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Showing content from https://github.com/rongxinzh/G4SNVHunter below:

GitHub - rongxinzh/G4SNVHunter

G4SNVHunter is an R package leveraging the G4Hunter algorithm to systematically identify single nucleotide variants (SNVs), along with other small-scale variants such as indels and MNVs, that have the potential to disrupt G-quadruplex (G4) formation propensity.

You can install the package directly from GitHub,

# install.packages("devtools")
devtools::install_github("rongxinzh/G4SNVHunter")

To run the sample code in our vignette, set the dependencies parameter to TRUE,

# install.packages("devtools")
devtools::install_github("rongxinzh/G4SNVHunter", dependencies = TRUE)

NOTE

• Your R version must be ≥ 4.3.
• If you are using Windows, please install Rtools before proceeding.

The package is also available on Bioconductor as a development (devel) version. To install it, follow these steps,

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install(version = 'devel')

BiocManager::install("G4SNVHunter")

If you want to run the sample code in our vignette, set dependencies to TRUE,

BiocManager::install("G4SNVHunter", dependencies = TRUE)

NOTE

• The Bioconductor installation (version = 'devel') requires the latest version of R.
• Make sure to initialize Bioconductor devel using BiocManager::install(version = 'devel') before installing the package.

First, you need to library the G4SNVHunter package,

In this example, we need to use the human genome sequence of chromosome 16 (hg19 version).

if (!requireNamespace("BSgenome.Hsapiens.UCSC.hg19", quietly = TRUE)) {
    BiocManager::install("BSgenome.Hsapiens.UCSC.hg19")
}
library(BSgenome.Hsapiens.UCSC.hg19)

# Load sequence for chromosome 16 (hg19)
hg19 <- BSgenome.Hsapiens.UCSC.hg19

chr16_seq <- DNAStringSet(hg19$chr16)

# Chromosome names are needed for analysis
names(chr16_seq) <- "chr16"

We have prepared some sample variant data, which can be loaded as,

vcf_file <- system.file("extdata", 
                        "example_variants_chr16.vcf", 
                        package = "G4SNVHunter")
variants <- loadVariant(vcf_file, file_type = "vcf")
seqlevels(variants) <- paste0("chr", seqlevels(variants))

Before assessing the impact of SNV on G4, we need to predict the G4-prone regions,

chr16_G4 <- G4HunterDetect(chr16_seq)

Then, the impact of variants on the formation of G4 structures can be evaluated as,

result <- G4VarImpact(G4 = chr16_G4, 
                      variants = variants, 
                      ref_col = "REF",
                      alt_col = "ALT")

Since not all variants can significantly impact the formation of G4s, we need to filter the output. While the filtering parameters can be set flexibly, a set of recommended parameters is:

filtered_var_eff <- filterVarImpact(result, 
                                    raw_score_threshold = 1.5,
                                    mut_score_threshold = 1.2)

print(filtered_var_eff)

Generally, G4 structures with an absolute G4Hunter score above 1.5 are considered to have a high confidence in forming stable structures. In contrast, those with scores below 1.2, or more conservatively, below 1.0, are typically less likely to form stable structures.

For full use of our package, please refer to our vignette (highly recommended).

If you encounter any issues, have questions, or would like to make suggestions, feel free to report them at our bug tracker.

For additional inquiries, contact us at: Email: rongxinzhang@outlook.com

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