3.2 从圆圈到多层注释

本页目录

本页准备
第一步：双线骨架与中心名称
第二步：加入特征形状
第三步：为特征适配标签
第四步：引物与选定酶切位点

这一页只关注每次新增的视觉信息。准备时计算输入，绘图仍由独立图层完成。

本页准备

准备代码 · 在本页运行一次

代码library(ggchord)
feature_palette <- c(
  CDS = "#993366", promoter = "#31849B", rep_origin = "#F1E51D",
  primer_bind = "#A020F0", misc_feature = "#A6ACB3", protein_bind = "#31849B"
)

library(ggchord)
doc_background <- "#FFFCF5"
identity_palette <- c("#303B71", "#36537D", "#269B82", "#A5D52C", "#ECEB31")
identity_values <- c(0, 0.5, 0.8, 0.9, 1)
identity_breaks <- c(0, 50, 80, 90, 100)
doc_legend_theme <- theme(
  legend.title = element_text(size = 13, face = "bold"),
  legend.text = element_text(size = 11),
  legend.key.width = unit(9, "mm"), legend.key.height = unit(5, "mm"),
  legend.spacing.x = unit(9, "mm"), legend.spacing.y = unit(7, "mm"),
  legend.margin = margin(l = 4, r = 4, unit = "mm")
)
sequence_guide <- guide_ggchord_legend(nrow = 1, theme = doc_legend_theme)
strand_guide <- guide_ggchord_legend(
  nrow = 1, override.aes = list(strand = c("+", "-")), theme = doc_legend_theme
)
identity_guide <- guide_ggchord_colourbar(
  position = "left", direction = "vertical",
  theme = theme(
    legend.key.height = unit(75, "mm"), legend.key.width = unit(5, "mm"),
    legend.title = element_text(size = 13, face = "bold"),
    legend.text = element_text(size = 11), legend.margin = margin(r = 7, unit = "mm")
  )
)
doc_theme <- theme_ggchord_publication(
  axis.text = element_text(size = 11),
  axis.text.check.overlap = TRUE
) +
  theme(
    legend.position = "bottom",
    legend.title = element_text(size = 13, face = "bold"),
    legend.text = element_text(size = 11),
    legend.key.width = unit(9, "mm"), legend.key.height = unit(5, "mm"),
    legend.spacing.x = unit(9, "mm"), legend.spacing.y = unit(7, "mm"),
    legend.box.spacing = unit(6, "mm"),
    plot.background = element_rect(fill = doc_background, colour = NA),
    panel.background = element_blank(),
    plot.title = element_text(face = "bold", size = 18),
    plot.subtitle = element_text(size = 10, colour = "#5C625A"),
    plot.margin = margin(14, 18, 14, 18)
  )
doc_axis <- function(coordinates = "relative") {
  scale_seq_position_continuous(
    coordinates = coordinates, n.breaks = 3, minor_breaks = NULL,
    labels = function(x) format(x, scientific = FALSE, big.mark = ",", trim = TRUE)
  )
}

library(dplyr)
data(plasmid_example_pUC19)
plasmid_seq <- plasmid_example_pUC19 |>
  select(accver, length)
plasmid_feature_source <- find_common_features(plasmid_example_pUC19)
# Dedicated primer layers already show bindings; avoid duplicate primer annotations.
plasmid_features <- plasmid_feature_source |>
  filter(type != "primer_bind") |>
  select(accver, start, end, strand, type, anno)
plasmid_primers <- find_primer_bindings(plasmid_example_pUC19, set = "universal")
plasmid_sites <- find_restriction_sites(plasmid_example_pUC19)
display_sites <- filter_restriction_sites(plasmid_sites, enzymes = c("EcoRI", "BamHI", "HindIII"))
primer_names <- primer_catalog(set = "universal")

代码plasmid_position <- position_feature_stack(spacing = 0.085, base_position = position_plasmid())
p_plasmid_backbone <- ggchord(seq_data = plasmid_seq) +
  geom_seq(seq_style = "double", colour = "#252525") +
  geom_seq_center_label() +
  coord_circular(rotation = 90) +
  theme_ggchord_plasmid() +
  theme(
    panel.background = element_blank(),
    plot.background = element_rect(fill = doc_background, colour = NA)
  )
p_plasmid_features <- p_plasmid_backbone +
  geom_feature_plasmid(data = plasmid_features, position = plasmid_position)
p_plasmid_text <- p_plasmid_features +
  geom_feature_label_repel(
    data = plasmid_features, position = plasmid_position,
    external = TRUE, label_fit = "shrink", label_min_size = 2.5
  )
p_plasmid_text

第一步：双线骨架与中心名称

先检查长度和名称。coord_circular() 仅接受一条序列；双线样式由geom控制。

第二步：加入特征形状

position_feature_stack() 为重叠注释分层，geom_feature_plasmid() 按实际类别选择形状和配色。短特征可能使用紧凑形状，不等于缺失。

第三步：为特征适配标签

箭头层与标签层使用同一Position。允许文字在明确字号下限内缩小，必要时外置；尺寸变化后重新检查。

第四步：引物与选定酶切位点

代码plasmid_position <- position_feature_stack(spacing = .10, base_position = position_plasmid())
p_plasmid <- ggchord(seq_data = plasmid_seq) +
  geom_seq(seq_style = "double") +
  geom_feature_plasmid(aes(feature_fill = type), data = plasmid_features, position = plasmid_position) +
  geom_feature_label_repel(
    data = plasmid_features, position = plasmid_position,
    size = 4
  ) +
  geom_primer(data = plasmid_primers, colour = "#A020F0") +
  geom_primer_label_repel(data = plasmid_primers, colour = "#A020F0", max_overlaps = 0, size = 3) +
  geom_restriction_site(data = display_sites, colour = "#29364B", label_size = 3, leader = "radial") +
  geom_seq_center_label() +
  scale_feature_fill_manual(values = feature_palette) +
  scale_seq_position_continuous() +
  coord_circular(rotation = 90) +
  theme_ggchord_plasmid() +
  theme(
    legend.title = element_text(size = 13, face = "bold"),
    legend.text = element_text(size = 11),
    legend.spacing.x = unit(9, "mm"), legend.spacing.y = unit(7, "mm"),
    plot.background = element_rect(fill = doc_background, colour = NA),
    panel.background = element_blank()
  )
p_plasmid

只展示选定的三个酶切位点，完整搜索表仍保留。完整的识别方法、来源与假设见3.3。然后使用13个参考案例比较注释密度，最后完成pETDuet-1综合图。