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Massively Parallel Reporter Assays in Cultured Mammalian Cells
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Proformer: a hybrid macaron transformer model predicts expression values from promoter sequences.
Il-Youp Kwak1, Byeong-Chan Kim1, Juhyun Lee1
1Department of Applied Statistics, Chung‑Ang University, Seoul, Republic of Korea.
BMC Bioinformatics
|February 20, 2024
Summary
We developed Proformer, a novel deep learning model, to predict gene expression from DNA sequences. This transformer-based approach enhances understanding of cis-regulatory logic and gene expression determination.
Area of Science:
- Computational Biology
- Genomics
- Bioinformatics
Background:
- Understanding cis-regulatory logic is crucial for decoding gene expression.
- High-throughput measurements offer new avenues for studying gene regulation.
Purpose of the Study:
- To develop a novel deep learning model for predicting gene expression from DNA sequences.
- To systematically decode cis-regulatory logic using high-throughput data.
Main Methods:
- Developed Proformer, an end-to-end transformer encoder architecture.
- Utilized a Macaron-like Transformer encoder with specific layer configurations.
- Incorporated sliding k-mers, positional embeddings, and strand embeddings for sequence input.
- Implemented multiple expression heads with mask filling to stabilize training.
Main Results:
- Proformer accurately predicts gene expression values from DNA sequences.
- The novel architecture demonstrated superior performance compared to conventional designs.
- The model enhances the understanding of cis-regulatory sequence function.
Conclusions:
- Proformer offers a new computational method for analyzing gene regulation.
- The study advances our comprehension of how cis-regulatory sequences influence gene expression.
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