Extending Sequence Length is Not All You Need: Effective Integration of Multimodal Signals for Gene Expression Prediction

TL;DR

This paper shows that integrating proximal multimodal epigenomic signals with a novel framework called Prism significantly improves gene expression prediction accuracy using short DNA sequences, challenging the focus on long sequence modeling.

Contribution

The paper introduces Prism, a new framework that effectively integrates diverse epigenomic signals and mitigates confounding effects, achieving state-of-the-art gene expression prediction with short sequences.

Findings

Long sequence modeling can decrease prediction performance.

Proper integration of multimodal signals improves accuracy.

Prism outperforms existing methods on gene expression prediction.

Abstract

Gene expression prediction, which predicts mRNA expression levels from DNA sequences, presents significant challenges. Previous works often focus on extending input sequence length to locate distal enhancers, which may influence target genes from hundreds of kilobases away. Our work first reveals that for current models, long sequence modeling can decrease performance. Even carefully designed algorithms only mitigate the performance degradation caused by long sequences. Instead, we find that proximal multimodal epigenomic signals near target genes prove more essential. Hence we focus on how to better integrate these signals, which has been overlooked. We find that different signal types serve distinct biological roles, with some directly marking active regulatory elements while others reflect background chromatin patterns that may introduce confounding effects. Simple concatenation may lead models to develop spurious associations with these background patterns. To address this challenge, we propose Prism, a framework that learns multiple combinations of high-dimensional epigenomic features to represent distinct background chromatin states and uses backdoor adjustment to mitigate confounding effects. Our experimental results demonstrate that proper modeling of multimodal epigenomic signals achieves state-of-the-art performance using only short sequences for gene expression prediction.