标题： RNAMunin：一种用于非编码RNA发现的深度机器学习模型 显示英文标题

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

摘要： 微生物基因组的功能注释通常偏向于蛋白质编码基因，留下了大量未被探索的非编码RNA（ncRNAs），这些ncRNAs对于调节细菌和古菌的生理、应激反应和代谢至关重要。直接从基因组序列中识别ncRNAs是生物信息学和生物学中的一个关键挑战，这对于理解有机体的完整调控潜力至关重要。本文介绍了RNAMunin，这是一种机器学习（ML）模型，能够仅使用基因组序列来发现ncRNAs。它在处理大型序列数据集（如总长度达多个Gbp的长读宏基因组组装片段）时也具有计算可行性。RNAMunin是在从16个旧金山河口样本的约60 Gbp长读宏基因组中提取的Rfam序列上训练的。据我们所知，目前没有其他模型能够在如此大的规模上仅基于基因组序列检测ncRNAs。由于RNAMunin只需要基因组序列作为输入，因此我们不需要ncRNA被转录就可以找到它，即我们不需要转录组数据。我们以叙述风格撰写本文，以便最好地传达RNAMunin是如何开发以及如何详细工作的。与几乎所有当前的ML模型不同，RNAMunin大约有1M个参数，体积非常小且运行非常快。 显示英文摘要

摘要： 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.