Subverting the Canon: Novel Cancer-Promoting Functions and Mechanisms for snoRNAs

Huo, Matthew; Rai, Sudhir Kumar; Nakatsu, Ken; Deng, Youping; Jijiwa, Mayumi

doi:10.3390/ijms25052923

Cited by 3 publications

References 217 publications

(357 reference statements)

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Improving ncRNA family prediction using multi-modal contrastive learning of sequence and structure

Xu,

Li,

Yang

et al. 2024

Bioinformatics

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Motivation Recent advancements in high-throughput sequencing technology have significantly increased the focus on non-coding RNA (ncRNA) research within the life sciences. Despite this, the functions of many ncRNAs remain poorly understood. Research suggests that ncRNAs within the same family typically share similar functions, underlining the importance of understanding their roles. There are two primary methods for predicting ncRNA families: biological and computational. Traditional biological methods are not suitable for large-scale data prediction due to the significant human and resource requirements. Concurrently, most existing computational methods either rely solely on ncRNA sequence data or are exclusively based on the secondary structure of ncRNA molecules. These methods fail to fully utilize the rich multimodal information available from ncRNAs, thereby preventing them from learning more comprehensive and in-depth feature representations. Results To tackle these problems, we proposed MM-ncRNAFP, a multi-modal contrastive learning framework for ncRNA family prediction. We first used a pre-trained language model to encode the primary sequences of a large mammalian ncRNA dataset. Then, we adopted a contrastive learning framework with an attention mechanism to fuse the secondary structure information obtained by graph neural networks. The MM-ncRNAFP method can effectively fuse multi-modal information. Experimental comparisons with several competitive baselines demonstrated that MM-ncRNAFP can achieve more comprehensive representations of ncRNA features by integrating both sequence and structural information. This integration significantly enhances the performance of ncRNA family prediction. Ablation experiments and qualitative analyses were performed to verify the effectiveness of each component in our model. Moreover, since our model is pre-trained on a large amount of ncRNA data, it has the potential to bring significant improvements to other ncRNA-related tasks. Availability and Implementation MM-ncRNAFP and the datasets are available at https://github.com/xuruiting2/MM-ncRNAFP.

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Improving ncRNA family prediction using multi-modal contrastive learning of sequence and structure

Xu,

Li,

Yang

et al. 2024

Bioinformatics

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A bifunctional snoRNA with separable activities in guiding rRNA 2’-O-methylation and scaffolding gametogenesis effectors

Leroy,

Challal,

Pelletier

et al. 2024

Preprint

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Small nucleolar RNAs are non-coding transcripts that guide chemical modifications of RNA substrates and modulate gene expression at the epigenetic and post-transcriptional levels. However, the extent of their regulatory potential and the underlying molecular mechanisms remain poorly understood. Here, we identify a conserved, previously unannotated intronic C/D-box snoRNA, termed snR107, hosted in the fission yeast long non-coding RNA mamRNA and carrying two independent cellular functions. On the one hand, snR107 guides site-specific 25S rRNA 2′-O-methylation and promotes pre-rRNA processing and 60S subunit biogenesis. On the other hand, snR107 associates with the gametogenic RNA-binding proteins Mmi1 and Mei2, mediating their reciprocal inhibition and restricting meiotic gene expression during sexual differentiation. Both functions require distinct cis-motifs within snR107, including a conserved 2′-O-methylation guiding sequence. Together, our results position snR107 as a dual regulator of rRNA modification and gametogenesis effectors, expanding our vision on the non-canonical functions exerted by snoRNAs in cell fate decisions.

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LncRNA Genes of the SNHG Family: Co-methylation and Common Functions in Ovarian Cancer

Braga,

Filippova,

Uroshlev

et al. 2024

Biochemistry Moscow

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Subverting the Canon: Novel Cancer-Promoting Functions and Mechanisms for snoRNAs

Cited by 3 publications

References 217 publications

Improving ncRNA family prediction using multi-modal contrastive learning of sequence and structure

Improving ncRNA family prediction using multi-modal contrastive learning of sequence and structure

A bifunctional snoRNA with separable activities in guiding rRNA 2’-O-methylation and scaffolding gametogenesis effectors

LncRNA Genes of the SNHG Family: Co-methylation and Common Functions in Ovarian Cancer

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