metadata

configs:
  - config_name: expression_HEK
    data_files:
      - split: train
        path: expression_level/HEK_10fold_cv_split.tsv
  - config_name: expression_Muscle
    data_files:
      - split: train
        path: expression_level/Muscle_10fold_cv_split.tsv
  - config_name: expression_pc3
    data_files:
      - split: train
        path: expression_level/pc3_10fold_cv_split.tsv
  - config_name: translation_efficiency_HEK
    data_files:
      - split: train
        path: translation_efficiency/HEK_10fold_cv_split.tsv
  - config_name: translation_efficiency_Muscle
    data_files:
      - split: train
        path: translation_efficiency/Muscle_10fold_cv_split.tsv
  - config_name: translation_efficiency_pc3
    data_files:
      - split: train
        path: translation_efficiency/pc3_10fold_cv_split.tsv
  - config_name: modification_site
    data_files:
      - split: train
        path: modification_site_prediction/train.tsv
      - split: validation
        path: modification_site_prediction/valid.tsv
      - split: test
        path: modification_site_prediction/test.tsv
  - config_name: ncrna_family_bnoise0
    data_files:
      - split: train
        path: ncrna_family_classification/bnoise0/train.tsv
      - split: validation
        path: ncrna_family_classification/bnoise0/valid.tsv
      - split: test
        path: ncrna_family_classification/bnoise0/test.tsv
  - config_name: ncrna_family_bnoise200
    data_files:
      - split: train
        path: ncrna_family_classification/bnoise200/train.tsv
      - split: validation
        path: ncrna_family_classification/bnoise200/valid.tsv
      - split: test
        path: ncrna_family_classification/bnoise200/test.tsv
  - config_name: protein_abundance_athaliana
    data_files:
      - split: train
        path: protein_abundance/athaliana_5fold_cv_split.tsv
  - config_name: protein_abundance_dmelanogaster
    data_files:
      - split: train
        path: protein_abundance/dmelanogaster_5fold_cv_split.tsv
  - config_name: protein_abundance_ecoli
    data_files:
      - split: train
        path: protein_abundance/ecoli_5fold_cv_split.tsv
  - config_name: protein_abundance_hsapiens
    data_files:
      - split: train
        path: protein_abundance/hsapiens_5fold_cv_split.tsv
  - config_name: protein_abundance_scerevisiae
    data_files:
      - split: train
        path: protein_abundance/scerevisiae_5fold_cv_split.tsv
  - config_name: splice_site_acceptor
    data_files:
      - split: train
        path: splice_site_prediction/acceptor/train.tsv
      - split: validation
        path: splice_site_prediction/acceptor/valid.tsv
      - split: test_danio
        path: splice_site_prediction/acceptor/test_Danio.tsv
      - split: test_fly
        path: splice_site_prediction/acceptor/test_Fly.tsv
      - split: test_thaliana
        path: splice_site_prediction/acceptor/test_Thaliana.tsv
      - split: test_worm
        path: splice_site_prediction/acceptor/test_Worm.tsv
  - config_name: splice_site_donor
    data_files:
      - split: train
        path: splice_site_prediction/donor/train.tsv
      - split: validation
        path: splice_site_prediction/donor/valid.tsv
      - split: test_danio
        path: splice_site_prediction/donor/test_Danio.tsv
      - split: test_fly
        path: splice_site_prediction/donor/test_Fly.tsv
      - split: test_thaliana
        path: splice_site_prediction/donor/test_Thaliana.tsv
      - split: test_worm
        path: splice_site_prediction/donor/test_Worm.tsv
  - config_name: transcript_abundance_athaliana
    data_files:
      - split: train
        path: transcript_abundance/athaliana_5fold_cv_split.tsv
  - config_name: transcript_abundance_dmelanogaster
    data_files:
      - split: train
        path: transcript_abundance/dmelanogaster_5fold_cv_split.tsv
  - config_name: transcript_abundance_ecoli
    data_files:
      - split: train
        path: transcript_abundance/ecoli_5fold_cv_split.tsv
  - config_name: transcript_abundance_hsapiens
    data_files:
      - split: train
        path: transcript_abundance/hsapiens_5fold_cv_split.tsv
  - config_name: transcript_abundance_hvolcanii
    data_files:
      - split: train
        path: transcript_abundance/hvolcanii_5fold_cv_split.tsv
  - config_name: transcript_abundance_ppastoris
    data_files:
      - split: train
        path: transcript_abundance/ppastoris_5fold_cv_split.tsv
  - config_name: transcript_abundance_scerevisiae
    data_files:
      - split: train
        path: transcript_abundance/scerevisiae_5fold_cv_split.tsv
  - config_name: mean_ribosome_load
    data_files:
      - split: train
        path: mean_ribosome_load/train.tsv
      - split: validation
        path: mean_ribosome_load/validation_random7600.tsv
      - split: test
        path: mean_ribosome_load/test_human7600.tsv

AIDO.RNA Benchmark Datasets

mRNA related tasks

Translation efficiency prediction from Chu et al.(2024) [1]
- 3 cell lines: Muscle, pc3, HEK
- input sequence: 5'UTR
- 10-fold cross-validation split
mRNA expression level prediction from Chu et al.(2024) [1]
- 3 cell lines: Muscle, pc3, HEK
- input sequence: 5'UTR
- 10-fold cross-validation split
Mean ribosome load prediction from Sample et al. (2019) [2]
- input sequence: 5'UTR
- ouput: mean ribosome load
- the original data source: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE114002
- Similar to the previous studies [2, 4], we also split the data into the following three
  - train: total 76.3k samples
  - val: total 7600 samples (also called as Random 7600 in [4])
  - test: total 7600 samples (also called as Human 7600 in [4])
Transcript abundance prediction from Outeiral and Deane (2024) [3]
- 7 organisms: A. thaliana, D. melanogaster, E.coli, H. sapiens, S. cerevisiae, H. volcanii, and P. pastoris
- input sequence: CDS
- 5-fold cross-validation split
Protein abundance prediction from Outeiral and Deane (2024) [3]
- 5 organisms: A. thaliana, D. melanogaster, E.coli, H. sapiens, and S. cerevisiae
- input sequence: CDS
- 5-fold cross-validation split
- Note: We have transformed the label to logarithm space using the following function: log(1+x).

RNA function prediction tasks

The datasets listed below are collected following the setting in Wang et al. (2023) [4].

Cross-species splice site prediction
- 2 datasets: acceptor, donor
- 4 test species: zebrafish, fruit fly, worm, and plant
- input sequence: pre-mRNA fragment
ncRNA family classification
- 2 datasets: boundary noise 0, boundary noise 200
- input sequence: small noncoding RNA with different level of boundary noise
RNA modification site prediction
- 12 labels (modification sites): Am, Cm, Gm, Tm, m1A, m5C, m5U, m6A, m6Am, m7G, Φ, and I.

Reference

Yanyi Chu, Dan Yu, Yupeng Li, Kaixuan Huang, Yue Shen, Le Cong, Jason Zhang, and Mengdi Wang. A 5 utr language model for decoding untranslated regions of mrna and function predictions. Nature Machine Intelligence, pages 1–12, 2024.
Paul J Sample, Ban Wang, David W Reid, Vlad Presnyak, Iain J McFadyen, David R Morris, and Georg Seelig. Human 5 utr design and variant effect prediction from a massively parallel translation assay. Nature biotechnology, 37(7):803–809, 2019.
Carlos Outeiral and Charlotte M Deane. Codon language embeddings provide strong signals for use in protein engineering. Nature Machine Intelligence, 6(2):170–179, 2024.
Xi Wang, Ruichu Gu, Zhiyuan Chen, Yongge Li, Xiaohong Ji, Guolin Ke, and HanWen. Uni-rna: universal pre-trained models revolutionize rna research. bioRxiv, pages 2023–07, 2023.