MissensePathoR

Description

MissensePathoR is an R package designed to perform pathogenicity analysis of missense variants across the human proteome using AlphaMissense’s deep learning predictions, catering to both single variant and gene-level analyses. This package provides quantitative and qualitative pathogenicity analysis across user defined groups, and uniquely offers users a platform to explore differential distributions and patterns of pathogenicity scores, thus enriching the understanding of missense variants’ impact in genomics research. MissensePathoR is the first R package that facilitates comparisons between pathogenicity scores of variants in groups, further enriched by its provision for visualization of score and class distribution patterns, and ranked gene pathway enrichment analyses. The package analyses variant call format (vcf) file, which is the standardized text file format used in bioinformatics for storing gene sequence variations (Danecek et al., 2011), making it particularly valuable for researchers in genome-wide association studies (GWAS) and those involved in variant calling of whole-genome or single-cell RNA sequencing analysis. The MissensePathoR package is developed using R version 4.3.1 (2023-06-16), Platform: aarch64-apple-darwin22.4.0 and x86_64-pc-linux-gnu, and Running under: aarch64, darwin22.4.0 and x86_64, linux-gnu.

AlphaMissense is a state-of-the-art deep learning model that predicts the pathogenicity of single-amino acid changes in proteins using sequence data and structural context (Cheng et al., 2023). The variant effect prediction score is defined as the log-likelihood difference between the reference amino acid and the alternative amino acid, indicating likelihood of a variant being pathogenic (closer to 0 more likely benign variant, closer to 1 more likely pathogenic variant). The class prediction is to interpret a variant as “likely pathogenic,” “ambiguous,” or “likely benign.” with model define threshold score values.

Installation

You can install the development version of MissensePathoR from GitHub with:

# install.packages("devtools")
devtools::install_github("Lola-W/MissensePathoR", build_vignettes = TRUE)
library("MissensePathoR")

To run the Shiny app:

runMissensePathoR()

Overview

ls("package:MissensePathoR")
data(package = "MissensePathoR")

MissensePathoR contain functions to:

1. Reading Variant Call Format (VCF) Files: Enables the import and processing of VCF files. (See function readVCF)

2. Retrieving AlphaMissense Data: Acquires the AlphaMissense prediction dataset, essential for the subsequent annotation of variants. (See function readAlphaMissenseData)

3. Predicting Pathogenicity Scores: Annotates variants with pathogenicity scores derived from the AlphaMissense dataset. (See function predictPathoScore)

4. Summary of Variant Classes: Provides a tabular summary of the annotated SNPs between groups, including the proportions of benign, ambiguous, and pathogenic variants or the pathogenicity scores in scalar value. (See functions classSummary and scoreSummary)

5. Visualization of Pathogenicity Class Proportions: Utilizes stacked bar plots to visually represent the proportions of each pathogenicity class within and between groups. (See function classVis)

6. Visualization of Score Distributions: Employs box plots to show the distribution of pathogenicity scores within and between groups. (See function scoreViz)

7. Gene Mapping for Variants: Annotates all variants to located HGNC gene names. (See function mapGene)

8. Differential SNPs Analysis: Performs differential gene expression analysis between assigned groups, ranking genes based on variant frequency and pathogenicity score. (See function diffSNPs)

9. Pathway Enrichment Analysis: Conducts pathway enrichment analysis on the ranked gene list to identify significant pathways. (See function enrichSNP)

10. Pathway Visualization: Provides dot plots that display enriched pathways, enhancing the interpretability of pathway analysis results. (See function pathwayViz)

browseVignettes("MissensePathoR")

An overview of the package’s workflow and interaction between its various functions is illustrated below:

Contributions

The MissensePathoR package is authored by Jiaming Weng. The author wrote all the functions. All the functions depend on data.table package for data saving and reading and dplyr package for data manipulation, all the visualization functions (classViz, scoreViz, and pathwayViz) are powered by package ggplot2 for graph generating and RColorBrewer for coloring.readVCF used read.vcfR in vcfR package to read and process vcf files, readAlphaMissenseData make use of the publicly available AlphaMissense dataset provided by Google Deepmind. mapGene leverages the UniProt.ws package for mapping AlphaMissense predicted UniProt IDs to HGNC gene names. diffSNPs integrates the edgeR to perform counts-per-million(CPM) on pseudo gene counts and Quasi-Likelihood Model. gprofiler2 package is used for thresholded over-representation analysis. The dependencies of the underconstructing gene-level analyses and shiny app functionalities will be specified later. ChatGPT4 is used to provide insights for debug and documentation, and write error checking for all the functions, its contribution is cited in the comments of the code as # contributed by ChatGPT-4.

For the data:

• AlphaMissenseSample.rda is a partition of the dataset publically available by AlphaMissense in https://console.cloud.google.com/storage/browser/dm_alphamissense (Cheng et al., 2023).

• vcfSample.rda is Chromosomes 1-5 of 4 vcf files that generated from fastq files in GSE113171 (Rendleman et al., 2018) from Gene Expression Omnibus by the author.

References

• Carlson M, Ramos M (2022). UniProt.ws: R Interface to UniProt Web Services. R package version 2.38.1.
• Cheng, J., Novati, G., Pan, J., Bycroft, C., Žemgulytė, A., Applebaum, T., Pritzel, A., Wong, L. H., Zielinski, M., Sargeant, T., Schneider, R. G., Senior, A. W., Jumper, J., Hassabis, D., Kohli, P., & Avsec, Ž. (2023). Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science, 381(6664), eadg7492. https://doi.org/10.1126/science.adg7492
• Chen, Y., Lun, A. T., McCarthy, D. J., Ritchie, M. E., Phipson, B., Hu, Y., … & Smyth, G. K. (2015). edgeR: Empirical analysis of digital gene expression data in R. Bioconductor Version: Release (3.12).
• Danecek, P., Auton, A., Abecasis, G., Albers, C. A., Banks, E., DePristo, M. A., Handsaker, R. E., Lunter, G., Marth, G. T., Sherry, S. T., McVean, G., & Durbin, R. (2011). The variant call format and VCFtools. Bioinformatics, 27(15), 2156–2158. https://doi.org/10.1093/bioinformatics/btr330
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• Johansen, N., Somasundaram, S., Travaglini, K. J., Yanny, A. M., Shumyatcher, M., Casper, T., Cobbs, C., Dee, N., Ellenbogen, R., Ferreira, M., Goldy, J., Guzman, J., Gwinn, R., Hirschstein, D., Jorstad, N. L., Keene, C. D., Ko, A., Levi, B. P., Ojemann, J. G., … Miller, J. A. (2023). Interindividual variation in human cortical cell type abundance and expression. Science, 382(6667), eadf2359. https://doi.org/10.1126/science.adf2359
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• Knaus BJ, Grünwald NJ (2017). “VCFR: a package to manipulate and visualize variant call format data in R.” Molecular Ecology Resources, 17(1), 44-53. ISSN 757, https://dx.doi.org/10.1111/1755-0998.12549.
• Neuwirth E (2022). RColorBrewer: ColorBrewer Palettes. R package version 1.1-3, https://CRAN.R-project.org/package=RColorBrewer.
• Raudvere, Uku, Liis Kolberg, Ivan Kuzmin, Tambet Arak, Priit Adler, Hedi Peterson, and Jaak Vilo. 2019. “G: Profiler: A Web Server for Functional Enrichment Analysis and Conversions of Gene Lists (2019 Update).” Nucleic Acids Research 47 (W1): W191–98.
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Acknowledgements

This package was developed as part of an assessment for 2023 BCB410H: Applied Bioinformatics course at the University of Toronto, Toronto, CANADA. MissensePathoR welcomes issues, enhancement requests, and other contributions. To submit an issue, use the GitHub issues.