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Bayesian nonparametric discovery of isoforms and individual specific quantification.

Derek Aguiar1, Li-Fang Cheng2, Bianca Dumitrascu3

  • 1Department of Computer Science, Princeton University, Princeton, NJ, 08540, USA. daguiar@princeton.edu.

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Summary
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We developed BIISQ, a novel Bayesian method for discovering and quantifying mRNA isoforms from RNA-seq data. This approach accurately identifies low-abundance isoforms, improving our understanding of gene expression and disease.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Alternative splicing generates diverse mRNA isoforms from protein-coding genes, contributing to molecular diversity.
  • Dysregulation of mRNA isoform expression is implicated in various disease pathologies.
  • Characterizing isoforms from short-read RNA sequencing (RNA-seq) data is challenging due to shared subsequences and variable frequencies.

Purpose of the Study:

  • To develop a robust computational model for de novo isoform discovery and quantification using short-read RNA-seq data.
  • To address the limitations of existing methods in accurately characterizing low-abundance and novel mRNA isoforms.
  • To provide a method that does not rely on pre-existing isoform reference sequences.

Main Methods:

  • Developed BIISQ, a Bayesian nonparametric model for isoform discovery and quantification.
  • Employed stochastic variational inference for efficient posterior estimation of isoform abundance.
  • Estimated a shared isoform catalog across multiple samples without requiring reference sequences.

Main Results:

  • BIISQ demonstrated superior precision and recall in simulations compared to state-of-the-art isoform reconstruction methods.
  • The model showed significant improvements in inferring low-abundance isoforms, with 36% more isoforms correctly identified at low coverage compared to multi-sample methods.
  • BIISQ outperformed single-sample methods by 170% in inferring isoforms at low coverage.
  • Applied BIISQ to GEUVADIS RNA-seq data and validated inferred isoforms using genetic variant associations with isoform ratios.

Conclusions:

  • BIISQ offers a powerful and accurate approach for isoform discovery and quantification from RNA-seq data.
  • The method enhances the characterization of transcriptomic complexity, particularly for low-abundance and novel isoforms.
  • BIISQ facilitates a deeper understanding of isoform regulation in biological processes and disease.