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Improving the performance of supervised deep learning for regulatory genomics using phylogenetic augmentation.

Andrew G Duncan¹, Jennifer A Mitchell¹, Alan M Moses¹

¹Cell & Systems Biology, University of Toronto, Toronto, ON M5S 3G5, Canada.

Bioinformatics (Oxford, England)

|April 8, 2024

Summary

Phylogenetic augmentation, using evolutionarily related sequences, enhances deep learning models for genomic sequence analysis. This method improves data efficiency and model performance, especially for small datasets in genomics.

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Area of Science:

Genomics
Computational Biology
Machine Learning

Background:

Supervised deep learning models complex genomic sequence-regulatory function relationships.
Understanding these models offers biological insights into regulatory functions.
Limited sequence variation in genomes may hinder training complex models for the cis-regulatory code.

Purpose of the Study:

To address limitations in current data augmentation methods for genomic sequence data.
To improve the performance and data efficiency of deep learning models in genomics.
To introduce phylogenetic augmentation as a novel data augmentation technique.

Main Methods:

Developed phylogenetic augmentation by incorporating evolutionarily related sequences from different species.
Applied this method to augment genomic sequences for deep learning model training.
Evaluated model performance on predicting high-throughput functional assay measurements.

Main Results:

Phylogenetic augmentation significantly improves deep learning model performance on regulatory genomic sequences.
The method enhances data efficiency, rescuing model performance on down-sampled training sets.
Enables deep learning on small, real-world genomic datasets.

Conclusions:

Phylogenetic augmentation is an effective data augmentation strategy for supervised deep learning in genomics.
This approach overcomes limitations of insufficient sequence variation for complex genomic modeling.
The method is broadly applicable to various deep learning problems in the field of genomics.