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Related Experiment Video

Updated: Jan 14, 2026

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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Interpretable phenotype decoding from multicondition sequencing data with ALPINE.

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

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|October 22, 2025
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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.