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User Guide for ShiNyP: SNP Analysis and Visualization Platform

9 FAQ

If you encounter any issues or have suggestions for new features, please submit a request on the GitHub Issues page or email us at: teddyhuangyh@gmail.com

General Usage

  1. Can I use the online version of ShiNyP platform?

    Yes, a trial version of ShiNyP is available online at: https://teddyhuang.shinyapps.io/ShiNyP_Demo/

    This web-based DEMO is hosted on Shinyapps.io and is intended for trial purposes. Please note that, due to server memory limitations (1GB RAM), this version is not suitable for the analysis of large-scale SNP datasets. For complete functionality and to analyze larger datasets, we strongly recommend downloading ShiNyP from GitHub.

    For more information: Get Started

  2. Do I need programming skills to use ShiNyP?

    No programming experience is required. ShiNyP provides a user-friendly graphical interface that allows you to perform all analyses interactively without coding.

  3. How do I install and start the ShiNyP platform?

    You can install ShiNyP in R via:

    remotes::install_github("TeddYenn/ShiNyP")
library(ShiNyP)
ShiNyP::run_ShiNyP()

    Alternatively, use the Docker image for a one-step deployment:

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

    For more information: Get Started

  4. Is my data secure when using ShiNyP?

    All analyses are conducted locally on your machine or server. No user data is transmitted to third-party servers unless you explicitly use AI features (ShiNyP AI chatbot and reporting system).

  5. Does ShiNyP require internet access to function?

    ShiNyP runs locally and does not require internet access for core analyses. Internet is only needed when installing the packages and using AI features (ShiNyP AI chatbot and reporting system).

Analysis & Features

  1. What makes ShiNyP different from other SNP analysis tools?

    ShiNyP uniquely integrates a modular Graphical User Interface (GUI), ShiNyP AI (real-time chatbot), cross-species compatibility, AI-based interpretation, customizable visualizations, and open-source accessibility—all in one platform, making it a versatile tool for researchers in genomics, breeding, and evolutionary biology.

  2. What types of input data are supported by ShiNyP?

    ShiNyP supports genome-wide biallelic SNP datasets in Variant Call Format (VCF). It is also compatible with data.frame and genlight files, covering both diploid and polyploid species. NOTE: The diploidization processing simplifies genotype data and does not account for allelic dosage effects.

    For more information: Data Input

  3. What kind of output does the platform provide?

    The platform generates publication-ready figures (PDF, PNG, or JPEG) and tables, reusable R data objects, and AI-assisted reports that summarize analytical results in natural language.

  4. Can I customize analysis parameters?

    Yes, users can adjust analysis thresholds, models, and filtering criteria directly through the interface to suit their research needs.

  5. Can I analyze large SNP datasets with ShiNyP?

    Yes, ShiNyP is optimized for both moderate and large-scale SNP datasets. However, performance may depend on your system’s hardware specifications.

  6. Can I analyze datasets from different size/species in one session?

    Yes, as long as your data are properly formatted, ShiNyP supports datasets from different size/species. However, it is recommended to analyze each dataset separately for clearer results.

  7. Can ShiNyP export data to formats used by other genetics software?

    Yes, results and transformed data can be exported in formats compatible with tools such as R, STRUCTURE, PLINK, GenAlEx, and others.

    For more information: Data Transform

Support

  1. Is ShiNyP open-source? Is it possible to extend ShiNyP with custom modules or scripts?

    Yes, ShiNyP is released under an open-source license (GNU Affero General Public License). The source code is available on GitHub for transparency and community contributions.

  2. Where can I report bugs or request new features?

    If you encounter any issues or have suggestions for new features, please submit a request on the GitHub Issues page or email us at: . The project is actively maintained and welcomes community feedback and collaboration.

  3. Future?

Installation Issues

If you encounter any issues while installing ShiNyP, please don’t hesitate to let us know. The issue may not be unique to you, and by reporting it, you help improve the entire community. Below are some common installation issues:

  • R and Bioconductor version mismatch

    The installation specifies Bioconductor version 3.21, which requires R version ≥ 4.5. If your R version is below 4.5, update it from CRAN.

    # Check your R version 
R.version.string

  • Permission denied: curl.dll

    Please close all R/RStudio programs and terminal windows to ensure no sessions are using the package, then restart and try installing again.

  • Installation of dependencies

    Installing packages like shiny and dartR may require additional developer tools. You might encounter error messages such as:

    ERROR: dependencies 'shiny', 'dartR' are not available for package 'ShiNyP'

    • For Windows: Download Rtools from CRAN Rtools.

    • For macOS: Open Terminal and run the installation command:

      xcode-select --install

  • GitHub installation issues

    Verify that your system is connected to the internet. Check for any firewall or proxy settings that might block GitHub access.

  • Package loading issues

    Errors occur when loading ShiNyP or its dependencies, possibly due to outdated packages or conflicts. Update all installed packages.

Index

  • AI Report 8
  • AMOVA (Analysis of MOlecular VAriance) 5.4
  • API key 8
  • Bayesian Information Criterion (BIC) 4.2
  • Chromosome Info. 2.3 5.2 6.3 6.4 7.2
  • Circos Plot 5.2
  • Core Sample Set 7.1
  • Core SNP Set 7.2
  • DAPC (Discriminant Analysis of Principal Components) 4.2
  • data.frame 1.1
  • Demo Data 1.1
  • Diversity Parameter 5.1
  • Genetic Distance 5.3
  • genind 3
  • genlight 5
  • Group Info. 3 4.5 4.6 5.1 6.2
  • Hardy-Weinberg equilibrium (HWE) 2.2
  • Heterozygosity rate 2.1
  • IBS (Identity By State) 6.3
  • Kinship Analysis 4.5
  • Manhattan Plot 6.4
  • Minor allele frequency (MAF) 2.2
  • Missing rate 2.1
  • NJ (Neighbor-Joining) Tree 4.4
  • OutFLANK 6.2
  • PCA (Principal Component Analysis) 4.1
  • pcadapt 6.1
  • Permutation Test 5.4
  • Sample QC 2.1
  • Scatter Plot 4.6
  • ShiNyP ShiNyP
  • Site Info. 1.1 2.1 2.2 2.3 5.1 6.1 6.2 6.3 7.2
  • SNP Density 2.3
  • SNP QC 2.2
  • Tree Plot 4.7
  • UPGMA (Unweighted Pair Group Method with Arithmetic mean) Tree 4.3
  • VCF 1.1

Bibliography

Excoffier, L, P E Smouse, and J M Quattro. 1992. “Analysis of Molecular Variance Inferred from Metric Distances Among DNA Haplotypes: Application to Human Mitochondrial DNA Restriction Data.” Genetics 131 (2): 479–91. https://doi.org/10.1093/genetics/131.2.479.
GOUDET, JÉRÔME. 2004. “Hierfstat, a Package for r to Compute and Test Hierarchical F-Statistics.” Molecular Ecology Notes 5 (1): 184–86. https://doi.org/10.1111/j.1471-8286.2004.00828.x.
Granato, Italo S. C., Giovanni Galli, Evellyn Giselly de Oliveira Couto, Massaine Bandeira e Souza, Leandro Freitas Mendonça, and Roberto Fritsche-Neto. 2018. “snpReady: A Tool to Assist Breeders in Genomic Analysis.” Molecular Breeding 38 (8). https://doi.org/10.1007/s11032-018-0844-8.
Gu, Zuguang, Lei Gu, Roland Eils, Matthias Schlesner, and Benedikt Brors. 2014. “Circlizeimplements and Enhances Circular Visualization in R.” Bioinformatics 30 (19): 2811–12. https://doi.org/10.1093/bioinformatics/btu393.
Jeong, Seongmun, Jae-Yoon Kim, Soon-Chun Jeong, Sung-Taeg Kang, Jung-Kyung Moon, and Namshin Kim. 2017. “GenoCore: A Simple and Fast Algorithm for Core Subset Selection from Large Genotype Datasets.” Edited by Alexandre G. de Brevern. PLOS ONE 12 (7): e0181420. https://doi.org/10.1371/journal.pone.0181420.
Jombart, Thibaut, Sébastien Devillard, and François Balloux. 2010. “Discriminant Analysis of Principal Components: A New Method for the Analysis of Genetically Structured Populations.” BMC Genetics 11 (1): 94. https://doi.org/10.1186/1471-2156-11-94.
Kamvar, Zhian N., Javier F. Tabima, and Niklaus J. Grünwald. 2014. “Poppr: An R Package for Genetic Analysis of Populations with Clonal, Partially Clonal, and/or Sexual Reproduction.” PeerJ 2 (March): e281. https://doi.org/10.7717/peerj.281.
Kang, Hyun Min, Jae Hoon Sul, Susan K Service, Noah A Zaitlen, Sit-yee Kong, Nelson B Freimer, Chiara Sabatti, and Eleazar Eskin. 2010. “Variance Component Model to Account for Sample Structure in Genome-Wide Association Studies.” Nature Genetics 42 (4): 348–54. https://doi.org/10.1038/ng.548.
Luu, Keurcien, Eric Bazin, and Michael G. B. Blum. 2016. “Pcadapt: An R Package to Perform Genome Scans for Selection Based on Principal Component Analysis.” Molecular Ecology Resources 17 (1): 67–77. https://doi.org/10.1111/1755-0998.12592.
Paradis, Emmanuel, and Klaus Schliep. 2018. “Ape 5.0: An Environment for Modern Phylogenetics and Evolutionary Analyses in R.” Edited by Russell Schwartz. Bioinformatics 35 (3): 526–28. https://doi.org/10.1093/bioinformatics/bty633.
Whitlock, Michael C., and Katie E. Lotterhos. 2015. “Reliable Detection of Loci Responsible for Local Adaptation: Inference of a Null Model Through Trimming the Distribution ofFST.” The American Naturalist 186 (S1): S24–36. https://doi.org/10.1086/682949.
Yu, Guangchuang, David K. Smith, Huachen Zhu, Yi Guan, and Tommy Tsan-Yuk Lam. 2016. “Ggtree: An r Package for Visualization and Annotation of Phylogenetic Trees with Their Covariates and Other Associated Data.” Edited by Greg McInerny. Methods in Ecology and Evolution 8 (1): 28–36. https://doi.org/10.1111/2041-210x.12628.