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CircularChromosome_gb_Polydora.R
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CircularChromosome_gb_Polydora.R
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library(tidyverse)
# library(Rbowtie2)
# library(Rsamtools)
library(RColorBrewer)
library(Biostrings)
library(IRanges)
library(rtracklayer)
library(circlize)
library(genbankr)
library(zoo)
library(egg)
my_theme <- theme_bw() +
theme(axis.text.x = element_text(size=14, angle=00,hjust=0.95,vjust=0.5),
axis.text.y = element_text(size=14),
strip.text.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.title.x = element_blank(),
legend.position = "none")
#Set working directory & import files
setwd("/media/dima/KINGSTON/Papers_submition/0000_Polydora/Data/")
dir()
mtDNA <- readGenBank("submission1.gb", ret.seq = TRUE)
genes <- genes(mtDNA)
features <- otherFeatures(mtDNA)
sequence <- getSeq(mtDNA)
GenomeSize = length(sequence[[1]])
getSeq(mtDNA)
############# import and analyse coverage
coverage <- read.csv("depth.txt", sep = "\t")
names(coverage) <- c("seq", "pos", "count")
coverage <- left_join(cbind(data.frame(pos = seq(1,GenomeSize))),
coverage,
by = "pos") %>%
mutate(count = ifelse(is.na(count), 0, count)) %>%
mutate(low_cov = ifelse(count > 2, NA, 1))
coverage_smooth <- tibble(coverage) %>%
mutate(smooth_cov = rollmean(count, 25, na.pad = TRUE)) %>%
filter(pos %% 25 == 0) %>%
mutate(smooth_cov = ifelse(smooth_cov < 1, 1, smooth_cov))
############ #Analyze complete genome GCcontent v0.1
########### to fix: handling genome tail, overlapping window, ...
window = 100 #set the window for GC content
step = 100 #
steps <- GenomeSize %/% step
GCcontent = data.frame(pos = as.numeric(),
GC = as.numeric(),
ATskew = as.numeric(),
GCskew = as.numeric(),
coverage = as.numeric()
)
for (i in seq(1, steps))
{
position <- i*(step)
chunk <- sequence[[1]][(position-(window/2)):
min((position+(window/2)),
length(sequence[[1]]))]
GC <- letterFrequency(chunk, "GC", as.prob = TRUE)
Aa <- letterFrequency(chunk, "A")
Tt <- letterFrequency(chunk, "T")
Gg <- letterFrequency(chunk, "G")
Cc <- letterFrequency(chunk, "C")
ATskew <- (Aa-Tt)/(Aa+Tt) # AT skew = (A − T)/(A + T)
GCskew <- (Gg-Cc)/(Gg+Cc) # GC skew = (G − C)/(G + C)
chunk_cov <- mean(coverage$count[(position-(window/2)):
min((position+(window/2)),
length(sequence[[1]]))])
GCcontent <- rbind(GCcontent,
data.frame(pos = position,
GC = GC,
ATskew = ATskew,
GCskew = GCskew,
coverage = max(chunk_cov, 1)))
}
GCcontent <-GCcontent %>% mutate(cumulativeATskew = cumsum(GCcontent$ATskew)) %>%
mutate(cumulativeGCskew = cumsum(GCcontent$GCskew))
#####parse MtDNA genomic features
genes_t <- as.data.frame(genes) %>%
mutate(product = gene_id) %>%
select(start, end, width, strand, type, product)
mtDNAfeatures <- as.data.frame(features) %>%
mutate(product = ifelse(is.na(product), allele, product)) %>%
select(start, end, width, strand, type, product) %>%
rbind(genes_t) %>%
mutate(type = factor(type))
FeatureColor <- brewer.pal(length(levels(mtDNAfeatures$type)),"Set2") %>%
set_names(levels(mtDNAfeatures$type))
##############################
################ Circular maps
##############################
### BioCircus
circos.clear()
circos.par("gap.degree" = 0,
"cell.padding" = c(0, 0, 0, 0),
"start.degree" = 90,
"track.height" = 0.1)
circos.initialize("a", xlim = c(0, GenomeSize))
# circos.track(ylim = c(0,1),bg.border = "red")
# circos.text(mtDNAfeatures$coordinates,0.7,mtDNAfeatures$X_Gene, niceFacing = TRUE, facing = "clockwise", cex = 0.5)
BEDmtDNAfeatures <- mtDNAfeatures %>%
# filter(type != "tRNA_gene") %>%
# mutate(X_Gene = ifelse(type == "tRNA_gene", str_sub(X_Gene, 1,2),X_Gene)) %>%
mutate(chr = "a") %>%
select(chr, start, end, product)
circos.genomicLabels(BEDmtDNAfeatures,
labels.column = 4,
side = "outside",
# col = FeatureColor[mtDNAfeatures$type],
cex = 1)
circos.info()
circos.par("track.height" = 0.05)
circos.track(ylim = c(0,1),bg.border = "white")
for (row in 1:nrow(mtDNAfeatures))
{arrowsize = ifelse(mtDNAfeatures$type[row] == "tRNA", 35, 100)
circos.arrow(mtDNAfeatures$start[row],
mtDNAfeatures$end[row],
y= 0.5,
width = 0.8,
arrow.head.width = 0.8,
arrow.head.length = arrowsize,
arrow.position = ifelse(mtDNAfeatures$strand[row] == "+", "end", "start"),
col = FeatureColor[mtDNAfeatures$type[row]]
)
}
circos.par("gap.degree" = 0,
"cell.padding" = c(0, 0, 0, 0),
"start.degree" = 90,
"track.height" = 0.15)
### Coverage track
# #
circos.track(ylim = c(0,max(log2(coverage_smooth$smooth_cov))))
circos.lines(x = coverage_smooth$pos,
y = log2(coverage_smooth$smooth_cov),
area = TRUE,
type = 'h',
col = 'darkgreen', lwd = 2)
# circos.lines(x = coverage$pos,
# y = coverage$low_cov,
# area = TRUE,
# type = 'h',
# col = 'red',
# lwd = 1)
par(cex = 0.8)
circos.yaxis(side = "right", labels.cex = 0.5)
# # GC track
circos.par("gap.degree" = 0,
"cell.padding" = c(0, 0, 0, 0),
"start.degree" = 90,
"track.height" = 0.1)
circos.track(ylim = c(0,max(GCcontent$GC)))
circos.lines(x = GCcontent$pos, y = GCcontent$GC, area = TRUE, type = 'h', col = 'darkblue', lwd = 2)
par(cex = 0.8)
circos.yaxis(side = "right", labels.cex = 0.5)
# #AT skew track
circos.track(ylim = c(min(GCcontent$GCskew, na.rm = TRUE),-min(GCcontent$GCskew, na.rm = TRUE)))
circos.lines(x = GCcontent$pos, y = GCcontent$ATskew, type = 'l', col = 'orange', lwd = 2)
circos.segments(0, 0, max(GCcontent$pos),0, type = "l", lwd = 1, lty = 2, col = "red")
circos.lines(x = GCcontent$pos, y = GCcontent$GCskew, type = 'l', col = 'red', lwd = 2)
par(cex = 0.8)
circos.yaxis(side = "left", labels.cex = 0.5)
################################
################# GENE EXPRESSION
################################
expression <- mtDNAfeatures[1:42,] %>%
filter(type != "tRNA") %>%
filter(!is.na(product)) %>%
group_by(product) %>%
mutate(sum_count = sum(coverage$count[start:end])) %>%
mutate(expression = sum_count/width)
Figure5B <- expression %>%
ggplot(aes(x = log2(expression), y = product)) +
geom_col() +
my_theme
# expression %>%
# ggplot(aes(x = width, y = log2(expression), label = product)) +
# geom_text() +
# my_theme
FigureSXX1 <- coverage %>%
ggplot(aes(x= pos, y = count)) +
geom_line() +
xlim(1774-300, 2317+100) +
geom_vline(xintercept = c(1774,2317), size = 1.3, lty = 3,color = "coral4") +
ylim(0, 275) +
my_theme
FigureSXX2 <- coverage %>%
ggplot(aes(x= pos, y = count)) +
geom_line() +
xlim(4671-100, 5730+100) +
geom_vline(xintercept = c(4671,5274, 5730), size = 1.3, lty = 3,color = "coral4") +
ylim(0, 275) +
my_theme
FigureSXX3 <- coverage %>%
ggplot(aes(x= pos, y = count)) +
geom_line() +
xlim(11968-100, 12394+100) +
geom_vline(xintercept = c(11968,12394), size = 1.3, lty = 3,color = "coral4") +
ylim(0, 275) +
my_theme
FigureNad4l <- coverage %>%
ggplot(aes(x= pos, y = count)) +
geom_line() +
xlim(14256-100, 14538+100) +
geom_vline(xintercept = c(14256,14538), size = 1.3, lty = 3,color = "coral4") +
ylim(0, 275) +
my_theme
FigureATP6 <- coverage %>%
ggplot(aes(x= pos, y = count)) +
geom_line() +
scale_x_continuous(breaks = seq(11000, 12100, by = 50),
limits = c(11170-100, 11967+100)) +
geom_vline(xintercept = c(11170,11967), size = 1.3, lty = 3,color = "coral4") +
ylim(0, 1100) +
my_theme
ggarrange(FigureSXX1, FigureSXX2, FigureSXX3, FigureNad4l)