metadata
datasets:
- multimolecule/rnacentral
library_name: multimolecule
license: agpl-3.0
mask_token: <mask>
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: 0.2009
- label: '*'
score: 0.159673
- label: '|'
score: 0.118655
- label: I
score: 0.117554
- label: .
score: 0.093048
pipeline_tag: fill-mask
sequence_type: ncRNA
task: fill-mask
text: UAGCUUAUCAG<mask>CUGAUGUUGA
- example_title: microRNA 146a
mask_index: 10
mask_index_1based: 11
masked_char: A
output:
- label: '?'
score: 0.228405
- label: '*'
score: 0.181964
- label: '|'
score: 0.134386
- label: I
score: 0.132831
- label: .
score: 0.10698
pipeline_tag: fill-mask
sequence_type: ncRNA
task: fill-mask
text: UGAGAACUGA<mask>UUCCAUGGGUU
- example_title: microRNA 155
mask_index: 15
mask_index_1based: 16
masked_char: A
output:
- label: '?'
score: 0.235441
- label: '*'
score: 0.188319
- label: '|'
score: 0.141343
- label: I
score: 0.137523
- label: .
score: 0.111508
pipeline_tag: fill-mask
sequence_type: ncRNA
task: fill-mask
text: UUAAUGCUAAUCGUG<mask>UAGGGGUU
- example_title: RNA component of mitochondrial RNA processing endoribonuclease
mask_index: 11
mask_index_1based: 12
masked_char: A
output:
- label: A
score: 0.622152
- label: G
score: 0.074581
- label: X
score: 0.062167
- label: '?'
score: 0.059036
- label: '*'
score: 0.042494
pipeline_tag: fill-mask
sequence_type: ncRNA
task: fill-mask
text: >-
GGUUCGUGCUG<mask>AGGCCUGUAUCCUAGGCUACACACUGAGGACUCUGUUCCUCCCCUUUCCGCCUAGGGGAAAGUCCCCGGACCUCGGGCAGAGAGUGCCACGUGCAUACGCACGUAGACAUUCCCCGCUUCCCACUCCAAAGUCCGCCAAGAAGCGUAUCCCGCUGAGCGGCGUGGCGCGGGGGCGUCAUCCGUCAGCUCCCUCUAGUUACGCAGGCAGUGCGUGUCCGCGCACCAACCACACGGGGCUCAUUCUCAGCGCGGCUGUAAAAAAAAA
- example_title: 7SK small nuclear RNA
mask_index: 13
mask_index_1based: 14
masked_char: A
output:
- label: A
score: 0.855574
- label: '?'
score: 0.037714
- label: '*'
score: 0.032782
- label: I
score: 0.025124
- label: '|'
score: 0.023974
pipeline_tag: fill-mask
sequence_type: ncRNA
task: fill-mask
text: >-
GGAUGUGAGGGCG<mask>UCUGGCUGCGACAUCUGUCACCCCAUUGAUCGCCAGGGUUGAUUCGGCUGAUCUGGCUGGCUAGGCGGGUGUCCCCUUCCUCCCUCACCGCUCCAUGUGCGUCCCUCCCGAAGCUGCGCGCUCGGUCGAAGAGGACGACCAUCCCCGAUAGAGGAGGACCGGUCUUCGGUCAAGGGUAUACGAGUAGCUGCGCUCCCCUGCUAGAACCUCCAAACAAGCUCUCAAGGUCCAUUUGUAGGAGAACGUAGGGUAGUCAAGCUUCCAAGACUCCAGACACAUCCAAAUGAGGCGCUGCAUGUGGCAGUCUGCCUUUCUUUU
- example_title: telomerase RNA component
mask_index: 23
mask_index_1based: 24
masked_char: A
output:
- label: '?'
score: 0.227095
- label: '*'
score: 0.180311
- label: '|'
score: 0.134258
- label: I
score: 0.133277
- label: .
score: 0.105863
pipeline_tag: fill-mask
sequence_type: ncRNA
task: fill-mask
text: >-
GGGUUGCGGAGGGUGGGCCUGGG<mask>GGGGUGGUGGCCAUUUUUUGUCUAACCCUAACUGAGAAGGGCGUAGGCGCCGUGCUUUUGCUCCCCGCGCGCUGUUUUUCUCGCUGACUUUCAGCGGGCGGAAAAGCCUCGGCCUGCCGCCUUCCACCGUUCAUUCUAGAGCAAACAAAAAAUGUCAGCUGCUGGCCCGUUCGCCCCUCCCGGGGACCUGCGGCGGGUCGCCUGCCCAGCCCCCGAACCCCGCCUGGAGGCCGCGGUCGGCCCGGGGCUUCUCCGGAGGCACCCACUGCCACCGCGAAGAGUUGGGCUCUGUCAGCCGCGGGUCUCUCGGGGGCGAGGGCGAGGUUCAGGCCUUUCAGGCCGCAGGAAGAGGAACGGAGCGAGUCCCCGCGCGCGGCGCGAUUCCCUGAGCUGUGGGACGUGCACCCAGGACUCGGCUCACACAUGC
- example_title: vault RNA 2-1
mask_index: 12
mask_index_1based: 13
masked_char: A
output:
- label: A
score: 0.282629
- label: '?'
score: 0.141381
- label: '*'
score: 0.11284
- label: G
score: 0.085753
- label: '|'
score: 0.084345
pipeline_tag: fill-mask
sequence_type: ncRNA
task: fill-mask
text: >-
CGGGUCGGAGUU<mask>GCUCAAGCGGUUACCUCCUCAUGCCGGACUUUCUAUCUGUCCAUCUCUGUGCUGGGGUUCGAGACCCGCGGGUGCUUACUGACCCUUUUAUGCAA
- example_title: brain cytoplasmic RNA 1
mask_index: 18
mask_index_1based: 19
masked_char: A
output:
- label: A
score: 0.585389
- label: '?'
score: 0.098956
- label: '*'
score: 0.085764
- label: '|'
score: 0.062997
- label: I
score: 0.060588
pipeline_tag: fill-mask
sequence_type: ncRNA
task: fill-mask
text: >-
GGCCGGGCGCGGUGGCUC<mask>CGCCUGUAAUCCCAGCUCUCAGGGAGGCUAAGAGGCGGGAGGAUAGCUUGAGCCCAGGAGUUCGAGACCUGCCUGGGCAAUAUAGCGAGACCCCGUUCUCCAGAAAAAGGAAAAAAAAAAACAAAAGACAAAAAAAAAAUAAGCGUAACUUCCCUCAAAGCAACAACCCCCCCCCCCCUUU
- example_title: HIV-1 TAR-WT
mask_index: 13
mask_index_1based: 14
masked_char: A
output:
- label: A
score: 0.216119
- label: '?'
score: 0.172431
- label: '*'
score: 0.133169
- label: '|'
score: 0.107029
- label: I
score: 0.10469
pipeline_tag: fill-mask
sequence_type: ncRNA
task: fill-mask
text: GGUCUCUCUGGUU<mask>GACCAGAUCUGAGCCUGGGAGCUCUCUGGCUAACUAGGGAACC
- example_title: prion protein (Kanno blood group)
mask_index: 21
mask_index_1based: 22
masked_char: A
output:
- label: '?'
score: 0.19308
- label: '*'
score: 0.152793
- label: '|'
score: 0.114088
- label: I
score: 0.113779
- label: C
score: 0.095156
pipeline_tag: fill-mask
sequence_type: mRNA
task: fill-mask
text: AUGGCGAACCUUGGCUGCUGG<mask>UGCUGGUUCUCUUUGUGGCCACAUGGAGUGACCUGGGCCUCUGC
- example_title: interleukin 10
mask_index: 11
mask_index_1based: 12
masked_char: A
output:
- label: '?'
score: 0.208715
- label: '*'
score: 0.166324
- label: '|'
score: 0.125006
- label: I
score: 0.123702
- label: .
score: 0.099905
pipeline_tag: fill-mask
sequence_type: mRNA
task: fill-mask
text: AUGCACAGCUC<mask>GCACUGCUCUGUUGCCUGGUCCUCCUGACUGGGGUGAGGGCC
- example_title: Zaire ebolavirus
mask_index: 11
mask_index_1based: 12
masked_char: A
output:
- label: '?'
score: 0.197839
- label: '*'
score: 0.153979
- label: '|'
score: 0.113605
- label: I
score: 0.111712
- label: U
score: 0.103835
pipeline_tag: fill-mask
sequence_type: mRNA
task: fill-mask
text: >-
AAUGUUCAAAC<mask>CUUUGUGAAGCUCUGUUAGCUGAUGGUCUUGCUAAAGCAUUUCCUAGCAAUAUGAUGGUAGUCACAGAGCGUGAGCAAAAAGAAAGCUUAUUGCAUCAAGCAUCAUGGCACCACACAAGUGAUGAUUUUGGUGAGCAUGCCACAGUUAGAGGGAGUAGCUUUGUAACUGAUUUAGAGAAAUACAAUCUUGCAUUUAGAUAUGAGUUUACAGCACCUUUUAUAGAAUAUUGUAACCGUUGCUAUGGUGUUAAGAAUGUUUUUAAUUGGAUGCAUUAUACAAUCCCACAGUGUUAU
- example_title: SARS coronavirus
mask_index: 14
mask_index_1based: 15
masked_char: A
output:
- label: U
score: 0.194165
- label: '?'
score: 0.164707
- label: '*'
score: 0.129908
- label: '|'
score: 0.09555
- label: I
score: 0.094492
pipeline_tag: fill-mask
sequence_type: mRNA
task: fill-mask
text: >-
AUGUUUAUUUUCUU<mask>UUAUUUCUUACUCUCACUAGUGGUAGUGACCUUGACCGGUGCACCACUUUUGAUGAUGUUCAAGCUCCUAAUUACACUCAACAUACUUCAUCUAUGAGGGGGGUUUACUAUCCUGAUGAAAUUUUUAGAUCAGACACUCUUUAUUUAACUCAGGAUUUAUUUCUUCCAUUUUAUUCUAAUGUUACAGGGUUUCAUACUAUUAAUCAUACGUUUGACAACCCUGUCAUACCUUUUAAGGAUGGUAUUUAUUUUGCUGCCACAGAGAAAUCAAAUGUUGUCCGUGGUUGGGUUUUUGGUUCUACCAUGAACAACAAGUCACAGUCGGUGAUUAUUAUUAACAAUUCUACUAAUGUUGUUAUACGAGCAUGUAACUUUGAAUUGUGUGACAACCCUUUCUUUGCUGUUUCUAAACCCAUGGGUACACAGACACAUACUAUGAUAUUCGAUAAUGCAUUUAAAUGCACUUUCGAGUACAUAUCU
- example_title: insulin
mask_index: 12
mask_index_1based: 13
masked_char: A
output:
- label: C
score: 0.261781
- label: '?'
score: 0.167051
- label: '*'
score: 0.133536
- label: I
score: 0.09981
- label: '|'
score: 0.099001
pipeline_tag: fill-mask
sequence_type: mRNA
task: fill-mask
text: >-
AUGGCCCUGUGG<mask>UGCGCCUCCUGCCCCUGCUGGCGCUGCUGGCCCUCUGGGGACCUGACCCAGCCGCAGCCUUUGUGAACCAACACCUGUGCGGCUCACACCUGGUGGAAGCUCUCUACCUAGUGUGCGGGGAACGAGGCUUCUUCUACACACCCAAGACCCGCCGGGAGGCAGAGGACCUGCAGGUGGGGCAGGUGGAGCUGGGCGGGGGCCCUGGUGCAGGCAGCCUGCAGCCCUUGGCCCUGGAGGGGUCCCUGCAGAAGCGUGGCAUUGUGGAACAAUGCUGUACCAGCAUCUGCUCCCUCUACCAGCUGGAGAACUACUGCAACUAG
- example_title: cyclin dependent kinase inhibitor 2A
mask_index: 18
mask_index_1based: 19
masked_char: A
output:
- label: '?'
score: 0.190741
- label: '*'
score: 0.150517
- label: '|'
score: 0.113423
- label: I
score: 0.111556
- label: G
score: 0.095289
pipeline_tag: fill-mask
sequence_type: mRNA
task: fill-mask
text: >-
AUGGAGCCGGCGGCGGGG<mask>GCAGCAUGGAGCCUUCGGCUGACUGGCUGGCCACGGCCGCGGCCCGGGGUCGGGUAGAGGAGGUGCGGGCGCUGCUGGAGGCGGGGGCGCUGCCCAACGCACCGAAUAGUUACGGUCGGAGGCCGAUCCAGGUCAUGAUGAUGGGCAGCGCCCGAGUGGCGGAGCUGCUGCUGCUCCACGGCGCGGAGCCCAACUGCGCCGACCCCGCCACUCUCACCCGACCCGUGCACGACGCUGCCCGGGAGGGCUUCCUGGACACGCUGGUGGUGCUGCACCGGGCCGGGGCGCGGCUGGACGUGCGCGAUGCCUGGGGCCGUCUGCCCGUGGACCUGGCUGAGGAGCUGGGCCAUCGCGAUGUCGCACGGUACCUGCGCGCGGCUGCGGGGGGCACCAGAGGCAGUAACCAUGCCCGCAUAGAUGCCGCGGAAGGUCCCUCAGACAUCCCCGAUUGA
- example_title: human papillomavirus type 16 E6
mask_index: 10
mask_index_1based: 11
masked_char: A
output:
- label: '?'
score: 0.169333
- label: '*'
score: 0.133592
- label: A
score: 0.11662
- label: G
score: 0.109444
- label: '|'
score: 0.10071
pipeline_tag: fill-mask
sequence_type: mRNA
task: fill-mask
text: >-
AUGCACCAAA<mask>GAGAACUGCAAUGUUUCAGGACCCACAGGAGCGACCCAGAAAGUUACCACAGUUAUGCACAGAGCUGCAAACAACUAUACAUGAUAUAAUAUUAGAAUGUGUGUACUGCAAGCAACAGUUACUGCGACGUGAGGUAUAUGACUUUGCUUUUCGGGAUUUAUGCAUAGUAUAUAGAGAUGGGAAUCCAUAUGCUGUAUGUGAUAAAUGUUUAAAGUUUUAUUCUAAAAUUAGUGAGUAUAGACAUUAUUGUUAUAGUUUGUAUGGAACAACAUUAGAACAGCAAUACAACAAACCGUUGUGUGAUUUGUUAAUUAGGUGUAUUAACUGUCAAAAGCCACUGUGUCCUGAAGAAAAGCAAAGACAUCUGGACAAAAAGCAAAGAUUCCAUAAUAUAAGGGGUCGGUGGACCGGUCGAUGUAUGUCUUGUUGCAGAUCAUCAAGAACACGUAGAGAAACCCAGCUGUAA
- example_title: NRAS proto-oncogene
mask_index: 36
mask_index_1based: 37
masked_char: A
output:
- label: '?'
score: 0.20744
- label: '*'
score: 0.163305
- label: '|'
score: 0.122595
- label: I
score: 0.121137
- label: C
score: 0.108824
pipeline_tag: fill-mask
sequence_type: 5' UTR
task: fill-mask
text: >-
GGGGCCGGAAGUGCCGCUCCUUGGUGGGGGCUGUUC<mask>UGGCGGUUCCGGGGUCUCCAACAUUUUUCCCGGCUGUGGUCCUAAAUCUGUCCAAAGCAGAGGCAGUGGAGCUUGAGGUUCUUGCUGGUGUGAA
- example_title: amyloid beta precursor protein
mask_index: 15
mask_index_1based: 16
masked_char: A
output:
- label: '?'
score: 0.224441
- label: '*'
score: 0.176997
- label: '|'
score: 0.133761
- label: I
score: 0.131265
- label: .
score: 0.105797
pipeline_tag: fill-mask
sequence_type: 5' UTR
task: fill-mask
text: >-
GUCAGUUUCCUCGGC<mask>GCGGUAGGCGAGAGCACGCGGAGGAGCGUGCGCGGGGGCCCCGGGAGACGGCGGCGGUGGCGGCGCGGGCAGAGCAAGGACGCGGCGGAUCCCACUCGCACAGCAGCGCACUCGGUGCCCCGCGCAGGGUCGCG
- example_title: RUNX family transcription factor 1
mask_index: 15
mask_index_1based: 16
masked_char: A
output:
- label: '?'
score: 0.206538
- label: '*'
score: 0.160702
- label: '|'
score: 0.119129
- label: I
score: 0.118095
- label: .
score: 0.094544
pipeline_tag: fill-mask
sequence_type: 5' UTR
task: fill-mask
text: >-
ACUUCUUUGGGCCUC<mask>UAAACAACCACAGAACCACAAGUUGGGUAGCCUGGCAGUGUCAGAAGUCUGAACCCAGCAUAGUGGUCAGCAGGCAGGACGAAUCACACUGAAUGCAAACCACAGGGUUUCGCAGCGUGGUAAAAGAAAUCAUUGAGUCCCCCGCCUUCAGAAGAGGGUGCAUUUUCAGGAGGAAGCG
- example_title: fragile X messenger ribonucleoprotein 1
mask_index: 15
mask_index_1based: 16
masked_char: A
output:
- label: '?'
score: 0.195416
- label: '*'
score: 0.154775
- label: I
score: 0.11592
- label: '|'
score: 0.115607
- label: G
score: 0.105457
pipeline_tag: fill-mask
sequence_type: 5' UTR
task: fill-mask
text: >-
CUCAGUCAGGCGCUC<mask>GCUCCGUUUCGGUUUCACUUCCGGUGGAGGGCCGCCUCUGAGCGGGCGGCGGGCCGACGGCGAGCGCGGGCGGCGGCGGUGACGGAGGCGCCGCUGCCAGGGGGCGUGCGGCAGCGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGAGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCUGGGCCUCGAGCGCCCGCAGCCCACCUCUCGGGGGCGGGCUCCCGGCGCUAGCAGGGCUGAAGAGAAG
- example_title: MYC proto-oncogene
mask_index: 10
mask_index_1based: 11
masked_char: A
output:
- label: '?'
score: 0.202977
- label: '*'
score: 0.16026
- label: '|'
score: 0.119362
- label: I
score: 0.117585
- label: .
score: 0.094434
pipeline_tag: fill-mask
sequence_type: 5' UTR
task: fill-mask
text: >-
AACUCGCUGU<mask>GUAAUUCCAGCGAGAGGCAGAGGGAGCGAGCGGGCGGCCGGCUAGGGUGGAAGAGCCGGGCGAGCAGAGCUGCGCUGCGGGCGUCCUGGGAAGGGAGAUCCGGAGCGAAUAGGGGGCUUCGCCUCUGGCCCAGCCCUCCCGCUGAUCCCCCAGCCAGCGGUCCGCAACCCUUGCCGCAUCCACGAAACUUUGCCCAUAGCAGCGGGCGGGCACUUUGCACUGGAACUUACAACACCCGAGCAAGGACGCGACUCUCCCGACGCGGGGAGGCUAUUCUGCCCAUUUGGGGACACUUCCCCGCCGCUGCCAGGACCCGCUUCUCUGAAAGGCUCUCCUUGCAGCUGCUUAGACG
- example_title: activating transcription factor 4
mask_index: 20
mask_index_1based: 21
masked_char: A
output:
- label: '?'
score: 0.238453
- label: '*'
score: 0.189471
- label: '|'
score: 0.141617
- label: I
score: 0.139835
- label: .
score: 0.112254
pipeline_tag: fill-mask
sequence_type: 5' UTR
task: fill-mask
text: >-
CAUUUCUACUUUGCCCGCCC<mask>CAGAUGUAGUUUUCUCUGCGCGUGUGCGUUUUCCCUCCUCCCCGCCCUCAGGGUCCACGGCCACCAUGGCGUAUUAGGGGCAGCAGUGCCUGCGGCAGCAUUGGCCUUUGCAGCGGCGGCAGCAGCACCAGGCUCUGCAGCGGCAACCCCCAGCGGCUUAAGCCAUGGCGCUUCUCACGGCAUUCAGCAGCAGCGUUGCUGUAACCGACAAAGACACCUUCGAAUUAAGCACAUUCCUCGAUUCCAGCAAAGCACCGCAAC
- example_title: Human GPI protein p137
mask_index: 11
mask_index_1based: 12
masked_char: A
output:
- label: '?'
score: 0.203726
- label: '*'
score: 0.160149
- label: '|'
score: 0.119915
- label: I
score: 0.118089
- label: A
score: 0.100465
pipeline_tag: fill-mask
sequence_type: 3' UTR
task: fill-mask
text: >-
UUUUUAAAAGG<mask>AAAGAUACCAAAUGCCUGCUGCUACCACCCUUUUCAAUUGCUAUGUUUUGAAAGGCACCAGUAUGUGUUUUAGAUUGAUUUAAAUGUUUCAUUUAAAUCACGGACAGUAGUUUCAGUUCUGAUGGUAUAAGCAAAACAAAUAAAACGUUUAUAAAAGUUGUAUCUUGAAACACUGGUGUUCAACAGCUAGCAGCUUAUGUGAUUCACCCCAUGCCACGUUAGUGUCACAAAUUUUAUGGUUUAUCUCCAGCAACAUUUCUCUAGUACUUGCACUUAUUAUCUGAAUUC
- example_title: nucleophosmin 1
mask_index: 11
mask_index_1based: 12
masked_char: A
output:
- label: '?'
score: 0.175375
- label: '*'
score: 0.136246
- label: U
score: 0.107147
- label: '|'
score: 0.102165
- label: I
score: 0.100268
pipeline_tag: fill-mask
sequence_type: 3' UTR
task: fill-mask
text: >-
GAAAAUAGUUU<mask>AACAAUUUGUUAAAAAAUUUUCCGUCUUAUUUCAUUUCUGUAACAGUUGAUAUCUGGCUGUCCUUUUUAUAAUGCAGAGUGAGAACUUUCCCUACCGUGUUUGAUAAAUGUUGUCCAGGUUCUAUUGCCAAGAAUGUGUUGUCCAAAAUGCCUGUUUAGUUUUUAAAGAUGGAACUCCACCCUUUGCUUGGUUUUAAGUAUGUAUGGAAUGUUAUGAUAGGACAUAGUAGUAGCGGUGGUCAGACAUGGAAAUGGUGGGGAGACAAAAAUAUACAUGUGAAAUAAAACUCAGUAUUUUAAUAAAGUAGCACGGUUUCUAUUGA
- example_title: superoxide dismutase 1
mask_index: 12
mask_index_1based: 13
masked_char: A
output:
- label: '?'
score: 0.180425
- label: '*'
score: 0.142506
- label: '|'
score: 0.105775
- label: I
score: 0.105049
- label: C
score: 0.101959
pipeline_tag: fill-mask
sequence_type: 3' UTR
task: fill-mask
text: >-
ACAUUCCCUUGG<mask>UGUAGUCUGAGGCCCCUUAACUCAUCUGUUAUCCUGCUAGCUGUAGAAAUGUAUCCUGAUAAACAUUAAACACUGUAAUCUUAAAAGUGUAAUUGUGUGACUUUUUCAGAGUUGCUUUAAAGUACCUGUAGUGAGAAACUGAUUUAUGAUCACUUGGAAGAUUUGUAUAGUUUUAUAAAACUCAGUUAAAAUGUCUGUUUCAAUGACCUGUAUUUUGCCAGACUUAAAUCACAGAUGGGUAUUAAACUUGUCAGAAUUUCUUUGUCAUUCAAGCCUGUGAAUAAAAACCCUGUAUGGCACUUAUUAUGAGGCUAUUAAAAGAAUCCAAAUUCAAACUAAA
- example_title: hemoglobin subunit alpha 2
mask_index: 13
mask_index_1based: 14
masked_char: A
output:
- label: '?'
score: 0.174786
- label: G
score: 0.166439
- label: '*'
score: 0.139318
- label: '|'
score: 0.103622
- label: I
score: 0.101995
pipeline_tag: fill-mask
sequence_type: 3' UTR
task: fill-mask
text: >-
CUGGAGCCUCGGU<mask>GCCGUUCCUCCUGCCCGCUGGGCCUCCCAACGGGCCCUCCUCCCCUCCUUGCACCGGCCCUUCCUGGUCUUUGAAUAAAGUCUGAGUGGGCAGCA
- example_title: BRAF proto-oncogene
mask_index: 12
mask_index_1based: 13
masked_char: A
output:
- label: '?'
score: 0.182087
- label: '*'
score: 0.148305
- label: A
score: 0.147731
- label: '|'
score: 0.110948
- label: I
score: 0.106279
pipeline_tag: fill-mask
sequence_type: 3' UTR
task: fill-mask
text: >-
AACAAAUGAGUG<mask>GAGAGUUCAGGAGAGUAGCAACAAAAGGAAAAUAAAUGAACAUAUGUUUGCUUAUAUGUUAAAUUGAAUAAAAUACUCUCUUUUUUUUUAAGGUGAACCAAAGAACACUUGUGUGGUUAAAGACUAGAUAUAAUUUUUCCCCAAACUAAAAUUUAUACUUAACAUUGGAUUUUUAACAUCCAAGGGUUAAAAUACAUAGACAUUGCUAAAAAUUGGCAGAGCCUCUUCUAGAGGCUUUACUUUCUGUUCCGGGUUUGUAUCAUUCACUUGGUUAUUUUAAGUAGUAAACUUCAGUUUCUCAUGCAACUUUUGUUGCCAGCUAUCACAUGUCCACUAGGGACUCCAGAAGAAGACCCUACCUAUGCCUGUGUUUGCAGGUGAGAAGUUGGCAGUCGGUUAGCCUGGG
- example_title: H3 clustered histone 1
mask_index: 17
mask_index_1based: 18
masked_char: A
output:
- label: '?'
score: 0.199656
- label: '*'
score: 0.155685
- label: '|'
score: 0.118332
- label: I
score: 0.116603
- label: .
score: 0.092642
pipeline_tag: fill-mask
sequence_type: 3' UTR
task: fill-mask
text: UUACUGUGGUCUCUCUG<mask>CGGUCCAAGCAAAGGCUCUUUUCAGAGCCACCACCUUUUC
RNA-FM
Pre-trained model on non-coding RNA (ncRNA) using a masked language modeling (MLM) objective.
Disclaimer
This is an UNOFFICIAL implementation of the Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions by Jiayang Chen, Zhihang Hu, Siqi Sun, et al.
The OFFICIAL repository of RNA-FM is at ml4bio/RNA-FM.
The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation.
The team releasing RNA-FM did not write this model card for this model so this model card has been written by the MultiMolecule team.
Model Details
RNA-FM is a bert-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 section for more information on the training process.
Variants
- multimolecule/rnafm: The RNA-FM model pre-trained on non-coding RNA sequences.
- multimolecule/mrnafm: The RNA-FM model pre-trained on messenger RNA sequences.
Model Specification
| Variants | Num Layers | Hidden Size | Num Heads | Intermediate Size | Num Parameters (M) | FLOPs (G) | MACs (G) | Max Num Tokens |
|---|---|---|---|---|---|---|---|---|
| mRNA-FM | 12 | 1280 | 20 | 5120 | 239.26 | 258.08 | 128.85 | 1024 |
| RNA-FM | 640 | 99.52 | 109.02 | 54.36 |
Links
- Code: multimolecule.rnafm
- Data: multimolecule/rnacentral
- Paper: Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions
- Developed by: Jiayang Chen, Zhihang Hu, Siqi Sun, Qingxiong Tan, Yixuan Wang, Qinze Yu, Licheng Zong, Liang Hong, Jin Xiao, Tao Shen, Irwin King, Yu Li
- Model type: BERT - ESM
- Original Repository: ml4bio/RNA-FM
Usage
The model file depends on the multimolecule library. You can install it using pip:
pip install multimolecule
Direct Use
Masked Language Modeling
You can use this model directly with a pipeline for masked language modeling:
import multimolecule # you must import multimolecule to register models
from transformers import pipeline
predictor = pipeline("fill-mask", model="multimolecule/rnafm")
output = predictor("gguc<mask>cucugguuagaccagaucugagccu")
Downstream Use
Extract Features
Here is how to use this model to get the features of a given sequence in PyTorch:
from multimolecule import RnaTokenizer, RnaFmModel
tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnafm")
model = RnaFmModel.from_pretrained("multimolecule/rnafm")
text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
output = model(**input)
Sequence Classification / Regression
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:
import torch
from multimolecule import RnaTokenizer, RnaFmForSequencePrediction
tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnafm")
model = RnaFmForSequencePrediction.from_pretrained("multimolecule/rnafm")
text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
label = torch.tensor([1])
output = model(**input, labels=label)
Token Classification / Regression
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:
import torch
from multimolecule import RnaTokenizer, RnaFmForTokenPrediction
tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnafm")
model = RnaFmForTokenPrediction.from_pretrained("multimolecule/rnafm")
text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
label = torch.randint(2, (len(text), ))
output = model(**input, labels=label)
Contact Classification / Regression
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:
import torch
from multimolecule import RnaTokenizer, RnaFmForContactPrediction
tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnafm")
model = RnaFmForContactPrediction.from_pretrained("multimolecule/rnafm")
text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
label = torch.randint(2, (len(text), len(text)))
output = model(**input, labels=label)
Training Details
RNA-FM used Masked Language Modeling (MLM) as the pre-training objective: 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.
Training Data
The RNA-FM model was pre-trained on 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 representing a broad range of organisms and RNA types.
RNA-FM applied CD-HIT (CD-HIT-EST) with a cut-off at 100% sequence identity to remove redundancy from the RNAcentral. The final dataset contains 23.7 million non-redundant RNA sequences.
RNA-FM 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
RNA-FM used masked language modeling (MLM) as the pre-training objective. The masking procedure is similar to the one used in BERT:
- Mask rate: 15%
- Replacement:
<mask>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 8 NVIDIA A100 GPUs with 80GiB memories.
- Learning rate: 1e-4
- Learning rate scheduler: Inverse square root
- Learning rate warm-up: 10,000 steps
- Weight decay: 0.01
Citation
@article{chen2022interpretable,
title={Interpretable rna foundation model from unannotated data for highly accurate rna structure and function predictions},
author={Chen, Jiayang and Hu, Zhihang and Sun, Siqi and Tan, Qingxiong and Wang, Yixuan and Yu, Qinze and Zong, Licheng and Hong, Liang and Xiao, Jin and King, Irwin and others},
journal={arXiv preprint arXiv:2204.00300},
year={2022}
}
The artifacts distributed in this repository are part of the MultiMolecule project. If MultiMolecule supports your research, please cite the MultiMolecule project as follows:
@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 for any questions or comments on the model card.
Please contact the authors of the RNA-FM paper for questions or comments on the paper/model.
License
This model implementation is licensed under the GNU Affero General Public License.
For additional terms and clarifications, please refer to our License FAQ.
SPDX-License-Identifier: AGPL-3.0-or-later