Geno Plot R Manual

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Package ‘genoPlotR’
January 25, 2019
Type Package
Title Plot Publication-Grade Gene and Genome Maps
Version 0.8.9
Date 2019-01-16
Author Lionel Guy 
URL http://genoplotr.r-forge.r-project.org/
Depends R (>= 2.10.0), ade4, grid
Imports methods
Maintainer Lionel Guy 
Description Draws gene or genome maps and comparisons between these, in a
publication-grade manner. Starting from simple, common files, it will
draw postscript or PDF files that can be sent as such to journals.
License GPL (>= 2)
LazyLoad yes
NeedsCompilation no

R topics documented:
genoPlotR-package .
annotation . . . . . .
apply_color_scheme
artemisColors . . . .
auto_annotate . . . .
barto . . . . . . . . .
c.dna_seg . . . . . .
chrY_subseg . . . .
comparison . . . . .
dna_seg . . . . . . .
gene_types . . . . .
human_nt . . . . . .
mauve_bbone . . . .
middle . . . . . . . .
plot_gene_map . . .
range.dna_seg . . . .
read_functions . . . .

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2
4
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27
29

2

genoPlotR-package
reverse . . .
seg_plot . .
three_genes
trim . . . .

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33
35
38
38
41

genoPlotR-package

Plot Publication-Grade Gene and Genome Maps

Description
Draws gene or genome maps and comparisons between these, in a publication-grade manner. Starting from simple, common files, it will draw postscript or PDF files that can be sent as such to
journals.
Details
Package:
Type:
Title:
Version:
Date:
Author:
URL:
Depends:
Imports:
Maintainer:
Description:
License:
LazyLoad:

genoPlotR
Package
Plot Publication-Grade Gene and Genome Maps
0.8.9
2019-01-16
Lionel Guy 
http://genoplotr.r-forge.r-project.org/
R (>= 2.10.0), ade4, grid
methods
Lionel Guy 
Draws gene or genome maps and comparisons between these, in a publication-grade manner. Starting from
GPL (>= 2)
yes

Index of help topics:
annotation
apply_color_scheme
artemisColors
auto_annotate
barto
c.dna_seg
chrY_subseg
comparison
dna_seg
gene_types
genoPlotR-package
human_nt
mauve_bbone
middle
plot_gene_map

Annotation class and class functions
Apply a color scheme
Artemis Colors
Auto-annotate dna_segs
Comparison of 4 Bartonella genomes
Concatenate dna_seg objects
Comparisons of subsegments of the Y chromosome
in human and chimp
Comparison class and class functions
DNA segment (dna_seg) class and class functions
Gene types
Plot Publication-Grade Gene and Genome Maps
Human-readable nucleotide scale
Mauve backbone of 4 Bartonella genomes
Middles of a dna_seg
Plot gene and genome maps

genoPlotR-package
range.dna_seg
read_functions
reverse
seg_plot
three_genes
trim

3
Range calculation
Reading functions
Reverse objects
seg_plot class and class functions
Three genes data set
Trimming data frames or more complex objects
with >= 2 numeric columns

The only plotting function is plot_gene_map, which produces link[grid]{grid} graphics.
Data is composed mainly of DNA segments (dna_seg) objects, which represent collections of
genes or segments of genomes, and of comparison objects, which are the pairwise comparisons
between the dna_segs. Data can be read from files (see read_functions) or from R objects
like data.frames or lists, with dna_seg and comparison conversion functions.
Author(s)
Lionel Guy 
Maintainer: Lionel Guy 
References
Guy, L., Roat Kultima, J, and Andersson, S.G.E. (2010). genoPlotR: comparative gene and genome
visualization in R. Bioinformatics 26(18):2334-2335.
See Also
plot_gene_map for plotting. dna_seg and comparison for the base objects and conversion
functions. read_dna_seg_from_tab, read_dna_seg_from_ptt, read_comparison_from_tab
and read_comparison_from_blast to read from files.
Examples
## simple example
## dna segments
## data.frame with several genes
names1 <- c("feat1", "feat2", "feat3")
starts1 <- c(2, 1000, 1050)
ends1 <- c(600, 800, 1345)
strands1 <- c("-", -1, 1)
cols1 <- c("blue", "grey", "red")
df1 <- data.frame(name=names1, start=starts1, end=ends1,
strand=strands1, col=cols1)
dna_seg1 <- dna_seg(df1)
is.dna_seg(dna_seg1)
## with only one gene, or two, and merging
gene2a <- dna_seg(list(name="feat1", start=50, end=900, strand="-", col="blue"))
genes2b <- dna_seg(data.frame(name=c("feat2", "feat3"), start=c(800, 1200),
end=c(1100, 1322), strand=c("+", 1),
col=c("grey", "red")))
dna_seg2 <- c.dna_seg(gene2a, genes2b)
is.dna_seg(dna_seg2)
## reading from file
dna_seg3_file <- system.file('extdata/dna_seg3.tab', package = 'genoPlotR')

4

annotation
dna_seg3 <- read_dna_seg_from_tab(dna_seg3_file)
is.dna_seg(dna_seg3)
## comparison
## from a data.frame
comparison1 <- as.comparison(data.frame(start1=starts1, end1=ends1,
start2=dna_seg2$start,
end2=dna_seg2$end))
is.comparison(comparison1)
## from a file
comparison2_file <- system.file('extdata/comparison2.tab',
package = 'genoPlotR')
comparison2 <- read_comparison_from_tab(comparison2_file,
color_scheme="red_blue")
is.comparison(comparison1)
## plot
plot_gene_map(dna_segs=list(dna_seg1, dna_seg2, dna_seg3),
comparisons=list(comparison1, comparison2))

annotation

Annotation class and class functions

Description
An annotation describes a DNA segment. It has labels attached to positions. Each label can be
attached to a single position or to a range.
Usage
annotation(x1, x2 = NA, text, rot = 0, col = "black")
as.annotation(df, x2 = NA, rot = 0, col = "black")
is.annotation(annotation)
Arguments
x1

Numeric. A vector giving the first or only position of the label. Mandatory.

x2

Numeric. A vector of the same length as x1. If a row (or the whole column is
NA, then the annotation(s) will be attached to x0. Else, the annotation will be
attached to the range between both positions. NA by default.

text

Character of the same length as x0. Gives the text of the labels. Mandatory.

rot

Numeric of the same length as x0. Gives the rotation, in degrees, of the labels.
0 by default.

col

Vector of the same length as x0. The color of the labels. black by default.

df

A data frame to convert to an annotation object. Should have at least columns
x1 and text.

annotation

An object to test.

annotation

5

Details
An annotation object is a data frame with columns x0, x1, text, col and rot. They give,
respectively, the first (or only) position, eventually the second position, the text, the color and the
rotation of the annotation. When plotted with plot_gene_map, it will add an annotation row
on top of the first dna_seg. Labels for which only one position is given will be centered on that
position. Labels for which two positions are given are linked by an horizontal square bracket and
the label is plotted in the middle of the positions.
Value
annotation and as.annotation return an annotation object. is.annotation returns a
logical.
Author(s)
Lionel Guy
See Also
plot_gene_map, middle.
Examples
## loading data
data(three_genes)
## Calculating middle positions
mid_pos <- middle(dna_segs[[1]])
# Create first annotation
annot1 <- annotation(x1=mid_pos, text=dna_segs[[1]]$name)
plot_gene_map(dna_segs=dna_segs, comparisons=comparisons, annotations=annot1)
## Exploring options
annot2 <- annotation(x1=c(mid_pos[1], dna_segs[[1]]$end[2]),
x2=c(NA, dna_segs[[1]]$end[3]),
text=c(dna_segs[[1]]$name[1], "region1"),
rot=c(30, 0), col=c("grey", "black"))
plot_gene_map(dna_segs=dna_segs, comparisons=comparisons,
annotations=annot2, annotation_height=1.3)
## Annotations on all the segments
annots <- lapply(dna_segs, function(x){
mid <- middle(x)
annot <- annotation(x1=mid, text=x$name, rot=30)
})
plot_gene_map(dna_segs=dna_segs, comparisons=comparisons,
annotations=annots, annotation_height=1.8, annotation_cex=1)
##
## Using a bigger dataset from a 4-genome comparison
##
data(barto)
## Adding a tree
tree <- newick2phylog("(BB:2.5,(BG:1.8,(BH:1,BQ:0.8):1.9):3);")
## Showing several subsegments

6

apply_color_scheme
xlims2 <- list(c(1445000, 1415000, 1380000, 1412000),
c( 10000,
45000,
50000,
83000, 90000, 120000),
c( 15000,
36000,
90000, 120000, 74000, 98000),
c(
5000,
82000))
## Adding annotations for all genomes, allow segments to be placed out
## of the longest segment
annots <- lapply(barto$dna_segs, function(x){
mid <- middle(x)
annot <- annotation(x1=mid, text=x$name, rot=30)
# removing gene names starting with "B" and keeping 1 in 4
idx <- grep("^[^B]", annot$text, perl=TRUE)
annot[idx[idx %% 4 == 0],]
})
plot_gene_map(barto$dna_segs, barto$comparisons, tree=tree,
annotations=annots,
xlims=xlims2,
limit_to_longest_dna_seg=FALSE,
dna_seg_scale=TRUE)

apply_color_scheme Apply a color scheme

Description
Apply a color scheme to a numeric vector, eventually taking the direction into account.
Usage
apply_color_scheme(x, direction = NULL, color_scheme = "grey",
decreasing = FALSE, rng = NULL, transparency = 0.5)
Arguments
x

A numeric, that will be used to apply a gradient of colors to a comparison.

direction

If a red-blue scheme is choosen, the vector (composed of -1 and 1 values and of
same length as x) giving the direction of the comparison.

color_scheme Character. One of red_blue, blue_red, grey, gray.
decreasing

Logical. Are the values of the comparisons oriented such as the lower the value,
the closer the relationship (e.g. e-values, gaps, mismatches, etc)? FALSE by
default.

rng

Numeric of length 2. Gives the higher and lower limit to apply a color scheme.

transparency Numeric of length 1, between 0 and 1, or FALSE. Should the color scheme use
transparency, and if yes how much (ratio). 0.5 by default. Not supported on all
devices.

apply_color_scheme

7

Details
A color scale is calculated, with the darker color corresponding to the highest values of x, or the
contrary is decreasing is TRUE. For the moment, two schemes (red-blue and grey scale) are
used.
For the red-blue scale (as in ACT), the direct comparisons are colored in red hues, and the reversed
ones in blue hues.
This is especially useful to replace comparison values (such as BLAST percent identity values) by
color hues.
Value
A character vector of same length as x, representing colors.
Author(s)
Lionel Guy
References
Artemis Comparison Tool, http://www.sanger.ac.uk/Software/ACT/
See Also
comparison
Examples
## Load data
data(three_genes)
## Color schemes
## Greys
comparisons[[1]]$values <- c(70, 80, 90)
comparisons[[1]]$col <- apply_color_scheme(comparisons[[1]]$values,
color_scheme="grey")
plot_gene_map(dna_segs=dna_segs, comparisons=comparisons)
## Red-blue
comparisons[[1]]$col <- apply_color_scheme(comparisons[[1]]$values,
direction=comparisons[[1]]$direction,
color_scheme="red_blue")
plot_gene_map(dna_segs=dna_segs, comparisons=comparisons)
## Decreasing
comparisons[[1]]$col <- apply_color_scheme(comparisons[[1]]$values,
direction=comparisons[[1]]$direction,
color_scheme="red_blue",
decreasing=TRUE)
plot_gene_map(dna_segs=dna_segs, comparisons=comparisons)
## Range
comparisons[[1]]$col <- apply_color_scheme(comparisons[[1]]$values,
direction=comparisons[[1]]$direction,
color_scheme="red_blue",
rng=c(30,100))
plot_gene_map(dna_segs=dna_segs, comparisons=comparisons)
## Transparency
x1 <- seq(100, 600, by=50)

8

artemisColors
x2 <- seq(1100, 700, by=-50)
comparisons[[2]] <- as.comparison(data.frame(start1=c(x1, x2),
end1=c(x1+250, x2+300),
start2=c(x1+150, x2-300)+2000,
end2=c(x1+250, x2-500)+2000
))
comparisons[[2]]$col <- apply_color_scheme(1:nrow(comparisons[[2]]),
comparisons[[2]]$direction,
color_scheme="blue_red")
comparisons[[1]]$col <- apply_color_scheme(comparisons[[1]]$values,
color_scheme="grey",
transparency=0.8)
plot_gene_map(dna_segs=dna_segs, comparisons=comparisons)
comparisons[[1]]$col <- apply_color_scheme(comparisons[[1]]$values,
color_scheme="grey",
transparency=1)
comparisons[[2]]$col <- apply_color_scheme(1:nrow(comparisons[[2]]),
comparisons[[2]]$direction,
color_scheme="blue_red",
transparency=0.2)
plot_gene_map(dna_segs=dna_segs, comparisons=comparisons)

artemisColors

Artemis Colors

Description
Returns a data frame with the standard artemis colors.
Usage
artemisColors()
Value
A data.frame with the following columns: n, names, colors, r, g and g. The 3 first columns
give the Artemis color number, its name, and its equivalent in R. The 3 last give the r, g and b values.
Author(s)
Lionel Guy
References
Artemis website: http://www.sanger.ac.uk/resources/software/artemis/
Examples
artCol <- artemisColors()
plot(rep(1, nrow(artCol)), artCol$n, xlim=c(1, 2), type="n")
text(rep(1, nrow(artCol)), artCol$n, labels=artCol$n, col=artCol$colors)
text(rep(1, nrow(artCol)), artCol$n, labels=artCol$names, col=artCol$colors,
pos=4, offset=1)

auto_annotate

9

Auto-annotate dna_segs

auto_annotate

Description
Annotate dna_segs in a smart way. This is especially designed for dna_segs read from genbank
or embl files, but can be extended for other uses. In short, it produces annotations from dna_segs,
grouping the tags for operons (atpA, atpB, atC) into one tag (atpA-C), and similarly for numbered
genes (bep1-9).
Usage
auto_annotate(dna_seg, locus_tag_pattern=NULL, names=dna_seg$gene,
keep_genes_only=TRUE, ...)
Arguments
dna_seg
A dna_seg object.
locus_tag_pattern
NULL by default. A character giving a pattern, that is used to simplify names.
Specially useful to transform long locus tags into numbers (e.g. Eco003456
becomes 3456).
A character vector with as many elements as there are rows in the dna_seg.
By default, the gene column of the dna_seg is taken. Gives the names to be
summarized.
keep_genes_only
A logical, TRUE by default. If set, the genes that have a name that is "-" or
empty are not annotated.

names

...

Further arguments to be passed to annotation function, like rot or color.

Value
An annotation object.
Author(s)
Lionel Guy
See Also
annotation, dna_seg.
Examples
## Prepare dna_seg
names <- paste("Eco", sprintf("%04d", 1:20), sep="")
gene <- c("-", "atpC", "atpB", "atpA", "atp2",
"-", "-", "cda1", "cda2", "cda3",
"vcx23", "vcx22", "vcx21", "cde20",
"-", "gfrU", "gfrT", "gfrY", "gfrX", "gfrW")
ds <- dna_seg(data.frame(name=names, start=(1:20)*3, end=(1:20)*3+2,
strand=rep(1, 20), gene=gene,

10

barto
stringsAsFactors=FALSE))
## Original annotation
annot1 <- annotation(x1=middle(ds), text=ds$gene, rot=30)
## auto_annotate with various options
annot2 <- auto_annotate(ds)
annot3 <- auto_annotate(ds, keep_genes_only=FALSE, rot=45)
annot4 <- auto_annotate(ds, keep_genes_only=FALSE,
locus_tag_pattern="Eco", col="red")
## Plot
plot_gene_map(list(ds, ds, ds, ds),
annotations=list(annot1, annot2, annot3, annot4))

barto

Comparison of 4 Bartonella genomes

Description
Comparison of 4 Bartonella genomes by BLAST.
Usage
data(barto)
Format
barto, a list of three dataframes, representing the four genomes and their pairwise comparisons:
• dna_segswhich is a list of 4 dna_seg objects, containing all the protein genes for each
genome. Obtained by reading ptt files downloaded from NCBI with read_dna_seg_from_ptt.
• comparisonswhich is a list of 3 comparison objects, obtained by doing genome-togenome (fasta files) BLASTS, and then reading the resulting tab files with read_comparison_from_blast.
• rnt_segswhich is a list of 4 dna_seg objects, containing all the RNA genes of the four
genomes. Obtained by reading rnt files downloaded from NCBI with read_dna_seg_from_ptt.
A bash script to obtain the same file as in the dataset is available in the extdata folder of the package. Find its location by running system.file('extdata/barto.sh', package = 'genoPlotR').
References
BLAST: http://www.ncbi.nlm.nih.gov/blast/
Examples
data(barto)
plot_gene_map(barto$rnt_segs, barto$comparisons, gene_type="blocks")

c.dna_seg

11

Concatenate dna_seg objects

c.dna_seg

Description
Concatenate dna_seg objects.
Usage
## S3 method for class 'dna_seg'
c(...)
Arguments
...

dna_segs to be concatenated.

Value
A dna_seg object
Author(s)
Lionel Guy
See Also
dna_seg
Examples
## load data
data(three_genes)
dna_segs[1:2]
c(dna_segs[[1]], dna_segs[[2]])

chrY_subseg

Comparisons of subsegments of the Y chromosome in human and
chimp

Description
A subsegment of the Y chromosome in Homo sapiens and Pan troglodytes, to illustrate support for
exons and introns.
Usage
data(chrY_subseg)

12

comparison

Format
A list of two data frames, representing the Y segment in the two species, and containing:
• dna_segswhich is a list of two dna_seg objects, containing each three rows (or genes).
• comparisonwhich is a list of one comparison objects.
Details
Header for the Homo sapiens genbank file: LOCUS NC_000023 220001 bp DNA linear CON
10-JUN-2009 DEFINITION Homo sapiens chromosome X, GRCh37 primary reference assembly.
ACCESSION NC_000023 REGION: 2600000..2820000 GPC_000000047
Header for the Pan troglodytes file: LOCUS NC_006491 220001 bp DNA linear CON 18-SEP-2006
DEFINITION Pan troglodytes chromosome X, reference assembly (based on Pan_troglodytes-2.1).
ACCESSION NC_006491 REGION: 2620000..2840000
Examples
data(chrY_subseg)
plot_gene_map(chrY_subseg$dna_segs, chrY_subseg$comparison, dna_seg_scale=TRUE,
scale=FALSE)

comparison

Comparison class and class functions

Description
A comparison is a collection of similarities, representing the comparison between two DNA segments. These functions are class functions to create, convert and test comparison objects.
Usage
comparison(x)
as.comparison(df)
is.comparison(comparison)
Arguments
x
df
comparison

Can be a list or data.frame object. See the details for the columns in the
data.frame.
A data.frame object. See details for the required columns.
Any object to test.

Details
Objects (either data frames or lists) should have at least named elements start1, end1, start2
and end2. In addition, it can take a color column. Additional numeric columns can be used for
color-coding (via apply_color_scheme.
comparison tries to build a comparison object from either a data frame or a list, as.comparison
accepts only data.frames.
is.comparison returns TRUE if the object tested is a comparison object.
Read functions such as read_comparison_from_tab and read_comparison_from_blast
also return comparison objects.

dna_seg

13

Value
A comparison object for comparison and as.comparison. Comparison objects are also of
class data.frame. They contain the columns start1, end1, start2, end2, direction
and col (color).
A logical for is.comparison.
Author(s)
Lionel Guy
See Also
dna_seg, read_comparison_from_tab, read_comparison_from_blast, trim.comparison,
reverse.comparison.
Examples
## Get some values
starts1 <- c(2, 1000, 1050)
ends1 <- c(600, 800, 1345)
starts2 <- c(50, 800, 1200)
ends2 <- c(900, 1100, 1322)
## From a data.frame
comparison1 <- as.comparison(data.frame(start1=starts1, end1=ends1,
start2=starts2, end2=ends2))
comparison1
is.comparison(comparison1)
is.data.frame(comparison1)
comparison(data.frame(start1=starts1, end1=ends1,
start2=starts2, end2=ends2))
## From a list
comparison(list(start1=starts1, end1=ends1,
start2=starts2, end2=ends2))
## From a file
comparison2_file <- system.file('extdata/comparison2.tab',
package = 'genoPlotR')
comparison2 <- read_comparison_from_tab(comparison2_file)

dna_seg

DNA segment (dna_seg) class and class functions

Description
A DNA segment is a collection of genes or elements along a genome, to be represented on a map.
These functions are class functions to create, convert and test dna_seg objects.

14

dna_seg

Usage
dna_seg(x, ...)
as.dna_seg(df, col = "blue", fill = "blue", lty = 1, lwd = 1,
pch = 8, cex = 1, gene_type = "arrows")
is.dna_seg(dna_seg)
Arguments
x

A data.frame or list that can be coerced to a data frame.

...

Arguments further passed to as.dna_seg (see below).

df

A data frame representing the dna_seg object. See details for necessary columns.

col

Either a color vector of the same length as df or of length one, to be applied
to the whole object. Will be passed to corresponding grob object. Default to
blue.

fill

Either a fill vector of the same length as df or of length one, to be applied to the
whole object. Will be passed to corresponding grob object. Default to blue.

lty

A vector of the same length as df or of length one, giving the line type around
the objects.

lwd

Same as lty, giving the line width.

pch

Same as lty, giving the character representing each object. Goes with gene_type
points.

cex

Same as lty, giving the character size representing each object. Goes with
gene_type points.

gene_type

Vector of the same length as df or of length one, giving the type of representation of each object.

dna_seg

Object to test.

Details
Objects to be converted needs to have their first 4 columns named name, start, end and strand.
Extra columns with names col, lty, lwd, pch, cex, gene_type will be used in the plotting
process. Other extra columns will be kept in the object, but not used.
dna_seg tries to build a dna_seg object from a data frame or a list.
as.dna_seg tries to build a dna_seg object from a data frame.
Read functions such as read_dna_seg_from_tab and read_dna_seg_from_ptt also return dna_seg objects.
Value
A comparison object for comparison and as.comparison. DNA seg objects are also of class
data.frame. They contain the following columns: name, start, end, strand, col, lty,
lwd, pch, cex, gene_type.
A logical for is.comparison.
Author(s)
Lionel Guy

gene_types

15

See Also
read_dna_seg_from_tab, read_dna_seg_from_ptt, gene_types.
Examples
## generate data
names1 <- c("feat1", "feat2", "feat3")
starts1 <- c(2, 1000, 1050)
ends1 <- c(600, 800, 1345)
strands1 <- c("-", -1, 1)
cols1 <- c("blue", "grey", "red")
## create data.frame
df1 <- data.frame(name=names1, start=starts1, end=ends1,
strand=strands1, col=cols1)
## with dna_seg
dna_seg1 <- dna_seg(df1)
dna_seg1
as.dna_seg(df1)
## test
is.dna_seg(dna_seg1)
## directly readable with read_dna_seg_from_tab
## Not run:
write.table(x=dna_seg1, file="dna_seg1.tab", quote=FALSE,
row.names=FALSE, sep="\t")
## End(Not run)
## with only one gene and with list, or two, and merging with c.dna_seg
gene2a <- dna_seg(list(name="feat1", start=50, end=900, strand="-", col="blue"))
genes2b <- dna_seg(data.frame(name=c("feat2", "feat3"), start=c(800, 1200),
end=c(1100, 1322), strand=c("+", 1),
col=c("grey", "red"),
fill=c("transparent", "grey"),
gene_type=c("arrows", "blocks")))
dna_seg2 <- c(gene2a, genes2b)
## test
is.dna_seg(dna_seg2)
## reading from file
dna_seg3_file <- system.file('extdata/dna_seg3.tab', package = 'genoPlotR')
dna_seg3 <- read_dna_seg_from_tab(dna_seg3_file)
is.dna_seg(dna_seg3)

gene_types

Gene types

16

gene_types

Description
Returns a vector containing the available gene types. In addition to these gene types, the user can
provide graphical functions that return a list or a single grob object.
Usage
gene_types(auto = TRUE)
Arguments
auto

Logical. Should type "auto" be added?

Details
dna_segs may contain one character column gene_type. Elements in this column should either
be one of the predefined gene types, or refer to a graphical function that has exactly the same name
and that returns a grob or a gList object.
A gene object (i.e. a single row of a dna_seg) is passed to the graphical function, as well as the
contents of the . . . . The start and line width of an element can thus be accessed via gene$start
and gene$lwd. Extra columns that would be added in the dna_seg can be used similarly. Extra
arguments can also be globally passed via . . . when calling plot_gene_map.
Value
A character vector.
Author(s)
Lionel Guy
See Also
plot_gene_map, dna_seg
Examples
## To view pre-coded gene types:
gene_types()
## Load data
data(barto)
n <- length(gene_types(auto=FALSE))
## Get a small subset from the barto dataset
dna_seg <- barto$dna_segs[[3]][1:n,]
plot_gene_map(list(dna_seg))
## Change gene_types and plot again
dna_seg$gene_type <- gene_types(auto=FALSE)
dna_seg$fill <- rainbow(n)
dna_seg_r <- dna_seg
dna_seg_r$strand <- -dna_seg$strand
## Add an annotation
annot <- annotation(middle(dna_seg), text=dna_seg$gene_type, rot=45,

gene_types

17
col=dna_seg$col)

## Plot
plot_gene_map(list(dna_seg, dna_seg_r), annotations=list(annot, annot),
annotation_height=5, dna_seg_line=grey(0.7))

## Using home-made graphical functions
## Data
data(three_genes)
## Functions returning grobs.
## Creates a triangle
triangleGrob <- function(gene, ...) {
x <- c(gene$start, (gene$start+gene$end)/2, gene$end)
y1 <- 0.5 + 0.4*gene$strand
y <- c(y1, 0.5, y1)
polygonGrob(x, y, gp=gpar(fill=gene$fill, col=gene$col, lty=gene$lty,
lwd=gene$lwd), default.units="native")
}
## Draws a star. Note that the limits of the dna_seg region are
## voluntarily not respected
starGrob <- function(gene, ...){
## Coordinates for the star
x <- sin(((0:5)/2.5)*pi)*(gene$end-gene$start)/2 + (gene$end+gene$start)/2
y <- cos(((0:5)/2.5)*pi)*gene$strand*2 + 0.5
idx <- c(1, 3, 5, 2, 4, 1)
## Attribute line_col only if present in the gene
line_col <- if (!is.null(gene$line_col)) gene$line_col else gene$col
## Having a conditional transparency, depending on a length cut-off
## passed via dots
length_cutoff <- list(...)$length_cutoff
if (!is.null(length_cutoff)){
alpha <- if ((gene$end-gene$start) < length_cutoff) 0.3 else 0.8
} else alpha <- 1
## Grobs
g <- polygonGrob(x[idx], y[idx], gp=gpar(fill=gene$col, col=line_col,
lty=gene$lty, lwd=gene$lwd, alpha=alpha),
default.units="native")
t <- textGrob(label="***", x=(gene$end+gene$start)/2, y=0.5,
default.units="native")
gList(g, t)
}
## Replacing the standard types
dna_segs[[1]]$gene_type <- "triangleGrob"
dna_segs[[2]]$gene_type <- "starGrob"
## Adding more variables
dna_segs[[2]]$line_col <- c("black", grey(0.3), "blue")
## Mix of several types on the same line
dna_segs[[3]]$gene_type <- c("starGrob", "triangleGrob", "arrows")
## Plot
plot_gene_map(dna_segs, comparisons, length_cutoff=600)

18

human_nt

human_nt

Human-readable nucleotide scale

Description
Return a human readable list from a nucleotide position or lenght.
Usage
human_nt(nt, signif = FALSE)
Arguments
nt

A nucleotide position

signif

Either a logical or an integer. If FALSE (default), nt is not rounded. Else, it
returns signif significant digits.

Details
Return a nucleotide value in nt, kb, Mb or Gb, according to the value given. This is particularly
useful to display nice scales without too many trailing zeros.
Value
Returns a list with 4 elements
n

A numeric value corresponding to nt divided by mult (see below).

tag

A character, giving the multiplier used in text.

mult

The muliplier used, in numeric value.

text

A character, giving the value in a human readable format.

Author(s)
Lionel Guy
Examples
human_nt(123456)
human_nt(123456, signif=2)
human_nt(123456890, signif=2)

mauve_bbone

19

Mauve backbone of 4 Bartonella genomes

mauve_bbone

Description
The result of a multiple genome alignment with Mauve.
Usage
data(mauve_bbone)
Format
bbone, a list of two dataframes, representing the regions which are conserved in at least two
genomes:
• dna_segswhich is a list of 4 dna_seg objects, containing the mauve blocks for each
genome.
• comparisonswhich is a list of 3 comparison objects.
A bash script to obtain the same file as in the data is available in the extdata folder of the package.
Find its location by running system.file('extdata/mauve.sh', package = 'genoPlotR').
The resulting backone file can then be read with read_mauve_backbone.
References
Mauve: http://asap.ahabs.wisc.edu/mauve/
Examples
data(mauve_bbone)
plot_gene_map(bbone$dna_segs, bbone$comparisons)

Middles of a dna_seg

middle

Description
Returns a vector containing the middle of the genes of a dna_seg. Useful to prepare annotations,
for example.
Usage
middle(dna_seg)
Arguments
dna_seg

A dna_seg object.

20

plot_gene_map

Value
A numeric vector.
Author(s)
Lionel Guy
See Also
annotation, dna_seg
Examples
## Load data
data(barto)
## Get middles of the first dna_seg
mid <- middle(barto$dna_segs[[1]])

plot_gene_map

Plot gene and genome maps

Description
This plotting function represents linearly DNA segments and their comparisons. It will plot one
line per DNA segment, eventually separated by the comparisons. In addition, a tree can be plotted
on the left of the plot, and annotations on the top row. Since this is a grid plot, it can be placed into
other graphics, or modified subsequently.
Usage
plot_gene_map(dna_segs,
comparisons = NULL,
tree = NULL,
tree_width = NULL,
tree_branch_labels_cex = NULL,
tree_scale = FALSE,
legend = NULL,
annotations = NULL,
annotation_height = 1,
annotation_cex = 0.8,
seg_plots=NULL,
# user-defined plots
seg_plot_height=3, # height of plots (in lines)
seg_plot_height_unit="lines", # unit of preceding
seg_plot_yaxis=3, # if non-null or non false, ticks
seg_plot_yaxis_cex=scale_cex,
xlims = NULL,
offsets = NULL,
minimum_gap_size = 0.05,
fixed_gap_length = FALSE,

plot_gene_map

21
limit_to_longest_dna_seg = TRUE,
main = NULL,
main_pos = "centre",
dna_seg_labels = NULL,
dna_seg_label_cex=1,
dna_seg_label_col="black",
gene_type = NULL,
arrow_head_len = 200,
dna_seg_line = TRUE,
scale = TRUE,
dna_seg_scale = !scale,
n_scale_ticks=7,
scale_cex=0.6,
global_color_scheme = c("auto", "auto", "blue_red", 0.5),
override_color_schemes = FALSE,
plot_new=TRUE,
debug = 0,
...)

Arguments
dna_segs

A list of dna_seg objects. Mandatory.

comparisons

A list of comparison objects. Optional.

tree

A tree, under the form of a phylog object. If specified, takes place at the left
of the tags. See details below for more information.

Numeric. The width of the tree area in the plot, in inches. By default, takes 20
percent of the total plot.
tree_branch_labels_cex
Numeric or NULL (default). If the tree provided contains node annotations,
they will be displayed with this cex. If equal to 0, node annotations are not
displayed.
tree_width

tree_scale

Logical. Plot a scale for the tree? Default is FALSE.

legend

Yet unimplemented.

annotations An annotation object or a list of annotation objects. See details. Optional.
annotation_height
Numeric. The height, in lines, of the annotation line. One by default, if annotation is defined
annotation_cex
Numeric. The cex (i.e. the character expansion) of the annotation line.
A list of seg_plot objects of the length as dna_segs, a single seg_plot
or NULL (default). To draw plots associated to a dna_seg. See seg_plot
for more information and some examples.
seg_plot_height
The height of the seg_plot regions. By default, equals to 3 (lines, see next
argument).
seg_plot_height_unit
The unit of the height of the seg_plot regions. Should be a valid unit.
See the grid documentation for more information. If equals to "null", then
the height will be calculated as a proportion of the comparison region (i.e. 0.5
means the seg_plot region will be half the size of the comparison.
seg_plots

22

plot_gene_map
seg_plot_yaxis
Can be NULL, FALSE or a numercic. In the first two cases, no y-axis is drawn
for the seg_plots. If numeric, a axis is drawn with approximately that number
of ticks.
seg_plot_yaxis_cex
The character expansion of the seg_plot_yaxis labels. Equals to scale_cex
by default.
xlims

A list with as many elements as there are dna_segs, or NULL. If NULL, the
whole segment will be represented. If a list, each element of the list is a numeric vector, representing pairs of left and right limits for each subsegment. See
details.

A list or a vector with as many elements as there are dna_segs, or NULL. If is a
numeric vector, gives the offset of the first subsegment. If is a list, each element
should have the same length as there are subsegments (see xlims). Gives then
the length of each gap. If NULL, the size of the gaps is optimized to minimize
the lengths of the comparisons. See details.
minimum_gap_size
A numeric. How much of the plotting region should a gap be, at least. Default
is 0.05 (20% of the plotting region).
fixed_gap_length
Should the gaps have a fixed length? Otherwise, the gap length will be optimized
to minimize the size of comparisons. FALSE by default.
limit_to_longest_dna_seg
A logical. Should the plot be restricted to the longest dna_seg? If no, the
other segments can be extended to better fit comparisons.
offsets

main

A character. Main title of the plot.

main_pos
Position of the main title. One of centre, left or right.
dna_seg_labels
A character, same length as dna_segs. The names of the segments. If NULL,
the names of dna_segs will be used, if available. Else, no name are plotted.
If a tree is given, names must exist either in dna_seg_labels or in the
names of dna_segs.
dna_seg_label_cex
A numeric. The character size for the DNA segments labels, or tree labels.
Default is 1.
dna_seg_label_col
A color, of length 1 or of the same length as dna_segs. Gives the color of the
labels. Default is black.
A character. Describes the type of representation of genes or dna_seg elements. See details.
arrow_head_len
A numeric. Gives the length of arrow heads for gene type "arrows". The arrow
head extends at maximum at half of the gene. Set to Inf to have all arrow heads
covering the half of the gene. 200 by default.
gene_type

dna_seg_line A vector, either logical or giving colors, of length 1 or of same length as dna_segs.
Should the line in the middle of the segments be drawn, and if yes, in what color.
TRUE by default, which gives black lines. FALSE (logical, or as a string) results
in no plotting.
scale

A logical. Should the scale be displayed on the plot. TRUE by default.

plot_gene_map

23

dna_seg_scale
A logical, of length one or of the same length as dna_segs. Should a scale be
displayed below each or all dna segments, respectively. !scale by default.
n_scale_ticks
A integer. The (approximate) number of ticks on the longest segment. Default:
7.
scale_cex
A numeric. The character size for the scale labels. Default is 1.
global_color_scheme
A character of length 4. If no col column is present on any comparison or
is override_color_schemes is set, apply a global color scheme over all
comparions. See below for more details. c("auto", "auto", "blue_red")
by default.
override_color_schemes
A logical. If TRUE, apply a global color scheme even if there are comparisons
that have col columns. FALSE by default.
plot_new

Logical. Produce a new plot? If TRUE, uses grid.newpage before plotting.

debug

A numeric. If > 0, only that number of element will be plotted for each dna_seg
and comparison.

...

Further arguments to be passed to user-defined graphical functions.

Details
One line is plotted per dna_seg. Eventually, the space between the lines will be filled with the
comparisons. dna_segs can be annotated with annotations, and accompagnying data can
be plotted using seg_plot.
A phylogenetic tree (a phylog object from package ade4) can be drawn at the left of the plot.
The tree does not need to be ordered as the dna_seg_labels, but a permutation of the tree
with that order should exist. If the tree is large, the number of permutations become too large, and
the function will stop (>100000 permutations). The solution is then to provide segments that are
ordered in the same manner as the tree labels (or vice-versa).
There is an (experimental) support for branch annotations. These are given in the Newick tree, directly after the parenthesis closing a node. They can be characters or integers, but so far newick2phylog
doesn’t support decimal values. Tags will be ignored if they start with "I", and trimmed if they start
with "X".
The format of the elements of dna_segs is previously determined in the object or can be globally
set by gene_type. See the function gene_types to return the available types. Gene type can
also be user-defined, using a function returning a grob. See gene_types for more details.
xlims allow the user to plot subsegments of a dna_seg. xlims consists of a list composed of
as many numeric vectors as there are segments. Each of these numeric vectors give pairs of left and
right borders, and gives the direction. For example, c(1,2,6,4) will plot two subsegments, segment
1 to 2 which is plotted left to right and segment 4 to 6, plotted right to left. -Inf and Inf values
are accepted. NULL values will result in plotting the whole segment.
offsets allows to user to define the placement of the subsegments. If a list is provided, each
element of the list should have as many elements as there are subsegments. It will give the size of
the gaps, including the first one from the border of the plot to the first subsegment.
A main title (main) can also be added at the top of the plot, at the position defined by main_pos.
A general scale can be added at the bottom right of the plot (scale).
dna_seg_scale gives the ability to plot scales on one, some or every segment. c(TRUE, FALSE, TRUE)
will add scales to the first and third segments.

24

plot_gene_map
The four elements of global_color_scheme are (i) which column serves as scale to apply the color scheme, or "auto" (default); (ii) if the scale is "increasing" or "decreasing" (see
apply_color_scheme for more details), or "auto" (default); (iii) the color scheme to apply;
(iv) the transparency to apply (0.5 by default).

Value
Nothing. A lattice graphic is plotted on the current device.
Note
This plotting function has been tested as far as possible, but given its complexity and that the
package is young, bugs or strange behaviors are possible. Please report them to the author.
As of 10/3/2010, support for viewing exons/introns is only available using genbank and embl formats, not when importing ptt files.
Author(s)
Lionel Guy , Jens Roat Kultima
See Also
dna_seg and comparison for the base objects; read_dna_seg_from_tab, read_dna_seg_from_ptt,
read_comparison_from_tab and read_comparison_from_blast to read from files;
annotation to annotate dna_segs; seg_plot to draw plots next to dna_segs; gene_types
for gene_type argument; apply_color_scheme for color schemes;
Examples
old.par <- par(no.readonly=TRUE)
data("three_genes")
## Segments only
plot_gene_map(dna_segs=dna_segs)
## With comparisons
plot_gene_map(dna_segs=dna_segs, comparisons=comparisons)
## Tree
names <- c("A_aaa", "B_bbb", "C_ccc")
names(dna_segs) <- names
tree <- newick2phylog("(((A_aaa:4.2,B_bbb:3.9):3.1,C_ccc:7.3):1);")
plot_gene_map(dna_segs=dna_segs, comparisons=comparisons,
tree=tree)
## Increasing tree width
plot_gene_map(dna_segs=dna_segs, comparisons=comparisons,
tree=tree, tree_width=3)
## Annotations on the tree
tree2 <- newick2phylog("(((A_aaa:4.2,B_bbb:3.9)97:3.1,C_ccc:7.3)78:1);")
plot_gene_map(dna_segs=dna_segs, comparisons=comparisons,
tree=tree2, tree_width=3)
plot_gene_map(dna_segs=dna_segs, comparisons=comparisons,
tree=tree2, tree_width=3, tree_branch_labels_cex=0.5)
plot_gene_map(dna_segs=dna_segs, comparisons=comparisons,
tree=tree2, tree_width=3, tree_branch_labels_cex=0)

plot_gene_map

25

## Annotation
## Calculating middle positions
mid_pos <- middle(dna_segs[[1]])
# Create first annotation
annot1 <- annotation(x1=mid_pos, text=dna_segs[[1]]$name)
plot_gene_map(dna_segs=dna_segs, comparisons=comparisons, annotations=annot1)
## Exploring options
annot2 <- annotation(x1=c(mid_pos[1], dna_segs[[1]]$end[2]),
x2=c(NA, dna_segs[[1]]$end[3]),
text=c(dna_segs[[1]]$name[1], "region1"),
rot=c(30, 0), col=c("grey", "black"))
plot_gene_map(dna_segs=dna_segs, comparisons=comparisons,
annotations=annot2, annotation_height=1.3)
## xlims
## Just returning a segment
plot_gene_map(dna_segs, comparisons,
xlims=list(NULL, NULL, c(Inf,-Inf)),
dna_seg_scale=TRUE)
## Removing one gene
plot_gene_map(dna_segs, comparisons,
xlims=list(NULL, NULL, c(-Inf,2800)),
dna_seg_scale=TRUE)
## offsets
offsets <- c(0, 0, 0)
plot_gene_map(dna_segs=dna_segs, comparisons=comparisons, offsets=offsets)
offsets <- c(200, 400, 0)
plot_gene_map(dna_segs=dna_segs, comparisons=comparisons, offsets=offsets)
## main
plot_gene_map(dna_segs=dna_segs, comparisons=comparisons,
main="Comparison of A, B and C")
plot_gene_map(dna_segs=dna_segs, comparisons=comparisons,
main="Comparison of A, B and C", main_pos="left")
## dna_seg_labels
plot_gene_map(dna_segs=dna_segs, comparisons=comparisons,
dna_seg_labels=c("Huey", "Dewey", "Louie"))
## dna_seg_labels size
plot_gene_map(dna_segs=dna_segs, comparisons=comparisons,
dna_seg_labels=c("Huey", "Dewey", "Louie"),
dna_seg_label_cex=2)
## dna_seg_line
plot_gene_map(dna_segs=dna_segs, comparisons=comparisons,
dna_seg_line=c("FALSE", "red", grey(0.6)))
## gene_type
plot_gene_map(dna_segs=dna_segs, comparisons=comparisons,
gene_type="side_blocks")
##

26

plot_gene_map
## From here on, using a bigger dataset from a 4-genome comparison
##
data("barto")
## Adding a tree
tree <- newick2phylog("(BB:2.5,(BG:1.8,(BH:1,BQ:0.8):1.9):3);")
## Showing only subsegments
xlims1 <- list(c(1380000, 1445000),
c(10000, 83000),
c(15000, 98000),
c(5000, 82000))
## Reducing dataset size for speed purpose
for (i in 1:length(barto$dna_segs)){
barto$dna_segs[[i]] <- trim(barto$dna_segs[[i]], xlim=xlims1[[i]])
if (i < length(barto$dna_segs))
barto$comparisons[[i]] <- trim(barto$comparisons[[i]],
xlim1=xlims1[[i]], xlims1[[i+1]])
}
plot_gene_map(barto$dna_segs, barto$comparisons, tree=tree,
xlims=xlims1,
dna_seg_scale=TRUE)
## Showing several subsegments per genome
xlims2 <- list(c(1445000, 1415000, 1380000, 1412000),
c( 10000,
45000,
50000,
83000, 90000, 120000),
c( 15000,
36000,
90000, 120000, 74000, 98000),
c(
5000,
82000))
## dna_seg_scale, global_color_scheme, size, number, color of dna_seg_scale,
## size of dna_seg_scale labels
plot_gene_map(barto$dna_segs, barto$comparisons, tree=tree, xlims=xlims2,
dna_seg_scale=c(TRUE, FALSE, FALSE, TRUE), scale=FALSE,
dna_seg_label_cex=1.7,
dna_seg_label_col=c("black", "grey", "blue", "red"),
global_color_scheme=c("e_value", "auto", "grey", "0.7"),
n_scale_ticks=3, scale_cex=1)
## Hand-made offsets: size of all gaps
offsets2 <- list(c(10000, 10000),
c(2000, 2000, 2000),
c(10000, 5000, 2000),
c(10000))
plot_gene_map(barto$dna_segs, barto$comparisons, tree=tree,
#annotations=annots,
xlims=xlims2,
offsets=offsets2,
dna_seg_scale=TRUE)
##
## Exploring and modifying a previously plotted gene map plot
##
## View viewports
current.vpTree()
## Go down to one of the viewports, add an xaxis, go back up to root viewport
downViewport("dna_seg_scale.3.2")
grid.rect()
upViewport(0)
## Get all the names of the objects
grobNames <- getNames()
grobNames

range.dna_seg

27

## Change the color ot the scale line
grid.edit("scale.lines", gp=gpar(col="grey"))
## Remove first dna_seg_lines
grid.remove("dna_seg_line.1.1")
##
## Plot genoPlotR logo
##
col <- c("#B2182B", "#D6604D", "#F4A582", "#FDDBC7",
"#D1E5F0", "#92C5DE", "#4393C3", "#2166AC")
cex <- 2.3
## First segment
start1 <- c(150, 390, 570)
end1
<- c( 1, 490, 690)
genoR <- c(270, 530)
## Second segment
start2 <- c(100, 520, 550)
end2
<- c(240, 420, 650)
Plot <- c(330)
## dna_segs
ds1 <- as.dna_seg(data.frame(name=c("", "", ""),
start=start1, end=end1, strand=rep(1, 3),
col=col[c(2, 6, 1)], stringsAsFactor=FALSE))
ds_genoR <- as.dna_seg(data.frame(name=c("geno", "R"),
start=genoR, end=genoR, strand=rep(1, 2),
col=c(col[8], "black"),
stringsAsFactor=FALSE), cex=cex, gene_type="text")
ds2 <- as.dna_seg(data.frame(name=c("", "", ""),
start=start2, end=end2, strand=rep(1, 3),
col=col[c(5, 3, 7)],
stringsAsFactor=FALSE))
ds_Plot <- as.dna_seg(data.frame(name="Plot",
start=Plot, end=Plot, strand=1,
col=col[c(1)],
stringsAsFactor=FALSE), cex=cex, gene_type="text")
## comparison
c1 <- as.comparison(data.frame(start1=start1, end1=end1,
start2=start2, end2=end2,
col=grey(c(0.6, 0.8, 0.5))))
## Generate genoPlotR logo
## Not run:
cairo_pdf("logo.pdf", h=0.7, w=3)
## End(Not run)
par(fin=c(0.7, 3))
plot_gene_map(dna_segs=list(c(ds1, ds_genoR), c(ds2, ds_Plot)),
comparisons=list(c1), scale=FALSE, dna_seg_scale=FALSE,
dna_seg_line=grey(0.7), offsets=c(-20,160))
## Not run:
dev.off()
## End(Not run)
par(old.par)

range.dna_seg

Range calculation

28

range.dna_seg

Description
Calculate the range of dna_seg and comparisons.
Usage
## S3 method for class
range(x, ...)
## S3 method for class
range(x, overall=TRUE,
## S3 method for class
range(x, ...)

'dna_seg'
'comparison'
...)
'annotation'

Arguments
x

Object to calculate the range from.

overall

Logical, TRUE by default. Should the range be calculated over the whole object?
If FALSE, a range is calculated on each side of the comparison.

...

Unused.

Details
Calculate the overall range of a dna_seg, comparison or an annotation object.
Value
A numeric of length 2. For comparison, if overall is FALSE, a data frame with two rows and
two columns, xlim1 and xlim2.
Author(s)
Lionel Guy
See Also
dna_seg, comparison, trim for further examples.
Examples
## Load data
data(three_genes)
## On dna_seg
dna_segs[[1]]
range(dna_segs[[1]])
## On comparison
comparisons[[2]]
range(comparisons[[2]])
range(comparisons[[2]], overall=FALSE)

read_functions

read_functions

29

Reading functions

Description
Functions to parse dna_seg objects from tab, embl, genbank, fasta, ptt files or from mauve backbone
files, and comparison objects from tab or blast files.
Usage
read_dna_seg_from_tab(file, header = TRUE, ...)
read_dna_seg_from_file(file, tagsToParse=c("CDS"), fileType = "detect",
meta_lines = 2, gene_type = "auto", header = TRUE,
extra_fields = NULL, ...)
read_dna_seg_from_embl(file, tagsToParse=c("CDS"), ...)
read_dna_seg_from_genbank(file, tagsToParse=c("CDS"), ...)
read_dna_seg_from_fasta(file, ...)
read_dna_seg_from_ptt(file, meta_lines = 2, header = TRUE, ...)
read_comparison_from_tab(file, header = TRUE, ...)
read_comparison_from_blast(file, sort_by = "per_id",
filt_high_evalue = NULL,
filt_low_per_id = NULL,
filt_length = NULL,
color_scheme = NULL, ...)
read_mauve_backbone(file, ref = 1, gene_type = "side_blocks",
header = TRUE, filter_low = 0,
common_blocks_only = TRUE, ...)
Arguments
file

Path to file to load. URL are accepted.

header

Logical. Does the tab file has headers (column names)?

tagsToParse

Character vector. Tags to parse in embl or genbank files. Common tags are
’CDS’, ’gene’, ’misc_feature’.

fileType

Character string. Select file type, could be ’detect’ for automatic detection,
’embl’ for embl files, ’genbank’ for genbank files or ’ptt’ for ptt files.

meta_lines

The number of lines in the ptt file that represent "meta" data, not counting the
header lines. Standard for NCBI files is 2 (name and length, number of proteins.
Default is also 2.

gene_type

Determines how genes are visualized. If ’auto’ genes will appear as arrows
in there are no introns and as blocks if there are introns. Can also be set to
for example ’blocks’ or ’arrows’. Do note, currently introns are not supported
in the ptt file format. Default for mauve backbone is side_blocks. See
gene_types page for more details, or use function gene_types.

extra_fields NULL by default. If a character vector, parses extra fields in the genbank or embl
file that have corresponding keys and put them in the resulting dna_seg.
sort_by

In BLAST-like tabs, gives the name of the column that will be used to sort the
comparisons. Accepted values are per_id (percent identity, default), mism
(mismatches), gaps (gaps), e_value (E-value), bit_score (bit score).

30

read_functions
filt_high_evalue
A numerical, or NULL (default). Filters out all comparisons that have a e-value
higher than this one.
filt_low_per_id
A numerical, or NULL (default). Filters out all comparisons that have a percent
identity lower than this one.
filt_length

A numerical, or NULL (default). Filters out all comparisons that have alignments
shorter than this value.

color_scheme A color scheme to apply. See apply_color_scheme for more details. Possible values include grey and red_blue. NULL by default. Color schemes
can be applied while running plot_gene_map.
ref

In mauve backbone, which of the dna segments will be the reference, i.e. which
one will have its blocks in order.

...

Further arguments passed to generic reading functions and class conversion
functions. See as.dna_seg and as.comparison.
For read_comparison* functions, see details.

A numeric. If larger than 0, all blocks smaller that this number will be filtered
out. Defaults to 0.
common_blocks_only
A logical. If TRUE (by default), reads only common blocks (core blocks).
filter_low

Details
Tab files representing DNA segements should have at least the following columns: name, start, end,
strand (in that order. Additionally, if the tab file has headers, more columns will be used to define,
for example, the color, line width and type, pch and/or cex. See dna_seg for more information.
An example:
name
feat1A
feat1B
feat1C
feat1D

start
2
1399
2101
2800

end
1345
2034
2932
3120

strand
1
1
-1
1

col
blue
red
grey
green

Embl and Genbank files are two commonly used file types. These file types often contain a great
variety of information. To properly extract data from these files, the user has to choose which
features to extract. Commonly ’CDS’ features are of interest, but other feature tags such as ’gene’
or ’misc_feature’ may be of interest. Should a feature contain an inner "pseudo" tag indicating this
CDS or gene is a pseudo gene, this will be presented as a ’CDS_pseudo’ or a ’gene_pseudo’ feature
type respectively in the resulting table. Certain constraints apply to these file types, of which some
are: embl files must contain one and only one ID tag; genbank files may only contain one and only
one locus tag. In these two files, the following tags are parsed (in addition to the regular name, start,
end and strand): protein_id, product, color (or colour). In addition, extra tags can be parsed with
the argument extra_fields. If there are more than one field with such a tag, only the first one
is parsed.
Fasta files are read as one gene, as long as there are nucleotides in the fasta file.

Ptt (or protein table) files are a tabular format giving a bunch of information on each protein of a
genome (or plasmid, or virus, etc). They are available for each published genome on the NCBI ftp
site (ftp://ftp.ncbi.nlm.nih.gov/genomes/). As an example, look at ftp://ftp.ncbi.nlm.nih.gov/genomes/Bacteria/Bartonella

read_functions

31

1/NC_005956.ptt.
Tabular comparison files should have at least the following columns: start1, end1, start2, end2. If
no header is specified, the fifth column is parsed as the color.
start1
2
1399
500

end1
1345
2034
800

start2
10
2700
3000

end2
1210
1100
2500

col
red
blue
blue

BLAST tabular result files are produced either with blastall using -m8 or -m9 parameter, or with
any of the newer blastn/blastp/blastx/tblastx using -outfmt 6 or -outfmt 7.
In the subsequent plot_gene_map, the comparisons are drawn in the order of the comparison
object, i.e. the last rows of the comparison object are on the top in the plot. For comparisons read
from BLAST output, the order can be modified by using the argument sort_by. In any case, the
order of plotting can be modified by modifying the order of rows in the comparison object prior
to plotting.
Mauve backbone is another tabular data file that summarizes the blocks that are similar between
all compared genomes. Each genome gets two columns, one start and one end of the block.
There is one row per block and eventually a header row. If named, columns have sequence numbers, not actual names, so be careful to input the same order in both Mauve and genoPlotR. See
http://asap.ahabs.wisc.edu/mauve-aligner/mauve-user-guide/mauve-output-file-formats.html for more
info on the file format. Normally, the function should be able to read both progressiveMauve
and mauveAligner outputs. The function returns both the blocks as dna_segs and the links
between the blocks as comparisons.
Value
read_dna_seg_from_tab, read_dna_seg_from_file, read_dna_seg_from_embl,
read_dna_seg_from_genbank and read_dna_seg_from_ptt return dna_seg objects.
read_comparison_from_tab and read_comparison_from_blast return comparison
objects. read_mauve_backbone returns a list containing a list of dna_segs and comparisons.
objects.
Note
Formats are changing and it maybe that some functions are temporarily malfunctioning. Please
report any bug to the author. Mauve examples were prepared with Mauve 2.3.1.
Author(s)
Lionel Guy, Jens Roat Kultima
References
For BLAST: http://www.ncbi.nlm.nih.gov/blast/ For Mauve: http://asap.ahabs.wisc.edu/mauve/
See Also
comparison, dna_seg, apply_color_scheme.

32

read_functions

Examples
##
## From tabs
##
## Read DNA segment from tab
dna_seg3_file <- system.file('extdata/dna_seg3.tab', package = 'genoPlotR')
dna_seg3 <- read_dna_seg_from_tab(dna_seg3_file)
## Read comparison from tab
comparison2_file <- system.file('extdata/comparison2.tab',
package = 'genoPlotR')
comparison2 <- read_comparison_from_tab(comparison2_file)
##
## Mauve backbone
##
## File: this is only to retrieve the file from the genoPlotR
## installation folder.
bbone_file <- system.file('extdata/barto.backbone', package = 'genoPlotR')
## Read backbone
## To read your own backbone, run something like
## bbone_file <- "/path/to/my/file.bbone"
bbone <- read_mauve_backbone(bbone_file)
names <- c("B_bacilliformis", "B_grahamii", "B_henselae", "B_quintana")
names(bbone$dna_segs) <- names
## Plot
plot_gene_map(dna_segs=bbone$dna_segs, comparisons=bbone$comparisons)
## Using filter_low & changing reference sequence
bbone <- read_mauve_backbone(bbone_file, ref=2, filter_low=2000)
names(bbone$dna_segs) <- names
plot_gene_map(dna_segs=bbone$dna_segs, comparisons=bbone$comparisons)
## Read guide tree
tree_file <- system.file('extdata/barto.guide_tree', package = 'genoPlotR')
tree_str <- readLines(tree_file)
for (i in 1:length(names)){
tree_str <- gsub(paste("seq", i, sep=""), names[i], tree_str)
}
tree <- newick2phylog(tree_str)
## Plot
plot_gene_map(dna_segs=bbone$dna_segs, comparisons=bbone$comparisons,
tree=tree)
##
## From embl file
##
bq_embl_file <- system.file('extdata/BG_plasmid.embl', package = 'genoPlotR')
bq <- read_dna_seg_from_embl(bq_embl_file)
##
## From genbank file
##
bq_genbank_file <- system.file('extdata/BG_plasmid.gbk', package = 'genoPlotR')
bq <- read_dna_seg_from_file(bq_genbank_file, fileType="detect")

reverse

33

## Parsing extra fields in the genbank file
bq <- read_dna_seg_from_file(bq_genbank_file,
extra_fields=c("db_xref", "transl_table"))
names(bq)

##
## From ptt files
##
## From a file
bq_ptt_file <- system.file('extdata/BQ.ptt', package = 'genoPlotR')
bq <- read_dna_seg_from_ptt(bq_ptt_file)
## Read directly from NCBI ftp site:
url <- "ftp://ftp.ncbi.nih.gov/genomes/Bacteria/Bartonella_henselae_Houston-1/NC_005956.p
attempt <- 0
## Not run:
while (attempt < 5){
attempt <- attempt + 1
bh <- try(read_dna_seg_from_ptt(url))
if (!inherits(bh, "try-error")) {
attempt <- 99
} else {
print(paste("Tried", attempt, "times, retrying in 5s"))
Sys.sleep(5)
}
}
## End(Not run)
## If attempt to connect to internet fails
if (!exists("bh")){
data(barto)
bh <- barto$dna_segs[[3]]
}
##
## Read from blast
##
bh_vs_bq_file <- system.file('extdata/BH_vs_BQ.blastn.tab',
package = 'genoPlotR')
bh_vs_bq <- read_comparison_from_blast(bh_vs_bq_file, color_scheme="grey")
## Plot
plot_gene_map(dna_segs=list(BH=bh, BQ=bq), comparisons=list(bh_vs_bq),
xlims=list(c(1,50000), c(1, 50000)))

reverse

Reverse objects

Description
Reverse objects, mainly dna_seg and comparison

34

reverse

Usage
reverse(x, ...)
## Default S3 method:
reverse(x, ...)
## S3 method for class 'dna_seg'
reverse(x, ...)
## S3 method for class 'comparison'
reverse(x, side = 0, ...)
Arguments
x

The object to reverse.

...

Unused.

side

In the case of comparisons, the side of the comparison that should be reversed.
If side=1, the first side will be reversed. If side=2, the second side will be
reversed. If side<1, no side is reversed. If side>2, both sides are reversed.

Value
The same object as input.
Author(s)
Lionel Guy
See Also
dna_seg, comparison
Examples
## load data
data(three_genes)
## on dna_seg
dna_segs[[1]]
reverse(dna_segs[[1]])
## on comparison
reverse(comparisons[[2]], side=1)
reverse(comparisons[[2]], side=3)
## With mauve backbone
data(mauve_bbone)
## Plot
plot_gene_map(dna_segs=bbone$dna_segs, comparisons=bbone$comparisons)
## Reverse B_bacilliformis, and the corresponding comparison (first "side")
bbone$dna_segs[[1]] <- reverse(bbone$dna_segs[[1]])
bbone$comparisons[[1]] <- reverse(bbone$comparisons[[1]], 1)
plot_gene_map(dna_segs=bbone$dna_segs, comparisons=bbone$comparisons)

seg_plot

seg_plot

35

seg_plot class and class functions

Description
An seg_plot is an object to plot data associated to a dna_seg object. It is a list with mandatory
and optional arguments. The main arguments are func, which is a function returning a grob or a
gList, and args, which are arguments to be passed to this function.
Usage
seg_plot(func,
args = NULL,
xargs = c("x", "x0", "x1", "x2", "v"),
yargs = c("y", "y0", "y1", "y2", "h"),
ylim = NULL)
as.seg_plot(seg_plot)
is.seg_plot(seg_plot)
Arguments
func
args
xargs

yargs

ylim
seg_plot

Mandatory, with no defaults. A function that returns a grob object. See grid
documentation to find ready-made functions. User-defined functions work too.
A list, NULL by default. The arguments that will be passed to the function. It is
recommended that all arguments are named.
A vector giving the names of which of the arguments in args are defining the
x-axis. Used, among others, by the function trim.seg_plot. By default,
gives the most common x-defining arguments of the grid functions (x, x0, x1,
x2, v).
A vector giving the names of which of the arguments in args are defining the
y-axis. Used when plotting the graphs to define a sensible ylim if not defined.
By default, gives the most common y-defining arguments of the grid functions
(y, y0, y1, y2, h).
A numeric vector of length 2, defining the range of the plot when drawn with
plot_gene_map. Derived from yargs if not set.
In as.seg_plot, a list object to convert to seg_plot. See details below.
In is.seg_plot, an object to test.

Details
A seg_plot object is an object describing how to plot data associated to a dna_seg. It is a list
composed of a function, arguments to pass to this function, two arguments to define which of those
define x and y, and an eventual ylim to limit the plotting to a certain range when plotting.
The function func should return a grob object, or a gList list of grobs. The predefined functions of grid, such as linesGrob, pointsGrob, segmentsGrob, textGrob or polygonGrob
can be used, or user-defined functions can be defined.
The arguments in args should correspond to arguments passed to func. For example, if func = pointsGrob,
args could contain the elements x = 10:1, y = 1:10. It will often also contain a gp element,
the result of a call to the gpar function, to control graphical aspects of the plot such as color, fill,
line width and style, fonts, etc.

36

seg_plot

Value
seg_plot and as.seg_plot return a seg_plot object. is.seg_plot returns a logical.
Author(s)
Lionel Guy
See Also
plot_gene_map, trim.seg_plot.
Examples
## Using the existing pointsGrob
x <- 1:20
y <- rnorm(20)
sp <- seg_plot(func=pointsGrob, args=list(x=x, y=y,
gp=gpar(col=1:20, cex=1:3)))
is.seg_plot(sp)
## Function seg_plot(...) is identical to as.seg_plot(list(...))
sp2 <- as.seg_plot(list(func=pointsGrob, args=list(x=x, y=y,
gp=gpar(col=1:20, cex=1:3))))
identical(sp, sp2)
## For the show, plot the obtained result
grb <- do.call(sp$func, sp$args)
## Trim the seg_plot
sp_trim <- trim(sp, c(3, 10))
## Changing color and function "on the fly"
sp_trim$args$gp$col <- "blue"
sp_trim$func <- linesGrob
grb_trim <- do.call(sp_trim$func, sp_trim$args)
## Now plot
plot.new()
pushViewport(viewport(xscale=c(0,21), yscale=c(-4,4)))
grid.draw(grb)
grid.draw(grb_trim)
## Using home-made function
triangleGrob <- function(start, end, strand, col, ...) {
x <- c(start, (start+end)/2, end)
y1 <- 0.5 + 0.4*strand
y <- c(y1, rep(0.5, length(y1)), y1)
polygonGrob(x, y, gp=gpar(col=col), default.units="native",
id=rep(1:7, 3))
}
start <- seq(1, 19, by=3)+rnorm(7)/3
end <- start + 1 + rnorm(7)
strand <- sign(rnorm(7))
sp_tr <- seg_plot(func=triangleGrob,
args=list(start=start, end=end, strand=strand,
col=1:length(start)), xargs=c("start", "end"))
grb_tr <- do.call(sp_tr$func, sp_tr$args)
plot.new()
pushViewport(viewport(xscale=c(1,22), yscale=c(-2,2)))
grid.draw(grb_tr)
## Trim

seg_plot

37

sp_tr_trim <- trim(sp_tr, xlim=c(5, 15))
str(sp_tr_trim)
## If the correct xargs are not indicated, trimming won't work
sp_tr$xargs <- c("x")
sp_tr_trim2 <- trim(sp_tr, xlim=c(5, 15))
identical(sp_tr_trim, sp_tr_trim2)
y1 <- convertY(grobY(grb_tr, "south"), "native")
y2 <- convertY(grobY(grb_tr, "north"), "native")
heightDetails(grb)
grb
## Applying it to plot_gene_maps
data(three_genes)
## Build data to plot
xs <- lapply(dna_segs, range)
colors <- c("red", "blue", "green")
seg_plots <- list()
for (i in 1:length(xs)){
x <- seq(xs[[i]][1], xs[[i]][2], length=20)
seg_plots[[i]] <- seg_plot(func=pointsGrob,
args=list(x=x, y=rnorm(20)+2*i,
default.units="native", pch=3,
gp=gpar(col=colors[i], cex=0.5)))
}
plot_gene_map(dna_segs, comparisons,
seg_plots=seg_plots,
seg_plot_height=0.5,
seg_plot_height_unit="inches",
dna_seg_scale=TRUE)
## A more complicated example
data(barto)
tree <- newick2phylog("(BB:2.5,(BG:1.8,(BH:1,BQ:0.8):1.9):3);")
## Showing several subsegments per genome
xlims2 <- list(c(1445000, 1415000, 1380000, 1412000),
c( 10000,
45000,
50000,
83000, 90000, 120000),
c( 15000,
36000,
90000, 120000, 74000, 98000),
c(
5000,
82000))
## Adding fake data in 1kb windows
seg_plots <- lapply(barto$dna_segs, function(ds){
x <- seq(1, range(ds)[2], by=1000)
y <- jitter(seq(100, 300, length=length(x)), amount=50)
seg_plot(func=linesGrob, args=list(x=x, y=y, gp=gpar(col=grey(0.3), lty=2)))
})
plot_gene_map(barto$dna_segs, barto$comparisons, tree=tree,
seg_plots=seg_plots,
seg_plot_height=0.5,
seg_plot_height_unit="inches",
xlims=xlims2,
limit_to_longest_dna_seg=FALSE,
dna_seg_scale=TRUE,
main="Random plots for the same segment in 4 Bartonella genomes")

38

trim

three_genes

Three genes data set

Description
A set of three made-up genes, compared in three chromosomes.
Usage
data(three_genes)
Format
Two dataframes, representing the three genes in three DNA segments:
• dna_segswhich is a list of three dna_seg objects, containing each three rows (or genes).
• comparisonswhich is a list of two comparison objects.
Examples
data(three_genes)
plot_gene_map(dna_segs, comparisons)

trim

Trimming data frames or more complex objects with >= 2 numeric
columns

Description
Trims data frames with 2 or more numeric columns using a xlim. xlim(s) are as used to filter rows
whose numeric values are included in this interval.
Usage
trim(x, ...)
## Default S3 method:
trim(x, xlim = NULL, ...)
## S3 method for class 'dna_seg'
trim(x, xlim = NULL, ...)
## S3 method for class 'comparison'
trim(x, xlim1 = c(-Inf, Inf), xlim2 = c(-Inf, Inf), ...)
## S3 method for class 'annotation'
trim(x, xlim = NULL, ...)
## S3 method for class 'seg_plot'
trim(x, xlim = NULL, ...)

trim

39

Arguments
x

An object to trim,. generally a data frame or a matrix, or a seg_plot object.

xlim

A numeric of length 2. In a general case, the rows whose values are included in
this interval are returned.

...

Unused.

xlim1

A numeric of length 2. In the case of comparison, where the comparison can be
filtered on two sides, the interval to filter the first side.

xlim2

A numeric of length 2. The interval to filter the second side.

Details
In the case where x is a seg_plot object, the function uses the xargs argument to define what
are the vectors defining the x position (they should be the same length). Then, all the arguments
(including those inside an eventual gp argument) that are the same length as the x vectors are
trimmed, so that only the rows for which the x values are inside the xlim argument are kept.
Value
Returns the same object as input, with the rows (or subset) corresponding to the given interval.
Author(s)
Lionel Guy
See Also
dna_seg, comparison, seg_plot.
Examples
## Load
data(barto)
xlim_ref <- c(10000, 45000)
## Seg 2 (ref)
barto$dna_segs[[2]] <- trim(barto$dna_segs[[2]], xlim=xlim_ref)
## Seg 1
barto$comparisons[[1]] <- trim(barto$comparisons[[1]], xlim2=xlim_ref)
xlim1 <- range(barto$comparisons[[1]], overall=FALSE)$xlim1
barto$dna_segs[[1]] <- trim(barto$dna_segs[[1]], xlim=xlim1)
## Seg 3
barto$comparisons[[2]] <- trim(barto$comparisons[[2]], xlim1=xlim_ref)
xlim3 <- range(barto$comparisons[[2]], overall=FALSE)$xlim2
barto$dna_segs[[3]] <- trim(barto$dna_segs[[3]], xlim=xlim3)
## Seg 4
barto$comparisons[[3]] <- trim(barto$comparisons[[3]], xlim1=xlim3)
xlim4 <- range(barto$comparisons[[3]], overall=FALSE)$xlim2
barto$dna_segs[[4]] <- trim(barto$dna_segs[[4]], xlim=xlim4)
## Plot
plot_gene_map(barto$dna_segs, barto$comparisons)
## With seg_plot
x <- 1:20
y <- rnorm(20)
sp <- seg_plot(func=pointsGrob, args=list(x=x, y=y,

40

trim
gp=gpar(col=1:20, cex=1:3)))
## Trim
sp_trim <- trim(sp, c(3, 10))
str(sp_trim)
range(sp_trim$arg$x)

Index
∗Topic IO
read_functions, 29
∗Topic aplot
genoPlotR-package, 2
∗Topic datasets
barto, 10
chrY_subseg, 11
mauve_bbone, 19
three_genes, 38
∗Topic data
annotation, 4
apply_color_scheme, 6
artemisColors, 8
auto_annotate, 9
c.dna_seg, 11
comparison, 12
dna_seg, 13
gene_types, 15
human_nt, 18
middle, 19
range.dna_seg, 27
reverse, 33
seg_plot, 35
trim, 38
∗Topic file
read_functions, 29
∗Topic hplot
plot_gene_map, 20

chrY_subseg, 11
comparison, 3, 7, 12, 23, 24, 28, 31, 34, 39
comparisons (three_genes), 38

annotation, 4, 9, 20, 23, 24
apply_color_scheme, 6, 24, 31
artemisColors, 8
as.annotation (annotation), 4
as.comparison, 30
as.comparison (comparison), 12
as.dna_seg, 30
as.dna_seg (dna_seg), 13
as.seg_plot (seg_plot), 35
auto_annotate, 9

range.annotation (range.dna_seg),
27
range.comparison (range.dna_seg),
27
range.dna_seg, 27
read_comparison_from_blast, 3, 12,
13, 24
read_comparison_from_blast
(read_functions), 29
read_comparison_from_tab, 3, 12, 13,
24
read_comparison_from_tab
(read_functions), 29
read_dna_seg_from_embl
(read_functions), 29

dna_seg, 3, 9, 11, 13, 13, 16, 20, 23, 24, 28,
30, 31, 34, 39
dna_segs (three_genes), 38
gene_types, 15, 15, 23, 24, 29
genoPlotR (genoPlotR-package), 2
genoPlotR-package, 2
gList, 35
gpar, 35
grid, 35
grob, 35
human_nt, 18
is.annotation (annotation), 4
is.comparison (comparison), 12
is.dna_seg (dna_seg), 13
is.seg_plot (seg_plot), 35
mauve_bbone, 19
middle, 5, 19
phylog, 21
plot_gene_map, 3, 5, 16, 20, 35, 36

barto, 10
bbone (mauve_bbone), 19
c.dna_seg, 11
41

42
read_dna_seg_from_fasta
(read_functions), 29
read_dna_seg_from_file
(read_functions), 29
read_dna_seg_from_genbank
(read_functions), 29
read_dna_seg_from_ptt, 3, 14, 15, 24
read_dna_seg_from_ptt
(read_functions), 29
read_dna_seg_from_tab, 3, 14, 15, 24
read_dna_seg_from_tab
(read_functions), 29
read_functions, 3, 29
read_mauve_backbone, 19
read_mauve_backbone
(read_functions), 29
reverse, 33
reverse.comparison, 13
seg_plot, 21, 23, 24, 35, 39
three_genes, 38
trim, 28, 38
trim.comparison, 13
trim.seg_plot, 35, 36

INDEX



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