Introduction of ‘geneNR’

Rajamani Nirmalaruban

2026-07-05

Introduction

The geneNR package streamlines post-GWAS (Genome-Wide Association Studies) and post-QTL mapping analyses by automating candidate gene identification within custom search windows. It parses genomics files, visualizes marker densities, and targets stress-adaptive traits primarily in wheat and rice, serving as an asset for cereal improvement programs.

Key Features

Technical Pipeline & Parameter Configurations

The package exposes functions for gene identification (geneSNP(), geneQTL(), geneSNPcustom()), file conversion (import_hmp(), import_vcf()), data summarization (summariseSNP(), summariseSNP_vcf()), and visualization (plot_SNP(), plot_summariseSNP()).

Gene Mining Function Arguments

Argument Type Default Description
data_file character Required Built-in dataset name (e.g., "sample_data_wheat") or path to a custom .csv file.
upstream numeric 1000000 Search window extension upstream of the variant position (in base pairs).
downstream numeric 1000000 Search window extension downstream of the variant position (in base pairs).
crop character "wheat" Species target selector; choose either "wheat" or "rice".
genome_source character "online" Database routing engine; choose "online" (Ensembl server lookup) or "local".
gff3_file character NULL Complete system file path targeting your local custom .gff3 reference sheet.

Target Reference Genome Assemblies

When using the genome_source = "online" engine, geneNR automatically queries the most current stable database instances on Ensembl Plants:

Note: If your local input data was called using alternative genome coordinates (e.g., specific pan-genome cultivars, or custom assemblies), switch to genome_source = "local" and provide your targeted reference .gff3 file.

Step-by-Step Executions

Workflow 1: Instant Local Tracking Engine (CRAN Safe)

This mode executes completely offline. If genome_source = "local" is selected without passing a path, the function automatically loops back to its internal custom package database framework (sample_crop.gff3).

library(geneNR)

gff_path <- system.file("extdata", "sample_crop.gff3", package = "geneNR")

# Execute local mapping on sample datasets using the integrated reference sheet
local_results <- geneSNP(
  data_file     = "sample_data_wheat",
  upstream      = 50000,
  downstream    = 50000,
  genome_source = "local",
  gff3_file     = gff_path
)
#> SNPChrPostraits
#> Parsing local GFF3 reference file...
#> Warning: One or more parsing issues, call `problems()` on your data frame for details,
#> e.g.:
#>   dat <- vroom(...)
#>   problems(dat)
#> BLINK.SAAX-945240701B:567878516-5679785161B:567500000-568500000TraesCS1B02G339000MockSource
#> Filtered gene IDs saved to C:\Users\nirma\AppData\Local\Temp\RtmpyGlYSE\filtered_gene_id2ab46a476307.xlsx

# Display extracted candidate data matrix output lines
print(utils::head(local_results))
#>     traits         SNP          search_window              gene_size
#> 1 BLINK.SA AX-94524070 1B:567878516-567978516 1B:567500000-568500000
#>              gene_id  gene_type
#> 1 TraesCS1B02G339000 MockSource

Workflow 2: Online Real-Time Web Scraping Mode

This mode scrapes live genome layers from Ensembl Plants. Because it requires active server responses, it is skipped during check loops to comply with CRAN timing limits.

# Run online extraction tracking across regional rice variants
online_results <- geneSNP(
  data_file     = "sample_data_rice", 
  upstream      = 10000, 
  downstream    = 10000, 
  crop          = "rice",
  genome_source = "online"
)

print(utils::head(online_results))

Workflow 3: Custom Haplotype Range Testing (geneSNPcustom)

When dynamic search windows are not uniform across the dataset, use geneSNPcustom to read custom coordinate columns (start and stop) from the input table.

gff_path <- system.file("extdata", "sample_crop.gff3", package = "geneNR")

# Process dynamic uneven interval segments using the offline template pipeline
custom_range_results <- geneSNPcustom(
  data_file     = "sample_data_wheat_custom",
  crop          = "wheat",
  genome_source = "local",
  gff3_file     = gff_path
)
#> traitsSNPChrstartstop
#> Parsing local GFF3 reference file...
#> Warning: One or more parsing issues, call `problems()` on your data frame for details,
#> e.g.:
#>   dat <- vroom(...)
#>   problems(dat)

print(utils::head(custom_range_results))
#>   traits         SNP          search_window              gene_size
#> 1     SA AX-94524070 1B:565928516-569928516 1B:567500000-568500000
#>              gene_id  gene_type
#> 1 TraesCS1B02G339000 MockSource

Genomic Data Summarization & Visualization

Parsing and Summarizing Genotypic Data

You can easily import genomic marker distributions directly from standard formats like HapMap and VCF to compute chromosomal summary frames.

# Import a sample HapMap tracking dataset
demo_hmp <- system.file("extdata", "demo_SNP.hmp.txt", package = "geneNR")
imported_data <- import_hmp(demo_hmp)

# Summarize variant distributions across chromosomes
snp_summary <- summariseSNP(imported_data)
print(snp_summary)
#>   Chr SNP_Count
#> 1  1A         5
#> 2  1B         8
#> 3  1D         7
#> 4  2A        31
#> 5  2B        35
#> 6  2D        11
#> 7  3A         8
#> 8  3B        28
#> 9  3D        80

Mapping Chromosomal Density Charts (plot_SNP)

The package features native plotting methods to map physical variant spreads against chromosomal skeletons.

# Load internal mock layout references
chr_details <- read.csv(system.file("extdata", "chromosome_details.csv", package = "geneNR"))
snp_locations <- read.csv(system.file("extdata", "identified_SNP.csv", package = "geneNR"))

# Generate the physical density model distribution map
snp_map <- plot_SNP(
  chromosome_details = chr_details, 
  data               = snp_locations,  
  chromosome_color   = "steelblue",  
  title              = "Chromosome map with SNPs", 
  label_color        = "black"
)

# Render the plot layout
print(snp_map)
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_segment()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_text_repel()`).

Visualizing Frequency Histograms (plot_summariseSNP)

To view aggregate chromosomal frequencies side-by-side, use plot_summariseSNP to create customized summary charts.

# Generate a summary metric visualization chart
summary_chart <- plot_summariseSNP(
  snp_summary, 
  bar_color   = "skyblue",
  label_size  = 3, 
  label_color = "red"
)
#> Bar chart successfully saved at: C:\Users\nirma\AppData\Local\Temp\RtmpyGlYSE/snp_bar_chart.jpeg

# Render the layout chart
print(summary_chart)

System Output Layout

Upon evaluation, matches are structured cleanly and auto-exported into your working folder as a comprehensive Excel report:

Support & Package Maintenance

For code reporting, genomic coordinate requests, or to report structural workflow issues, contact the author and maintainer:

Glossary

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