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Updated: May 19, 2026

Dissecting Multi-protein Signaling Complexes by Bimolecular Complementation Affinity Purification (BiCAP)
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Multi-sample, multi-platform isoform quantification using empirical Bayes.

Arghamitra Talukder, Shree Thavarekere, Madison Mehlferber

    Biorxiv : the Preprint Server for Biology
    |May 18, 2026
    PubMed
    Summary
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    JOLI is a new model that combines short-read and long-read sequencing data for accurate RNA isoform quantification. It uses multi-sample learning to improve accuracy, especially for low-abundance transcripts.

    Area of Science:

    • Genomics
    • Computational Biology
    • Bioinformatics

    Background:

    • Accurate RNA isoform quantification is vital for understanding gene regulation and disease.
    • Short-read (SR) sequencing has limitations in resolving transcript ambiguity.
    • Long-read (LR) sequencing reduces ambiguity but has high error rates and lower throughput.

    Purpose of the Study:

    • To develop a novel method for enhancing transcript quantification by integrating SR and LR sequencing data.
    • To leverage multi-sample learning to improve accuracy and reproducibility in RNA isoform abundance estimation.
    • To address the limitations of existing methods in quantifying low- and moderate-abundance isoforms.

    Main Methods:

    • Introduced JOLI, a hierarchical model utilizing multi-sample learning for joint SR and LR data integration.

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    Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

    Published on: November 15, 2017

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    Last Updated: May 19, 2026

    Dissecting Multi-protein Signaling Complexes by Bimolecular Complementation Affinity Purification (BiCAP)
    06:45

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    Published on: June 15, 2018

    Using the E1A Minigene Tool to Study mRNA Splicing Changes
    10:25

    Using the E1A Minigene Tool to Study mRNA Splicing Changes

    Published on: April 22, 2021

    Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
    10:37

    Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

    Published on: November 15, 2017

  • Applied an empirical Bayes framework to learn shared priors across multiple samples for consistent inference.
  • Benchmarked JOLI against single-sample methods using simulated and real RNA-seq datasets.

    • Main Results:

    • JOLI demonstrated improved ranking consistency, proportional agreement, and estimation accuracy compared to single-sample methods.
    • In simulations, JOLI improved Spearman correlation by 9.8% for LR and 7.7% for SR data.
    • JOLI showed significant improvements in quantifying low- to moderate-abundance isoforms and enhanced reproducibility.

    Conclusions:

    • JOLI effectively leverages multi-sample learning and joint SR/LR data integration for robust transcript quantification.
    • The method offers superior accuracy and reproducibility, particularly for challenging low- and moderate-expression isoforms.
    • JOLI performs competitively with state-of-the-art approaches, highlighting its utility in transcriptomic studies.