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Decoding Natural Behavior from Neuroethological Embedding

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Published on: October 3, 2025

OpenSpliceAI provides an efficient modular implementation of SpliceAI enabling easy retraining across nonhuman

Kuan-Hao Chao^1,2, Alan Mao^1,2,3, Anqi Liu¹

¹Department of Computer Science, Johns Hopkins University, Baltimore, United States.

|October 30, 2025

Summary

OpenSpliceAI is a new, open-source deep learning tool for identifying DNA splicing signals. It offers faster processing and lower memory use than SpliceAI, enabling broader genomic analyses.

Keywords:

A. thaliana PyTorch Splice site prediction SpliceAI Transfer learning arabidopsis thaliana computational biology deep learning honeybee human mouse splice junctions systems biology zebrafish

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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

Area of Science:

Genomics
Bioinformatics
Machine Learning

Background:

SpliceAI is a leading deep learning method for identifying splicing signals in DNA.
Its utility is limited by outdated software and human-centric training data.

Purpose of the Study:

Introduce OpenSpliceAI, an open-source, trainable PyTorch version of SpliceAI.
Address limitations of SpliceAI by enabling species-specific retraining and mitigating human bias.

Main Methods:

Implemented SpliceAI in PyTorch as OpenSpliceAI.
Enabled training from scratch and transfer learning capabilities.
Performed comparative experiments on processing speed, memory usage, and concordance with SpliceAI.

Main Results:

OpenSpliceAI demonstrates faster processing speeds and lower memory usage compared to SpliceAI.
Achieved high concordance between OpenSpliceAI and SpliceAI outputs.
In silico mutagenesis and calibration experiments confirmed similar reliance on sequence features and score probability estimates.

Conclusions:

OpenSpliceAI provides a more efficient and flexible alternative to SpliceAI for splicing signal identification.
Its open-source nature and retraining capabilities facilitate large-scale, species-specific genomic analyses.
The tool integrates well with existing machine learning ecosystems for custom splicing model development.