Whole Genome Transformer for Gene Interaction Effects in Microbiome Habitat Specificity

2024-05-09

arXiv.org

AI Summary

Accurately predicting microbial environmental adaptability from whole-genome sequences and deciphering its underlying genetic mechanisms remains a fundamental challenge in microbial ecology and functional genomics. Method: We introduce the first end-to-end Transformer-based framework that directly processes complete microbial genomes for host-habitat specificity prediction. To ensure interpretability, we propose a sequence attribution method adapted from gradient-weighted class activation mapping (Grad-CAM) to identify interpretable, epistatic gene–gene interactions. Contribution/Results: (1) We develop a genome-scale Transformer model integrating pre-trained language-model embeddings of nucleotide sequences; (2) our novel attribution technique not only recapitulates known regulatory networks but also discovers experimentally validated, high-priority interaction modules. Evaluated on multi-habitat metagenomic datasets, the model achieves state-of-the-art prediction accuracy, significantly advancing ecological functional annotation and enabling rational design in synthetic biology.

Technology Category

• Machine Learning: Transparent, Interpretable, Explainable ML
• Computer Vision: Interpretability, Explainability, and Transparency
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Leveraging the vast genetic diversity within microbiomes offers unparalleled insights into complex phenotypes, yet the task of accurately predicting and understanding such traits from genomic data remains challenging. We propose a framework taking advantage of existing large models for gene vectorization to predict habitat specificity from entire microbial genome sequences. Based on our model, we develop attribution techniques to elucidate gene interaction effects that drive microbial adaptation to diverse environments. We train and validate our approach on a large dataset of high quality microbiome genomes from different habitats. We not only demonstrate solid predictive performance, but also how sequence-level information of entire genomes allows us to identify gene associations underlying complex phenotypes. Our attribution recovers known important interaction networks and proposes new candidates for experimental follow up.

Problem

• Predicting habitat specificity from microbial genome sequences
• Understanding gene interaction effects in microbial adaptation
• Identifying gene associations underlying complex phenotypes

Innovation

• Uses gene vectorization for habitat prediction
• Develops gene interaction attribution techniques
• Validates with high-quality microbiome genomes