RNAMunin: A Deep Machine Learning Model for Non-coding RNA Discovery

TL;DR

RNAMunin is a scalable, fast deep learning model that identifies non-coding RNAs directly from genomic sequences without transcriptomics data, aiding comprehensive genome annotation.

Contribution

It introduces RNAMunin, a novel, efficient ML model capable of detecting ncRNAs from large-scale genomic data solely based on sequence information.

Findings

Able to process large metagenomic datasets efficiently

Detects ncRNAs without requiring transcription data

Operates with approximately 1 million parameters

Abstract

Functional annotation of microbial genomes is often biased toward protein-coding genes, leaving a vast, unexplored landscape of non-coding RNAs (ncRNAs) that are critical for regulating bacterial and archaeal physiology, stress response and metabolism. Identifying ncRNAs directly from genomic sequence is a paramount challenge in bioinformatics and biology, essential for understanding the complete regulatory potential of an organism. This paper presents RNAMunin, a machine learning (ML) model that is capable of finding ncRNAs using genomic sequence alone. It is also computationally viable for large sequence datasets such as long read metagenomic assemblies with contigs totaling multiple Gbp. RNAMunin is trained on Rfam sequences extracted from approximately 60 Gbp of long read metagenomes from 16 San Francisco Estuary samples. We know of no other model that can detect ncRNAs based solely on genomic sequence at this scale. Since RNAMunin only requires genomic sequence as input, we do not need for an ncRNA to be transcribed to find it, i.e., we do not need transcriptomics data. We wrote this manuscript in a narrative style in order to best convey how RNAMunin was developed and how it works in detail. Unlike almost all current ML models, at approximately 1M parameters, RNAMunin is very small and very fast.