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Updated: Jan 21, 2026

Detection of Alternative Splicing During Epithelial-Mesenchymal Transition

Detection of Alternative Splicing During Epithelial-Mesenchymal Transition

Published on: October 9, 2014

Yanagi: Fast and interpretable segment-based alternative splicing and gene expression analysis.

Mohamed K Gunady^1,2, Stephen M Mount³, Héctor Corrada Bravo^4,5

¹Department of Computer Science, University of Maryland, College Park, Maryland, USA.

BMC Bioinformatics

|August 15, 2019

Summary

This summary is machine-generated.

This study introduces transcriptome segmentation to separate pseudo-alignment from transcript quantification in RNA sequencing (RNA-seq). This method enhances analyses like alternative splicing and differential gene expression without quantification, improving local splicing detection.

Keywords:

Alternative splicing Differential gene expression Pseudo-alignment RNA-seq Transcriptome quantification

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Last Updated: Jan 21, 2026

Detection of Alternative Splicing During Epithelial-Mesenchymal Transition

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Published on: October 9, 2014

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Engineering Artificial Factors to Specifically Manipulate Alternative Splicing in Human Cells

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Published on: April 26, 2017

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Ultra-fast pseudo-alignment is standard for RNA sequencing (RNA-seq) but couples alignment with transcript quantification.
This coupling limits direct application in alternative splicing and differential gene expression analyses without an extra quantification step.

Purpose of the Study:

To decouple pseudo-alignment and transcript quantification in RNA sequencing (RNA-seq) analyses.
To enable direct application of pseudo-alignment in downstream analyses like alternative splicing and differential gene expression.

Main Methods:

Introduced a transcriptome segmentation approach to separate alignment and quantification tasks.
Developed an efficient algorithm for generating maximal disjoint segments for pseudo-alignment.
Generated per-sample segment counts using ultra-fast pseudo-alignment on the transcriptome reference library.

Main Results:

Demonstrated the application of segment counts in alternative splicing and differential gene expression analyses.
Showed that segment counts improve detection and estimation of local splicing compared to transcript quantification, especially with incomplete annotations.
Validated findings using simulated and experimental RNA-seq data.

Conclusions:

The Yanagi transcriptome segmentation approach leverages pseudo-alignment efficiency.
It expands RNA-seq analysis applicability and interpretability by modeling local coverage variation.
This method offers advantages for alternative splicing and differential gene expression studies.