High-Performance Proteomics at Scale: An R Package and Interactive Web Portal for Standardised Analysis
Project Overview
Mass spectrometry-based proteomics can measure thousands of proteins across hundreds of samples in a single experiment. However, when samples are collected at different times, in different labs, or
across multiple patient donors, systematic technical variation, known as batch effects, can obscure true biological signals. Popular proteomics tools handle upstream steps like peptide identification and
protein quantification well but lack support for batch correction and the downstream statistical analyses essential for multi-batch or multi-donor studies. To address this gap, the Grey lab at the University of York has developed an R-based proteomics analysis pipeline. This project now aims to make it accessible to other researchers by creating a Shiny web portal for interactive analysis without writing code.
What were the key results of your research project?
- I developed a robust and reproducible web portal that allows users to analyse their proteomic data without requiring any computational experience.
- The Shiny app makes pipeline steps from quality control, all the way to machine learning analyses accessible to users.
A presentation of this research will be shared here and on our YouTube site when available.
How do you feel you have benefitted from completing this internship and has it made you consider future career paths?
One of the most valuable things I have taken away with me from this internship is a clearer understanding of what it means to build software for a wider community rather than solely for my own use. Prior to this internship, any pipeline I developed was written with my own datasets and assumptions in mind. Understanding how to move from simply ‘writing scripts’ to ‘engineering software’ has been an invaluable experience, and it is a skill I will carry forward into all my future projects.
Working with the Grey lab at the University of York has given me valuable insight into what a career as a research software engineering or bioinformatician could look like. I particularly enjoyed the collaborative nature of computational research and the opportunity to contribute to lots of projects at once. This internship has definitely made a career in computational biology feel far more attainable, and it has encouraged me to pursue these option further in the future.