公平分割颠覆排名格局：CHANRG模型揭示RNA二级结构预测泛化能力有限

摘要

RNA二级结构的精准预测是转录组注释、非编码RNA机制分析和RNA治疗设计的基石。基于深度学习和RNA基础模型的最新成果难以阐释，因为现有基准测试可能高估了跨RNA家族的泛化能力。我们推出综合层级非编码RNA群组注释库（CHANRG），该基准包含170,083个结构非冗余RNA，通过结构感知去重、基因组感知分割设计和多尺度结构评估，从Rfam 15.0的逾千万条序列中筛选而成。在29种预测工具的测试中，基础模型方法在保留集上获得最高精度，但在分布外数据中丧失大部分优势；而结构化解码器和直接神经预测器仍保持显著更强的鲁棒性。这种差距在控制序列长度后依然存在，既反映了结构覆盖度的损失，也体现了高阶构象连接的错误识别。CHANRG与无填充对称感知评估栈共同构建了更严格、批处理不变的框架，可助力开发具有可验证分布外鲁棒性的RNA结构预测工具。

English

Accurate prediction of RNA secondary structure underpins transcriptome annotation, mechanistic analysis of non-coding RNAs, and RNA therapeutic design. Recent gains from deep learning and RNA foundation models are difficult to interpret because current benchmarks may overestimate generalization across RNA families. We present the Comprehensive Hierarchical Annotation of Non-coding RNA Groups (CHANRG), a benchmark of 170{,}083 structurally non-redundant RNAs curated from more than 10 million sequences in Rfam~15.0 using structure-aware deduplication, genome-aware split design and multiscale structural evaluation. Across 29 predictors, foundation-model methods achieved the highest held-out accuracy but lost most of that advantage out of distribution, whereas structured decoders and direct neural predictors remained markedly more robust. This gap persisted after controlling for sequence length and reflected both loss of structural coverage and incorrect higher-order wiring. Together, CHANRG and a padding-free, symmetry-aware evaluation stack provide a stricter and batch-invariant framework for developing RNA structure predictors with demonstrable out-of-distribution robustness.

公平分割颠覆排名格局：CHANRG模型揭示RNA二级结构预测泛化能力有限

Fair splits flip the leaderboard: CHANRG reveals limited generalization in RNA secondary-structure prediction

摘要

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