---
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
1. 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.
2. 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.
3. 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.
4. 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.