RNA-GPT: Multimodal Generative System for RNA Sequence Understanding
This work addresses a domain-specific problem for RNA researchers by streamlining literature access and sequence analysis, though it appears incremental as it builds on existing LLM and encoder methods.
The authors tackled the challenge of navigating vast RNA literature by introducing RNA-GPT, a multimodal chat model that integrates RNA sequence encoders with LLMs to process user-uploaded sequences and provide accurate responses, resulting in the creation of a dataset of 407,616 RNA samples to support RNA research.
RNAs are essential molecules that carry genetic information vital for life, with profound implications for drug development and biotechnology. Despite this importance, RNA research is often hindered by the vast literature available on the topic. To streamline this process, we introduce RNA-GPT, a multi-modal RNA chat model designed to simplify RNA discovery by leveraging extensive RNA literature. RNA-GPT integrates RNA sequence encoders with linear projection layers and state-of-the-art large language models (LLMs) for precise representation alignment, enabling it to process user-uploaded RNA sequences and deliver concise, accurate responses. Built on a scalable training pipeline, RNA-GPT utilizes RNA-QA, an automated system that gathers RNA annotations from RNACentral using a divide-and-conquer approach with GPT-4o and latent Dirichlet allocation (LDA) to efficiently handle large datasets and generate instruction-tuning samples. Our experiments indicate that RNA-GPT effectively addresses complex RNA queries, thereby facilitating RNA research. Additionally, we present RNA-QA, a dataset of 407,616 RNA samples for modality alignment and instruction tuning, further advancing the potential of RNA research tools.