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IsoBayes: a Bayesian approach for single-isoform proteomics inference.

Jordy Bollon1,2, Michael R Shortreed3, Ben T Jordan4

  • 1Computational and Chemical Biology, Italian Institute of Technology, CMPVdA, Aosta, Italy.

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|June 25, 2024
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Summary
This summary is machine-generated.

IsoBayes is a new statistical method that integrates mass spectrometry proteomics and transcriptomics data to accurately detect and quantify protein isoforms. This approach improves upon current methods by directly inferring protein isoform presence and abundance from peptide data.

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Systems Biology

Background:

  • Studying protein isoforms is crucial in biomedical research.
  • Current bottom-up mass spectrometry proteomics infers protein isoforms indirectly from peptide identifications, facing challenges with noisy detection and shared peptides.
  • Existing methods often abstract results to gene-level or protein isoform groups, hindering isoform-specific analysis.

Purpose of the Study:

  • To introduce IsoBayes, a novel statistical method for direct protein isoform-level inference.
  • To integrate mass spectrometry proteomics and transcriptomics data within a Bayesian probabilistic framework.
  • To enhance the accuracy of protein isoform detection and quantification.

Main Methods:

  • Developed a Bayesian probabilistic framework integrating proteomics and transcriptomics data.
  • Employed a two-layer latent variable approach to address measurement uncertainty: peptide detection validation and abundance allocation.
  • Inferred protein isoform presence/absence (posterior probability) and estimated abundance with credible intervals.

Main Results:

  • IsoBayes demonstrated good sensitivity and specificity in detecting protein isoforms through simulations and real datasets.
  • Estimated protein isoform abundances showed high correlation with ground truth data.
  • The method successfully identified isoforms with significant differences between transcript and protein relative abundances.

Conclusions:

  • IsoBayes enables accurate inference at the protein isoform level, overcoming limitations of current proteomics approaches.
  • The integration of transcriptomics data enhances the robustness of protein isoform analysis.
  • IsoBayes is available as a Bioconductor R package for broader research application.