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assembly_stats.R
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assembly_stats.R
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# originally by Ruben Garrido-Oter
# garridoo@mpipz.mpg.de
options(warn=-1)
library(ggplot2, quietly=T, warn.conflicts=F)
library(scales, quietly=T, warn.conflicts=F)
library(Biostrings, quietly=T, warn.conflicts=F)
library(gridExtra, quietly=T, warn.conflicts=F)
library(MASS, quietly=T, warn.conflicts=F)
library(diptest, quietly=T, warn.conflicts=F)
kmer_chunks <- function(seqs, width, k){
x <- unlist(seqs)
kmers <- matrix(ncol=length(x)/width-1, nrow=k^k)
start <- 1
end <- 0
for (i in 1:(length(x)/width-1)) {
start <- end + 1
end <- end + width
if (end < length(x) & start < end)
kmers[, i] <- oligonucleotideFrequency(x[start:end], width=k)
}
return(kmers)
}
args <- commandArgs(TRUE)
if(length(args) < 2) {
cat("usage: assembly_stats.R assembly_file genome_id output_dir\n")
} else {
# parse arguments
assembly.file <- args[1]
genome.id <- args[2]
output.dir <- args[3]
min.length <- 0
width <- 10000
# generate a vector of contig lengths
seqs <- readDNAStringSet(assembly.file)
seqs <- seqs[width(seqs) >= min.length]
n.contigs <- sum(width(seqs) >= min.length)
names(seqs) <- gsub("^", "contig_", 1:length(seqs))
# calculate assembly statistics from contig lengths
lengths.table <- sort(width(seqs))
lengths <- data.frame(ctg_number=1:length(seqs), acc=cumsum(lengths.table))
tot.length <- lengths[dim(lengths)[1], 2]
n50.idx <- which(lengths[, 2] >= tot.length * .50)[1]
n50 <- lengths.table[n50.idx]
n90.idx <- which(lengths[, 2] >= tot.length * .10)[1]
n90 <- lengths.table[n90.idx]
# GC content spectrum
window.size <- 200
gc <- rowSums(letterFrequencyInSlidingView(unlist(seqs),
window.size, c("G","C"))) / window.size
# kmer spectrum MDS
if (sum(width(seqs)) < width)
width <- sum(width(seqs)) / 10
kmers <- kmer_chunks(seqs, width, k=4)
d <- dist(t(kmers))
d[d==0] <- 0.0001
points <- isoMDS(d, k=2)$points
points <- as.data.frame(points)
colnames(points) <- c("x", "y")
# Hartigan's dip test of multimodality
alpha <- 0.03
dtest_kmer <- dip(isoMDS(d, k=1)$points)
gc_coarse <- rowSums(letterFrequencyInSlidingView(unlist(seqs),
width, c("G","C"))) / width
dtest_gc <- dip(gc_coarse[seq(1, length(gc_coarse), window.size)])
### plotting
main_theme <- theme(panel.background=element_blank(),
panel.grid=element_blank(),
axis.line=element_line(color="black"),
axis.ticks=element_line(color="black"),
axis.text=element_text(colour="black", size=10),
legend.position="top")
p1 <- ggplot(lengths, aes(x=ctg_number, y=acc)) +
geom_point(cex=2, color="blue", alpha=.7) +
geom_line(color="blue") +
geom_vline(xintercept=n50.idx, linetype=2, color="grey") +
annotate("text", x=n50.idx, y=1, hjust=0,
label=paste("N50 (", n50, " bp)", sep=""),
size=3, angle=90) +
geom_vline(xintercept=n90.idx, linetype=2, color="grey") +
annotate("text", x=n90.idx, y=1, hjust=0,
label=paste("N90 (", n90, " bp)", sep=""),
size=3, angle=90) +
labs(x="number of contigs (sorted by increasing length)",
y="accumulated total contig length") +
ggtitle(genome.id) +
main_theme
df <- as.data.frame(gc)
p2 <- ggplot(data=df, aes(x=gc)) +
geom_histogram(binwidth=.01, size=.5,
colour="red", fill="transparent") +
xlim(0, 1) +
labs(x="GC content (%)", y="") +
theme(axis.ticks.y=element_blank(),
axis.text.y=element_blank(),
axis.line.y=element_blank()) +
main_theme
p3 <- ggplot(points, aes(x=x, y=y)) +
geom_point(alpha=.6) +
labs(x="tetranucleotide composition (MDS)", y=" ") +
main_theme
pbottom <- arrangeGrob(p3, p2, nrow=1, ncol=2, heights=4, widths=c(4, 4))
p <- arrangeGrob(p1, pbottom, ncol=1, heights=c(5, 4), widths=8)
ggsave(filename=paste(output.dir, "/", genome.id, ".pdf", sep=""),
plot=p, width=8, height=9)
# output statistics in text file
file <- paste(output.dir, "/", genome.id, ".txt", sep="")
sink(file, append=F)
cat(paste(genome.id, length(seqs), tot.length, n50, n90,
format(dtest_kmer, digits=2), format(dtest_gc, digits=2), sep="\t"))
cat("\n")
}