Package {geneviewer}

BRCA1 Splice Variants

Description

This dataset contains detailed information on five different splice variants of the BRCA1 gene. It includes data on the types of transcript features (exon, intron, UTR), chromosomal positions, strand orientations, and transcript lengths.

Usage

BRCA1_splice_variants

Format

A data frame with 119 observations and 6 variables:

ensembl_transcript_id

Unique Ensembl transcript identifiers. A character vector.

type

Type of the transcript feature, indicating whether it is an "exon", or "UTR". A character vector.

start

The start position of the feature on the chromosome. A numeric vector.

end

The end position of the feature on the chromosome. A numeric vector.

strand

The strand orientation of the feature, with -1 indicating the reverse strand. An integer vector.

transcript_length

The total length of the transcript in base pairs. An integer vector.

Source

Derived from the Ensembl website

Align gene clusters

Description

This function aligns clusters based on a specified gene id. The clusters are shifted so the gene sits at the same place in each of them, and all clusters keep the same scale: genes of the same length are drawn at the same width. The alignment is kept when GC_scale() is called afterwards, also with axis_type = "range".

Usage

GC_align(GC_chart, id_column, id, align = "left")

Arguments

Value

The modified GC_chart object with updated genomic coordinates.

Examples

genes_data <- data.frame(
  start = c(10, 90, 130, 170, 210),
  end = c(40, 120, 160, 200, 240),
  name = c('Gene 1', 'Gene 2', 'Gene 3', 'Gene 4', 'Gene 5'),
  group = c('A', 'B', 'C', 'C', 'A'),
  cluster =  c(1, 1, 1, 2, 2)
)


GC_chart(genes_data, group = "group", cluster = "cluster", height = "150px") %>%
  GC_align("group", "A", align = "left") %>%
  GC_legend(FALSE)

Add Annotations to a GC_chart

Description

This function adds annotations to specified clusters within a GC chart. Annotations can be of various types and are positioned based on provided coordinates. The types of annotations available are: text, textMarker, line, arrow, symbol, rectangle, promoter, terminator and arc.

Usage

GC_annotation(GC_chart, type = "text", cluster = NULL, ...)

Arguments

Value

Updated GC chart object with added annotations.

See Also

GC_trackMouse

Examples

genes_data <- data.frame(
  start = c(10, 50, 90, 130, 170, 210),
  end = c(40, 80, 120, 160, 200, 240),
  name = c('Gene 1', 'Gene 2', 'Gene 3', 'Gene 4', 'Gene 5', 'Gene 6'),
  group = c('A', 'A', 'B', 'B', 'A', 'C'),
  cluster = c(1, 1, 1, 2, 2, 2)
)

# Adding annotations to a GC chart
GC_chart(genes_data, cluster = "cluster", group = "group", height = "220px") %>%
  GC_annotation(
    type = "textMarker",
    cluster = 1,
    position = 24,
    text = "Gene 1",
    arrowSize = 8
  ) %>%
  GC_annotation(
    type = "text",
    text = "feature 1",
    x = 91,
    y = 71
  ) %>%
  GC_annotation(
    type = "symbol",
    symbol = "triangle",
    x = 95,
    y = 64,
    size = 10,
    rotation = 180
  ) %>%
  GC_annotation(
    type = "terminator",
    x = 81
  ) %>%
  GC_annotation(
    type = "promoter",
    x = 49
  ) %>%
  # Convenience function to track mouse position on hoover
  GC_trackMouse()

Create a GC Chart Visualization

Description

Generates an interactive GC chart for genomic data.

Usage

GC_chart(
  data,
  start = "start",
  end = "end",
  cluster = NULL,
  group = NULL,
  strand = NULL,
  width = "100%",
  height = "400px",
  style = list(),
  elementId = NULL,
  save_button = TRUE
)

Arguments

Value

A GC chart widget.

Examples

genes_data <- data.frame(
  start = c(10, 50, 90, 130, 170, 210),
  end = c(40, 80, 120, 160, 200, 240),
  name = c('Gene 1', 'Gene 2', 'Gene 3', 'Gene 4', 'Gene 5', 'Gene 6'),
  group = c('A', 'A', 'B', 'B', 'A', 'C'),
  cluster = c(1, 1, 1, 2, 2, 2)
)
# Load from data.frame
GC_chart(genes_data, group = "group", cluster = "cluster", height = "200px") %>%
GC_labels("name")

# Load from a GenBank file (or a folder of .gbk files)
gbk_file <- system.file("extdata", "BGC0000001.gbk", package = "geneviewer")
GC_chart(gbk_file, height = "200px")

Modify Cluster Settings

Description

This function can switch prevention of gene overlap on, adjust the spacing between tracks and alter the styling of specified clusters.

Usage

GC_cluster(
  GC_chart,
  separate_strands = NULL,
  strand_spacing = NULL,
  prevent_gene_overlap = NULL,
  overlap_min = NULL,
  overlap_spacing = NULL,
  cluster = NULL,
  style = list(),
  ...
)

Arguments

Value

Returns the modified gene chart object.

Examples

genes_data <- data.frame(
  start = c(1, 10, 200, 220, 600),
  end = c(10, 150, 180, 400, 400),
  name = c('Gene 1', 'Gene 2', 'Gene 3', 'Gene 4', 'Gene 5'),
  group = c('A', 'A', 'A', 'A', 'A')
)


GC_chart(genes_data, group = "group", height = "150px") %>%
  GC_cluster(separate_strands=TRUE, strand_spacing = 0) %>%
  GC_legend(FALSE)

# Only the gene inside another one goes below, drawn smaller
nested_genes <- data.frame(
  start = c(1, 120, 300, 340), end = c(125, 400, 380, 600),
  name = c("A", "B", "B2", "C")
)
GC_chart(nested_genes, height = "150px") %>%
  GC_cluster(prevent_gene_overlap = "nested") %>%
  GC_genes(style_by = "geneTrack",
           groupStyle = list("2" = list(markerHeight = 8, arrowheadHeight = 8))) %>%
  GC_labels("name")

Add a Footer to Each Cluster in a GC Chart

Description

This function allows you to add a footer to all or specific clusters within a GC chart. You can specify titles, subtitles, and control the display and styling.

Usage

GC_clusterFooter(
  GC_chart,
  title = NULL,
  subtitle = NULL,
  show = TRUE,
  cluster = NULL,
  subtitleFont = list(),
  titleFont = list(),
  align = NULL,
  spacing = NULL,
  ...
)

Arguments

Value

A GC chart object with updated footer settings for each specified cluster.

Examples

genes_data <- data.frame(
  start = c(10, 90, 130, 170, 210),
  end = c(40, 120, 160, 200, 240),
  name = c('Gene 1', 'Gene 3', 'Gene 4', 'Gene 5', 'Gene 6'),
  group = c('A', 'B', 'B', 'A', 'C'),
  cluster = c(1, 1, 2, 2, 2)
)

# Add a simple footer with subtitle to all clusters
GC_chart(genes_data, cluster = "cluster", group = "group") %>%
  GC_clusterFooter(
    title = "Cluster Footer",
    subtitle = "Cluster subtitle"
  )
# Add styling to the title and subtitle
GC_chart(genes_data, cluster = "cluster", group = "group") %>%
  GC_clusterFooter(
    title = "This is a footer",
    subtitle = "Subtitle for the footer",
    show = TRUE,
    cluster = 1,
    x = 6,
    y = -20,
    align = "center", # left / right
    spacing = 12,
    titleFont = list(
      fontSize = "12px",
      fontWeight = "bold",
      fontFamily = "sans-serif",
      fill = "black",
      cursor = "default"
    ),
    subtitleFont = list(
      fill = "grey",
      fontSize = "10px",
      fontStyle = "normal",
      fontFamily = "sans-serif",
      cursor = "default"
    )
  )

Set or Update Cluster Labels for a GC Chart

Description

This function allows you to set or update the labels for specified clusters within a GC chart. It provides flexibility in terms of the title, visibility, width, position, and other additional customization options.

Usage

GC_clusterLabel(
  GC_chart,
  title = NULL,
  show = TRUE,
  width = NULL,
  cluster = NULL,
  position = NULL,
  align = NULL,
  wrapLabel = NULL,
  wrapOptions = list(),
  subtitle = NULL,
  subtitleFont = list(),
  spacing = NULL,
  ...
)

Arguments

Value

Updated GC chart with new or modified cluster labels.

Examples

genes_data <- data.frame(
  start = c(10, 90, 130, 170, 210),
  end = c(40, 120, 160, 200, 240),
  name = c('Gene 1', 'Gene 3', 'Gene 4', 'Gene 5', 'Gene 6'),
  group = c('A', 'B', 'B', 'A', 'C'),
  cluster = c(1, 1, 2, 2, 2)
)

# Set cluster labels
GC_chart(genes_data, cluster ="cluster", group = "group", height = "200px") %>%
GC_clusterLabel(title = unique(genes_data$cluster))

# Set label for a specific cluster
GC_chart(genes_data, cluster ="cluster", group = "group", height = "200px") %>%
GC_clusterLabel(title = "Cluster 1", cluster = 1)

# Add a subtitle below each label
GC_chart(genes_data, cluster ="cluster", group = "group", height = "200px") %>%
  GC_clusterLabel(
    title = c("pQEB1", "pQEB1_inv1"),
    subtitle = c("E. cloacae", "K. oxytoca"),
    subtitleFont = list(fontStyle = "italic"),
    spacing = 14
  )

# Style labels
GC_chart(genes_data, cluster ="cluster", group = "group", height = "200px") %>%
  GC_clusterLabel(
    title = c("Cluster 1", "Cluster 2"),
    width = "100px",
    x = 0,
    y = 0,
    position = "left",
    align = "left", # center / right
    wrapLabel = TRUE,
    wrapOptions = list(
      dyAdjust = 0,
      lineHeightEms = 1.05,
      lineHeightSquishFactor =  1,
      splitOnHyphen =  TRUE,
      centreVertically = TRUE
     ),
    fontSize = "12px",
    fontStyle = "normal",
    fontWeight = "bold",
    fontFamily = "sans-serif",
    cursor = "default"
  )

Update cluster Title of a GC Chart Cluster

Description

Modify the cluster title and subtitle of specified clusters within a GC chart and adjust the display settings.

Usage

GC_clusterTitle(
  GC_chart,
  title = NULL,
  subtitle = NULL,
  subtitleFont = list(),
  titleFont = list(),
  show = TRUE,
  height = NULL,
  cluster = NULL,
  align = NULL,
  spacing = NULL,
  ...
)

Arguments

Value

Updated GC chart with new title settings.

Examples

genes_data <- data.frame(
  start = c(10, 50, 90, 130, 170, 210),
  end = c(40, 80, 120, 160, 200, 240),
  name = c('Gene 1', 'Gene 2', 'Gene 3', 'Gene 4', 'Gene 5', 'Gene 6'),
  group = c('A', 'A', 'B', 'B', 'A', 'C'),
  cluster = c(1, 1, 1, 2, 2, 2)
)

# Basic usage
GC_chart(genes_data, cluster ="cluster", group = "group", height = "400px") %>%
GC_labels("name") %>%
GC_clusterTitle(
  title = "Cluster 1 Data",
  subtitle = "Detailed View",
  show = TRUE,
  cluster = 1
)

# Customizing title style
GC_chart(genes_data, cluster = "cluster", group = "group", height = "400px") %>%
  GC_labels("name") %>%
  GC_clusterTitle(
    title = "Cluster 1 Data",
    subtitle = "Detailed View",
    show = TRUE,
    cluster = 1,
    x = 0,
    y = 5,
    align = "center",
    spacing = 20,
    titleFont = list(
      fontSize = "16px",
      fontStyle = "normal",
      fontWeight = "bold",
      textDecoration = "normal",
      fontFamily = "sans-serif",
      cursor = "default",
      fill = "black"
      # Any other CSS styles
    ),
    subtitleFont = list(
      fontSize = "14px",
      fontStyle = "normal",
      fontWeight = "bold",
      textDecoration = "normal",
      fontFamily = "sans-serif",
      cursor = "default",
      fill = "black"
      # Any other CSS styles
    )
  )

Update Color Scheme in Gene Chart

Description

This function updates the color scheme of the legend and genes in a gene chart.

Usage

GC_color(GC_chart, colorScheme = NULL, customColors = NULL)

Arguments

Value

Returns the gene chart object with updated color settings for the genes.

Examples

genes_data <- data.frame(
  start = c(10, 50, 90, 130, 170, 210),
  end = c(40, 80, 120, 160, 200, 240),
  name = c('Gene 1', 'Gene 2', 'Gene 3', 'Gene 4', 'Gene 5', 'Gene 6'),
  group = c('A', 'A', 'B', 'B', 'A', 'C'),
  cluster = c(1, 1, 1, 2, 2, 2)
)

GC_chart(genes_data, cluster = "cluster", group = "group", height = "100px") %>%
  GC_color(colorScheme = "schemeCategory10")

GC_chart(genes_data, cluster = "cluster", group = "group", height = "100px") %>%
  GC_color(customColors = c("red", "orange", "green"))

GC_chart(genes_data, cluster = "cluster", group = "group", height = "100px") %>%
  GC_color(customColors = list(A = "yellow", B = "pink", C =  "purple"))

# Set the colour of one group; the others keep the colours of the scheme
GC_chart(genes_data, cluster = "cluster", group = "group", height = "100px") %>%
  GC_color(colorScheme = "schemeSet2", customColors = list(C = "grey"))

Modify Coordinates in a GC Chart

Description

This function updates a GC chart by modifying the coordinates settings. It allows for showing or hiding tick values, applying custom tick values for the top and bottom axes, and supports several other customizations for specific or all clusters in the chart.

Usage

GC_coordinates(
  GC_chart,
  show = TRUE,
  tickValuesTop = NULL,
  tickValuesBottom = NULL,
  ticksFormat = NULL,
  tickStyle = list(),
  textStyle = list(),
  cluster = NULL,
  groupStyle = NULL,
  style_by = NULL,
  ...
)

Arguments

Value

Returns the GC chart object with updated coordinates.

Examples

genes_data <- data.frame(
  start = c(10, 90, 130, 170, 210),
  end = c(40, 120, 160, 200, 240),
  name = c('Gene 1', 'Gene 2', 'Gene 3', 'Gene 4', 'Gene 5'),
  cluster = c(1, 1, 2, 2, 2)
)

# Add coordinates to all clusters
GC_chart(genes_data, cluster = "cluster", group = "name", height = "200px") %>%
GC_coordinates()

# Modify coordinates of a specific cluster
GC_chart(genes_data, cluster = "cluster", group = "name", height = "200px") %>%
GC_coordinates() %>%
GC_coordinates(
  cluster = 2,
  show = TRUE,
  tickValuesTop = c(130, 170, 210, 240),
  tickValuesBottom = c(160, 200),
  ticksFormat = ",.0f", # ".2s",
  rotate = -45,
  yPositionTop = 55,
  yPositionBottom = 45,
  overlapThreshold = 20,
  tickStyle = list(
  stroke = "black",
  strokeWidth = 1,
  lineLength = 6
  ),
  textStyle = list(
  fill = "black",
  fontSize = "12px",
  fontFamily = "Arial",
  cursor = "default"
  )
)

Style an Individual Element

Description

Adds or updates an override for one element of a chart, without replacing the styles of other elements: a gene, a gene label, a gene coordinate, or the title or subtitle of a cluster title, footer or label. Use this in an editor and in the R code generated by it. Legend labels and markers can also be edited individually by group and part.

Usage

GC_element_style(
  GC_chart,
  component = c("genes", "labels", "coordinates", "clusterTitle", "clusterFooter",
    "clusterLabel", "legend", "title", "scaleBar", "scale"),
  row_id = NULL,
  ...,
  cluster = NULL,
  part = NULL,
  end = NULL,
  reset = FALSE,
  group = NULL
)

Arguments

Details

Overrides of genes, labels and coordinates match the row ID rather than the element's drawing index, so sorting, reverse clusters, resize and repeated gene names do not change their target. Row IDs are assigned by GC_chart() after filtering missing coordinates. Reproducing an edit requires the same input rows in the same order; replacing or reordering the input data requires remapping edits. These overrides take precedence over group styles and index-based styles. Reset removes the override, restoring underlying styles. Call this after the component's normal configuration (e.g. GC_clusterTitle()), which replaces its overrides. Legend overrides also take precedence over GC_legend(groupStyle = ...); resetting one part restores its group and global styles.

Value

The chart with the element override updated.

Examples

genes <- data.frame(start = c(90, 10), end = c(130, 50), name = c("a", "a"))
GC_chart(genes) %>%
  GC_labels("name") %>%
  GC_clusterTitle("Cluster", subtitle = "Subtitle") %>%
  GC_element_style("genes", row_id = 1, fill = "red", marker = "box") %>%
  GC_element_style("labels", row_id = 1, text = "Edited", fontSize = "16px") %>%
  GC_element_style("clusterTitle", cluster = 1, part = "subtitle",
                   fontStyle = "italic", fill = "grey")

GC_chart(data.frame(start = 10, end = 40, family = "A"), group = "family") %>%
  GC_element_style("legend", group = "A", part = "label", text = "Family A") %>%
  GC_element_style("legend", group = "A", part = "marker", fill = "red")

Report Chart Interactions and Completed Updates

Description

Makes a chart report clicks to Shiny, so an app can react to the part of the chart that was clicked: show the details of a gene, open the settings of the clicked part, and so on. Charts are not clickable unless this is set, and click reporting has no effect outside Shiny.

Usage

GC_events(
  GC_chart,
  click = TRUE,
  highlight = FALSE,
  rendered = FALSE,
  render_id = NULL,
  dblclick = FALSE,
  select = FALSE,
  legend_click = TRUE,
  drag = FALSE,
  zoom = FALSE
)

Arguments

Details

Each click sets ⁠input$<outputId>_click⁠, e.g. input$chart_click for GC_chartOutput("chart"), to a list with:

component

The part of the chart that was clicked: "genes", "transcript", "labels", "coordinates", "links", "legend", "scale", "scaleBar", "sequence", "title" (the chart title), "clusterTitle", "clusterFooter", "clusterLabel" or "annotations".

cluster

The cluster that was clicked, or NULL for the chart title, the legend and links between clusters.

data

The data of the clicked element, or NULL: the row of the gene for genes, transcripts and labels, the row of the link for links, and the group for the legend.

Every part of the chart is marked with a data-component attribute and each cluster with data-cluster, also without this function, so they can be selected with CSS or JavaScript.

With dblclick = TRUE, double-clicks independently report the same payload to ⁠input$<outputId>_dblclick⁠ and the bubbling browser event geneviewer:dblclick. Browsers also send clicks during a double-click: use clicks for selection, and double-clicks for editing, rather than opening different settings panels on each event.

With select = TRUE, a click selects an individual editable element and outlines it. Clicking it again, empty chart space or a noneditable part of the chart, or pressing Escape, clears selection. Shift-, Ctrl- or Cmd-click adds an element to the selection or takes it out, and dragging on empty chart space draws a box that selects the elements entirely inside it (with Shift, Ctrl or Cmd, in addition to the selection). With drag = TRUE as well, dragging one of the selected elements moves all of them that can be moved, and the arrow keys move them together; such a move of several elements is one step to undo, reported as ⁠input$<outputId>_moves⁠. Selection reports the payload of the element selected last (or NULL when cleared) to ⁠input$<outputId>_select⁠ and geneviewer:select, and a list of the payloads of all selected elements, in the order they were selected, to ⁠input$<outputId>_selection⁠ and geneviewer:selection. It survives redraws of the same data and resize. When a redraw changes the selected element's style, its updated payload is reported again. Selected items can be opened with Enter when dblclick = TRUE; items are keyboard focusable in selection mode. Use click = FALSE to avoid legacy single-click settings handlers. legend_click = FALSE excludes legend clicks from click reporting without enabling legend click editing. Legend labels and patches are individually selectable, with part set to "label" or "marker" and data set to the original group value. Legend clicks edit instead of toggling visibility when selection or double-click events are enabled, or when click reporting is enabled with legend_click = TRUE. Otherwise they hide/show genes. Double-click and selection browser events also work outside Shiny.

Click, double-click and selection payloads of editable elements also contain style: how it is drawn, with the names of GC_element_style() options, so an editor can show the current values. For a gene these are fill, opacity, stroke, strokeWidth, strokeDasharray, marker, markerSize, markerHeight, arrowheadWidth, arrowheadHeight, cornerRadius and y; for a label fill, opacity, text, fontSize (in pixels), fontWeight, fontStyle and fontFamily. Colours are hex codes, except transparent colours, "none" and patterns. Legend labels report the same text options; legend markers report fill, opacity, stroke, strokeWidth and strokeDasharray.

With drag = TRUE, the undo, redo and reset icons next to the download icon undo and redo moves, and move every element back to where it was first drawn, so positions set with GC_element_style() are kept. They show while the mouse is over the chart, once there is a move. After clicking the chart, Ctrl+Z (Cmd+Z on a Mac) undoes and Ctrl+Shift+Z or Ctrl+Y redoes. With select = TRUE as well, the arrow keys move the selected text or scale by a pixel, or 10 pixels with Shift. Each drag is one step to undo, arrow-key moves of one element within a second are one step, and a reset is one step. A step that moves one element reports it like a drag, with action set to "undo", "redo" or "nudge". A step that moves more, such as a reset, reports ⁠input$<outputId>_moves⁠ and geneviewer:moves once, with action ("reset", "undo" or "redo") and moves, a list of drag payloads, so an app that keeps drags keeps these too. Steps whose element was since moved by something else are dropped. Outside Shiny, moves are kept when the chart is resized.

With rendered = TRUE, each successful update also sets ⁠input$<outputId>_rendered⁠ to a list containing id (the supplied render_id, or a generated ID), chartId (the chart's DOM element ID), width and height (the widget size in pixels). When render_id is NULL, a new ID is generated in the browser for each successful update, unique across charts on the page. chartId stays the same when that chart updates; generated render IDs change. Explicit IDs, including 0 and "", are kept. It fires after the chart, legend, links and click handlers have been built, once per data update, including repeated identifiers, but not on resize or a failed draw. It does not wait for browser paint or external image loading.

The widget also dispatches a bubbling geneviewer:rendered browser event with the same fields in event.detail, including outside Shiny. A loading indicator can listen to it without inspecting the widget payload. Use a different render_id for each load and ignore events for older loads.

Value

The GC chart object with the event settings.

Examples

genes_data <- data.frame(
  start = c(10, 50, 90, 130),
  end = c(40, 80, 120, 160),
  name = c("Gene 1", "Gene 2", "Gene 3", "Gene 4"),
  group = c("A", "A", "B", "C")
)

if (interactive()) {
  library(shiny)
  ui <- fluidPage(
    GC_chartOutput("chart", height = "200px"),
    verbatimTextOutput("clicked")
  )
  server <- function(input, output, session) {
    output$chart <- renderGC_chart(
      GC_chart(genes_data, group = "group") %>%
        GC_labels("name") %>%
        GC_events(highlight = TRUE)
    )
    output$clicked <- renderPrint(str(input$chart_click))
  }
  shinyApp(ui, server)
}

Modify Gene Characteristics within a Chart

Description

This function updates a gene chart with specific characteristics for genes based on the given parameters. It can show/hide genes, apply a color scheme, assign custom colors, filter by cluster, and accept additional options.

Usage

GC_genes(
  GC_chart,
  group = NULL,
  marker = NULL,
  marker_size = NULL,
  show = TRUE,
  colorScheme = NULL,
  customColors = NULL,
  cluster = NULL,
  itemStyle = list(),
  groupStyle = NULL,
  style_by = NULL,
  corner_radius = NULL,
  ...
)

Arguments

Value

Returns the modified gene chart object.

Examples

genes_data <- data.frame(
  start = c(10, 90, 130, 170, 210),
  end = c(40, 120, 160, 200, 240),
  name = c('Gene 1', 'Gene 3', 'Gene 4', 'Gene 5', 'Gene 6'),
  group = c('A', 'B', 'B', 'A', 'C'),
  cluster = c(1, 1, 2, 2, 2)
)

# Change the appearance of a specific gene cluster
GC_chart(genes_data, cluster = "cluster", group = "group", height = "200px") %>%
  GC_genes(
    group = "group",
    show = TRUE,
    marker = "arrow",
    marker_size = "medium",
    colorScheme = NULL, # One of D3.js build in colorSchemes
                        # (eg. "schemeCategory10",
                        # "schemeAccent", "schemeTableau10")
    customColors = NULL, # A vector of color names
    prevent_overlap = FALSE,
    gene_overlap_spacing = 40,
    cluster = 1, # Specify a specific cluster
    x = 1,
    y = 50,
    stroke = "black",
    strokeWidth = 1,
    arrowheadWidth = NULL,
    arrowheadHeight = NULL,
    arrowHeight = NULL,
    markerHeight = NULL # overwrites marker_size
   )

# Change the appearance of a specific gene
GC_chart(genes_data, cluster = "cluster", group = "group", height = "200px") %>%
GC_genes(
  cluster = 2,
  itemStyle = list(list(index = 2, fill = "red", stroke = "blue")
  )
 )

# Change the appearance of all genes of a group
GC_chart(genes_data, cluster = "cluster", group = "group", height = "200px") %>%
  GC_genes(groupStyle = list(C = list(marker = "box", stroke = "red")))

Update Grid Display of a GC Chart Cluster

Description

Modify the grid display of specified clusters within a GC chart. This function allows users to adjust the margins, width, and height of the grid for each cluster.

Usage

GC_grid(
  GC_chart,
  margin = NULL,
  width = NULL,
  height = NULL,
  direction = "column",
  cluster = NULL
)

Arguments

Value

Updated GC chart with new grid display settings.

Examples

genes_data <- data.frame(
  start = c(10, 90, 130, 170, 210),
  end = c(40, 120, 160, 200, 240),
  name = c('Gene 1', 'Gene 3', 'Gene 4', 'Gene 5', 'Gene 6'),
  group = c('A', 'B', 'B', 'A', 'C'),
  cluster = c(1, 1, 2, 2, 2)
)

# Set Margin of clusters
GC_chart(genes_data, cluster ="cluster", group = "group", height = "200px") %>%
GC_grid(margin = list(left = "50px", right = "0px"))

# Set height of a specific cluster
GC_chart(genes_data, cluster ="cluster", group = "group", height = "200px") %>%
GC_grid(height = "120px", cluster = 2)

Add Labels to Each Cluster in a GC Chart

Description

This function adds labels to each cluster within a GC chart. It provides the option to show or hide the labels and supports customization of label properties. It can automatically pick up group names as labels from the GC_chart object if not provided.

Usage

GC_labels(
  GC_chart,
  label = NULL,
  show = TRUE,
  cluster = NULL,
  itemStyle = list(),
  groupStyle = NULL,
  style_by = NULL,
  ...
)

Arguments

Value

A ⁠GC chart⁠ object with updated label settings for each specified cluster.

Examples

genes_data <- data.frame(
  start = c(10, 90, 130, 170, 210),
  end = c(40, 120, 160, 200, 240),
  name = c('Gene 1', 'Gene 3', 'Gene 4', 'Gene 5', 'Gene 6'),
  group = c('A', 'B', 'B', 'A', 'C'),
  cluster = c(1, 1, 2, 2, 2)
)

# Add labels to all clusters
GC_chart(genes_data, cluster = "cluster", group = "group", height = "200px") %>%
GC_labels()

# Bold labels for the genes of group A
GC_chart(genes_data, cluster = "cluster", group = "group", height = "200px") %>%
  GC_labels(groupStyle = list(A = list(fontWeight = "bold")))

# Add labels and styling
GC_chart(genes_data, cluster = "cluster", group = "group", height = "200px") %>%
GC_labels(
 label = "group",
 show = TRUE,
 x = 0,
 y = 50,
 dy = "-1.2em",
 dx = "0em",
 rotate = 0,
 adjustLabels = TRUE, # Rotate labels to prevent overlap; "hide" hides them
 fontSize = "12px",
 fontStyle = "italic",
 fill = "black",
 fontFamily = "sans-serif",
 textAnchor = "middle",
 cursor = "default",
)

# Alter style of a specific label
GC_chart(genes_data, cluster = "cluster", group = "group", height = "200px") %>%
GC_labels(label = "group") %>%
GC_labels(
  cluster = 1,
  itemStyle = list(
    list(index = 0, fill = "red", fontSize = "14px", fontWeight = "bold")
  )
)

Set Legend for a Gene Chart

Description

This function configures the legend for a gene chart. It allows toggling the legend's visibility, setting a background color, and assigning custom labels for the legend entries. The function can also handle additional customizations through various options.

Usage

GC_legend(
  GC_chart,
  group = NULL,
  show = TRUE,
  backgroundColor = NULL,
  order = list(),
  position = NULL,
  align = NULL,
  style = list(),
  legendOptions = list(),
  legendTextOptions = list(),
  corner_radius = NULL,
  groupStyle = NULL,
  ...
)

Arguments

Value

Returns the modified gene chart object with the legend configured.

Examples

genes_data <- data.frame(
  start = c(10, 90, 130, 170, 210),
  end = c(40, 120, 160, 200, 240),
  name = c('Gene 1', 'Gene 3', 'Gene 4', 'Gene 5', 'Gene 6'),
  group = c('A', 'B', 'B', 'A', 'C'),
  cluster = c('A', 'A', 'A', 'B', 'B')
)

# Customize legend
GC_chart(genes_data, cluster = "cluster", group = "group", height = "200px") %>%
  GC_legend(
    position = "top", #bottom
    orientation = "horizontal", #vertical
    align = "center", #left / right
    x = 0,
    y = 0,
    adjustHeight = TRUE,
    backgroundColor = "#0000",
    order = list(),
    style = list(
      backgroundColor = "#0000"
      # Any other CSS styles
    ),
    legendOptions = list(
      cursor = "pointer",
      colorScheme = NULL,
      customColors = NULL # c("red", "green", "orange")
      # Additional styles
    ),
    legendTextOptions = list(
      cursor = "pointer",
      textAnchor = "start",
      dy = ".35em",
      fontSize = "12px",
      fontFamily = "sans-serif",
      fill = "black"
      # Additional styles
    )
  )

Add Links to GC Chart

Description

Add links generated by get_links to a GC_chart object. Links are added to the graph by their respective rowIDs.

Usage

GC_links(
  GC_chart,
  group = NULL,
  data = NULL,
  value1 = NULL,
  value2 = NULL,
  cluster = NULL,
  curve = NULL,
  measure = "identity",
  show_links = TRUE,
  label = TRUE,
  label_position = "gene",
  normal_color = NULL,
  inverted_color = NULL,
  low_color = NULL,
  vmin = NULL,
  vmax = NULL,
  use_group_colors = FALSE,
  color_bar = NULL,
  colorBarOptions = list(),
  linkWidth = NULL,
  linkStyle = list(),
  labelStyle = list(),
  ...
)

Arguments

Value

Modified GC_chart object with added links.

Examples

# Add links between all groups in each cluster
genes_data <- data.frame(
  start = c(10, 90, 130, 170, 240, 250, 300, 340, 380, 420),
  end = c(40, 120, 160, 200, 210, 270, 330, 370, 410, 450),
  name = c('Gene 1', 'Gene 2', 'Gene 3', 'Gene 4', 'Gene 5',
           'Gene 6', 'Gene 7', 'Gene 8', 'Gene 9', 'Gene 10'),
  group = c('A', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'C', 'D'),
  identity = c(NA, NA, NA, 50, 40, 100, 60, 65, 20, NA),
  similarity = c(NA, NA, NA, 40, 30, 90, 50, 55, 10, NA),
  cluster = c(1, 1, 1, 2, 2, 2, 3, 3, 3, 3)
)
GC_chart(genes_data,
         cluster = "cluster",
         height = "200px") %>%
  GC_links(
    group = "group",
    value1 = "A",
    value2 = "B",
    measure = "identity",
    label = FALSE) %>%
  GC_labels(label = "group")

# Add links between group A of cluster 1 and A and B of cluster 2
GC_chart(genes_data,
         cluster = "cluster",
         height = "200px") %>%
         GC_labels(label = "group") %>%
         GC_links(group = "group",
                   value1 = c("A", "A"),
                   value2 = c("B", "A"),
                   label = FALSE,
                   cluster = c(1,2))

# A horizontal color bar in the legend
GC_chart(genes_data, cluster = "cluster", group = "group",
         height = "220px") %>%
  GC_links(group = "group", measure = "identity", label = FALSE,
           colorBarOptions = list(position = "legend")) %>%
  GC_legend(position = "top")

# Style links and color bar
GC_chart(genes_data,
         cluster = "cluster",
         height = "200px"
) %>%
  GC_links(
    group = "group",
    data = NULL,
    curve = TRUE,
    measure = "identity",
    show_links = TRUE,
    label = TRUE,
    normal_color = "#1f77b4",
    inverted_color = "#d62728",
    use_group_colors = FALSE,
    color_bar = TRUE,
    colorBarOptions = list(
      x = 0,
      y = 24,
      width = 10,
      height = 60,
      labelOptions = list(
        fontSize = 8,
        xOffset = 2,
        yOffset = 0
        # Any other CSS style
      ),
      titleOptions = list(
        fontSize = 10,
        xOffset = 2,
        yOffset = 0
        # Any other CSS style
      ),
      barOptions = list(
        stroke = "#000",
        strokeWidth = 0.5,
        opacity = 1
        # Any other CSS style
      )
    ),
    linkWidth = 1,
    linkStyle = list(
      stroke = "black",
      strokeWidth = 0.5,
      fillOpacity = 0.4
      # Any other CSS style
    ),
    labelStyle = list(
      fontSize = "8px"
      # Any other CSS style
    )
  ) %>%
  GC_labels(label = "group", cluster = 1) %>%
  GC_clusterLabel()

Normalize Gene Clusters in a Genomic Chart

Description

This function normalizes the genomic coordinates of a set of genes within a cluster, ensuring that the genes are evenly spaced along the entire range of the cluster. The function allows for the option to preserve the original gene lengths and to introduce customized gaps between the genes.

Usage

GC_normalize(
  GC_chart,
  group = NULL,
  cluster = NULL,
  normalize = TRUE,
  preserve_gene_length = TRUE,
  gap = NULL,
  custom_gaps = list()
)

Arguments

Value

The modified GC_chart object with normalized gene coordinates for the specified clusters.

Examples

genes_data <- data.frame(
  start = c(10, 90, 130, 10, 90, 130),
  end = c(40, 120, 160, 40, 120, 160),
  name = c('Gene 1', 'Gene 2', 'Gene 3','Gene 1', 'Gene 2', 'Gene 3'),
  group = c('A', 'B', 'C', 'A', 'B', 'C'),
  cluster =  c(1, 1, 1, 2, 2, 2)
)

GC_chart(genes_data, group = "group", cluster = "cluster", height = "150px") %>%
  GC_normalize(
    cluster = 2,
    preserve_gene_length = TRUE,
    gap = NULL
 )

Fill Genes with Patterns

Description

Fills the genes, exons and legend markers with a pattern per group, for example for black and white figures. Patterns can also be drawn on top of the gene colours to show each group with both a colour and a pattern.

Usage

GC_pattern(
  GC_chart,
  patterns = NULL,
  color = "black",
  background = "white",
  size = 6,
  strokeWidth = 1
)

Arguments

Value

Returns the gene chart object with updated pattern settings.

Examples

genes_data <- data.frame(
  start = c(10, 50, 90, 130, 170, 210),
  end = c(40, 80, 120, 160, 200, 240),
  name = c('Gene 1', 'Gene 2', 'Gene 3', 'Gene 4', 'Gene 5', 'Gene 6'),
  group = c('A', 'A', 'B', 'B', 'A', 'C'),
  cluster = c(1, 1, 1, 2, 2, 2)
)

# Black and white
GC_chart(genes_data, cluster = "cluster", group = "group", height = "150px") %>%
  GC_pattern()

# Chosen patterns on grey backgrounds
GC_chart(genes_data, cluster = "cluster", group = "group", height = "150px") %>%
  GC_pattern(
    patterns = list(A = "dots", B = "crosshatch", C = "none"),
    background = c("white", "grey80")
  )

# Patterns on top of the gene colours
GC_chart(genes_data, cluster = "cluster", group = "group", height = "150px") %>%
  GC_pattern(background = NULL)

# White patterns on black for group C
GC_chart(genes_data, cluster = "cluster", group = "group", height = "150px") %>%
  GC_pattern(
    patterns = list(A = "checkerboard", B = "rings", C = "bricks"),
    color = list(A = "black", B = "black", C = "white"),
    background = list(A = "white", B = "white", C = "black")
  )

Update Scale of a GC Chart Cluster

Description

Modify the scale settings for specified clusters within a GC chart.

Usage

GC_scale(
  GC_chart,
  cluster = NULL,
  start = NULL,
  end = NULL,
  padding = NULL,
  hidden = FALSE,
  breaks = list(),
  tickValues = NULL,
  reverse = FALSE,
  axis_position = NULL,
  axis_type = "position",
  y = NULL,
  scale_breaks = FALSE,
  scale_break_threshold = 20,
  scale_break_padding = 1,
  ticksCount = NULL,
  ticksFormat = NULL,
  tickStyle = list(),
  textStyle = list(),
  lineStyle = list(),
  ...
)

Arguments

Value

Updated GC chart with new scale settings.

Examples

genes_data <- data.frame(
  start = c(100, 1000, 2000),
  end = c(150, 1500, 2500),
  name = c('Gene 4', 'Gene 5', 'Gene 6'),
  group = c('B', 'A', 'C'),
  cluster = c(2, 2, 2)
)
#Example usage with all custom options
GC_chart(genes_data, cluster = "cluster", group = "group", height = "200px") %>%
  GC_scale(
    start = 1,
    end = 2600,
    padding = 2,
    hidden = FALSE,
    reverse = FALSE,
    axis_position = "bottom",
    # breaks = list(
    #  list(start = 160, end = 900),
    #  list(start = 1600, end = 1900)
    # ),
    # tickValues = c(1, 2600),
    scale_breaks = TRUE,
    scale_break_threshold = 20,
    scale_break_padding = 1,
    ticksCount = 10,
    ticksFormat = ",.0f",
    y = NULL,
    tickStyle =
      list(
        stroke = "grey",
        strokeWidth = 1,
        lineLength = 6
        # Any other CSS styles
      ),
    textStyle =
      list(
        fill = "black",
        fontSize = "10px",
        fontFamily = "Arial",
        cursor = "default"
        # Any other CSS styles
      ),
    lineStyle = list(
      stroke = "grey",
      strokeWidth = 1
      # Any other CSS styles
    )
  ) %>%
  GC_legend(FALSE)

Update Scale Bar of a GC Chart Cluster

Description

Modify the scale bar settings for specified clusters within a GC chart.

Usage

GC_scaleBar(
  GC_chart,
  show = TRUE,
  cluster = NULL,
  scaleBarLineStyle = list(),
  scaleBarTickStyle = list(),
  labelStyle = list(),
  ...
)

Arguments

Value

Updated GC chart with new scale bar settings.

Examples

genes_data <- data.frame(
 start = c(1000, 9000, 13000, 17000, 21000),
 end = c(4000, 12000, 16000, 20000, 24000),
 name = c('Gene 1', 'Gene 3', 'Gene 4', 'Gene 5', 'Gene 6'),
 group = c('A', 'B', 'B', 'A', 'C'),
 cluster = c(1, 1, 2, 2, 2)
)

# Set scale bar for individual clusters
GC_chart(genes_data, cluster ="cluster", group = "group") %>%
GC_scaleBar(cluster = 1, title = "1 kb", scaleBarUnit = 1000) %>%
GC_scaleBar(cluster = 2, title = "2 kb", scaleBarUnit = 2000)

# Style scale bar
GC_chart(genes_data, cluster ="cluster", group = "group", height = "400px") %>%
  GC_scaleBar(
    title = "1kb",
    scaleBarUnit = 1000,
    cluster = NULL,
    x = 0,
    y = 50,
    labelStyle = list(
      labelPosition =  "left",
      fontSize = "10px",
      fontFamily = "sans-serif",
      fill = "red", # Text color
      cursor = "default"
      # Any other CSS style for the label
    ),
    textPadding = -2,
    scaleBarLineStyle = list(
      stroke = "black",
      strokeWidth = 1
      # Any other CSS style for the line
    ),
    scaleBarTickStyle = list(
      stroke = "black",
      strokeWidth = 1
      # Any other CSS style for the tick
    )
  )

Update Sequence Display of a GC Chart Cluster

Description

Modify the sequence display and break markers of specified clusters within a GC chart.

Usage

GC_sequence(
  GC_chart,
  show = TRUE,
  cluster = NULL,
  y = NULL,
  sequenceStyle = list(),
  markerStyle = list(),
  ...
)

Arguments

Details

This function allows customization of the sequence line and break markers in a GC chart. It offers options to adjust the sequence line (sequenceStyle) and break markers (markerStyle). The y parameter can be used to set the vertical position of the sequence.

Value

An updated GC chart with modified sequence display settings.

Examples

genes_data <- data.frame(
  start = c(10, 90, 130, 170, 210),
  end = c(40, 120, 160, 200, 240),
  name = c('Gene 1', 'Gene 3', 'Gene 4', 'Gene 5', 'Gene 6'),
  group = c('A', 'B', 'B', 'A', 'C'),
  cluster = c(1, 1, 2, 2, 2)
)

# Basic usage
GC_chart(genes_data, cluster ="cluster", group = "group", height = "200px") %>%
GC_labels("name") %>%
GC_sequence(show = TRUE, y = 50, cluster = NULL)

# Customize sequence and marker styles
GC_chart(genes_data, cluster="cluster", group = "group", height = "200px") %>%
  GC_scale(hidden = TRUE, scale_breaks = TRUE) %>%
  GC_sequence(
    start = NULL,
    end = NULL,
    sequenceStyle = list(
      stroke = "blue",
      strokeWidth = 1
      # Any other CSS style
    ),
    markerStyle = list(
      stroke = "blue",
      strokeWidth = 1,
      gap = 3,
      tiltAmount = 5
      # Any other CSS style
    )
  ) %>%
  GC_legend(FALSE)

Set the Theme of a GC Chart

Description

A theme sets the default look of every part of a chart: fonts, text and line colours, the background, the gene colours and the options of each component. Options set with the other GC_ functions (e.g. GC_labels(fontSize = )) always take precedence over the theme, whatever the order of the calls.

Usage

GC_theme(
  GC_chart,
  theme = NULL,
  textScale = NULL,
  fontFamily = NULL,
  textColor = NULL,
  lineColor = NULL,
  background = NULL,
  palette = NULL,
  pattern = NULL,
  ...
)

Arguments

Details

Call GC_theme() with a theme to start from that theme, or without one to change the current theme. The global settings (fontFamily, textColor, lineColor, background, palette, pattern) are applied to all components; options set for a component, in the theme or in ..., take precedence over them. textScale scales all text of the chart: the default font sizes and those set in the theme, not those set with the other GC_ functions.

The options of each component are those of its GC_ function, by their argument name (e.g. normal_color) or their name in the browser (e.g. normalColor). Components and their functions:

annotations takes a list per annotation type: text, line, arrow, textMarker, symbol, rectangle, promoter, terminator and arc.

A theme only sets how things look: it does not add components to the chart (e.g. a scale needs GC_scale()) or change the data.

The "publication" theme follows common journal figure guidelines: Arial or Helvetica, 7 pt text when the chart is used at its final print size, black text and keylines, visible axis ticks, no patterns or shadows, and the colour-blind-safe Wong palette. Gene labels are italic, as gene symbols are in journals; use GC_labels(fontStyle = "normal") for protein names or other labels. Journals often also require axis labels with units in parentheses and bold panel labels; these are content-specific and must be added by the author. Export as SVG to retain editable vector text and artwork, then check that fonts are embedded when converting to the PDF or EPS supplied to the journal.

Value

The GC chart object with the theme.

See Also

geneviewer_theme() to see the settings of a built-in theme.

Examples

genes_data <- data.frame(
  start = c(10, 90, 130, 170, 210),
  end = c(40, 120, 160, 200, 240),
  name = c('Gene 1', 'Gene 3', 'Gene 4', 'Gene 5', 'Gene 6'),
  group = c('A', 'B', 'B', 'A', 'C'),
  cluster = c(1, 1, 2, 2, 2)
)

chart <- GC_chart(genes_data, cluster = "cluster", group = "group",
                  height = "200px") %>%
  GC_labels("name") %>%
  GC_clusterLabel()

# A built-in theme
chart %>% GC_theme("dark")

# A built-in theme with other fonts and colours
chart %>% GC_theme("classic", fontFamily = "Georgia",
                   palette = c("#1b9e77", "#d95f02", "#7570b3"))

# Black and white with patterns, and other patterns
chart %>% GC_theme("bw")
chart %>% GC_theme("bw", pattern = list(patterns = c("dots", "crosshatch", "none")))

# Larger text, e.g. for a slide or a figure that is printed small
chart %>% GC_theme("classic", textScale = 1.4)

# Change one component; options of the GC_ functions still take precedence
chart %>%
  GC_theme("classic", labels = list(fontSize = "14px", fill = "grey")) %>%
  GC_labels("name", fontSize = "10px")

Add Title to a GC_chart

Description

Modify the cluster title and subtitle of specified clusters within a GC chart and adjust the display settings.

Usage

GC_title(
  GC_chart,
  title = NULL,
  subtitle = NULL,
  style = list(),
  subtitleFont = list(),
  titleFont = list(),
  show = TRUE,
  height = NULL,
  align = NULL,
  spacing = NULL,
  ...
)

Arguments

Value

Updated GC chart with new title settings.

Examples

genes_data <- data.frame(
  start = c(10, 50, 90, 130, 170, 210),
  end = c(40, 80, 120, 160, 200, 240),
  name = c('Gene 1', 'Gene 2', 'Gene 3', 'Gene 4', 'Gene 5', 'Gene 6'),
  group = c('A', 'A', 'B', 'B', 'A', 'C'),
  cluster = c(1, 1, 1, 2, 2, 2)
)

# Basic usage
GC_chart(genes_data, cluster ="cluster", group = "group", height = "400px") %>%
GC_labels("name") %>%
GC_title(
  title = "Cluster 1 Data",
  subtitle = "Detailed View",
  show = TRUE
)

# Customizing title style
GC_chart(genes_data, cluster = "cluster", group = "group", height = "500px") %>%
  GC_labels("name") %>%
  GC_title(
    title = "Cluster 1 Data",
    subtitle = "Detailed View",
    show = TRUE,
    height = "50px",
    cluster = 1,
    x = 0,
    y = 25, # height / 2
    align = "center",
    spacing = 20,
    style = list(
      backgroundColor = "#0000"
      # Any other CSS styles
    ),
    titleFont = list(
      fontSize = "16px",
      fontStyle = "normal",
      fontWeight = "bold",
      textDecoration = "normal",
      fontFamily = "sans-serif",
      cursor = "default",
      fill = "black"
      # Any other CSS styles
    ),
    subtitleFont = list(
      fontSize = "14px",
      fontStyle = "normal",
      fontWeight = "bold",
      textDecoration = "normal",
      fontFamily = "sans-serif",
      cursor = "default",
      fill = "black"
      # Any other CSS styles
    )
  )

Set the Toolbar of a GC Chart

Description

The toolbar holds the chart's own controls, in a corner of the chart: undo, redo and reset of moves, with GC_events()(drag = TRUE), and the save button of GC_chart()(save_button = TRUE). GC_toolbar() sets when and where it shows, which buttons it has, its colours and size, and the icons of its buttons. Colours and size can also be set for all charts of a theme, with GC_theme()(toolbar = list(...)); GC_toolbar() takes precedence.

Usage

GC_toolbar(
  GC_chart,
  show = NULL,
  position = NULL,
  buttons = NULL,
  size = NULL,
  color = NULL,
  hoverColor = NULL,
  tooltipBackground = NULL,
  tooltipColor = NULL,
  tooltipBorder = NULL,
  tooltipRadius = NULL,
  tooltipShadow = NULL,
  fontFamily = NULL,
  icons = NULL
)

Arguments

Details

Icons are Font Awesome icons, by name, e.g. "arrow-rotate-left" for a solid icon, or by their classes, e.g. "fa-regular fa-floppy-disk" for another style.

Value

The GC chart object with the toolbar settings.

Examples

genes_data <- data.frame(
  start = c(10, 50, 90),
  end = c(40, 80, 120),
  name = c("Gene 1", "Gene 2", "Gene 3")
)
GC_chart(genes_data, height = "150px") %>%
  GC_labels("name") %>%
  GC_events(select = TRUE, drag = TRUE) %>%
  GC_toolbar(show = "always", position = "topleft", size = 16,
             color = "#9e9e9e", hoverColor = "black",
             icons = list(reset = "arrows-to-circle", save = "camera"))

Set Tooltip for a Gene Chart

Description

This function configures the tooltip for a gene chart.

Usage

GC_tooltip(
  GC_chart,
  formatter = "<b>Start:</b> {start}<br><b>End:</b> {end}",
  show = TRUE,
  cluster = NULL,
  ...
)

Arguments

Value

Returns the gene chart object with the tooltip configured.

Examples

# Set tooltip
genes_data <- data.frame(
  start = c(10, 90, 130, 170, 210),
  end = c(40, 120, 160, 200, 240),
  name = c('Gene 1', 'Gene 2', 'Gene 3', 'Gene 4', 'Gene 5'),
  group = c('A', 'B', 'B', 'A', 'C')
)

# Add tooltips to the gene chart
GC_chart(genes_data, group = "group", height = "200px") %>%
GC_tooltip(formatter = " <b>Start:</b> {start}<br><b>end:</b> {end}")

Track Mouse Movement in a GC_chart

Description

This function enables or disables mouse tracking on specified clusters within a GC chart. When enabled, the x and y coordinates of the mouse are displayed which can be used to place annotations.

Usage

GC_trackMouse(GC_chart, show = TRUE, cluster = NULL, ...)

Arguments

Value

Updated GC chart object with mouse tracking settings.

See Also

GC_annotation

Examples

genes_data <- data.frame(
  start = c(10, 50, 90, 130, 170, 210),
  end = c(40, 80, 120, 160, 200, 240),
  name = c('Gene 1', 'Gene 2', 'Gene 3', 'Gene 4', 'Gene 5', 'Gene 6'),
  group = c('A', 'A', 'B', 'B', 'A', 'C'),
  cluster = c(1, 1, 1, 2, 2, 2)
)

# Enable mouse tracking
GC_chart(genes_data, cluster = "cluster", group = "group", height = "220px") %>%
GC_trackMouse()

Modify Transcript Characteristics within a Chart

Description

This function updates the chart with specific characteristics for transcripts based on the given parameters. It can show/hide transcripts, apply a color scheme, assign custom colors, filter by cluster, and accept additional options.

Usage

GC_transcript(
  GC_chart,
  transcript = NULL,
  type = NULL,
  strand = NULL,
  group = NULL,
  selection = NULL,
  show = TRUE,
  colorScheme = NULL,
  customColors = NULL,
  styleExons = list(),
  styleIntrons = list(),
  styleUTRs = list(),
  itemStyleExons = list(),
  itemStyleIntrons = list(),
  itemStyleUTRs = list(),
  groupStyleExons = NULL,
  groupStyleIntrons = NULL,
  groupStyleUTRs = NULL,
  style_by = NULL,
  labelOptions = list(),
  ...
)

Arguments

Details

The first call of GC_transcript() makes the chart again with the transcripts as clusters. Settings of the whole chart made before it (e.g. GC_title(), GC_legend(), GC_theme() and the margins of GC_grid()) are kept. Settings per cluster (e.g. GC_labels(), GC_clusterTitle()) are for the old clusters: set them after GC_transcript().

Value

Returns the modified gene chart object.

Examples

transcript_data <- data.frame(
  transcript = c("transcript1", "transcript1", "transcript1", "transcript1",
                 "transcript2", "transcript2", "transcript2"),
  type = c("5_utr", "exon", "exon", "3_utr",
           "5_utr", "exon", "3_utr"),
  start = c(1, 101, 201, 301,
            1, 101, 301),
  end = c(50, 150, 250, 350,
          50, 150, 350),
  strand = rep("forward", 7)
)

# All default transcript settings
GC_chart(
  transcript_data,
  start = "start",
  end = "end",
  height = "200px"
) %>%
  GC_transcript(
    transcript = "transcript",
    strand = "strand",
    type = "type",
    group = NULL,
    show = TRUE,
    selection = NULL,
    colorScheme = NULL,
    customColors = NULL,
    styleExons = list(
      show = TRUE,
      strokeWidth = 0,
      cursor = "default",
      marker = "box",
      markerSize = "medium",
      arrowheadWidth = NULL,
      arrowheadHeight = NULL,
      markerHeight = NULL,
      cornerRadius = NULL
      # Any other CSS style
    ),
    styleIntrons = list(
      show = TRUE,
      strokeWidth = 1,
      fill = "none",
      cursor = "default",
      marker = "intron",
      markerSize = "medium",
      arrowheadWidth = NULL,
      arrowheadHeight = NULL,
      markerHeight = NULL,
      cornerRadius = NULL
      # Any other CSS style
    ),
    styleUTRs = list(
      show = TRUE,
      fontSize = "10px",
      fontStyle = "normal",
      fontFamily = "sans-serif",
      cursor = "default",
      color = "black",
      fill = "#FFF",
      strokeWidth = 1,
      marker = "box",
      markerSize = "medium",
      arrowheadWidth = NULL,
      arrowheadHeight = NULL,
      markerHeight = NULL,
      cornerRadius = NULL
      # Any other CSS style
    ),
    labelOptions = list(
      show = TRUE,
      xOffset = 2,
      yOffset = 0,
      fontSize = "12px",
      fontStyle = "normal",
      fontWeight = "normal",
      fontFamily = "sans-serif",
      cursor = "default",
      color = "black"
    ),
    itemStyleExons = list(),
    itemStyleIntrons = list(),
    itemStyleUTRs = list()
  )
# Change the appearance of a specific intron
GC_chart(transcript_data,
         start = "start",
         end = "end",
         height = "200px"
) %>%
  GC_transcript(
    transcript = "transcript",
    type = "type",
    selection = 2,
    itemStyleExons = list(list(index = 0, fill = "red")
    )
  )

Add a Zoom Slider to a GC Chart

Description

GC_zoom() adds a slider below the clusters to zoom in on part of them. Drag a handle to change the window, drag the window to move it, click the track to move the window there, and double-click the slider or use the reset zoom button of the toolbar (GC_toolbar()) to show everything again. With the keyboard, the arrow keys move a handle or the window by 1% of the slider (Shift: 10%), Home and End to the ends.

Usage

GC_zoom(
  GC_chart,
  show = TRUE,
  range = NULL,
  mode = NULL,
  min = NULL,
  update = NULL,
  height = NULL,
  overview = NULL,
  color = NULL,
  style = NULL,
  windowStyle = NULL,
  handleStyle = NULL
)

Arguments

Details

The chart is drawn again for each window: genes, labels, links, coordinates and annotations follow the window, and parts outside it are cut off at the edges of the plot area. The axis shows the ticks of the window. The window of GC_scale()⁠(start, end)⁠ is the whole range the slider covers. Text moved by dragging (GC_events()(drag = TRUE)) keeps its offset. Saved images leave out the slider and the room it takes.

In Shiny, GC_events()(zoom = TRUE) reports the window, and GC_zoom_to() sets it from the server.

Value

The GC chart object with the zoom slider.

See Also

GC_zoom_to(), GC_events(), GC_scale()

Examples

GC_chart(ophA_clusters, cluster = "cluster", group = "class",
         height = "300px") %>%
  GC_labels("name") %>%
  GC_zoom(range = c(0.2, 0.6))

# The same base pairs in every cluster
GC_chart(ophA_clusters, cluster = "cluster", group = "class",
         height = "300px") %>%
  GC_zoom(mode = "absolute", update = "release", height = 30,
          handleStyle = list(backgroundColor = "#d9d9d9"))

Set the Zoom of a GC Chart in Shiny

Description

Moves the zoom slider of GC_zoom() in a running Shiny app, without drawing the chart again from R.

Usage

GC_zoom_to(outputId, range = NULL, session = shiny::getDefaultReactiveDomain())

Arguments

Value

Nothing; called for its effect.

See Also

GC_zoom()

Examples

if (interactive()) {
  library(shiny)
  ui <- fluidPage(
    actionButton("first", "First half"),
    actionButton("all", "Everything"),
    GC_chartOutput("chart", height = "300px")
  )
  server <- function(input, output, session) {
    output$chart <- renderGC_chart(
      GC_chart(ophA_clusters, cluster = "cluster", group = "class") %>%
        GC_zoom() %>%
        GC_events(zoom = TRUE)
    )
    observeEvent(input$first, GC_zoom_to("chart", c(0, 0.5)))
    observeEvent(input$all, GC_zoom_to("chart", NULL))
    observeEvent(input$chart_zoom, str(input$chart_zoom))
  }
  shinyApp(ui, server)
}

Genes of eleven coronaviruses from the four genera

Description

The genes of eleven coronaviruses, the genomes of Fig. 1 of the reference: HCoV-229E and HCoV-NL63 (Alphacoronavirus), MHV, HCoV-OC43, HCoV-HKU1, MERS-CoV, SARS-CoV-1, SARS-CoV-2 and the bat coronavirus RaTG13 (Betacoronavirus), IBV (Gammacoronavirus) and PDCoV (Deltacoronavirus). They are in that order, by genus and subgenus, so that each genome sits next to its closest relatives here. Made by data-raw/coronavirus_genomes.R.

Usage

coronavirus_genomes

Format

A data frame with 148 rows and 15 columns:

cluster

Short name of the virus, e.g. "SARS-CoV-2". A character vector.

genus

Genus of the virus, e.g. "Betacoronavirus". A character vector.

subgenus

Subgenus of the virus, e.g. "Sarbecovirus". A character vector.

virus

Name of the virus. A character vector.

strain

Strain or isolate, as in the record. A character vector.

host

Host of the virus, e.g. "human" or "bat". A character vector.

accession

GenBank or RefSeq accession of the genome. A character vector.

gene

Gene name, e.g. "ORF1a", "S", "3a" or "9b", or "L" and "3'UTR" for the untranslated ends. A character vector.

product

Description of the gene product, from the record. A character vector.

category

Role of the gene: "5' UTR", "ORF1a", "ORF1b", "spike", "hemagglutinin-esterase", "envelope", "membrane", "nucleocapsid", "accessory" or "3' UTR". A character vector.

start

Start position of the gene in the genome. A numeric vector.

end

End position of the gene in the genome. A numeric vector.

strand

Strand of the gene; all are "forward". A character vector.

homolog

Chain of homologous genes across neighbouring genomes of the same genus, named after its first gene, e.g. "MHV 5a" for 5a of MHV, ns12.9 of HCoV-OC43 and 4 of HCoV-HKU1, for GC_links()(group = "homolog"); NA for a gene without a homolog next to it. A character vector.

identity

Protein identity, in percent, of the gene to its homolog in the genome above; NA for the first gene of a chain. A numeric vector.

Details

The genes are read from the GenBank records and named as in the literature, e.g. "3a" for the "Orf3" of SARS-CoV-1 and the "NS3" of RaTG13. ORF1ab is split at its -1 ribosomal frameshift into ORF1a and ORF1b. Fourteen accessory ORFs that the records do not annotate are added from their position in the genome (product ends in "(not annotated in the record)"): the ORFs that lie inside N in another reading frame (Ia and Ib of HCoV-229E and HCoV-NL63, 9b and 9c of RaTG13 and SARS-CoV-2, 8c of MERS-CoV), 3b, 3c and 3d inside ORF3a of SARS-CoV-2 and 3b of RaTG13 (the SARS-CoV-2 ORFs are described by Jungreis et al., 2021), and ORF10 of RaTG13, which matches ORF10 of SARS-CoV-2. Each runs from an ATG to its first stop codon. MHV is strain JHM, as the records of strain A59 leave out HE, 4 and 5a.

Homologous genes of closely related neighbouring genomes share a homolog. Every gene is translated from the genome and aligned to the genes of the next genome with protein_blast(). Only neighbours whose ORF1b, which holds the polymerase and is the most conserved protein, is at least 70% identical are joined: the genomes of a subgenus and the two alphacoronaviruses (82%), but not the subgenera of the betacoronaviruses (62-66%) or the genera (50-56%), whose proteins are so far apart that their identity says little. The cut-off is chosen for these genomes, not an official one. Between close relatives, the core genes (ORF1a, ORF1b, S, HE, E, M and N) are homologous and are joined to the same gene of the next genome, however low their identity. Accessory genes are joined when each is the other's best hit, with at least 30% identity, or 50% when either protein is shorter than 50 amino acids, as short alignments reach a third identity by chance.

Each genome also has a row for its 5' untranslated region, named "L" after the leader sequence it starts with, from the start of the genome to ORF1a, and one for its 3' untranslated region ("3'UTR"), from the last gene to the end of the genome.

Source

GenBank accessions AF304460, AY567487, AY903460, AY597011, MN996532 and AY274119 and RefSeq accessions AC_000192, NC_019843, NC_045512, NC_001451 and NC_039208, read with read_gbk().

References

Chazal N. Coronavirus, the King Who Wanted More Than a Crown: From Common to the Highly Pathogenic SARS-CoV-2, Is the Key in the Accessory Genes? Front Microbiol. 2021;12:682603.

Jungreis I, Sealfon R, Kellis M. SARS-CoV-2 gene content and COVID-19 mutation impact by comparing 44 Sarbecovirus genomes. Nat Commun. 2021;12:2642.

Examples

GC_chart(coronavirus_genomes, cluster = "cluster", group = "category",
         height = "700px") %>%
  GC_labels("gene") %>%
  GC_clusterLabel() %>%
  GC_zoom(range = c(0.75, 1))

Erythromycin protein alignment results

Description

This dataset contains detailed information on genes involved in the biosynthesis of Erythromycin, an antibiotic produced by the bacterium Saccharopolyspora erythraea and several homologous gene clusters identified by antiSMASH. It includes gene identifiers, chromosomal positions, orientations, and annotations regarding their products and functions, as well as identity and similarity scores from protein_blast(), which uses global pairwise protein alignments.

Usage

erythromycin_BlastP

Format

A data frame with 148 observations and 16 variables:

protein_id

Unique protein identifiers. A character vector.

region

The chromosomal region of the gene, indicating start and end positions and strand. A character vector.

translation

Amino acid sequence of the protein encoded by the gene. A character vector.

cluster

Identifier of the gene cluster to which the gene belongs. A character vector.

strand

The strand orientation ("forward" or "complement") of the gene. A character vector.

start

The start position of the gene on the chromosome. A numeric vector.

end

The end position of the gene on the chromosome. A numeric vector.

rowID

A unique identifier for each row in the dataset. An integer vector.

identity

The percentage of identical positions in the global alignment with the best hit. A numeric vector.

similarity

The percentage of identical or similar amino acids in the global alignment with the best hit. A numeric vector.

BlastP

The protein_id of the best hit in the query cluster, or NA if there is none. A character vector.

score

Synteny score of the cluster with the query cluster, based on the hits. A numeric vector.

Gene

Gene name or identifier if available, otherwise NA. A character vector.

Position

Formatted string indicating the gene's position and orientation on the chromosome. A character vector.

Product

Description of the gene product. A character vector.

Functions

Functional categorization of the gene. A character vector.

Source

Derived from protein_blast() alignments of Saccharopolyspora erythraea genes involved in Erythromycin production.

Erythromycin Gene Cluster Data

Description

This dataset contains information about various genes involved in the biosynthesis of Erythromycin, an antibiotic produced by the bacterium Saccharopolyspora erythraea. It includes details such as gene identifiers, start and end positions of genes, and their associated functions and products.

Usage

erythromycin_cluster

Format

A data frame with 23 rows and 6 columns:

Identifiers

Unique identifiers for the genes. A character vector.

Start

Start positions of the genes on the chromosome. A numeric vector.

End

End positions of the genes on the chromosome. A numeric vector.

Strand

Strand orientation of the gene, either "+" or "-". A character vector.

Product

Description of the gene products. A character vector.

Functions

Functional categorization of the genes. A character vector.

Convert GenBank Features to Data Frame

Description

This function processes a list of GenBank features (loaded by read_gbk()) and converts selected features into a data frame. It supports processing multiple gene clusters.

Usage

gbk_features_to_df(
  gbk_list,
  feature = "CDS",
  keys = NULL,
  process_region = TRUE
)

Arguments

Value

A data frame where each row corresponds to a feature from the input list. The data frame includes a 'cluster' column indicating the source gbk file.

Examples

gbk_file <- system.file("extdata", "BGC0000001.gbk", package = "geneviewer")
gbk <- read_gbk(gbk_file)
df <- gbk_features_to_df(gbk)

# To extract only specific keys within the "CDS" feature
df <- gbk_features_to_df(gbk, feature = "CDS", keys = c("gene", "region"))

# To disable special processing of the 'region' key
df <- gbk_features_to_df(gbk, process_region = FALSE)

Convert GenBank to FASTA Format

Description

This function reads a GenBank file, extracts the sequence and writes it to a new file in FASTA format. It parses the DEFINITION and VERSION for the header and sequences from the ORIGIN section.

Usage

genbank_to_fasta(path, output_dir = tempdir())

Arguments

Value

The path of the FASTA file, invisibly.

Examples

# Path to the example GenBank file in the package
gbk_file <- system.file("extdata", "BGC0000001.gbk", package = "geneviewer")

# Convert the GenBank file to FASTA format in a temporary folder
genbank_to_fasta(gbk_file, output_dir = tempdir())

Shiny bindings for geneviewer

Description

Output and render functions for using geneviewer within Shiny applications and interactive Rmd documents.

Usage

GC_chartOutput(outputId, width = "100%", height = "400px")

renderGC_chart(expr, env = parent.frame(), quoted = FALSE)

Arguments

Details

In Shiny, a fixed pixel width set by GC_chart() (or GC_grid()) takes precedence over the output width. Put the output in a container with overflow-x: auto to scroll a chart wider than the available space. Responsive charts keep the width set by GC_chartOutput(), including when switching back from a fixed width. Standalone widgets keep their usual htmlwidgets sizing. Height behavior is unchanged.

Value

GC_chart widget that can be placed in the UI.

See Also

GC_chart()

Examples

if (interactive()) {
  library(shiny)
  library(geneviewer)

  ui <- fluidPage(
    titlePanel("Omphalotin Gene Cluster Visualization"),
    mainPanel(
      GC_chartOutput("gcChart", width = "100%", height = "500px")
    )
  )

  server <- function(input, output) {
    output$gcChart <- renderGC_chart({
      GC_chart(
        ophA_clusters, # Ensure 'ophA_clusters' data is defined or available
        cluster = "cluster",
        group = "class"
      ) %>%
      GC_title(title = c("<i>O. olearius</i>", "<i>D. bispora</i>")) %>%
      GC_labels("name") %>%
      GC_legend(position = "bottom") %>%
      GC_scaleBar() %>%
      GC_clusterLabel(title = "ophA")
    })
  }

  shinyApp(ui = ui, server = server)
}

Colours of a geneviewer Palette

Description

Besides the d3 colour schemes (e.g. "schemeSet2"), the gene colours can be set by the name of a geneviewer palette, in GC_theme() (palette) and in the colorScheme of GC_genes(), GC_transcript() and GC_color():

Usage

geneviewer_palette(name = NULL)

Arguments

Details

• "okabeIto": 8 colours that stay distinct with colour blindness (Okabe & Ito, also known as the Wong palette), the palette of the "publication" theme.

• "classic": 9 clear colours, the palette of the "classic" theme.

• "vivid": 9 bright colours for dark backgrounds.

• "dark": 11 soft colours for dark backgrounds, the palette of the "dark" theme.

• "vintage": 10 muted colours, the palette of the "vintage" theme.

• "infographic": 15 strong colours, the palette of the "infographic" theme.

• "greys": 6 greys, from dark to light.

• "blues": 7 blues, from dark to light.

Value

The colours of the palette, or the names of all palettes.

Examples

geneviewer_palette("okabeIto")
geneviewer_palette()

genes_data <- data.frame(
  start = c(10, 90, 130, 170, 210),
  end = c(40, 120, 160, 200, 240),
  group = c('A', 'B', 'B', 'A', 'C')
)
GC_chart(genes_data, group = "group", height = "100px") %>%
  GC_genes(colorScheme = "okabeIto")

Settings of a Built-in Theme

Description

Returns the settings of a built-in theme of GC_theme(), to see them or to start a theme of your own.

Usage

geneviewer_theme(name = "default")

Arguments

Value

A list of theme settings, to use as the theme of GC_theme().

Examples

geneviewer_theme("dark")

# A theme of your own, based on "classic"
my_theme <- utils::modifyList(geneviewer_theme("classic"),
                              list(textColor = "navy"))

Calculate Intron Positions Based on Exons

Description

This function takes a dataset containing transcript information and calculates the start and end positions of introns based on the positions of exons. It generates a combined dataframe including both the original exons and the calculated introns. Overlapping, nested, and adjacent exon/UTR intervals are treated as a continuous covered region; only positive gaps become introns.

Usage

get_introns(data)

Arguments

Value

A dataframe with the original exons and the calculated introns, sorted by transcript and start position. Each row includes the start, end, type (exon, UTR or intron), transcript, and strand for each segment.

Examples

exons <- data.frame(
  transcript = "tx1",
  type = "exon",
  start = c(100, 400, 900),
  end = c(250, 600, 1100),
  strand = "forward"
)
get_introns(exons)

Human Dystrophin Transcripts Data

Description

This dataset contains the Exon positions on human Dystrophin transcripts 'Dp427p2', 'Dp260-2', 'Dp140', 'Dp116', and 'Dp71'.

Usage

hs_dystrophin_transcripts

Format

A data frame with 202 rows and 5 columns:

transcript

Transcript names. A character vector representing the names of the transcripts.

type

Transcript type.

start

Start positions. An integer vector showing the starting positions of each Exon.

end

End positions. An integer vector showing the ending positions of each Exon.

length

Transcript lengths. An integer vector indicating the length of each transcript.

Human HOX Gene Cluster Data

Description

This dataset represents the positions and clusters of various human HOX genes. HOX genes are a group of related genes that control the body plan of an embryo along the head-tail axis. The dataset includes genes from HOXA, HOXB, HOXC, and HOXD clusters.

Usage

human_hox_genes

Format

A data frame with 39 rows and 6 columns:

symbol

HGNC symbols for the HOX genes. A character vector.

chromosome_name

Chromosome number where each gene is located. A numeric vector.

start

Start positions of the genes on the chromosome. A numeric vector.

end

End positions of the genes on the chromosome. A numeric vector.

strand

-1 for reverse and 1 for forward.

cluster

Cluster identification as a character vector, specifying the HOX gene cluster (HOXA, HOXB, HOXC, HOXD).

name

Simplified names for the HOX genes. A character vector.

Human and Locust Mitochondrial Gene Order

Description

The 13 protein-coding genes and two ribosomal RNAs of each mitochondrial genome compared in Figure 3 of Jameson et al. (2003). Rows follow circular gene order, starting at COX1, with the locust first. tRNAs are excluded. Coordinates remain those of the original RefSeq records, before rotation. Blocks reproduce the colours of the published comparison; they do not imply a reconstructed sequence of evolutionary events.

Usage

mitochondrial_gene_order

Format

A data frame with 30 rows and 9 columns:

cluster

Species name.

accession

Versioned RefSeq accession.

genome_length

Complete mitochondrial genome length in base pairs.

gene

Gene name; RNS and RNL denote the small and large rRNAs.

product

Gene product from the RefSeq annotation.

block

Gene block coloured in the published comparison.

start, end

Original 1-based inclusive genomic coordinates.

strand

Either "forward" or "complement".

Source

RefSeq NC_001712.1 and NC_012920.1, read with read_gbk().

References

Jameson D, Gibson AP, Hudelot C, Higgs PG. OGRe: a relational database for comparative analysis of mitochondrial genomes. Nucleic Acids Res. 2003;31:202-206. doi:10.1093/nar/gkg077.

Examples

# Rotate each circular genome to start at COX1
cox1 <- with(subset(mitochondrial_gene_order, gene == "COX1"),
             setNames(start, cluster))
genes <- within(mitochondrial_gene_order, {
  plot_start <- (start - cox1[cluster]) %% genome_length + 1
  plot_end <- plot_start + end - start
})
GC_chart(genes, start = "plot_start", end = "plot_end", cluster = "cluster",
         group = "block", strand = "strand") %>%
  GC_labels("gene") %>%
  GC_clusterLabel()

Perform Sequence Alignment Using MUMmer

Description

This function orchestrates the alignment of sequences in a specified directory using MUMmer, a tool for aligning large DNA or protein sequences. It can handle GenBank and FASTA file formats. and performs checks to ensure necessary files are present.

Usage

mummer_alignment(
  path,
  cluster = NULL,
  maptype = "many-to-many",
  seqtype = "protein",
  mummer_options = "",
  filter_options = "",
  remove_files = TRUE,
  output_dir = tempdir()
)

Arguments

Value

A data frame combining all alignment results, or NULL if errors occur during processing.

References

Kurtz S, Phillippy A, Delcher AL, Smoot M, Shumway M, Antonescu C, Salzberg SL (2004). Versatile and open software for comparing large genomes. Genome Biology, 5(R12).

Examples

## Not run: 
# Requires the external MUMmer programs (nucmer or promer, delta-filter and
# show-coords) on the PATH.

# Basic alignment with default options
mummer_alignment(
  path = "/path/to/sequences",
  maptype = "many-to-many",
  seqtype = "protein"
)

# Alignment with specific MUMmer options
mummer_alignment(
  path = "/path/to/sequences",
  maptype = "one-to-one",
  seqtype = "protein",
  mummer_options = "--maxgap=500 --mincluster=100",
  filter_options = "-i 90"
)

## End(Not run)

ophA Gene Cluster from Omphalotus olearius

Description

This dataset represents the ophA gene cluster from Omphalotus olearius and Dendrothele bispora, involved in the production of omphalotin, a cyclic N-methylated peptide.

Usage

ophA_clusters

Format

A data frame with 17 rows and 5 columns:

name

Gene names. A character vector.

start

Start positions of the genes. An integer vector.

end

End positions of the genes. An integer vector.

class

Classifications of the genes. A character vector indicating the type of protein or function associated with each gene.

cluster

Cluster identification as a character vector, specifying whether the gene belongs to the ophA or dbophA gene cluster.

References

van der Velden NS et al. Autocatalytic backbone N-methylation in a family of ribosomal peptide natural products. Nat Chem Biol. 2017 Aug;13(8):833-835. doi: 10.1038/nchembio.2393. Epub 2017 Jun 5. PMID: 28581484.

Nucleotide alignment of the pQEB1 plasmids

Description

The aligned regions between neighbouring plasmids of pQEB1_plasmids, in the order pQEB1_inv3_Tn5403, pQEB1, pQEB1_inv1, pQEB1_inv1_inv2 and pQEB1_inv1_arsTn, from a one-to-one nucleotide alignment with MUMmer (nucmer, see mummer_alignment()). Regions shorter than 500 bp are left out. Regions with start2 larger than end2 are inverted.

Usage

pQEB1_alignment

Format

A data frame with 14 rows and 9 columns:

cluster1, cluster2

Names of the two plasmids. Character vectors.

start1, end1

Start and end of the region in cluster1. Integer vectors.

start2, end2

Start and end of the region in cluster2. Integer vectors.

length1, length2

Length of the region in each plasmid. Integer vectors.

identity

Percentage identity of the region. A numeric vector.

Source

Made by data-raw/pQEB1_plasmids.R.

Genes of the blaKPC-2 plasmid pQEB1 and four of its variants

Description

The genes of five IncN plasmids of the pQEB1 lineage, which carries the carbapenemase gene blaKPC-2, from a hospital outbreak of Enterobacter cloacae and Klebsiella oxytoca. The variants differ from pQEB1 by inversions and transposon insertions. Made by data-raw/pQEB1_plasmids.R from the GenBank files, together with pQEB1_alignment.

Usage

pQEB1_plasmids

Format

A data frame with 401 rows and 10 columns:

cluster

Name of the plasmid. A character vector.

host

Species and strain the plasmid was found in. A character vector.

accession

GenBank accession of the plasmid. A character vector.

locus_tag

Locus tag of the gene. A character vector.

gene

Gene name, or "" when the gene has none. A character vector.

product

Description of the gene product. A character vector.

category

Function of the gene from its name and product, e.g. "Carbapenemase (KPC)", "Mobile element" or "Conjugation". A character vector.

start

Start position of the gene. A numeric vector.

end

End position of the gene. A numeric vector.

strand

Strand of the gene, "forward" or "complement". A character vector.

Source

GenBank accessions NZ_PQ162285, NZ_PQ162289, NZ_PQ162291, NZ_PQ162292 and NZ_PQ162293.

References

Moran RA, Behruznia M, Holden E, Garvey MI, McNally A. pQEB1: a hospital outbreak plasmid lineage carrying blaKPC-2. Microb Genom. 2024;10(9).

Find Homologous Proteins Between Clusters by Pairwise Alignment

Description

Compares the proteins of a query cluster with those of the other clusters. It generates all protein combinations between the query cluster and each other cluster, aligns each pair with a global (Needleman-Wunsch) pairwise alignment from pwalign::pairwiseAlignment(), calculates sequence identity and similarity, and keeps the best hit of each gene above a minimum identity threshold.

Usage

protein_blast(
  data,
  query,
  id = "protein_id",
  start = "start",
  end = "end",
  cluster = "cluster",
  genes = NULL,
  identity = 30,
  parallel = TRUE
)

Arguments

Details

Despite its name, the function does not run BLAST: there is no seed search, local alignment, substitution matrix or E-value. Identity and similarity are percentages of the full length of the global alignment, gaps included, so proteins of very different length, or that share only one domain, get lower values than they would get from BLASTP. Set identity with this in mind.

Value

A modified version of the input data dataframe, including additional columns BlastP (the protein of the query cluster that is the best hit; the name is kept for compatibility), identity, similarity and score (the synteny score of the cluster).

Note

This function relies on the Biostrings and pwalign packages from Bioconductor for sequence alignment. Ensure these packages are installed.

References

Needleman, S. B. and Wunsch, C. D. (1970). A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology, 48(3), 443-453. doi:10.1016/0022-2836(70)90057-4

Examples

if (requireNamespace("Biostrings", quietly = TRUE) &&
    requireNamespace("pwalign", quietly = TRUE)) {
  genes <- data.frame(
    protein_id = c("A1", "A2", "B1", "B2"),
    cluster = c("A", "A", "B", "B"),
    start = c(1, 200, 1, 200),
    end = c(150, 350, 150, 350),
    translation = c("MKTAYIAKQRQISFVKSHFSRQ", "MSDNELLKRLGEAVLAHPTTG",
                    "MKTAYIAKQRQISFVKSHFSRQ", "MLPGGRTSWVQEHCDKAYNIF")
  )
  protein_blast(genes, query = "A", identity = 30)
}

Read BED Files

Description

This function reads BED files from a specified directory or file path and combines them into a single data frame. BED files use 0-based coordinate starts, while this function transforms the coordinates to 1-based during import. chromStart and thickStart are increased by 1; end positions and blockSizes are kept as they are. blockStarts become 1-based positions relative to chromStart (the first block starts at 1), as in rtracklayer::import.bed(), so the start of a block is chromStart + blockStart - 1.

Usage

read_bed(path)

Arguments

Details

This function can read multiple BED files from a directory or a single BED file from a specified path. It adds a 'filename' column with the name of the file, and combines the data frames from all files into one.

Value

A data frame combining data from the BED files.

Examples

bed_file <- tempfile(fileext = ".bed")
writeLines(c(
  "chr1\t100\t500\tgeneA\t0\t+",
  "chr1\t700\t1200\tgeneB\t0\t-"
), bed_file)

# Read a single BED file
bed_data <- read_bed(bed_file)

# Read all BED files in a directory
bed_data <- read_bed(dirname(bed_file))

unlink(bed_file)

Read Protein Sequences from FASTA Files

Description

This function reads protein sequences from the specified FASTA file or all FASTA files within a directory. It specifically looks for metadata in the FASTA headers with key-value pairs separated by an equals sign =. For example, from the header '>protein1 [gene=scnD] [protein=ScnD]', it extracts 'gene' as the key and 'scnD' as its value, and similarly for other key-value pairs.

Usage

read_fasta(fasta_path, sequence = TRUE, keys = NULL, file_extension = "fasta")

Arguments

Details

The Biostrings package is required to run this function.

Value

A data frame with columns for each piece of information extracted from the FASTA headers.

Examples

if (requireNamespace("Biostrings", quietly = TRUE)) {
  fasta_file <- system.file("extdata", "BGC0000108.fasta", package = "geneviewer")

  # Read sequences from a single FASTA file
  sequences_df <- read_fasta(fasta_file)

  # Read all FASTA files in a directory, without the sequences
  sequences_df <- read_fasta(dirname(fasta_file), sequence = FALSE)
}

Read Data from GenBank Files

Description

This function reads data from a single GenBank file or directory with GenBank files. It allows selective extraction of information by specifying sections and features.

Usage

read_gbk(path, sections = NULL, features = NULL, origin = TRUE)

Arguments

Value

A list containing the contents of the specified sections and features of the GenBank file. Each section and feature is returned as a separate list element.

Examples

# Path to example GenBank file in the package
genbank_file <- system.file(
  "extdata",
  "BGC0000001.gbk",
  package = "geneviewer"
  )

# Read all data from the example GenBank file
gbk_data <- read_gbk(genbank_file)

# Read only specific sections from the example GenBank file
gbk_data <- read_gbk(genbank_file, sections = c("LOCUS", "DEFINITION"))

# Read specific features from the FEATURES section of the example GenBank file
gbk_data <- read_gbk(genbank_file, features = c("gene", "CDS"))

# Read data without the origin sequence
gbk_data <- read_gbk(genbank_file, origin = FALSE)

Read GFF Files

Description

This function reads GFF files from a specified directory or file path and combines them into a single data frame.

Usage

read_gff(path, fields = NULL)

Arguments

Details

This function can read multiple GFF files from a directory or a single GFF file. It processes each file, extracts the specified fields (if provided), adds a 'name' column with the filename, and combines the data frames from all files into one.

Value

A data frame containing the combined data from the GFF files.

Examples

gff_file <- system.file("extdata", "HQ113105.1.gff3", package = "geneviewer")

# Read a single GFF file
gff_data <- read_gff(gff_file)

# Read GFF files from a directory
gff_data <- read_gff(dirname(gff_file))

# Read specific fields
gff_data <- read_gff(
  gff_file,
  fields = c("seqid", "type", "start", "end", "strand", "Name")
)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

magrittr

%>%

Genes of Tn7 and of Tn7-like transposons carrying a CRISPR-Cas system

Description

The genes of Tn7, of Tn6022 and of fourteen Tn7-like transposons that have picked up a minimal CRISPR-Cas system, which has lost the genes needed to acquire spacers and to cut the target and is thought to guide the transposon to its insertion site instead. Each element is drawn from its right end, next to the gene it inserted beside, so that tnsA is at the left. The elements, the genomes they sit in and the insertion sites are those of Table S1 and Fig. 3 of the reference below, and the rows are named after the TnsA tree node that figure labels them with. Each region starts at the insertion site and ends at the left end of the element or after the cas genes, as in that figure. Only the first part of each element is covered, up to the end of the cas genes; the longer elements carry further cargo beyond it. Made by data-raw/tn7_crispr_elements.R.

Usage

tn7_crispr_elements

Format

A data frame with 307 rows and 13 columns:

cluster

Name of the element, as in Fig. 3 of the reference. A character vector.

strain

Species and strain the element was found in. A character vector.

strain_short

The strain with its genus abbreviated, e.g. "V. cholerae VC35", for short labels. A character vector.

accession

Accession of the sequence the region comes from. A character vector.

site

Gene the element has inserted next to, e.g. "glmS", "yciA" or "SRP-RNA". A character vector.

locus_tag

Locus tag of the gene. A character vector.

gene

Gene name, or "" when the gene has none; "R" or "L" for the right or left end of the element, and "CRISPR" for a CRISPR array. A character vector.

label

The name Fig. 3 of the reference writes on the gene, or "" when it names none: the transposition and cas genes, the insertion sites in short (e.g. "glmS" for "attTn7 (glmS)") and the resistance genes and integrase of Tn7. A character vector.

product

Description of the gene product. A character vector.

category

Role of the gene, e.g. "tnsA", "tniQ / tnsD", "cas8f / cas5f", "CRISPR array", "insertion site", "right end" or "left end". A character vector.

start

Start position of the gene in the region. A numeric vector.

end

End position of the gene in the region. A numeric vector.

strand

Strand of the gene, "forward" or "complement". A character vector.

Details

Tn7 itself carries tnsE and no cas genes. It is taken from the chromosome of Acinetobacter baumannii UC20804, where it sits at its insertion site attTn7, next to glmS; this Tn7 is 99.9% identical over its whole length to the Tn7 of GenBank DQ176450. Tn6022 is the other element without cas genes; it sits inside yifB (comM), which the annotation splits in two, and only the fragment at its right end is kept here. Its ends are the window between those two fragments and are not verified to the base. In element #2757 tnsA is fused to tnsB: the annotated gene is longer than the tnsB of Tn7 by about the size of a TnsA, and that extra length at its start is given as a separate row, with gene name "tnsA (fused)" and label "tnsA". The boundary between the two is an estimate.

Besides the genes, each element has a row for its right end (gene R, category "right end") and its left end (gene L, category "left end"). Both are single positions, with start equal to end. The left end of most elements lies far beyond the last gene covered here (up to about 100 kb), at its true position. Genes that run on past the left end are cut off there.

Source

GenBank accession CP076807, RefSeq accession NZ_KB849325 and the RefSeq genomes of Table S1 of the reference, read with read_gbk(). The CRISPR arrays that RefSeq does not annotate were found from the repeat consensus of the type I-F arrays these elements carry.

References

Peters JE, Makarova KS, Shmakov S, Koonin EV. Recruitment of CRISPR-Cas systems by Tn7-like transposons. Proc Natl Acad Sci USA. 2017;114(35):E7358-E7366.