File size: 28,023 Bytes

8e3f4e5

---
datasets:
- multimolecule/ucsc-genome-browser
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: U
    score: 0.061495
  - label: W
    score: 0.056873
  - label: A
    score: 0.052597
  - label: D
    score: 0.05131
  - label: K
    score: 0.050678
  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: U
    score: 0.05288
  - label: K
    score: 0.051471
  - label: W
    score: 0.05044
  - label: D
    score: 0.050326
  - label: G
    score: 0.0501
  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: U
    score: 0.066864
  - label: K
    score: 0.060454
  - label: G
    score: 0.054659
  - label: D
    score: 0.052998
  - label: W
    score: 0.052187
  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: C
    score: 0.067699
  - label: S
    score: 0.066549
  - label: G
    score: 0.065419
  - label: V
    score: 0.055928
  - label: M
    score: 0.051713
  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: C
    score: 0.056555
  - label: M
    score: 0.05517
  - label: S
    score: 0.054635
  - label: V
    score: 0.054361
  - label: A
    score: 0.053818
  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: C
    score: 0.062204
  - label: Y
    score: 0.058459
  - label: M
    score: 0.058118
  - label: H
    score: 0.057039
  - label: U
    score: 0.05494
  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: U
    score: 0.065605
  - label: K
    score: 0.06546
  - label: G
    score: 0.065316
  - label: B
    score: 0.056461
  - label: Y
    score: 0.052495
  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: 0.474908
  - label: R
    score: 0.088644
  - label: M
    score: 0.054261
  - label: W
    score: 0.044605
  - label: I
    score: 0.036764
  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: G
    score: 0.092236
  - label: R
    score: 0.091493
  - label: A
    score: 0.090755
  - label: D
    score: 0.075213
  - label: K
    score: 0.06847
  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: C
    score: 0.10045
  - label: S
    score: 0.082719
  - label: G
    score: 0.068118
  - label: V
    score: 0.060734
  - label: M
    score: 0.057348
  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: A
    score: 0.118906
  - label: W
    score: 0.115972
  - label: U
    score: 0.11311
  - label: H
    score: 0.05462
  - label: D
    score: 0.05389
  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: U
    score: 0.089669
  - label: W
    score: 0.087718
  - label: A
    score: 0.085809
  - label: H
    score: 0.066491
  - label: Y
    score: 0.058529
  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: 0.11542
  - label: W
    score: 0.065628
  - label: Y
    score: 0.063558
  - label: K
    score: 0.056925
  - label: H
    score: 0.05322
  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: 0.244674
  - label: S
    score: 0.105466
  - label: M
    score: 0.062689
  - label: V
    score: 0.056321
  - label: Y
    score: 0.05153
  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: G
    score: 0.093041
  - label: S
    score: 0.080106
  - label: C
    score: 0.068969
  - label: V
    score: 0.065866
  - label: R
    score: 0.064367
  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: A
    score: 0.077326
  - label: W
    score: 0.069774
  - label: R
    score: 0.066748
  - label: D
    score: 0.06546
  - label: U
    score: 0.062959
  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: C
    score: 0.125206
  - label: Y
    score: 0.082017
  - label: S
    score: 0.054425
  - label: B
    score: 0.054191
  - label: U
    score: 0.053726
  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: G
    score: 0.061595
  - label: S
    score: 0.060502
  - label: C
    score: 0.059429
  - label: V
    score: 0.052651
  - label: I
    score: 0.052487
  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: A
    score: 0.076755
  - label: W
    score: 0.071536
  - label: U
    score: 0.066671
  - label: H
    score: 0.062477
  - label: M
    score: 0.06048
  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: G
    score: 0.074085
  - label: S
    score: 0.06125
  - label: K
    score: 0.053493
  - label: B
    score: 0.052524
  - label: R
    score: 0.052083
  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: U
    score: 0.075318
  - label: K
    score: 0.071299
  - label: G
    score: 0.067494
  - label: B
    score: 0.057229
  - label: Y
    score: 0.052697
  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: C
    score: 0.071015
  - label: S
    score: 0.057857
  - label: Y
    score: 0.057416
  - label: B
    score: 0.053762
  - label: I
    score: 0.051768
  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: A
    score: 0.096845
  - label: R
    score: 0.065494
  - label: M
    score: 0.058856
  - label: W
    score: 0.054076
  - label: V
    score: 0.053535
  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: U
    score: 0.075736
  - label: W
    score: 0.065745
  - label: A
    score: 0.057072
  - label: D
    score: 0.05355
  - label: K
    score: 0.051872
  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: C
    score: 0.060693
  - label: Y
    score: 0.058021
  - label: U
    score: 0.055467
  - label: H
    score: 0.054192
  - label: M
    score: 0.053565
  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: G
    score: 0.172817
  - label: S
    score: 0.074829
  - label: K
    score: 0.072905
  - label: B
    score: 0.055636
  - label: R
    score: 0.05549
  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: A
    score: 0.118074
  - label: R
    score: 0.085726
  - label: W
    score: 0.085458
  - label: D
    score: 0.076888
  - label: G
    score: 0.06224
  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: C
    score: 0.051725
  - label: Y
    score: 0.050437
  - label: I
    score: 0.04982
  - label: S
    score: 0.049815
  - label: B
    score: 0.049603
  pipeline_tag: fill-mask
  sequence_type: 3' UTR
  task: fill-mask
  text: UUACUGUGGUCUCUCUG<mask>CGGUCCAAGCAAAGGCUCUUUUCAGAGCCACCACCUUUUC
---

# SpliceBERT

Pre-trained model on messenger RNA precursor (pre-mRNA) using a masked language modeling (MLM) objective.

## Disclaimer

This is an UNOFFICIAL implementation of the [Self-supervised learning on millions of pre-mRNA sequences improves sequence-based RNA splicing prediction](https://doi.org/10.1101/2023.01.31.526427) by Ken Chen, et al.

The OFFICIAL repository of SpliceBERT is at [chenkenbio/SpliceBERT](https://github.com/chenkenbio/SpliceBERT).

> [!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 SpliceBERT did not write this model card for this model so this model card has been written by the MultiMolecule team.**

## Model Details

SpliceBERT is a [bert](https://huggingface.co/google-bert/bert-base-uncased)-style model pre-trained on a large corpus of messenger RNA precursor 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.

### Variants

- **[multimolecule/splicebert](https://huggingface.co/multimolecule/splicebert)**: The SpliceBERT model.
- **[multimolecule/splicebert.510](https://huggingface.co/multimolecule/splicebert.510)**: The intermediate SpliceBERT model.
- **[multimolecule/splicebert-human.510](https://huggingface.co/multimolecule/splicebert-human.510)**: The intermediate SpliceBERT model pre-trained on human data only.

### Model Specification

<table>
<thead>
  <tr>
    <th>Variants</th>
    <th>Num Layers</th>
    <th>Hidden Size</th>
    <th>Num Heads</th>
    <th>Intermediate Size</th>
    <th>Num Parameters (M)</th>
    <th>FLOPs (G)</th>
    <th>MACs (G)</th>
    <th>Max Num Tokens</th>
  </tr>
</thead>
<tbody>
  <tr>
    <td><b>splicebert</b></td>
    <td rowspan="3">6</td>
    <td rowspan="3">512</td>
    <td rowspan="3">16</td>
    <td rowspan="3">2048</td>
    <td>19.72</td>
    <td>22.66</td>
    <td>11.27</td>
    <td>1024</td>
  </tr>
  <tr>
    <td>splicebert.510</td>
    <td rowspan="2">19.45</td>
    <td rowspan="2">22.56</td>
    <td rowspan="2">11.22</td>
    <td rowspan="2">510</td>
  </tr>
  <tr>
    <td>splicebert-human.510</td>
  </tr>
</tbody>
</table>

### Links

- **Code**: [multimolecule.splicebert](https://github.com/DLS5-Omics/multimolecule/tree/master/multimolecule/models/splicebert)
- **Data**: [UCSC Genome Browser](https://genome.ucsc.edu)
- **Paper**: [Self-supervised learning on millions of pre-mRNA sequences improves sequence-based RNA splicing prediction](https://doi.org/10.1101/2023.01.31.526427)
- **Developed by**: Ken Chen, Yue Zhou, Maolin Ding, Yu Wang, Zhixiang Ren, Yuedong Yang
- **Model type**: [BERT](https://huggingface.co/google-bert/bert-base-uncased)
- **Original Repository**: [chenkenbio/SpliceBERT](https://github.com/chenkenbio/SpliceBERT)

## 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/splicebert")
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:

```python
from multimolecule import RnaTokenizer, SpliceBertModel


tokenizer = RnaTokenizer.from_pretrained("multimolecule/splicebert")
model = SpliceBertModel.from_pretrained("multimolecule/splicebert")

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, SpliceBertForSequencePrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/splicebert")
model = SpliceBertForSequencePrediction.from_pretrained("multimolecule/splicebert")

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, SpliceBertForTokenPrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/splicebert")
model = SpliceBertForTokenPrediction.from_pretrained("multimolecule/splicebert")

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, SpliceBertForContactPrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/splicebert")
model = SpliceBertForContactPrediction.from_pretrained("multimolecule/splicebert")

text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
label = torch.randint(2, (len(text), len(text)))

output = model(**input, labels=label)
```

## Training Details

SpliceBERT 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 SpliceBERT model was pre-trained on messenger RNA precursor sequences from [UCSC Genome Browser](https://genome.ucsc.edu).
UCSC Genome Browser provides visualization, analysis, and download of comprehensive vertebrate genome data with aligned annotation tracks (known genes, predicted genes, ESTs, mRNAs, CpG islands, etc.).

SpliceBERT collected reference genomes and gene annotations from the UCSC Genome Browser for 72 vertebrate species. It applied [bedtools getfasta](https://bedtools.readthedocs.io/en/latest/content/tools/getfasta.html) to extract pre-mRNA sequences from the reference genomes based on the gene annotations. The pre-mRNA sequences are then used to pre-train SpliceBERT. The pre-training data contains 2 million pre-mRNA sequences with a total length of 65 billion nucleotides.

Note [`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

SpliceBERT 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 V100 GPUs.

- Optimizer: AdamW
- Learning rate: 1e-4
- Learning rate scheduler: ReduceLROnPlateau(patience=3)

SpliceBERT trained model in a two-stage training process:

1. Pre-train with sequences of a fixed length of 510 nucleotides.
2. Pre-train with sequences of a variable length between 64 and 1024 nucleotides.

The intermediate model after the first stage is available as `multimolecule/splicebert.510`.

SpliceBERT also pre-trained a model on human data only to validate the contribution of multi-species pre-training. The intermediate model after the first stage is available as `multimolecule/splicebert-human.510`.

## Citation

```bibtex
@article {chen2023self,
	author = {Chen, Ken and Zhou, Yue and Ding, Maolin and Wang, Yu and Ren, Zhixiang and Yang, Yuedong},
	title = {Self-supervised learning on millions of pre-mRNA sequences improves sequence-based RNA splicing prediction},
	elocation-id = {2023.01.31.526427},
	year = {2023},
	doi = {10.1101/2023.01.31.526427},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {RNA splicing is an important post-transcriptional process of gene expression in eukaryotic cells. Predicting RNA splicing from primary sequences can facilitate the interpretation of genomic variants. In this study, we developed a novel self-supervised pre-trained language model, SpliceBERT, to improve sequence-based RNA splicing prediction. Pre-training on pre-mRNA sequences from vertebrates enables SpliceBERT to capture evolutionary conservation information and characterize the unique property of splice sites. SpliceBERT also improves zero-shot prediction of variant effects on splicing by considering sequence context information, and achieves superior performance for predicting branchpoint in the human genome and splice sites across species. Our study highlighted the importance of pre-training genomic language models on a diverse range of species and suggested that pre-trained language models were promising for deciphering the sequence logic of RNA splicing.Competing Interest StatementThe authors have declared no competing interest.},
	URL = {https://www.biorxiv.org/content/early/2023/05/09/2023.01.31.526427},
	eprint = {https://www.biorxiv.org/content/early/2023/05/09/2023.01.31.526427.full.pdf},
	journal = {bioRxiv}
}
```

> [!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 [SpliceBERT paper](https://doi.org/10.1101/2023.01.31.526427) 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
```