750, replace=T))
# rownames(d) = tree$tip.label
# g2 = phylo4d(g1, d)
# rNodeData <- data.frame(randomTrait = nNodes(g1),
# color = sample(c('black'), nNodes(g1), replace=T),
# row.names = nodeId(g1, "internal"))
#
# nodeData(g2) <- rNodeData
# ggtree(g2, aes(color=sample)) + scale_colour_brewer(palette="Dark2")
#
# tree <- read.newick(AAtree_outfile)
# tree$data
# treetib <- as_tibble(tree)
# treetib$label
# shortsample <- sapply(strsplit(treetib$label,"_"),"[",1)
# treetib <- add_column(treetib,sample = shortsample)
# tree_phylo <- as.phylo(treetib)
# treetib$sample
#
#
# ggplot(tree_phylo, aes(x, y)) + geom_tree() + theme_tree() + geom_tippoint(aes(color=sample), size=1.5)
#
# tree <- read.newick(AAtree_outfile)
# g1 = as(tree,'phylo4')
# g1 = as(tr, 'phylo4')
# shortsample <- sapply(strsplit(tree$tip.label,"_"),"[",1)