Assessing Identification and Quantification Errors in Mass Spectrometry-Based Proteomics
Develop and apply hierarchical Bayesian networks to assess and quantify errors in proteomic data analysis. Build scalable tools improving protein identification accuracy and fostering more reliable biological insights.
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Project Description
Project Overview
This PhD project aims to develop robust probabilistic methods to assess identification and quantification errors in mass spectrometry-based shotgun proteomics. Proteomics is vital for biological research but is often compromised by systematic and random errors in peptide and protein identification.
What You Will Do
The doctoral candidate will devise probabilistic regression models to estimate error rates in proteomics datasets, using hierarchical Bayesian networks to model peptide-protein dependencies and error propagation. They will test models on benchmark datasets, compare with current methods, and develop scalable software tools to integrate these models into existing workflows.
Expected Outcomes
The research will produce improved software that offers more accurate peptide and protein identifications and reliable protein-level quantifications. These tools will increase robustness and confidence in biological insights from large-scale proteomics data.
Why This Matters
Accurate error assessment in proteomics is crucial for trustworthy biological interpretations and downstream research. This project addresses a critical limitation in proteomic analyses, enhancing the reliability of studies that influence biomedical research and systems biology.
Entry Requirements
How to Apply
Eligibility
Supervisor Profile
Prof. Lukas Käll leads research at the Science for Life Laboratory and KTH Royal Institute of Technology focusing on computational biology, proteomics, and machine learning. His work integrates statistical and machine learning methods to improve mass spectrometry data analysis. He is recognized for advancing probabilistic models in proteomics and has a strong publication record with high citations.