Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification

2025-02-11

AI Summary

This work addresses the limitation of existing biomolecular pre-trained models, which neglect cross-omics interactions among DNA, RNA, and proteins. We propose the first unified multi-omics modeling framework grounded in the Central Dogma. Methodologically: (1) we introduce a novel nucleotide representation paradigm driven by reverse transcription and reverse translation; (2) we design a codon-aware tokenizer and a hybrid long-sequence Transformer architecture; and (3) we integrate masked modeling pre-training with knowledge distillation from protein language models to enable end-to-end modeling—from coding sequences to tertiary protein structures. Our framework achieves state-of-the-art performance across 12 downstream tasks spanning genomics, transcriptomics, and proteomics. It significantly improves accuracy in cross-omics functional prediction and enhances biological interpretability through mechanistically grounded representations.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Computer Vision: Multi-modal Vision

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

The interactions between DNA, RNA, and proteins are fundamental to biological processes, as illustrated by the central dogma of molecular biology. While modern biological pre-trained models have achieved great success in analyzing these macromolecules individually, their interconnected nature remains under-explored. In this paper, we follow the guidance of the central dogma to redesign both the data and model pipeline and offer a comprehensive framework, Life-Code, that spans different biological functions. As for data flow, we propose a unified pipeline to integrate multi-omics data by reverse-transcribing RNA and reverse-translating amino acids into nucleotide-based sequences. As for the model, we design a codon tokenizer and a hybrid long-sequence architecture to encode the interactions of both coding and non-coding regions with masked modeling pre-training. To model the translation and folding process with coding sequences, Life-Code learns protein structures of the corresponding amino acids by knowledge distillation from off-the-shelf protein language models. Such designs enable Life-Code to capture complex interactions within genetic sequences, providing a more comprehensive understanding of multi-omics with the central dogma. Extensive Experiments show that Life-Code achieves state-of-the-art performance on various tasks across three omics, highlighting its potential for advancing multi-omics analysis and interpretation.

Problem

• Unify multi-omics data integration
• Model DNA, RNA, protein interactions
• Advance multi-omics analysis interpretation

Innovation

• Unified multi-omics data pipeline
• Codon tokenizer and hybrid architecture
• Knowledge distillation for protein structures