Title: Automated Gene Identification for Post-GWAS and QTL Analysis
Version: 2.0.1
Description: Facilitates the post-Genome Wide Association Studies (GWAS) and Quantitative Trait Loci (QTL) analysis of identifying candidate genes within user-defined search window, based on the identified Single Nucleotide Polymorphisms (SNPs) as given by Mazumder AK (2024) <doi:10.1038/s41598-024-66903-3>. It supports candidate gene analysis for wheat and rice. Just import your GWAS result as explained in the sample_data file and the function does all the manual search and retrieve candidate genes for you, while exporting the results into ready-to-use output.
License: CC BY 4.0
Copyright: (C) 2025 Nirmalaruban
Encoding: UTF-8
RoxygenNote: 7.3.2
Depends: R (≥ 3.5)
LazyData: TRUE
Imports: readr, stringr, utils, httr, rvest, xml2, writexl, vcfR, ggplot2, ggrepel
Suggests: knitr, rmarkdown, devtools
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2025-03-29 16:27:50 UTC; nirma
Author: Rajamani Nirmalaruban [aut, cre, cph], R. Suvitha [aut], Rajbir Yadav [aut], Meda Alekya [aut], Amit Kumar Mazumder [aut], Subramani Sugumar [aut], Prashanth babu [aut], Manjeet Kumar [aut], Kiran B Gaikwad [aut], Naresh Kumar Bainsla [aut], S. Bhaskar Reddy [aut]
Maintainer: Rajamani Nirmalaruban <nirmalaruban97@gmail.com>
Repository: CRAN
Date/Publication: 2025-03-29 16:50:02 UTC

Identifies Candidate Genes based on identified Quantitative Trati Loci (QTL) analysis

Description

Identifies Candidate Genes based on identified Quantitative Trati Loci (QTL) analysis

Usage

geneQTL(data_file, crop = "wheat")

Arguments

data_file

The input data in .csv format. (sample_data_wheat_qtl or sample_data_rice_qtl for demo purpose)

crop

Either "wheat" or "rice". (by default it will be wheat)

Value

A data frame containing traits, QTL, gene_id, gene_size, and gene_type.

Examples

load(system.file("extdata", "precomputed_sample_results_qtl.rda", package = "geneNR"))
message(sample_results)

result <- geneQTL("sample_data_wheat_qtl", crop="wheat")
result <- geneQTL("sample_data_rice_qtl", crop="rice")
#result <- geneQTL("your_results.csv", crop="wheat")

Identifies Candidate Genes based on identified Single Nucleotide Ploymorphisms (SNPs) from Genome Wide Association Stuides (GWAS) Analysis

Description

Identifies Candidate Genes based on identified Single Nucleotide Ploymorphisms (SNPs) from Genome Wide Association Stuides (GWAS) Analysis

Usage

geneSNP(data_file, upstream = 1e+06, downstream = 1e+06, crop = "wheat")

Arguments

data_file

The input data in .csv format. (sample_data_wheat or sample_data_rice for demo purpose)

upstream

The search window upstream of the current position of the SNP. (default: 1000000)

downstream

The search window downstream of the current position of the SNP. (default: 1000000)

crop

Either "wheat" or "rice". (default: wheat)

Value

A data frame containing traits, SNP, gene_id, gene_size, and gene_type.

Examples

load(system.file("extdata", "precomputed_sample_results.rda", package = "geneNR"))
message(sample_results)

result <- geneSNP("sample_data_wheat", 10000, 10000, crop = "wheat")
result <- geneSNP("sample_data_rice", 10000, 10000, crop = "rice")

Identifies Candidate Genes based on identified Single Nucleotide Ploymorphisms (SNPs) from Genome Wide Association Stuides (GWAS) Analysis

Description

Identifies Candidate Genes based on identified Single Nucleotide Ploymorphisms (SNPs) from Genome Wide Association Stuides (GWAS) Analysis

Usage

geneSNPcustom(data_file, crop = "wheat")

Arguments

data_file

The input data in .csv format. (sample_data_wheat_custom for demo purpose)

crop

Either "wheat" or "rice". (default: wheat)

Value

A data frame containing traits, SNP, gene_id, gene_size, and gene_type.

Examples

load(system.file("extdata", "precomputed_sample_results_custom.rda", package = "geneNR"))
message(sample_results)

result <- geneSNPcustom("sample_data_wheat_custom", crop = "wheat")

Imports Hapmap genotypic data file

Description

Imports Hapmap genotypic data file

Usage

import_hmp(file_path, header = TRUE, sep = "\t", stringsAsFactors = FALSE)

Arguments

file_path

Provide the actual path of Hapmap genotypic data file

header

by default it will be True

sep

by default it will be tab separated

stringsAsFactors

by default it will be False

Value

Hampmap genotypic data

Examples


demo_SNP <- system.file("extdata", "demo_SNP.hmp.txt", package = "geneNR")
hapmap_data <- import_hmp(demo_SNP)
head(hapmap_data)

Imports VCF (Variant Call Format) data file

Description

Imports VCF (Variant Call Format) data file

Usage

import_vcf(file_path)

Arguments

file_path

Provide the actual path of the VCF file

Value

A vcfR object containing the imported data

Examples


demo_SNP <- system.file("extdata", "demo_SNP.vcf", package = "geneNR")
vcf_data <- import_vcf(demo_SNP)
vcf_data

Plot SNP Distribution on Chromosome Map

Description

Plots SNP positions across chromosomes with centromere markers using given chromosome details and SNP data.

Usage

plot_SNP(
  chromosome_details,
  data,
  chromosome_color = "steelblue",
  title = "Chromosome map with SNPs",
  label_color = "black",
  image_width = 10,
  image_height = 10
)

Arguments

chromosome_details

A data frame containing chromosome details with columns Chr, start and stop

data

A data frame containing SNP data with columns Chr, Pos, and SNP.

chromosome_color

Color of the chromosome bars (default: "skyblue").

title

Title of the chromosome plot depicting the identified SNPs

label_color

Color of the SNP labels (default: "black").

image_width

width of the chromosome plot

image_height

height of the chromosome plot

Value

A ggplot object for the SNP distribution plot.

Examples


chromosome_details <- read.csv(system.file("extdata", "chromosome_details.csv", package = "geneNR"))
data <- read.csv(system.file("extdata", "identified_SNP.csv", package = "geneNR"))
chromosome_plot <- plot_SNP(chromosome_details = chromosome_details, data = data,
chromosome_color = "steelblue" ,title = "Chromosome map with SNPs", label_color = "black",
image_width = 15, image_height = 10)
print(chromosome_plot)

Plot SNP Distribution Across Chromosomes

Description

Creates a bar chart representing the distribution of SNPs across chromosomes. Allows customization of bar color, label size, and label color. Saves the plot to a user-specified directory or a temporary directory.

Usage

plot_summariseSNP(
  snp_distribution,
  file_name = "snp_bar_chart.jpeg",
  output_dir = tempdir(),
  bar_color = "lightblue",
  label_size = 3,
  label_color = "black"
)

Arguments

snp_distribution

A data frame with columns Chr and SNP_Count.

file_name

The name of the file to save the plot (default: "snp_bar_chart.jpeg").

output_dir

The directory to save the file (default: tempdir()).

bar_color

The color of the bars (default: "lightblue").

label_size

The size of the text labels on the bars (default: 3).

label_color

The color of the text labels on the bars (default: "black").

Value

A ggplot object for the created bar chart.

Examples


demo_SNP <- system.file("extdata", "demo_SNP.hmp.txt", package = "geneNR")
data <- import_hmp(demo_SNP)
snp_distribution <- summariseSNP(data)
plot <- plot_summariseSNP(snp_distribution, bar_color = "skyblue",
label_size = 3, label_color = "red")
print(plot)

Sample Data

Description

A dataset containing sample data related to genetic markers and associated traits.

Usage

sample_data_rice

Format

A data frame with columns:

SNP

SNP identifier, character.

Chr

Chromosome location, character.

Pos

Position on the chromosome, numeric.

traits

Associated traits, character.

Source

Basha FTM, Sar P, Bhowmick PK, Mahato A, Bisht DS, Iquebal MA, Chakraborty K, Banerjee A, Verma BC, Bhaduri D, Kumar J, Ngangkham U, Saha S, Priyamedha, Mandal NP, Roy S. Genome-wide association study identified QTLs and genes underlying early seedling vigour in aus rice (Oryza sativa L.). Mol Genet Genomics. 2024 Dec 3;299(1):112. doi: 10.1007/s00438-024-02204-8. PMID: 39625651.

Examples

data(sample_data_rice)    #lazy loading

Sample Data

Description

A dataset containing sample data related to genetic markers and associated traits.

Usage

sample_data_rice_qtl

Format

A data frame with columns:

traits

Associated traits, character.

Chr

Chromosome location, character.

start

Position on the chromosome where QTL starts, numeric.

stop

Position on the chromosome where QTL stops, numeric.

Source

Generated for demonstration purposes

Examples

data(sample_data_rice_qtl)    #lazy loading

Sample Data

Description

A dataset containing sample data related to genetic markers and associated traits.

Usage

sample_data_wheat

Format

A data frame with columns:

SNP

SNP identifier, character.

Chr

Chromosome location, character.

Pos

Position on the chromosome, numeric.

traits

Associated traits, character.

Source

Generated for demonstration purposes

Examples

data(sample_data_wheat)    #lazy loading

Sample Data

Description

A dataset containing sample data related to genetic markers and associated traits.

Usage

sample_data_wheat_custom

Format

A data frame with columns:

traits

Associated traits, character.

SNP

SNP identifier, character.

Chr

Chromosome location, character.

start

Position on the chromosome where search window starts, numeric.

stop

Position on the chromosome where search window stops, numeric.

Source

Generated for demonstration purposes

Examples

data(sample_data_wheat_custom)    #lazy loading

Sample Data

Description

A dataset containing sample data related to genetic markers and associated traits.

Usage

sample_data_wheat_qtl

Format

A data frame with columns:

traits

Associated traits, character.

Chr

Chromosome location, character.

start

Position on the chromosome where QTL starts, numeric.

stop

Position on the chromosome where QTL stops, numeric.

Source

Generated for demonstration purposes

Examples

data(sample_data_wheat_qtl)    #lazy loading

Distribution of SNPs Across Chromosomes

Description

Distribution of SNPs Across Chromosomes

Usage

summariseSNP(data)

Arguments

data

A data frame containing a column named Chr

Value

A data frame with chromosome names and the count of SNPs for each chromosome

Examples


demo_SNP <- system.file("extdata", "demo_SNP.hmp.txt", package = "geneNR")
data <- import_hmp(demo_SNP)
snp_distribution <- summariseSNP(data)
print(snp_distribution)

Distribution of SNPs Across Chromosomes from VCF

Description

Distribution of SNPs Across Chromosomes from VCF

Usage

summariseSNP_vcf(vcf_data)

Arguments

vcf_data

A vcfR object containing VCF data.

Value

A data frame with chromosome names and the count of SNPs for each chromosome.

Examples


demo_SNP <- system.file("extdata", "demo_SNP.vcf", package = "geneNR")
vcf_data <- import_vcf(demo_SNP)
snp_distribution <- summariseSNP_vcf(vcf_data)
print(snp_distribution)