genbio-ai/rna-downstream-tasks

GB.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).
• —mRNA half-life prediction from Agarwal and Kelley [8]
• —2 species: human, mouse
• —input sequences: 5'UTR, CDS, 3'UTR
• —10-fold cross-validation split
• —Note: We also included splicing features: n_splice_site(number of splice sites for the mRNA sequence) and splice_site_intensity (n_splice_site/ CDS_length).

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.
• —RNA-protein interaction prediction [9]
• —17 datasets: see rna_protein_interaction/hela.list

Reference

1. 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. 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. 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. 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. 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. 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. 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.
8. 8.Vikram Agarwal and David R Kelley. The genetic and biochemical determinants of mRNA degradation rates in mammals. Genome Biology, 2022.
9. 9.Yiran Xu, Jianghui Zhu, Wenze Huang, Kui Xu, Rui Yang, Qiangfeng Cliff Zhang, and Lei Sun. PrismNet: predicting protein–RNA interaction using in vivo RNA structural information. Nucleic Acids Research, 2023.