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User Guide for ShiNyP: SNP Analysis and Visualization Platform

Get Started 🚀

There are two easy ways to install and run ShiNyP:

  1. Using R:
    This method is suitable if you already have R installed or prefer working within the R environment. You’ll need to install some R packages and then launch ShiNyP directly from R environment. ▸ R/RStudio/Rtools Tutorial for Beginners! (#Developing)

  2. Using Docker:
    This is the simpler option if you’d rather skip installing R or any packages. With Docker, you can run ShiNyP in a ready-to-use setup with just one command. ▸ Docker Tutorial for Beginners! (#Developing)


🔘 Run ShiNyP via R

✅ Prerequisites

Before installing ShiNyP, ensure your system meets the following requirements:

  • R: Version ≥ 4.4.

    Check your current version in R: getRversion()

  • Bioconductor: Version ≥ 3.20.

    Match your Bioconductor version with your R version (e.g., use Bioconductor 3.21 if R = 4.5).

1️⃣ Install Required Package 

install.packages("BiocManager")
BiocManager::install(version = "3.21") # Use the version that matches your R
BiocManager::install(c("qvalue", "SNPRelate", "ggtree", "snpStats"), force = TRUE)

2️⃣ Install the ShiNyP Package 

install.packages("remotes")
remotes::install_github("TeddYenn/ShiNyP", force = TRUE)

3️⃣ Start the ShiNyP Platform 

library(ShiNyP)
ShiNyP::run_ShiNyP()

4️⃣ Run Analysis on ShiNyP

Input your SNP dataset in VCF, or try the built-in Demo Data.

🔘 Run ShiNyP via Docker

If you have 🐳 Docker installed, you can launch ShiNyP without installing R.

✅ Prerequisite

  • Docker.

    Verify your Docker installation in Terminal: docker --version

1️⃣ Pull the Docker Image 

docker run -d -p 3838:3838 teddyenn/shinyp-platform

2️⃣ Start the ShiNyP Platform

Open your browser and visit 👉 http://localhost:3838


Main Features

🔼 Overview of the ShiNyP Platform Workflow for SNP Analysis.

▸ Data Input & Processing:
The workflow begins with Variant Call Format (VCF) Data Input, followed by essential steps such as Data Quality Control (QC) and Data Transformation to prepare the data for analysis.
▸ Modular Analysis & Output:
Analytical functions are organized into distinct modules—each accessible as a separate page within the platform. These include: Population Structure, Genetic Diversity, Selection Sweep, and Core Collection. Each module contains multiple subpages offering specialized tools for detailed analysis.
▸ Customizable Output:
ShiNyP delivers publication-ready visualizations and AI Report that summarize analytical results in clear, structured narratives. Users can tailor output formats to fit specific research needs.