3.4 13个质粒参考案例

本页目录

一次准备全部案例
输入与识别结果
pSB1C3：小载体与基本骨架
pUC19：常见注释与复制起点
pBR322：多个抗性区与方向
pBluescript II SK(+)：局部调控与短特征
pET-28a(+)：表达区与标签
pETDuet-1：双表达单元，进入密集案例
pDONR221：重组相关结构
pcDNA3.1(+)：表达载体的注释层级
pTRE-Tight-BI：双向调控布局
pTRIPZ：更长序列的显示密度
pCAMBIA1300：较大植物载体的结构
pEarleyGate 201：另一植物载体的类别对照
pSpCas9(BB)-2A-GFP (PX458)：大型表达载体与文字适配
下一步

本图库包含13个独立的仓库FASTA参考记录，全部由当前代码重绘。旧版PNG和.dna文件不是本轮输出；下方只将载体用途作为学习线索，不由图形证明实验功能。完整来源和长度由随包数据及生成规则保留。

一次准备全部案例

准备代码 · 在本页运行一次

代码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)
  )
}

代码library(ggchord)
library(dplyr)
library(purrr)
# Thirteen unique records; the compatibility spelling pUC19c is not an extra plasmid.
catalog_objects <- c(
  "plasmid_example_pSB1C3", "plasmid_example_pUC19", "plasmid_example_pBR322",
  "plasmid_example_pBluescript_II_SK_plus", "plasmid_example_pET_28a_plus",
  "plasmid_example_pETDuet_1", "plasmid_example_pDONR221",
  "plasmid_example_pcDNA3_1_plus", "plasmid_example_pTRE_Tight_BI",
  "plasmid_example_pTRIPZ", "plasmid_example_pCAMBIA1300",
  "plasmid_example_pEarleyGate_201", "plasmid_example_pSpCas9_BB_2A_GFP_PX458"
)
read_catalog_record <- function(object) {
  target <- new.env(parent = emptyenv())
  data(list = object, package = "ggchord", envir = target)
  target[[object]]
}
catalog_sequences <- map(catalog_objects, read_catalog_record)
catalog_features <- map(catalog_sequences, find_common_features)
catalog_summary <- map2(catalog_sequences, catalog_features, function(sequence, features) {
  tibble(accver = sequence$accver, length = sequence$length, features = nrow(features))
}) |>
  bind_rows()

代码make_catalog_plot <- function(sequence, features) {
  placement <- position_feature_stack(spacing = 0.085, base_position = position_plasmid())
  ggchord(seq_data = sequence |> select(accver, length)) +
    geom_seq(seq_style = "double", colour = "#252525") +
    geom_feature_plasmid(data = features, position = placement) +
    geom_feature_label_repel(
      data = features, position = placement, external = FALSE,
      label_fit = "shrink", label_min_size = 2.5, max_overlaps = 0
    ) +
    geom_seq_center_label() +
    coord_circular(rotation = 90, fit = "manual", xlim = c(-1.35, 1.35), ylim = c(-1.35, 1.35)) +
    theme_ggchord_plasmid() +
    theme(
      panel.background = element_blank(),
      plot.background = element_rect(fill = doc_background, colour = NA)
    )
}
catalog_plots <- map2(catalog_sequences, catalog_features, make_catalog_plot)
names(catalog_plots) <- catalog_summary$accver
# Save any record at the same size used in the gallery.
# ggsave("pBR322.png", catalog_plots[["pBR322"]], width = 10, height = 8, dpi = 144)

同一个小函数组合独立图层，所有识别结果保留在catalog_features，实际数量见下表。这里保留全部特征几何，标签优先在图内适配，空间不足时可隐藏；引物与密集酶切的完整示例在综合实战。选择任一名字即可用注释掉的ggsave调用保存。

输入与识别结果

参考序列	长度（bp）	识别特征数
pSB1C3	2070	7
pUC19	2686	9
pBR322	4361	6
pBluescript II SK(+)	2961	14
pET-28a(+)	5369	16
pETDuet-1	5420	20
pDONR221	4761	11
pcDNA3.1(+)	5428	13
pTRE-Tight-BI	2856	15
pTRIPZ	13320	33
pCAMBIA1300	8959	17
pEarleyGate 201	11779	21
pSpCas9(BB)-2A-GFP (PX458)	9288	19