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Updated: Dec 6, 2025

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Published on: May 9, 2017

IntAPT: integrated assembly of phenotype-specific transcripts from multiple RNA-seq profiles.

Xu Shi^1,2, Andrew F Neuwald³, Xiao Wang¹

¹Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.

Bioinformatics (Oxford, England)

|October 5, 2020

Summary

This summary is machine-generated.

Accurate transcriptome analysis from noisy RNA-seq data is challenging. We developed integrated assembly of phenotype-specific transcripts (IntAPT), a Bayesian method that reliably identifies phenotype-specific isoforms, improving low-abundance transcript detection.

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Area of Science:

Computational Biology
Bioinformatics
Transcriptomics

Background:

High-throughput RNA sequencing (RNA-seq) enables deep transcriptome analysis.
Reconstructing accurate phenotype-specific transcriptomes is difficult due to RNA-seq data noise and variability.
Computational identification of transcripts across multiple samples requires robust methods.

Purpose of the Study:

To develop a computational method for identifying phenotype-specific transcript isoforms from multiple RNA-seq profiles.
To improve the accuracy and reliability of transcript isoform detection, especially for low-abundance isoforms.
To provide a robust tool for analyzing complex transcriptomes in the presence of noise and variability.

Main Methods:

Developed integrated assembly of phenotype-specific transcripts (IntAPT), a novel two-layer Bayesian model.
The model captures isoform presence at the group layer and quantifies abundance at the sample layer.
Employed spike-and-slab priors for isoform expression and Gibbs sampling for parameter estimation.

Main Results:

IntAPT consistently outperforms existing methods in simulations and real-world datasets.
Demonstrated robust performance across multiple samples, even with sequencing errors.
Achieved notably improved identification of low-abundance expressed isoforms.

Conclusions:

IntAPT provides a reliable method for identifying phenotype-specific transcript isoforms from RNA-seq data.
The Bayesian approach effectively handles noise and variability, enhancing transcript discovery.
IntAPT offers improved accuracy for detecting low-abundance isoforms, advancing transcriptome analysis.