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--- |
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configs: |
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- config_name: expression_HEK |
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data_files: |
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- split: train |
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path: expression_level/HEK_10fold_cv_split.tsv |
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- config_name: expression_Muscle |
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data_files: |
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- split: train |
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path: expression_level/Muscle_10fold_cv_split.tsv |
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- config_name: expression_pc3 |
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data_files: |
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- split: train |
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path: expression_level/pc3_10fold_cv_split.tsv |
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- config_name: translation_efficiency_HEK |
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data_files: |
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- split: train |
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path: translation_efficiency/HEK_10fold_cv_split.tsv |
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- config_name: translation_efficiency_Muscle |
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data_files: |
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- split: train |
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path: translation_efficiency/Muscle_10fold_cv_split.tsv |
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- config_name: translation_efficiency_pc3 |
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data_files: |
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- split: train |
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path: translation_efficiency/pc3_10fold_cv_split.tsv |
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- config_name: modification_site |
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data_files: |
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- split: train |
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path: modification_site_prediction/train.tsv |
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- split: validation |
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path: modification_site_prediction/valid.tsv |
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- split: test |
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path: modification_site_prediction/test.tsv |
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- config_name: ncrna_family_bnoise0 |
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data_files: |
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- split: train |
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path: ncrna_family_classification/bnoise0/train.tsv |
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- split: validation |
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path: ncrna_family_classification/bnoise0/valid.tsv |
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- split: test |
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path: ncrna_family_classification/bnoise0/test.tsv |
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- config_name: ncrna_family_bnoise200 |
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data_files: |
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- split: train |
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path: ncrna_family_classification/bnoise200/train.tsv |
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- split: validation |
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path: ncrna_family_classification/bnoise200/valid.tsv |
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- split: test |
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path: ncrna_family_classification/bnoise200/test.tsv |
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- config_name: protein_abundance_athaliana |
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data_files: |
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- split: train |
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path: protein_abundance/athaliana_5fold_cv_split.tsv |
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- config_name: protein_abundance_dmelanogaster |
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data_files: |
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- split: train |
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path: protein_abundance/dmelanogaster_5fold_cv_split.tsv |
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- config_name: protein_abundance_ecoli |
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data_files: |
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- split: train |
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path: protein_abundance/ecoli_5fold_cv_split.tsv |
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- config_name: protein_abundance_hsapiens |
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data_files: |
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- split: train |
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path: protein_abundance/hsapiens_5fold_cv_split.tsv |
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- config_name: protein_abundance_scerevisiae |
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data_files: |
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- split: train |
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path: protein_abundance/scerevisiae_5fold_cv_split.tsv |
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- config_name: splice_site_acceptor |
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data_files: |
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- split: train |
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path: splice_site_prediction/acceptor/train.tsv |
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- split: validation |
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path: splice_site_prediction/acceptor/valid.tsv |
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- split: test_danio |
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path: splice_site_prediction/acceptor/test_Danio.tsv |
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- split: test_fly |
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path: splice_site_prediction/acceptor/test_Fly.tsv |
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- split: test_thaliana |
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path: splice_site_prediction/acceptor/test_Thaliana.tsv |
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- split: test_worm |
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path: splice_site_prediction/acceptor/test_Worm.tsv |
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- config_name: splice_site_donor |
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data_files: |
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- split: train |
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path: splice_site_prediction/donor/train.tsv |
|
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- split: validation |
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path: splice_site_prediction/donor/valid.tsv |
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- split: test_danio |
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|
path: splice_site_prediction/donor/test_Danio.tsv |
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- split: test_fly |
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|
path: splice_site_prediction/donor/test_Fly.tsv |
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- split: test_thaliana |
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|
path: splice_site_prediction/donor/test_Thaliana.tsv |
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- split: test_worm |
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|
path: splice_site_prediction/donor/test_Worm.tsv |
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- config_name: transcript_abundance_athaliana |
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data_files: |
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- split: train |
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|
path: transcript_abundance/athaliana_5fold_cv_split.tsv |
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- config_name: transcript_abundance_dmelanogaster |
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data_files: |
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- split: train |
|
|
path: transcript_abundance/dmelanogaster_5fold_cv_split.tsv |
|
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- config_name: transcript_abundance_ecoli |
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data_files: |
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|
- split: train |
|
|
path: transcript_abundance/ecoli_5fold_cv_split.tsv |
|
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- config_name: transcript_abundance_hsapiens |
|
|
data_files: |
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|
- split: train |
|
|
path: transcript_abundance/hsapiens_5fold_cv_split.tsv |
|
|
- config_name: transcript_abundance_hvolcanii |
|
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data_files: |
|
|
- split: train |
|
|
path: transcript_abundance/hvolcanii_5fold_cv_split.tsv |
|
|
- config_name: transcript_abundance_ppastoris |
|
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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 |
|
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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 |
|
|
tags: |
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|
- biology |
|
|
--- |
|
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|
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# AIDO.RNA Benchmark Datasets |
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|
|
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## 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 [5] |
|
|
* 2 datasets: acceptor, donor |
|
|
* 4 test species: zebrafish, fruit fly, worm, and plant |
|
|
* input sequence: pre-mRNA fragment |
|
|
* ncRNA family classification [6] |
|
|
* 2 datasets: boundary noise 0, boundary noise 200 |
|
|
* input sequence: small noncoding RNA with different level of boundary noise |
|
|
* RNA modification site prediction [7] |
|
|
* 12 labels (modification sites): Am, Cm, Gm, Tm, m1A, m5C, m5U, m6A, m6Am, m7G, Φ, and I. |
|
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|
## 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. |
|
|
5. Nicolas Scalzitti, Arnaud Kress, Romain Orhand, Thomas Weber, Luc Moulinier, Anne Jeannin-Girardon, Pierre Collet, Olivier Poch, and Julie D Thompson. Spliceator: Multi-species splice site prediction using convolutional neural networks. BMC bioinformatics, 22(1):1–26, 2021. |
|
|
6. Teresa Maria Rosaria Noviello, Francesco Ceccarelli, Michele Ceccarelli, and Luigi Cerulo. Deep learning predicts short non-coding rna functions from only raw sequence data. PLoS computational biology, 16(11):e1008415, 2020. |
|
|
7. Zitao Song, Daiyun Huang, Bowen Song, Kunqi Chen, Yiyou Song, Gang Liu, Jionglong Su, João Pedro de Magalhães, Daniel J Rigden, and Jia Meng. Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring rna modifications. Nature communications, 12(1):4011, 2021. |
