SNVariome

SNVariome

A shiny app that integrates protein-protein interactions, clinical variants, & protein structure to enhance the interactome.

Table of Contents

• SNVariome
  • Background
  • Hackathon Goals
  • Data
  • Usage
    • Installation
    • Dependencies
  • Results
  • Team Members
  • References

Background

Most existing PPI databases only curate a binary interactome. However, integrating protein structure is critical in understanding complex human diseases because variants (GOF/LOF) have significant impacts on protein structure. Thus, having a more complete interactome will aid in the identification of pathogenic variants.

SNVariome is a tool that seeks to integrate protein structure, protein-protein interactions, drug-gene interaction data, variant data (using ClinVar, GOF/LOF data), and phenotype data (HPO terms) to develop a comprehensive interactome of clinical variants. We will utilize the SigCom LINCS API and integrate a publicly available dataset that has already examined GOF/LOF impacts on protein structure (pmcid: PMC9259657). Our first test case will be a list of genes/variants associated with breast cancer which should validate and expand the findings of DOI: 10.1126/science.abf3066.

Hackathon Goals

During Hackin’ Omics, we’d like to:

1. Determine an appropriate way or ways to utilize the EDC values generated regarding GOF/LOF impacts on protein structure of disease genes
2. Integrate the SigCom LINCS API with EDC values for disease genes to identify signatures associated with diseases
3. Implement the Extent of Disease Clustering (EDC) method in a shiny app that incorporates the SigCom LINCS API and other data sources to predict or identify novel pathogenic variants of diseases.

Data

• SigCom LINCS is a web-based search engine or API that serves over 1.5 million gene expression signatures processed, analyzed, and visualized from LINCS, GTEx, and GEO.
• Extent of Disease Clustering (EDC) values for disease genes via an excel sheet.
• Manually curated PPIs from STRING and four additional proteomes-scale interactome studies, i.e., Human Interactome I and II, BioPlex, QUBIC, and CoFrac.

Usage

No instructions yet.

Dependencies

• R 4.2.1
• RStudio

R Packages

install.packages(c('shiny', 'BiocManager', 'shinyjs', 'shinythemes', 'bslib', 'httr', 'jsonlite', 'xml2', 'DT'))

BiocManager::install(c('wppi'))

Steps to run

To run this app, open RStudio and use the below code in the console:

library(shiny)
shiny::runGitHub(username = "u-brite", repo = "SNVariome" )

Results

Team Members

Shaurita Hutchins	Team leader
Bernhard Hane	Team member
Oladosu Tosin Ayodeji	Team member
Bharat Mishra	Team member
Hailey Levi	Team member
Pooja Singaravelu	Team member
Maria Jose	Team member

References

Gerasimavicius L, Livesey BJ, Marsh JA. Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure. Nat Commun. 2022 Jul 6;13(1):3895. doi: 10.1038/s41467-022-31686-6. PMID: 35794153; PMCID: PMC9259657.

Kim M, Park J, Bouhaddou M, Kim K, Rojc A, Modak M, Soucheray M, McGregor MJ, O’Leary P, Wolf D, Stevenson E, Foo TK, Mitchell D, Herrington KA, Muñoz DP, Tutuncuoglu B, Chen KH, Zheng F, Kreisberg JF, Diolaiti ME, Gordan JD, Coppé JP, Swaney DL, Xia B, van ‘t Veer L, Ashworth A, Ideker T, Krogan NJ. A protein interaction landscape of breast cancer. Science. 2021 Oct;374(6563):eabf3066. doi: 10.1126/science.abf3066. Epub 2021 Oct 1. PMID: 34591612; PMCID: PMC9040556.

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