--- datasets: - multimolecule/rnacentral library_name: multimolecule license: agpl-3.0 mask_token: pipeline_tag: fill-mask tags: - Biology - RNA - ncRNA - rna widget: - example_title: microRNA 21 mask_index: 11 mask_index_1based: 12 masked_char: A output: - label: score: 0.035777 - label: '|' score: 0.035764 - label: '*' score: 0.03576 - label: score: 0.035756 - label: score: 0.03575 pipeline_tag: fill-mask sequence_type: ncRNA task: fill-mask text: UAGCUUAUCAGCUGAUGUUGA - example_title: microRNA 146a mask_index: 10 mask_index_1based: 11 masked_char: A output: - label: score: 0.035803 - label: '*' score: 0.035778 - label: score: 0.035771 - label: '|' score: 0.035765 - label: '?' score: 0.035745 pipeline_tag: fill-mask sequence_type: ncRNA task: fill-mask text: UGAGAACUGAUUCCAUGGGUU - example_title: microRNA 155 mask_index: 15 mask_index_1based: 16 masked_char: A output: - label: score: 0.035797 - label: '|' score: 0.035794 - label: score: 0.035773 - label: '*' score: 0.03577 - label: score: 0.035731 pipeline_tag: fill-mask sequence_type: ncRNA task: fill-mask text: UUAAUGCUAAUCGUGUAGGGGUU - example_title: RNA component of mitochondrial RNA processing endoribonuclease mask_index: 11 mask_index_1based: 12 masked_char: A output: - label: '*' score: 0.035793 - label: score: 0.035782 - label: I score: 0.035752 - label: score: 0.035744 - label: score: 0.035741 pipeline_tag: fill-mask sequence_type: ncRNA task: fill-mask text: GGUUCGUGCUGAGGCCUGUAUCCUAGGCUACACACUGAGGACUCUGUUCCUCCCCUUUCCGCCUAGGGGAAAGUCCCCGGACCUCGGGCAGAGAGUGCCACGUGCAUACGCACGUAGACAUUCCCCGCUUCCCACUCCAAAGUCCGCCAAGAAGCGUAUCCCGCUGAGCGGCGUGGCGCGGGGGCGUCAUCCGUCAGCUCCCUCUAGUUACGCAGGCAGUGCGUGUCCGCGCACCAACCACACGGGGCUCAUUCUCAGCGCGGCUGUAAAAAAAAA - example_title: 7SK small nuclear RNA mask_index: 13 mask_index_1based: 14 masked_char: A output: - label: score: 0.035776 - label: score: 0.035756 - label: '|' score: 0.035749 - label: . score: 0.035743 - label: '?' score: 0.035732 pipeline_tag: fill-mask sequence_type: ncRNA task: fill-mask text: GGAUGUGAGGGCGUCUGGCUGCGACAUCUGUCACCCCAUUGAUCGCCAGGGUUGAUUCGGCUGAUCUGGCUGGCUAGGCGGGUGUCCCCUUCCUCCCUCACCGCUCCAUGUGCGUCCCUCCCGAAGCUGCGCGCUCGGUCGAAGAGGACGACCAUCCCCGAUAGAGGAGGACCGGUCUUCGGUCAAGGGUAUACGAGUAGCUGCGCUCCCCUGCUAGAACCUCCAAACAAGCUCUCAAGGUCCAUUUGUAGGAGAACGUAGGGUAGUCAAGCUUCCAAGACUCCAGACACAUCCAAAUGAGGCGCUGCAUGUGGCAGUCUGCCUUUCUUUU - example_title: telomerase RNA component mask_index: 23 mask_index_1based: 24 masked_char: A output: - label: score: 0.035805 - label: score: 0.035779 - label: '|' score: 0.035775 - label: score: 0.035762 - label: score: 0.035739 pipeline_tag: fill-mask sequence_type: ncRNA task: fill-mask text: GGGUUGCGGAGGGUGGGCCUGGGGGGGUGGUGGCCAUUUUUUGUCUAACCCUAACUGAGAAGGGCGUAGGCGCCGUGCUUUUGCUCCCCGCGCGCUGUUUUUCUCGCUGACUUUCAGCGGGCGGAAAAGCCUCGGCCUGCCGCCUUCCACCGUUCAUUCUAGAGCAAACAAAAAAUGUCAGCUGCUGGCCCGUUCGCCCCUCCCGGGGACCUGCGGCGGGUCGCCUGCCCAGCCCCCGAACCCCGCCUGGAGGCCGCGGUCGGCCCGGGGCUUCUCCGGAGGCACCCACUGCCACCGCGAAGAGUUGGGCUCUGUCAGCCGCGGGUCUCUCGGGGGCGAGGGCGAGGUUCAGGCCUUUCAGGCCGCAGGAAGAGGAACGGAGCGAGUCCCCGCGCGCGGCGCGAUUCCCUGAGCUGUGGGACGUGCACCCAGGACUCGGCUCACACAUGC - example_title: vault RNA 2-1 mask_index: 12 mask_index_1based: 13 masked_char: A output: - label: '*' score: 0.035797 - label: score: 0.035778 - label: score: 0.035763 - label: score: 0.035723 - label: I score: 0.035718 pipeline_tag: fill-mask sequence_type: ncRNA task: fill-mask text: CGGGUCGGAGUUGCUCAAGCGGUUACCUCCUCAUGCCGGACUUUCUAUCUGUCCAUCUCUGUGCUGGGGUUCGAGACCCGCGGGUGCUUACUGACCCUUUUAUGCAA - example_title: brain cytoplasmic RNA 1 mask_index: 18 mask_index_1based: 19 masked_char: A output: - label: score: 0.035795 - label: '|' score: 0.035761 - label: score: 0.035739 - label: score: 0.035733 - label: score: 0.035727 pipeline_tag: fill-mask sequence_type: ncRNA task: fill-mask text: GGCCGGGCGCGGUGGCUCCGCCUGUAAUCCCAGCUCUCAGGGAGGCUAAGAGGCGGGAGGAUAGCUUGAGCCCAGGAGUUCGAGACCUGCCUGGGCAAUAUAGCGAGACCCCGUUCUCCAGAAAAAGGAAAAAAAAAAACAAAAGACAAAAAAAAAAUAAGCGUAACUUCCCUCAAAGCAACAACCCCCCCCCCCCUUU - example_title: HIV-1 TAR-WT mask_index: 13 mask_index_1based: 14 masked_char: A output: - label: score: 0.035794 - label: '*' score: 0.035775 - label: '|' score: 0.035741 - label: score: 0.035738 - label: score: 0.035716 pipeline_tag: fill-mask sequence_type: ncRNA task: fill-mask text: GGUCUCUCUGGUUGACCAGAUCUGAGCCUGGGAGCUCUCUGGCUAACUAGGGAACC - example_title: prion protein (Kanno blood group) mask_index: 21 mask_index_1based: 22 masked_char: A output: - label: score: 0.035799 - label: score: 0.035798 - label: '|' score: 0.035754 - label: '*' score: 0.035751 - label: score: 0.035742 pipeline_tag: fill-mask sequence_type: mRNA task: fill-mask text: AUGGCGAACCUUGGCUGCUGGUGCUGGUUCUCUUUGUGGCCACAUGGAGUGACCUGGGCCUCUGC - example_title: interleukin 10 mask_index: 11 mask_index_1based: 12 masked_char: A output: - label: score: 0.035801 - label: score: 0.035789 - label: score: 0.035753 - label: score: 0.035749 - label: '|' score: 0.035736 pipeline_tag: fill-mask sequence_type: mRNA task: fill-mask text: AUGCACAGCUCGCACUGCUCUGUUGCCUGGUCCUCCUGACUGGGGUGAGGGCC - example_title: Zaire ebolavirus mask_index: 11 mask_index_1based: 12 masked_char: A output: - label: '|' score: 0.035774 - label: score: 0.035769 - label: score: 0.035749 - label: '*' score: 0.035746 - label: score: 0.035732 pipeline_tag: fill-mask sequence_type: mRNA task: fill-mask text: AAUGUUCAAACCUUUGUGAAGCUCUGUUAGCUGAUGGUCUUGCUAAAGCAUUUCCUAGCAAUAUGAUGGUAGUCACAGAGCGUGAGCAAAAAGAAAGCUUAUUGCAUCAAGCAUCAUGGCACCACACAAGUGAUGAUUUUGGUGAGCAUGCCACAGUUAGAGGGAGUAGCUUUGUAACUGAUUUAGAGAAAUACAAUCUUGCAUUUAGAUAUGAGUUUACAGCACCUUUUAUAGAAUAUUGUAACCGUUGCUAUGGUGUUAAGAAUGUUUUUAAUUGGAUGCAUUAUACAAUCCCACAGUGUUAU - example_title: SARS coronavirus mask_index: 14 mask_index_1based: 15 masked_char: A output: - label: score: 0.03579 - label: '|' score: 0.035758 - label: score: 0.035753 - label: score: 0.035753 - label: '*' score: 0.035746 pipeline_tag: fill-mask sequence_type: mRNA task: fill-mask text: AUGUUUAUUUUCUUUUAUUUCUUACUCUCACUAGUGGUAGUGACCUUGACCGGUGCACCACUUUUGAUGAUGUUCAAGCUCCUAAUUACACUCAACAUACUUCAUCUAUGAGGGGGGUUUACUAUCCUGAUGAAAUUUUUAGAUCAGACACUCUUUAUUUAACUCAGGAUUUAUUUCUUCCAUUUUAUUCUAAUGUUACAGGGUUUCAUACUAUUAAUCAUACGUUUGACAACCCUGUCAUACCUUUUAAGGAUGGUAUUUAUUUUGCUGCCACAGAGAAAUCAAAUGUUGUCCGUGGUUGGGUUUUUGGUUCUACCAUGAACAACAAGUCACAGUCGGUGAUUAUUAUUAACAAUUCUACUAAUGUUGUUAUACGAGCAUGUAACUUUGAAUUGUGUGACAACCCUUUCUUUGCUGUUUCUAAACCCAUGGGUACACAGACACAUACUAUGAUAUUCGAUAAUGCAUUUAAAUGCACUUUCGAGUACAUAUCU - example_title: insulin mask_index: 12 mask_index_1based: 13 masked_char: A output: - label: score: 0.035796 - label: score: 0.035762 - label: . score: 0.035751 - label: '|' score: 0.035748 - label: score: 0.035747 pipeline_tag: fill-mask sequence_type: mRNA task: fill-mask text: AUGGCCCUGUGGUGCGCCUCCUGCCCCUGCUGGCGCUGCUGGCCCUCUGGGGACCUGACCCAGCCGCAGCCUUUGUGAACCAACACCUGUGCGGCUCACACCUGGUGGAAGCUCUCUACCUAGUGUGCGGGGAACGAGGCUUCUUCUACACACCCAAGACCCGCCGGGAGGCAGAGGACCUGCAGGUGGGGCAGGUGGAGCUGGGCGGGGGCCCUGGUGCAGGCAGCCUGCAGCCCUUGGCCCUGGAGGGGUCCCUGCAGAAGCGUGGCAUUGUGGAACAAUGCUGUACCAGCAUCUGCUCCCUCUACCAGCUGGAGAACUACUGCAACUAG - example_title: cyclin dependent kinase inhibitor 2A mask_index: 18 mask_index_1based: 19 masked_char: A output: - label: score: 0.03579 - label: '*' score: 0.035734 - label: score: 0.035726 - label: score: 0.035722 - label: C score: 0.03572 pipeline_tag: fill-mask sequence_type: mRNA task: fill-mask text: AUGGAGCCGGCGGCGGGGGCAGCAUGGAGCCUUCGGCUGACUGGCUGGCCACGGCCGCGGCCCGGGGUCGGGUAGAGGAGGUGCGGGCGCUGCUGGAGGCGGGGGCGCUGCCCAACGCACCGAAUAGUUACGGUCGGAGGCCGAUCCAGGUCAUGAUGAUGGGCAGCGCCCGAGUGGCGGAGCUGCUGCUGCUCCACGGCGCGGAGCCCAACUGCGCCGACCCCGCCACUCUCACCCGACCCGUGCACGACGCUGCCCGGGAGGGCUUCCUGGACACGCUGGUGGUGCUGCACCGGGCCGGGGCGCGGCUGGACGUGCGCGAUGCCUGGGGCCGUCUGCCCGUGGACCUGGCUGAGGAGCUGGGCCAUCGCGAUGUCGCACGGUACCUGCGCGCGGCUGCGGGGGGCACCAGAGGCAGUAACCAUGCCCGCAUAGAUGCCGCGGAAGGUCCCUCAGACAUCCCCGAUUGA - example_title: human papillomavirus type 16 E6 mask_index: 10 mask_index_1based: 11 masked_char: A output: - label: score: 0.035779 - label: '|' score: 0.035758 - label: score: 0.035744 - label: . score: 0.035729 - label: I score: 0.035728 pipeline_tag: fill-mask sequence_type: mRNA task: fill-mask text: AUGCACCAAAGAGAACUGCAAUGUUUCAGGACCCACAGGAGCGACCCAGAAAGUUACCACAGUUAUGCACAGAGCUGCAAACAACUAUACAUGAUAUAAUAUUAGAAUGUGUGUACUGCAAGCAACAGUUACUGCGACGUGAGGUAUAUGACUUUGCUUUUCGGGAUUUAUGCAUAGUAUAUAGAGAUGGGAAUCCAUAUGCUGUAUGUGAUAAAUGUUUAAAGUUUUAUUCUAAAAUUAGUGAGUAUAGACAUUAUUGUUAUAGUUUGUAUGGAACAACAUUAGAACAGCAAUACAACAAACCGUUGUGUGAUUUGUUAAUUAGGUGUAUUAACUGUCAAAAGCCACUGUGUCCUGAAGAAAAGCAAAGACAUCUGGACAAAAAGCAAAGAUUCCAUAAUAUAAGGGGUCGGUGGACCGGUCGAUGUAUGUCUUGUUGCAGAUCAUCAAGAACACGUAGAGAAACCCAGCUGUAA - example_title: NRAS proto-oncogene mask_index: 36 mask_index_1based: 37 masked_char: A output: - label: score: 0.035787 - label: '|' score: 0.035755 - label: score: 0.03575 - label: score: 0.035745 - label: '*' score: 0.035744 pipeline_tag: fill-mask sequence_type: 5' UTR task: fill-mask text: GGGGCCGGAAGUGCCGCUCCUUGGUGGGGGCUGUUCUGGCGGUUCCGGGGUCUCCAACAUUUUUCCCGGCUGUGGUCCUAAAUCUGUCCAAAGCAGAGGCAGUGGAGCUUGAGGUUCUUGCUGGUGUGAA - example_title: amyloid beta precursor protein mask_index: 15 mask_index_1based: 16 masked_char: A output: - label: score: 0.035785 - label: '*' score: 0.035768 - label: '?' score: 0.035765 - label: '|' score: 0.035756 - label: score: 0.035752 pipeline_tag: fill-mask sequence_type: 5' UTR task: fill-mask text: GUCAGUUUCCUCGGCGCGGUAGGCGAGAGCACGCGGAGGAGCGUGCGCGGGGGCCCCGGGAGACGGCGGCGGUGGCGGCGCGGGCAGAGCAAGGACGCGGCGGAUCCCACUCGCACAGCAGCGCACUCGGUGCCCCGCGCAGGGUCGCG - example_title: RUNX family transcription factor 1 mask_index: 15 mask_index_1based: 16 masked_char: A output: - label: score: 0.035809 - label: '*' score: 0.035797 - label: score: 0.035737 - label: '|' score: 0.035719 - label: I score: 0.035716 pipeline_tag: fill-mask sequence_type: 5' UTR task: fill-mask text: ACUUCUUUGGGCCUCUAAACAACCACAGAACCACAAGUUGGGUAGCCUGGCAGUGUCAGAAGUCUGAACCCAGCAUAGUGGUCAGCAGGCAGGACGAAUCACACUGAAUGCAAACCACAGGGUUUCGCAGCGUGGUAAAAGAAAUCAUUGAGUCCCCCGCCUUCAGAAGAGGGUGCAUUUUCAGGAGGAAGCG - example_title: fragile X messenger ribonucleoprotein 1 mask_index: 15 mask_index_1based: 16 masked_char: A output: - label: '*' score: 0.035775 - label: '|' score: 0.035763 - label: score: 0.035749 - label: score: 0.035744 - label: '?' score: 0.035741 pipeline_tag: fill-mask sequence_type: 5' UTR task: fill-mask text: CUCAGUCAGGCGCUCGCUCCGUUUCGGUUUCACUUCCGGUGGAGGGCCGCCUCUGAGCGGGCGGCGGGCCGACGGCGAGCGCGGGCGGCGGCGGUGACGGAGGCGCCGCUGCCAGGGGGCGUGCGGCAGCGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGAGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCUGGGCCUCGAGCGCCCGCAGCCCACCUCUCGGGGGCGGGCUCCCGGCGCUAGCAGGGCUGAAGAGAAG - example_title: MYC proto-oncogene mask_index: 10 mask_index_1based: 11 masked_char: A output: - label: score: 0.03581 - label: score: 0.035767 - label: score: 0.035753 - label: '|' score: 0.035736 - label: score: 0.03573 pipeline_tag: fill-mask sequence_type: 5' UTR task: fill-mask text: AACUCGCUGUGUAAUUCCAGCGAGAGGCAGAGGGAGCGAGCGGGCGGCCGGCUAGGGUGGAAGAGCCGGGCGAGCAGAGCUGCGCUGCGGGCGUCCUGGGAAGGGAGAUCCGGAGCGAAUAGGGGGCUUCGCCUCUGGCCCAGCCCUCCCGCUGAUCCCCCAGCCAGCGGUCCGCAACCCUUGCCGCAUCCACGAAACUUUGCCCAUAGCAGCGGGCGGGCACUUUGCACUGGAACUUACAACACCCGAGCAAGGACGCGACUCUCCCGACGCGGGGAGGCUAUUCUGCCCAUUUGGGGACACUUCCCCGCCGCUGCCAGGACCCGCUUCUCUGAAAGGCUCUCCUUGCAGCUGCUUAGACG - example_title: activating transcription factor 4 mask_index: 20 mask_index_1based: 21 masked_char: A output: - label: '*' score: 0.035781 - label: '|' score: 0.035781 - label: score: 0.035733 - label: score: 0.03573 - label: score: 0.035728 pipeline_tag: fill-mask sequence_type: 5' UTR task: fill-mask text: CAUUUCUACUUUGCCCGCCCCAGAUGUAGUUUUCUCUGCGCGUGUGCGUUUUCCCUCCUCCCCGCCCUCAGGGUCCACGGCCACCAUGGCGUAUUAGGGGCAGCAGUGCCUGCGGCAGCAUUGGCCUUUGCAGCGGCGGCAGCAGCACCAGGCUCUGCAGCGGCAACCCCCAGCGGCUUAAGCCAUGGCGCUUCUCACGGCAUUCAGCAGCAGCGUUGCUGUAACCGACAAAGACACCUUCGAAUUAAGCACAUUCCUCGAUUCCAGCAAAGCACCGCAAC - example_title: Human GPI protein p137 mask_index: 11 mask_index_1based: 12 masked_char: A output: - label: '*' score: 0.035785 - label: score: 0.035775 - label: score: 0.03576 - label: score: 0.035741 - label: '|' score: 0.035736 pipeline_tag: fill-mask sequence_type: 3' UTR task: fill-mask text: UUUUUAAAAGGAAAGAUACCAAAUGCCUGCUGCUACCACCCUUUUCAAUUGCUAUGUUUUGAAAGGCACCAGUAUGUGUUUUAGAUUGAUUUAAAUGUUUCAUUUAAAUCACGGACAGUAGUUUCAGUUCUGAUGGUAUAAGCAAAACAAAUAAAACGUUUAUAAAAGUUGUAUCUUGAAACACUGGUGUUCAACAGCUAGCAGCUUAUGUGAUUCACCCCAUGCCACGUUAGUGUCACAAAUUUUAUGGUUUAUCUCCAGCAACAUUUCUCUAGUACUUGCACUUAUUAUCUGAAUUC - example_title: nucleophosmin 1 mask_index: 11 mask_index_1based: 12 masked_char: A output: - label: '*' score: 0.035774 - label: score: 0.03577 - label: score: 0.035763 - label: '|' score: 0.035747 - label: score: 0.035716 pipeline_tag: fill-mask sequence_type: 3' UTR task: fill-mask text: GAAAAUAGUUUAACAAUUUGUUAAAAAAUUUUCCGUCUUAUUUCAUUUCUGUAACAGUUGAUAUCUGGCUGUCCUUUUUAUAAUGCAGAGUGAGAACUUUCCCUACCGUGUUUGAUAAAUGUUGUCCAGGUUCUAUUGCCAAGAAUGUGUUGUCCAAAAUGCCUGUUUAGUUUUUAAAGAUGGAACUCCACCCUUUGCUUGGUUUUAAGUAUGUAUGGAAUGUUAUGAUAGGACAUAGUAGUAGCGGUGGUCAGACAUGGAAAUGGUGGGGAGACAAAAAUAUACAUGUGAAAUAAAACUCAGUAUUUUAAUAAAGUAGCACGGUUUCUAUUGA - example_title: superoxide dismutase 1 mask_index: 12 mask_index_1based: 13 masked_char: A output: - label: score: 0.035775 - label: '|' score: 0.035767 - label: '*' score: 0.035747 - label: score: 0.035747 - label: score: 0.035729 pipeline_tag: fill-mask sequence_type: 3' UTR task: fill-mask text: ACAUUCCCUUGGUGUAGUCUGAGGCCCCUUAACUCAUCUGUUAUCCUGCUAGCUGUAGAAAUGUAUCCUGAUAAACAUUAAACACUGUAAUCUUAAAAGUGUAAUUGUGUGACUUUUUCAGAGUUGCUUUAAAGUACCUGUAGUGAGAAACUGAUUUAUGAUCACUUGGAAGAUUUGUAUAGUUUUAUAAAACUCAGUUAAAAUGUCUGUUUCAAUGACCUGUAUUUUGCCAGACUUAAAUCACAGAUGGGUAUUAAACUUGUCAGAAUUUCUUUGUCAUUCAAGCCUGUGAAUAAAAACCCUGUAUGGCACUUAUUAUGAGGCUAUUAAAAGAAUCCAAAUUCAAACUAAA - example_title: hemoglobin subunit alpha 2 mask_index: 13 mask_index_1based: 14 masked_char: A output: - label: '*' score: 0.035797 - label: score: 0.03578 - label: score: 0.035776 - label: score: 0.035756 - label: '?' score: 0.035725 pipeline_tag: fill-mask sequence_type: 3' UTR task: fill-mask text: CUGGAGCCUCGGUGCCGUUCCUCCUGCCCGCUGGGCCUCCCAACGGGCCCUCCUCCCCUCCUUGCACCGGCCCUUCCUGGUCUUUGAAUAAAGUCUGAGUGGGCAGCA - example_title: BRAF proto-oncogene mask_index: 12 mask_index_1based: 13 masked_char: A output: - label: score: 0.035826 - label: score: 0.035759 - label: '*' score: 0.035756 - label: '?' score: 0.035748 - label: '|' score: 0.035747 pipeline_tag: fill-mask sequence_type: 3' UTR task: fill-mask text: AACAAAUGAGUGGAGAGUUCAGGAGAGUAGCAACAAAAGGAAAAUAAAUGAACAUAUGUUUGCUUAUAUGUUAAAUUGAAUAAAAUACUCUCUUUUUUUUUAAGGUGAACCAAAGAACACUUGUGUGGUUAAAGACUAGAUAUAAUUUUUCCCCAAACUAAAAUUUAUACUUAACAUUGGAUUUUUAACAUCCAAGGGUUAAAAUACAUAGACAUUGCUAAAAAUUGGCAGAGCCUCUUCUAGAGGCUUUACUUUCUGUUCCGGGUUUGUAUCAUUCACUUGGUUAUUUUAAGUAGUAAACUUCAGUUUCUCAUGCAACUUUUGUUGCCAGCUAUCACAUGUCCACUAGGGACUCCAGAAGAAGACCCUACCUAUGCCUGUGUUUGCAGGUGAGAAGUUGGCAGUCGGUUAGCCUGGG - example_title: H3 clustered histone 1 mask_index: 17 mask_index_1based: 18 masked_char: A output: - label: score: 0.035798 - label: score: 0.035776 - label: '|' score: 0.035758 - label: score: 0.03575 - label: '*' score: 0.035735 pipeline_tag: fill-mask sequence_type: 3' UTR task: fill-mask text: UUACUGUGGUCUCUCUGCGGUCCAAGCAAAGGCUCUUUUCAGAGCCACCACCUUUUC --- # RNABERT Pre-trained model on non-coding RNA (ncRNA) using masked language modeling (MLM) and structural alignment learning (SAL) objectives. ## Disclaimer This is an UNOFFICIAL implementation of the [Informative RNA-base embedding for functional RNA clustering and structural alignment](https://doi.org/10.1093/nargab/lqac012) by Manato Akiyama, et al. The OFFICIAL repository of RNABERT is at [mana438/RNABERT](https://github.com/mana438/RNABERT). > [!CAUTION] > The MultiMolecule team is aware of a potential risk in reproducing the results of RNABERT. > > The original implementation of RNABERT does not prepend `` (``) and append `` tokens to the input sequence. > This should not affect the performance of the model in most cases, but it can lead to unexpected behavior in some cases. > > Please set `bos_token=None, eos_token=None` in the tokenizer and set `bos_token_id=None, eos_token_id=None` in the model configuration if you want the exact behavior of the original implementation. > [!TIP] > The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation. **The team releasing RNABERT did not write this model card for this model so this model card has been written by the MultiMolecule team.** ## Model Details RNABERT is a [bert](https://huggingface.co/google-bert/bert-base-uncased)-style model pre-trained on a large corpus of non-coding RNA sequences in a self-supervised fashion. This means that the model was trained on the raw nucleotides of RNA sequences only, with an automatic process to generate inputs and labels from those texts. Please refer to the [Training Details](#training-details) section for more information on the training process. ### Model Specification | Num Layers | Hidden Size | Num Heads | Intermediate Size | Num Parameters (M) | FLOPs (G) | MACs (G) | Max Num Tokens | | ---------- | ----------- | --------- | ----------------- | ------------------ | --------- | -------- | -------------- | | 6 | 120 | 12 | 40 | 0.48 | 0.96 | 0.46 | 440 | ### Links - **Code**: [multimolecule.rnabert](https://github.com/DLS5-Omics/multimolecule/tree/master/multimolecule/models/rnabert) - **Weights**: [multimolecule/rnabert](https://huggingface.co/multimolecule/rnabert) - **Data**: [multimolecule/rnacentral](https://huggingface.co/datasets/multimolecule/rnacentral) - **Paper**: [Informative RNA-base embedding for functional RNA clustering and structural alignment](https://doi.org/10.1093/nargab/lqac012) - **Developed by**: Manato Akiyama and Yasubumi Sakakibara - **Model type**: [BERT](https://huggingface.co/google-bert/bert-base-uncased) - **Original Repository**: [mana438/RNABERT](https://github.com/mana438/RNABERT) ## Usage The model file depends on the [`multimolecule`](https://multimolecule.danling.org) library. You can install it using pip: ```bash pip install multimolecule ``` ### Direct Use #### Masked Language Modeling You can use this model directly with a pipeline for masked language modeling: ```python import multimolecule # you must import multimolecule to register models from transformers import pipeline predictor = pipeline("fill-mask", model="multimolecule/rnabert") output = predictor("gguccucugguuagaccagaucugagccu") ``` ### Downstream Use #### Extract Features Here is how to use this model to get the features of a given sequence in PyTorch: ```python from multimolecule import RnaTokenizer, RnaBertModel tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnabert") model = RnaBertModel.from_pretrained("multimolecule/rnabert") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") output = model(**input) ``` #### Sequence Classification / Regression > [!NOTE] > This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for sequence classification or regression. Here is how to use this model as backbone to fine-tune for a sequence-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, RnaBertForSequencePrediction tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnabert") model = RnaBertForSequencePrediction.from_pretrained("multimolecule/rnabert") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") label = torch.tensor([1]) output = model(**input, labels=label) ``` #### Token Classification / Regression > [!NOTE] > This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for token classification or regression. Here is how to use this model as backbone to fine-tune for a nucleotide-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, RnaBertForTokenPrediction tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnabert") model = RnaBertForTokenPrediction.from_pretrained("multimolecule/rnabert") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") label = torch.randint(2, (len(text), )) output = model(**input, labels=label) ``` #### Contact Classification / Regression > [!NOTE] > This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for contact classification or regression. Here is how to use this model as backbone to fine-tune for a contact-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, RnaBertForContactPrediction tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnabert") model = RnaBertForContactPrediction.from_pretrained("multimolecule/rnabert") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") label = torch.randint(2, (len(text), len(text))) output = model(**input, labels=label) ``` ## Training Details RNABERT has two pre-training objectives: masked language modeling (MLM) and structural alignment learning (SAL). - **Masked Language Modeling (MLM)**: taking a sequence, the model randomly masks 15% of the tokens in the input then runs the entire masked sentence through the model and has to predict the masked tokens. This is comparable to the Cloze task in language modeling. - **Structural Alignment Learning (SAL)**: the model learns to predict the structural alignment of two RNA sequences. The model is trained to predict the alignment score of two RNA sequences using the Needleman-Wunsch algorithm. ### Training Data The RNABERT model was pre-trained on [RNAcentral](https://multimolecule.danling.org/datasets/rnacentral). RNAcentral is a free, public resource that offers integrated access to a comprehensive and up-to-date set of non-coding RNA sequences provided by a collaborating group of [Expert Databases](https://rnacentral.org/expert-databases) representing a broad range of organisms and RNA types. RNABERT used a subset of 76, 237 human ncRNA sequences from RNAcentral for pre-training. RNABERT preprocessed all tokens by replacing "U"s with "T"s. Note that during model conversions, "T" is replaced with "U". [`RnaTokenizer`][multimolecule.RnaTokenizer] will convert "T"s to "U"s for you, you may disable this behaviour by passing `replace_T_with_U=False`. ### Training Procedure #### Preprocessing RNABERT preprocess the dataset by applying 10 different mask patterns to the 72, 237 human ncRNA sequences. The final dataset contains 722, 370 sequences. The masking procedure is similar to the one used in BERT: - Mask rate: 15% - Replacement: `` for 80% of masked tokens - Replacement: random token for 10% of masked tokens - Replacement: unchanged token for 10% of masked tokens #### Pre-training The model was trained on 1 NVIDIA V100 GPU. ## Citation ```bibtex @article{akiyama2022informative, author = {Akiyama, Manato and Sakakibara, Yasubumi}, title = "{Informative RNA base embedding for RNA structural alignment and clustering by deep representation learning}", journal = {NAR Genomics and Bioinformatics}, volume = {4}, number = {1}, pages = {lqac012}, year = {2022}, month = {02}, abstract = "{Effective embedding is actively conducted by applying deep learning to biomolecular information. Obtaining better embeddings enhances the quality of downstream analyses, such as DNA sequence motif detection and protein function prediction. In this study, we adopt a pre-training algorithm for the effective embedding of RNA bases to acquire semantically rich representations and apply this algorithm to two fundamental RNA sequence problems: structural alignment and clustering. By using the pre-training algorithm to embed the four bases of RNA in a position-dependent manner using a large number of RNA sequences from various RNA families, a context-sensitive embedding representation is obtained. As a result, not only base information but also secondary structure and context information of RNA sequences are embedded for each base. We call this ‘informative base embedding’ and use it to achieve accuracies superior to those of existing state-of-the-art methods on RNA structural alignment and RNA family clustering tasks. Furthermore, upon performing RNA sequence alignment by combining this informative base embedding with a simple Needleman–Wunsch alignment algorithm, we succeed in calculating structural alignments with a time complexity of O(n2) instead of the O(n6) time complexity of the naive implementation of Sankoff-style algorithm for input RNA sequence of length n.}", issn = {2631-9268}, doi = {10.1093/nargab/lqac012}, url = {https://doi.org/10.1093/nargab/lqac012}, eprint = {https://academic.oup.com/nargab/article-pdf/4/1/lqac012/42577168/lqac012.pdf}, } ``` > [!NOTE] > The artifacts distributed in this repository are part of the MultiMolecule project. > If MultiMolecule supports your research, please cite the MultiMolecule project as follows: ```bibtex @software{chen_2024_12638419, author = {Chen, Zhiyuan and Zhu, Sophia Y.}, title = {MultiMolecule}, doi = {10.5281/zenodo.12638419}, publisher = {Zenodo}, url = {https://doi.org/10.5281/zenodo.12638419}, year = 2024, month = may, day = 4 } ``` ## Contact Please use GitHub issues of [MultiMolecule](https://github.com/DLS5-Omics/multimolecule/issues) for any questions or comments on the model card. Please contact the authors of the [RNABERT paper](https://doi.org/10.1093/nargab/lqac012) for questions or comments on the paper/model. ## License This model implementation is licensed under the [GNU Affero General Public License](license.md). For additional terms and clarifications, please refer to our [License FAQ](license-faq.md). ```spdx SPDX-License-Identifier: AGPL-3.0-or-later ```