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bone_proteogenomics_manuscript

Code accompanying the manuscript

https://github.com/aa9gj/bone_proteogenomics_manuscript

Science Score: 67.0%

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    CITATION.cff file
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    DOI references
    Found 4 DOI reference(s) in README
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    Links to: ncbi.nlm.nih.gov, nature.com, plos.org, zenodo.org
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    Low similarity (9.8%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Code accompanying the manuscript

Basic Info
  • Host: GitHub
  • Owner: aa9gj
  • License: mit
  • Language: R
  • Default Branch: main
  • Size: 144 KB
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  • Stars: 0
  • Watchers: 2
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Created about 3 years ago · Last pushed 10 months ago
Metadata Files
Readme License Citation

README.md

DOI

Code accompanying the manuscript "Long read proteogenomics to connect disease-associated sQTLs to the protein isoform effectors in disease"

The full text can be found in Abood et al. 2024, AJGH

Purpose

We present a novel generalizable approach that integrates information from GWAS, splicing QTL (sQTL), and PacBio long-read RNA-seq in a disease relevant model to infer the effects of sQTLs on the ultimate protein isoform products they encode

Data availability

  1. Processed and input data is found in DOI
  2. Raw long-read sequencing data is found in GSE224588

How to use this repository

  1. Use setuprenv.R to set up the R environment with all the needed packages.
  2. The repo is broken down into three major sections:
    • sQTLcolocalizationanalysis: This directory contains code needed to replicate Bayesian colocalization analysis with Coloc. Please refer to the README.md within directory for further information
    • Step 0: Perform bayesian colocalization analysis using summary statistics from the latest BMD GWAS with summary statistics from sQTL data for all 49 GTEx tissues.
    • Referencetranscriptomegeneration: This directory contains code to generate the reference transcriptome from long-read RNAseq data. Please refer to the README.md within directory for further information
    • Isoseq analysis: from raw reads to isoform classification
    • Step 1: Perform analyses on outputs from SQANTI and cDNA_cupcake
    • sQTLtoisoform_mapping
    • Step 2: Characterize full-length isoforms (known and novel) containing the colocalized junctions
    • Step 3: Add effect size and direction of effect to colocalized junctions
    • Step 4: Annotate lead sQTLs and their proxy, follow with positional and enrichment analyses
    • Step 5: Differential analyses (DE and DIU) using tappAS
    • Step 6: Integrating multiple datasets from the literature and within our analyses to prioritize the isoforms for experimental validation
    • Step 7: ORF analyses including: NMD and truncation analysis was performed using a beta version of Biosurfer

Owner

  • Name: Arby Abood
  • Login: aa9gj
  • Kind: user
  • Location: Charlottesville,VA
  • Company: University of Virginia

Graduate student @cphg | big data enthusiast

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this code, please cite the associated publication."
title: "Long read proteogenomics to connect disease-associated sQTLs to the protein isoform effectors in disease"
authors:
  - family-names: Abood
    given-names: Arby
# Additional authors omitted for brevity
version: "1.0"
doi: "10.1016/j.ajhg.2024.03.001"
url: "https://www.cell.com/ajhg/abstract/S0002-9297(24)00227-1"
date-released: 2024-01-01

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