The DATASET_DOMAIN template are used to visualize schematic representations of proteins, with a protein backbone and various shapes depicting the locations of individual domains. Even though its primary use is for the display of protein domain architectures, it can be used for various other purposes, as it offers a lot of flexibility. The DATASET_DOMAIN template belongs to the “Basic graphics” class (refer to the Class for detail information).

To add a schematic representation of a protein to its branch, users must input the branch name, protein length, marker shape, start and end position, color, and label for each structural domain.

This section shows how to use the itol.toolkit to add schematic representations of proteins to the tree. Without itol.toolkit, users need to manually input information such as the color and the shape of protein domains one by one. When faced with a large amount of data, this process is very time consuming. The itol.toolkit simplifies the data processing procedure and integrates it seamlessly with visual preparation. To use the itol.toolkit, users need to prepare the label, length, start, and end position of the corresponding protein domain for each tip, as the program automatically sets the shape and color.

Draw schematic representation of protein

This section uses dataset 1 to show the visualization of schematic representations of proteins (refer to the Dataset for detail information).

The first step is to load the newick format tree file tree_of_itol_templates.tree and its corresponding metadata parameter_usage_raw.txt. The following example data parameter_usage_raw.txt contains the types of parameters and their usage in each template.

The purpose of our data processing in this section is to demonstrate the use of various types of parameters in various templates using schematic representation of protein.

tree <- system.file("extdata",
                    package = "itol.toolkit")
tab_tmp <- system.file("extdata",
                       package = "itol.toolkit")
tab_tmp <- fread(tab_tmp)

We represent protein domains using the type of parameter and determine the length of each domain by the number of parameters used by the template for that type. The final result of our data processing is shown in variable template_end_group_long. The first column is the template names (corresponding to the tips of the tree), the second column is the length of the protein domain, the three and four columns are the starting and ending positions of the protein domain, and the fifth column is the parameter types (i.e., the domain label).

tab_id_group <- tab_tmp[,c(1,2)]
tab_tmp <- tab_tmp[,-c(1,2)]
tab_tmp_01 <- convert_01(object = tab_tmp)
tab_tmp_01 <- cbind(tab_id_group,tab_tmp_01)
para_order <- c("type",
                "common themes",
                "specific themes",
template_with_start <- tab_tmp_01 %>%
                   pivot_longer(-c('parameter', 'group'), 
                                names_to = 'variable',
                                values_to = 'value') %>%
                   group_by(group,variable) %>%
                   summarise(sublen = sum(value)) %>%
                          value=sublen) %>%
                   mutate(group=factor(group,levels = order)) %>%
group_start <- data.frame(group = template_with_start$group, 
                          Freq = apply(template_with_start[,-1], 1, max)) %>%
               mutate(start=lag(cumsum(Freq))) %>%
               mutate(start=replace_na(start, 0))
template_with_start_and_end <- sapply(as.list(template_with_start[,-1]), 
                                      function(x)x + group_start$start) %>% %>%
  mutate(group=template_with_start$group) %>%
  relocate(group) %>%
               names_to = 'variable',
               values_to = "end") %>%
  left_join(group_start[c('group','start')]) %>%
         group =factor(.$group, levels=para_order)) %>%
  relocate(variable, length, start, end, group)

Using prepared data to create unit

unit_28 <- create_unit(data = template_end_group_long,
                       key = "E028_domains_1",
                       type = "DATASET_DOMAINS",
                       tree = tree)

Style modification

Users can add a column to the long table used to generate templates to change the shape of each protein domain. By assigning the domain label column to a new column and using the stringr::str_replace_all function to replace each label with a shape, you can quickly modify the shape.

Domain shape codes are as follows:

Character Shape
RE rectangle
HH horizontal hexagon
HV vertical hexagon
EL ellipse
DI rhombus (diamond)
TR right pointing triangle
TL left pointing triangle
PL left pointing pentagram
PR right pointing pentagram
PU up pointing pentagram
PD down pointing pentagram
OC octagon
GP rectangle
template_end_group_long$shape <- template_end_group_long$group
template_end_group_long$shape <- stringr::str_replace_all(template_end_group_long$shape,"type","HH")
template_end_group_long$shape <- stringr::str_replace_all(template_end_group_long$shape,"separator","HV")
template_end_group_long$shape <- stringr::str_replace_all(template_end_group_long$shape,"profile","EL")
template_end_group_long$shape <- stringr::str_replace_all(template_end_group_long$shape,"field","DI")
template_end_group_long$shape <- stringr::str_replace_all(template_end_group_long$shape,"common themes","TR")
template_end_group_long$shape <- stringr::str_replace_all(template_end_group_long$shape,"specific themes","TL")
template_end_group_long$shape <- stringr::str_replace_all(template_end_group_long$shape,"data","PL")


group2shape <- tribble(
  ~group,               ~shape,
  "type",               "HH",
  "separator",          "HV",
  "profile",            "EL",
  "field",              "DI",
  "common themes",      "TR",
  "specific themes",    "TL",
  "data",               "PL"
template_with_start_and_end  %<>%