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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Interpretable phenotype decoding from multicondition sequencing data with ALPINE.

Wei-Hao Lee¹, Lechuan Li², Ruth Dannenfelser²

¹Systems, Synthetic, and Physical Biology, Rice University, Houston, Texas 77005, USA.

Genome Research

|October 22, 2025

Summary

This summary is machine-generated.

We developed ALPINE, a new method to analyze complex sequencing data, effectively separating technical noise and identifying condition-specific genes. This approach enhances biological insights from individual studies and cross-study integrations.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Advancing sequencing technologies generate vast, heterogeneous data with complex biological perturbations.
Analyzing high-resolution data requires methods to disentangle technical factors from biological signals.
Integrating multiple studies offers opportunities to explore molecular underpinnings of health and disease.

Purpose of the Study:

To develop a robust methodology for analyzing complex sequencing data.
To disentangle technical and non-relevant phenotypic factors from condition-specific signals.
To provide interpretable insights into genetic effects of biological conditions.

Main Methods:

Developed ALPINE, a supervised non-negative matrix factorization (NMF) framework.
Evaluated ALPINE's performance through simulations across four different scenarios.
Compared ALPINE with existing methods for isolating phenotypic effects and removing batch effects.

Main Results:

ALPHINE effectively separates technical and non-technical factors, providing interpretable condition-associated genes.
ALPHINE outperforms existing methods in isolating phenotypic condition effects and prioritizing condition-associated genes.
ALPHINE demonstrates favorable performance in batch effect removal compared to state-of-the-art integration methods.

Conclusions:

ALPHINE is a powerful tool for analyzing high-resolution sequencing data.
The framework facilitates the extraction of biological insights from complex datasets.
ALPHINE enhances the potential of both individual high-resolution studies and large cross-study integrations.