gggenomes (version 1.0.1)

gggenomes: Plot genomes, features and synteny maps

Description

gggenomes() initializes a gggenomes-flavored ggplot object. It is used to declare the input data for gggenomes' track system.

(See for more details on the track system, gggenomes vignette or the Details/Arguments section)

Usage

gggenomes(
  genes = NULL,
  seqs = NULL,
  feats = NULL,
  links = NULL,
  .id = "file_id",
  spacing = 0.05,
  wrap = NULL,
  adjacent_only = TRUE,
  infer_bin_id = seq_id,
  infer_start = min(start, end),
  infer_end = max(start, end),
  infer_length = max(start, end),
  theme = c("clean", NULL),
  .layout = NULL,
  ...
)

Value

gggenomes-flavored ggplot object

Arguments

genes, feats

A data.frame, a list of data.frames, or a character vector with paths to files containing gene data. Each item is added as feature track.

For a single data.frame the track_id will be "genes" and "feats", respectively. For a list, track_ids are parsed from the list names, or if names are missing from the name of the variable containing each data.frame. Data columns:

• required: seq_id,start,end

• recognized: strand,bin_id,feat_id,introns

seqs

A data.frame or a character vector with paths to files containing sequence data. Data columns:

• required: seq_id,length

• recognized: bin_id,start,end,strand

links

A data.frame or a character vector with paths to files containing link data. Each item is added as links track. Data columns:

• required: seq_id,seq_id2

• recognized: start,end,bin_id,start2,end2,bin_id2,strand

.id

The name of the column for file labels that are created when reading directly from files. Defaults to "file_id". Set to "bin_id" if every file represents a different bin.

spacing

between sequences in bases (>1) or relative to longest bin (<1)

wrap

wrap bins into multiple lines with at most this many nucleotides per lin.

adjacent_only

Indicates whether links should be created between adjacent sequences/chromosomes only. By default it is set to adjacent_only = TRUE. If FALSE, links will be created between all sequences

(not recommended for large data sets)

infer_length, infer_start, infer_end, infer_bin_id

used to infer pseudo seqs if only feats or links are provided, or if no bin_id column was provided. The expressions are evaluated in the context of the first feat or link track.

By default subregions of sequences from the first to the last feat/link are generated. Set infer_start to 0 to show all sequences from their true beginning.

theme

choose a gggenomes default theme, NULL to omit.

.layout

a pre-computed layout from layout_genomes(). Useful for developmental purposes.

...

additional parameters, passed to layout

Details

gggenomes::gggenomes() resembles the functionality of ggplot2::ggplot(). It is used to construct the initial plot object, and is often followed by "+" to add components to the plot (e.g. "+ geom_gene()").

A big difference between the two is that gggenomes has a multi-track setup ('seqs', 'feats', 'genes' and 'links'). gggenomes() pre-computes a layout and adds coordinates (y,x,xend) to each data frame prior to the actual plot construction. This has some implications for the usage of gggenomes:

• Data frames for tracks have required variables. These predefined variables are used during import to compute x/y coordinates (see arguments).

• gggenomes' geoms can often be used without explicit aes() mappings This works because we always know the names of the plot variables ahead of time: they originate from the pre-computed layout, and we can use that information to set sensible default aesthetic mappings for most cases.

Examples

# Compare the genomic organization of three viral elements
# EMALEs: endogenous mavirus-like elements (example data shipped with gggenomes)
gggenomes(emale_genes, emale_seqs, emale_tirs, emale_ava) +
  geom_seq() + geom_bin_label() + # chromosomes and labels
  geom_feat(size = 8) + # terminal inverted repeats
  geom_gene(aes(fill = strand), position = "strand") + # genes
  geom_link(offset = 0.15) # synteny-blocks

# with some more information
gggenomes(emale_genes, emale_seqs, emale_tirs, emale_ava) %>%
  add_feats(emale_ngaros, emale_gc) %>%
  add_clusters(emale_cogs) %>%
  sync() +
  geom_link(offset = 0.15, color = "white") + # synteny-blocks
  geom_seq() + geom_bin_label() + # chromosomes and labels
  # thistle4, salmon4, burlywood4
  geom_feat(size = 6, position = "identity") + # terminal inverted repeats
  geom_feat(
    data = feats(emale_ngaros), color = "turquoise4", alpha = .3,
    position = "strand", size = 16
  ) +
  geom_feat_note(aes(label = type),
    data = feats(emale_ngaros),
    position = "strand", nudge_y = .3
  ) +
  geom_gene(aes(fill = cluster_id), position = "strand") + # genes
  geom_wiggle(aes(z = score, linetype = "GC-content"), feats(emale_gc),
    fill = "lavenderblush4", position = position_nudge(y = -.2), height = .2
  ) +
  scale_fill_brewer("Conserved genes", palette = "Dark2", na.value = "cornsilk3")

# initialize plot directly from files
gggenomes(
  ex("emales/emales.gff"),
  ex("emales/emales.gff"),
  ex("emales/emales-tirs.gff"),
  ex("emales/emales.paf")
) + geom_seq() + geom_gene() + geom_feat() + geom_link()

# multi-contig genomes wrap to fixed width
s0 <- read_seqs(list.files(ex("cafeteria"), "Cr.*\\.fa.fai$", full.names = TRUE))
s1 <- s0 %>% dplyr::filter(length > 5e5)
gggenomes(seqs = s1, infer_bin_id = file_id, wrap = 5e6) +
  geom_seq() + geom_bin_label() + geom_seq_label()