Package 'geneviewer'

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

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 similarity and identity scores from BlastP analysis.

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 identity score from BlastP analysis, representing the percentage of identical matches. A numeric vector.

similarity

The similarity score from BlastP analysis, often reflecting the functional or structural similarity. A numeric vector.

BlastP

Reference to the protein_id after BlastP comparison, or NA if not applicable. A character vector.

score

Score assigned based on the BlastP analysis, quantifying the match quality. 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 BlastP analysis of Saccharopolyspora erythraea genes involved in Erythromycin production.

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.

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

## Not run: 
gbk <- read_gbk("path/to/genbank_file.gbk")
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)

## End(Not run)

Description

This function aligns clusters based on a specified gene id.

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)

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, and terminator.

Usage

GC_annotation(GC_chart, type = "textAnnotation", 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()

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 folder containing .gbk files
# file_path <- "~/path/to/folder/"
# GC_chart(file_path) %>%

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_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)

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(),
  ...
)

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",
    spacing = 15,
    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"
    )
  )

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 = "100px",
  cluster = NULL,
  position = "left",
  wrapLabel = TRUE,
  wrapOptions = list(),
  ...
)

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)

# 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",
    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"
  )

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,
  ...
)

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
    )
  )

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"))

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,
  ...
)

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"
  )
)

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(),
  ...
)

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")
  )
 )

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)

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(),
  ...
)

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()

# 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
 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")
  )
)

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 = "#0000",
  order = list(),
  position = "bottom",
  style = list(),
  legendOptions = list(),
  legendTextOptions = list(),
  ...
)

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
    x = 0,
    y = 0,
    adjustHeight = TRUE,
    backgroundColor = "#0000",
    order = list(),
    positions = "bottom",
    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
    )
  )

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 = TRUE,
  measure = "identity",
  show_links = TRUE,
  label = TRUE,
  normal_color = "#969696",
  inverted_color = "#d62728",
  use_group_colors = FALSE,
  color_bar = TRUE,
  colorBarOptions = list(),
  linkWidth = NULL,
  linkStyle = list(),
  labelStyle = list(),
  ...
)

Arguments

Value

Modified 'GC_chart' object with added links.

See Also

* [get_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))

# 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()

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
 )

Description

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

Usage

GC_scale(
  GC_chart,
  cluster = NULL,
  start = NULL,
  end = NULL,
  padding = 2,
  hidden = FALSE,
  breaks = list(),
  tickValues = NULL,
  reverse = FALSE,
  axis_position = "bottom",
  axis_type = "position",
  y = NULL,
  scale_breaks = FALSE,
  scale_break_threshold = 20,
  scale_break_padding = 1,
  ticksCount = 10,
  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)

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
    )
  )

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 = 50,
  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)

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,
  ...
)

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
    )
  )

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}")

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()

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(),
  labelOptions = list(),
  ...
)

Arguments

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")
    )
  )

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 = NULL)

Arguments

Value

None explicitly, but writes the FASTA formatted data to a file.

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
genbank_to_fasta(gbk_file)

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

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)
}

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.

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.

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.

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.

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: 
# 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)

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.

Description

This function conducts a BLAST analysis for protein sequences within specified clusters. It generates all possible protein combinations between a query cluster and other clusters, performs pairwise alignments, calculates sequence identity and similarity, and filters results based on 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

Value

A modified version of the input 'data' dataframe, including additional columns for BLAST results (identity, similarity).

Note

This function relies on the Biostrings and pwalign package for sequence alignment and the dplyr package for data manipulation. Ensure these packages are installed and loaded into your R session.

Examples

## Not run: 
path_to_folder <- "path/to/gbk/folder/"
data_updated <- protein_blast(
                         path_to_folder,
                         id = "protein_id",
                         query = "cluster A",
                         identity = 30
                         )

## End(Not run)

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.

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

## Not run: 
# Read BED files from a directory
bed_data <- read_bed("path/to/directory")

# Read a single BED file
bed_data <- read_bed("path/to/file.bed")

## End(Not run)

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

## Not run: 
# Read sequences from a single FASTA file
sequences_df <- read_fasta("path/to/single_file.fasta")

# Read all sequences from a directory of FASTA files
sequences_df <- read_fasta("path/to/directory/", file_extension = "fa")

# Read sequences and include the protein sequences in the output
sequences_df <- read_fasta("path/to/directory/", sequence = TRUE)

## End(Not run)

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)

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

## Not run: 
# Read GFF files from a directory
gff_data <- read_gff("path/to/directory")

# Read a single GFF file
gff_data <- read_gff("path/to/file.gff")

# Read specific fields from GFF files
gff_data <- read_gff(
"path/to/directory",
fields = c("seqid", "start", "end", "attributes")
)

## End(Not run)