🐝 chromR

🐝 chromR is an R script designed for visualizing gene or domain positions on chromosomes or contigs. It allows users to customize their plots using keywords, file-based inputs, and color options, making it ideal for genomic data exploration.

📖 Features

📊 Visualization Option

• Plot genes, domains, or specific regions on chromosomes or contigs.
• Highlight specific genes, regions, or domains using keyword filtering.
• Additional plot types:
  • line_plot: display features as lines instead of points.
  • accumulated_plot: generate an accumulated gene count plot. (Fig. 2)
  • folded_plot: generate an accumulated gene count plot but folded in center of chromosome. (Fig. 2)

🎨 Color Customization

• Define colors for categories using R color names (e.g., "black") or hex codes (e.g., #1f77b4).
• Support for RColorBrewer palettes via palette

📁 Data Handling

• Accepts standard GFF (General Feature Format) files as input gff_file.
• Support for fill files fill_file in CSV/TSV formats.
• Export processed data as tables in CSV or TSV formats CSV/TSV with table

🔍 Filtering Option

• Strict mode strict to show only chromosomes containing keywords
• High point support via layout file layout
• Custom chromosome ordering order_file
• Filter by number of chromosomes to display number

⚙️ Integration & Compatibility

• 🚀 Command-Line Friendly. Run directly from the terminal or integrate into pipelines.
• 🖥️ RStudio Compatible. Easy to modify, debug, or execute within RStudio.

🖼️ Example Output

• Description: This graph shows the position of two transposons in Saccharomyces cerevisiae R64, Ty1 (Ty1, gene) and TYA (TYA, gene) highlighted in green and orange.
• Command used:

Rscript chromR.R -g ../genome/Saccharomyces_cerevisiae.R64-1-1.114.gff3 --keywords Ty1 gene TYA gene

• Genome example: Download Saccharomyces cerevisiae GFF

Figure 1.

Figure 2.

🧪 Installation

1. Clone this repository:

git clone https://github.com/mathiashole/chromR.git
cd chromR

2. Install required R packages:

Open R or RStudio and run:

install.packages(c("dplyr", "readr", "ggplot2", "yaml"))

🔧 Usage

Requirements:

Ensure the following R packages are installed: dplyr, readr, ggplot2

Execution Options:

1. Using the Terminal

• Navigate to the folder containing chromR.R and execute the script with the necessary arguments:

Rscript chromR.R --gff_file </path/to/file.gff> --keywords <keyword1> <keyword2> --colors <color1>

2. Using RStudio

• Open chromR.R in RStudio.
• Set the arguments manually in the script or pass them interactively.

⚙️ Arguments and configurations

Command-line arguments

Argument	Description	Example
--config / -c	Path to the YAML configuration file. All program options are read from this file.	Rscript chromR.R --config config.yaml

YAML configuration options

YAML key	Description	Example
gff_file	Path to the GFF annotation file.	data/genome.gff
feature_mode	Feature selection mode (fill or keyword).	fill
feature_file	File containing genomic regions (used with fill mode).	regions.tsv
keywords	Attribute/type pairs used to select features (used with keyword mode).	["gene", "TYA"]
strict_filter	Require exact keyword matching.	true
max_chromosomes	Maximum number of chromosomes to plot.	5 or N
palette	Name of an RColorBrewer palette. Ignored if colors is provided.	Dark2
colors	Custom colors (hex codes or color names).	["#d95f02", "#1b9e77"]
line_plot	Generate a line plot instead of a histogram.	true
accumulated_plot	Plot accumulated densities instead of individual densities.	false
density_mode	Density representation (hist or density).	hist
facet_plot	Generate faceted plots.	true
facet_mode	Facet representation (hist or density).	hist
facet_scales	Scale behavior for facets (fixed or free).	free
interactive	Enable interactive plotting.	false
window_mode	Enable sliding-window analysis.	true
window_size	Sliding-window size (bp).	5000
gene_list	List of genes for sliding-window analysis.	["TYA", "Ty1"]
min_genes	Minimum number of genes required per window.	2

💡 Note: Colors must match the number of keywords provided. They can be standard R color names or hex codes. In the case of hexadecimal colors, they must be put in quotes (e.g. "#F4A582").

💡 Bash Quick Examples

Example 1: Basic Plot

Highlight gene1 and CDS in orange:

Rscript chromR.R --gff_file data.gff --keywords gene1 CDS --colors orange

Rscript chromR.R -g </path/of/file.gff> -k <keyword1> <keyword1_asociated> <keyword2> <keyword2_asociated>

If you have any genome data, put this data in TSV or CSV format and use GFF to map the chromosome.

Example 2: Fill data to Plot

Rscript chromR.R --gff_file data.gff --fill_file data_to_fill_information.tsv/.csv --format tsv/csv

Rscript chromR.R -g </path/of/file.gff> -ff <path/of/data_to_fill> -f <tsv/csv>

💡 R Quick Examples

Example 1: Basic Plot

Highlight gene1 and CDS in orange:

args <- c("chromR.R", "--gff_file", "data.gff", "--keywords", "gene1 CDS", "--colors, "orange")

# Execution command
system2("Rscript", args = args)

If you have any genome data, put this data in TSV or CSV format and use GFF to map the chromosome.

Example 2: Fill data to Plot

args <- c("chromR.R", "--gff_file", "data.gff", "--fill_file", "data_to_fill_information.tsv/.csv", "--format", "tsv/csv")

# Execution command
system2("Rscript", args = args)

💖 Contributing

We welcome contributions!

• Pull requests and 🌟 stars are always welcome.
• For major changes, please open an issue first to discuss what you would like to change.
• Please make sure to update tests as appropriate.

🚧 Roadmap

See the full roadmap here

📜 License

MIT © Mathias Mangino