## this function integrates 3 steps to creating a genome plot
## 1 query bioMart to get a GFF like data-frame from ensembl
## 2 add new elements to the gff to make it plot with ggplot
## 3 plot it with ggplot2
## the reason to split it like this is so that basic users may use this simple function
## and advanced users can try custom queries or altered plotting themes
 
gnmplot= function(filters='hgnc_symbol', values=c('TP53', 'WRAP53', 'EFNB3'), species='human')
{
 
gff= getGFF(filters=filters, values=values, species=species)
 
gff.new=alterGFF(gff)
 
gnm.plot=gffplot(gff.new)
 
return(gnm.plot)
 
}
 
 
getGFF=function(filters=filters, values=values, species=species)
{
 
## getGFF uses biomaRt getBM() to query ensembl and return a GFF like dataframe
## to integrate with alterGFF() the attributes returned must be stable
## however with a reasonable understanding of biomaRt you can use different filters and values
 
require(biomaRt)
 
## built-in shortcuts for species names
if(species=='human') species.str="hsapiens_gene_ensembl"
if(species=='mouse') species.str="mmusculus_gene_ensembl"
if(species=='drosophila') species.str="dmelanogaster_gene_ensembl"
if(!any(species==c('human', 'mouse', 'drosphila'))) species.str=species
 
## we are restricted to using ensembl datbases
mart<-useDataset(species.str, useMart("ensembl"))
 
gff=getBM(attributes=c('chromosome_name','ensembl_gene_id', 'ensembl_transcript_id','5_utr_start', '5_utr_end', 
'3_utr_start', '3_utr_end', 'start_position','end_position', 'exon_chrom_start', 'exon_chrom_end', 'strand'), 
filters = filters, values = values, mart=mart)
 
return(gff)
}
 
 
alterGFF= function(gff=gff)
{
## this function adds columns to a getGFF() dataframe fro plotting by ggplot2
## this function requires specific named columns so is very customisable
 
## R does not like variables or column names that begin with numbers e.g. 5_utr_start
 
	colnames(gff)[4:7]=c('utr5_start', 'utr5_end', 'utr3_start', 'utr3_end')
 
## categorise which rows are exons and which utrs and add a column
	type=rep('exon', dim(gff)[1])
	type[which(!is.na(gff$utr5_start))]='utr5'
	type[which(!is.na(gff$utr3_start))]='utr3'
	gff=data.frame(gff,type=type)
 
 
## to plot a line the length of each gene I need to add this data to each row
## so I need to find the max and min exons for each transcript
## the merge them back into each row of the original data
	ag.min=aggregate(gff, list(gff$ensembl_transcript_id), function(x)min(as.numeric(x),na.rm=T))
	ag.max=aggregate(gff, list(gff$ensembl_transcript_id), function(x)max(as.numeric(x),na.rm=T))
	ag=data.frame(ensembl_transcript_id= ag.min$Group.1, start=ag.min$exon_chrom_start, end=ag.max$exon_chrom_end, strand=ag.max$strand)
	ag= ag[order(ag$start),]
 
## We are adding a y offset for each transcript element 
## So we have to see whether they are overlapping i.e. if so it needs a new row	##
## If it is on the pos or neg strand											##
## good luck to anyone who wants to try unlooping this							##
 
	yneg=-1; ypos=1; mem=vector(); y=vector()
	for(i in 1:length(ag$start))
	{
		genevec=ag$start[i]:ag$end[i]
		ynew=ifelse(length(intersect(genevec, mem))>0, TRUE, FALSE)
		strandpos=ifelse(ag$strand[i]>0, TRUE, FALSE)
 
		if(ynew && strandpos)
			ypos=ypos+1
		if(ynew && !strandpos)
			yneg=yneg-1
		if(!ynew && strandpos)	
			ypos= 1
		if(!ynew && !strandpos)	
			yneg= -1	
 
		if(strandpos)
			y[i]=ypos
		if(!strandpos)
			y[i]=yneg
 
		mem=c(mem,genevec)
	}
 
	ag=data.frame(ag,y=y)
 
## here we are merging the start end and the y offset for each transcript back into the original data
## so it is associated with every feature-row
	gff=merge(ag,gff) 
 
## to plot the line we also need to show arrow direction which requires an option first or last
	ardir=ifelse(gff$strand==1,'last','first')
	gff=data.frame(gff,ardir=ardir)
 
## OK there is an annoying glitch- that if you use strand for facet_grid then -1 will be on top
## the easiest workround is to create a dummy char variable that is alphabetically the right way!!!!!
 
	strandChar=gff$strand
	strandChar[strandChar==1]<-'forward'
	strandChar[strandChar==(-1)]<-'reverse'
	gff.new=data.frame(gff, strandChar=strandChar)
 
	return(gff.new)
}
 
 
 
gffplot=function(gff.new=gff.new)
{
## the ggplot package is used to create genomeplots
## geom_rect is used to create each seq element (e.g. UTRs, miRNAs, exons) i.e. with a enesembl_transcript ID
## geom_segment is used to create a line that links from the start to end of each gene or miRNA i.e. with a ensembl_gene_id
##I buid the plot in stages as the options are a bit lengthy 
 
require(ggplot2)
 
 
gnm1=ggplot(aes(xmin=exon_chrom_start, xmax=exon_chrom_end, ymin=y+0.25, ymax= y-0.25, fill=type, label=ensembl_transcript_id), data=gff.new)
 
gnm2= gnm1+ geom_rect()+geom_segment(aes(x=start,xend=end, y=y, yend= y))+theme_bw()+facet_grid(strandChar~., scales='free_y')
 
gnm.plot= gnm2+ ylab('')+xlab(paste('chromosome',gff$chromosome_name[1]))+scale_x_continuous(formatter = "comma")
 
return(gnm.plot)