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

TL;DR

Life-Code introduces a unified multi-omics modeling framework based on the central dogma, integrating DNA, RNA, and proteins through novel data pipelines and architectures to better understand genetic interactions.

Contribution

It presents a comprehensive framework that unifies multi-omics data and models the interactions between genetic molecules using innovative sequence transformation and hybrid architectures.

Findings

Achieves state-of-the-art results across multiple multi-omics tasks

Effectively models interactions between coding and non-coding regions

Demonstrates improved understanding of genetic sequence complexities

Abstract

The interactions between DNA, RNA, and proteins are fundamental to biological processes, as illustrated by the central dogma of molecular biology. Although modern biological pre-trained models have achieved great success in analyzing these macromolecules individually, their interconnected nature remains underexplored. This paper follows the guidance of the central dogma to redesign both the data and model pipeline and offers 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 between coding and non-coding regions through 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 results on various tasks across three omics, highlighting its potential for advancing multi-omics analysis and interpretation.