A Large-Scale Foundation Model for RNA Enables Diverse Function and Structure Prediction Article Swipe
Eric P. Xing
,
S. Zou
,
Tianhua Tao
,
Parvez Mahbub
,
Caleb N. Ellington
,
Robin Algayres
,
Dian Li
,
Yonghao Zhuang
,
Hongyi Wang
,
Le Song
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-6445344/v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-6445344/v1
Accurately predicting RNA structures and functions from nucleotide sequences, or conversely, designing sequences to meet structural and functional requirements, remains a fundamental challenge in RNA biology, largely due to limited annotated data and the poor efficiency of \textit{ab initio} modeling approaches. Here, we introduce AIDO.RNA, a large-scale RNA foundation model that leverages self-supervised pre-training to learn general and effective RNA representations, which can be transferred to tackle a wide range of RNA prediction and design tasks. AIDO.RNA is a 1.6-billion-parameter transformer-based language model, pre-trained on 42 million non-coding RNA (ncRNA) sequences at single-nucleotide resolution. It can be adapted to achieve state-of-the-art performance on 26 out of 28 diverse tasks, including RNA structure and function prediction, mRNA expression modeling, multi-modal RNA isoform expression prediction, and RNA inverse folding, demonstrating its effectiveness and versatility across the board. We find that beyond excelling in ncRNA-related tasks that directly reside in the pre-training data space, AIDO.RNA can be efficiently adapted to new domains with continued domain-specific pre-training to generalize toward untranslated regions and coding regions of mRNA, suggesting a promising pathway to continue to level up biological foundation models in general. We make AIDO.RNA open source and release the utility of the model in AIDO.ModelGenerator, a Python package enabling easy reproduction, application, and extension of our results.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-6445344/v1
- https://www.researchsquare.com/article/rs-6445344/latest.pdf
- OA Status
- gold
- Cited By
- 3
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- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4410165516Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-6445344/v1Digital Object Identifier
- Title
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A Large-Scale Foundation Model for RNA Enables Diverse Function and Structure PredictionWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-07Full publication date if available
- Authors
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Eric P. Xing, S. Zou, Tianhua Tao, Parvez Mahbub, Caleb N. Ellington, Robin Algayres, Dian Li, Yonghao Zhuang, Hongyi Wang, Le SongList of authors in order
- Landing page
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https://doi.org/10.21203/rs.3.rs-6445344/v1Publisher landing page
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https://www.researchsquare.com/article/rs-6445344/latest.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.researchsquare.com/article/rs-6445344/latest.pdfDirect OA link when available
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Foundation (evidence), Scale (ratio), Function (biology), Computer science, Computational biology, Biology, Evolutionary biology, Geography, Cartography, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.find | 137 |
| abstract_inverted_index.from | 7 |
| abstract_inverted_index.mRNA | 116 |
| abstract_inverted_index.make | 189 |
| abstract_inverted_index.meet | 15 |
| abstract_inverted_index.open | 191 |
| abstract_inverted_index.poor | 35 |
| abstract_inverted_index.that | 51, 138, 144 |
| abstract_inverted_index.wide | 69 |
| abstract_inverted_index.with | 160 |
| abstract_inverted_index.Here, | 42 |
| abstract_inverted_index.learn | 56 |
| abstract_inverted_index.level | 181 |
| abstract_inverted_index.mRNA, | 173 |
| abstract_inverted_index.model | 50, 199 |
| abstract_inverted_index.range | 70 |
| abstract_inverted_index.tasks | 143 |
| abstract_inverted_index.which | 62 |
| abstract_inverted_index.Python | 203 |
| abstract_inverted_index.across | 133 |
| abstract_inverted_index.beyond | 139 |
| abstract_inverted_index.board. | 135 |
| abstract_inverted_index.coding | 170 |
| abstract_inverted_index.design | 75 |
| abstract_inverted_index.model, | 83 |
| abstract_inverted_index.models | 185 |
| abstract_inverted_index.reside | 146 |
| abstract_inverted_index.source | 192 |
| abstract_inverted_index.space, | 151 |
| abstract_inverted_index.tackle | 67 |
| abstract_inverted_index.tasks, | 109 |
| abstract_inverted_index.tasks. | 76 |
| abstract_inverted_index.toward | 166 |
| abstract_inverted_index.(ncRNA) | 90 |
| abstract_inverted_index.achieve | 100 |
| abstract_inverted_index.adapted | 98, 156 |
| abstract_inverted_index.diverse | 108 |
| abstract_inverted_index.domains | 159 |
| abstract_inverted_index.general | 57 |
| abstract_inverted_index.initio} | 39 |
| abstract_inverted_index.inverse | 126 |
| abstract_inverted_index.isoform | 121 |
| abstract_inverted_index.largely | 27 |
| abstract_inverted_index.limited | 30 |
| abstract_inverted_index.million | 87 |
| abstract_inverted_index.package | 204 |
| abstract_inverted_index.pathway | 177 |
| abstract_inverted_index.regions | 168, 171 |
| abstract_inverted_index.release | 194 |
| abstract_inverted_index.remains | 20 |
| abstract_inverted_index.utility | 196 |
| abstract_inverted_index.AIDO.RNA | 77, 152, 190 |
| abstract_inverted_index.biology, | 26 |
| abstract_inverted_index.continue | 179 |
| abstract_inverted_index.directly | 145 |
| abstract_inverted_index.enabling | 205 |
| abstract_inverted_index.folding, | 127 |
| abstract_inverted_index.function | 114 |
| abstract_inverted_index.general. | 187 |
| abstract_inverted_index.language | 82 |
| abstract_inverted_index.modeling | 40 |
| abstract_inverted_index.results. | 213 |
| abstract_inverted_index.AIDO.RNA, | 45 |
| abstract_inverted_index.annotated | 31 |
| abstract_inverted_index.challenge | 23 |
| abstract_inverted_index.continued | 161 |
| abstract_inverted_index.designing | 12 |
| abstract_inverted_index.effective | 59 |
| abstract_inverted_index.excelling | 140 |
| abstract_inverted_index.extension | 210 |
| abstract_inverted_index.functions | 6 |
| abstract_inverted_index.including | 110 |
| abstract_inverted_index.introduce | 44 |
| abstract_inverted_index.leverages | 52 |
| abstract_inverted_index.modeling, | 118 |
| abstract_inverted_index.promising | 176 |
| abstract_inverted_index.sequences | 13, 91 |
| abstract_inverted_index.structure | 112 |
| abstract_inverted_index.Accurately | 1 |
| abstract_inverted_index.\textit{ab | 38 |
| abstract_inverted_index.biological | 183 |
| abstract_inverted_index.efficiency | 36 |
| abstract_inverted_index.expression | 117, 122 |
| abstract_inverted_index.foundation | 49, 184 |
| abstract_inverted_index.functional | 18 |
| abstract_inverted_index.generalize | 165 |
| abstract_inverted_index.non-coding | 88 |
| abstract_inverted_index.nucleotide | 8 |
| abstract_inverted_index.predicting | 2 |
| abstract_inverted_index.prediction | 73 |
| abstract_inverted_index.sequences, | 9 |
| abstract_inverted_index.structural | 16 |
| abstract_inverted_index.structures | 4 |
| abstract_inverted_index.suggesting | 174 |
| abstract_inverted_index.approaches. | 41 |
| abstract_inverted_index.conversely, | 11 |
| abstract_inverted_index.efficiently | 155 |
| abstract_inverted_index.fundamental | 22 |
| abstract_inverted_index.large-scale | 47 |
| abstract_inverted_index.multi-modal | 119 |
| abstract_inverted_index.performance | 102 |
| abstract_inverted_index.pre-trained | 84 |
| abstract_inverted_index.prediction, | 115, 123 |
| abstract_inverted_index.resolution. | 94 |
| abstract_inverted_index.transferred | 65 |
| abstract_inverted_index.versatility | 132 |
| abstract_inverted_index.application, | 208 |
| abstract_inverted_index.pre-training | 54, 149, 163 |
| abstract_inverted_index.untranslated | 167 |
| abstract_inverted_index.demonstrating | 128 |
| abstract_inverted_index.effectiveness | 130 |
| abstract_inverted_index.ncRNA-related | 142 |
| abstract_inverted_index.reproduction, | 207 |
| abstract_inverted_index.requirements, | 19 |
| abstract_inverted_index.domain-specific | 162 |
| abstract_inverted_index.self-supervised | 53 |
| abstract_inverted_index.representations, | 61 |
| abstract_inverted_index.state-of-the-art | 101 |
| abstract_inverted_index.single-nucleotide | 93 |
| abstract_inverted_index.transformer-based | 81 |
| abstract_inverted_index.AIDO.ModelGenerator, | 201 |
| abstract_inverted_index.1.6-billion-parameter | 80 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 10 |
| citation_normalized_percentile.value | 0.93681399 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |