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Dataset Card for Dataset Name
The nucleotide_transformer_downstream_tasks dataset features the 18 downstream tasks presented in the Nucleotide Transformer paper. They consist of both binary and multi-class classification tasks that aim at providing a consistent genomics benchmark.
Dataset Description
- Repository: Nucleotide Transformer
- Paper: The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics
Dataset Summary
The different datasets are collected from 4 different genomics papers:
- DeePromoter: Robust Promoter Predictor Using Deep Learning: The datasets features 3,065 TATA promoters and 26,532 non-TATA promoters, with each promoter yielding a negative sequence by randomly sampling parts of the sequence. The
promoter_alldataset will feature all the promoters and their negative counterparts, while thepromoter_tataandpromoter_no_tatarespectively provide the TATA and non-TATA parts of the dataset. - A deep learning framework for enhancer prediction using word embedding and sequence generation: To build the training dataset, the authors collect 742 strong
enhancers, 742 weak enhancers and 1484 non-enhancers, and augment the dataset with 6000 synthetic enhancers and 6000 synthetic non-enhancers produced with a generative model. The test dataset is comprised of 100 strong enhancers, 100 weak enhancers and 200 non enhancers. The original paper uses this dataset to do both binary classification (i.e a sample gets classified as non-enhancer or enhancer) and 3-class classification (i.e a sample gets classified as non-enhancer, weak enhancer or strong enhancer). Both tasks are respectively tackled in the
enhancersandenhancers_typesdatasets. - SpliceFinder: ab initio prediction of splice sites using convolutional neural network: The authors introduce a dataset containing 10,000 samples of donor site, acceptor site, and non-splice-site, resulting in 30,000 total samples that are featured in the
splice_sites_alldataset. - Spliceator: multi-species splice site prediction using convolutional neural networks: Two datasets are introduced by this paper, each of them contain splice sites and their corresponding negative datasets. The dataset
splice_sites_acceptorfeatures acceptor splice sites and the other,splice_sites_donor, donor splice sites. - Qualitatively predicting acetylation and methylation areas in DNA sequences: The paper introduces a set of datasets featuring epigenetic marks identified in the yeast genome, namely acetylation and metylation nucleosome occupancies. Nucleosome occupancy values in these ten datasets were obtained with Chip-Chip experiments and further processed into positive and negative observations to provide the datasets corresponding to the following histone marks:
H3,H4,H3K9ac,H3K14ac,H4ac,H3K4me1,H3K4me2,H3K4me3,H3K36me3andH3K79me3
Dataset Structure
| Task | Number of train sequences | Number of test sequences | Number of labels | Sequence length |
| --------------------- | ------------------------- | ------------------------ | ---------------- | --------------- |
| promoter_all | 30,000 | 1,584 | 2 | 300 |
| promoter_tata | 5,062 | 212 | 2 | 300 |
| promoter_no_tata | 30,000 | 1,372 | 2 | 300 |
| enhancers | 30,000 | 3,000 | 2 | 400 |
| enhancers_types | 30,000 | 3,000 | 3 | 400 |
| splice_sites_all | 30,000 | 3,000 | 3 | 600 |
| splice_sites_acceptor | 30,000 | 3,000 | 2 | 600 |
| splice_sites_donor | 30,000 | 3,000 | 2 | 600 |
| H2AFZ | 30,000 | 3,000 | 2 | 1,000 |
| H3K27ac | 30,000 | 1,616 | 2 | 1,000 |
| H3K27me3 | 30,000 | 3,000 | 2 | 1,000 |
| H3K36me3 | 30,000 | 3,000 | 2 | 1,000 |
| H3K4me1 | 30,000 | 3,000 | 2 | 1,000 |
| H3K4me2 | 30,000 | 2,138 | 2 | 1,000 |
| H3K4me3 | 30,000 | 776 | 2 | 1,000 |
| H3K9ac | 23,274 | 1,004 | 2 | 1,000 |
| H3K9me3 | 27,438 | 850 | 2 | 1,000 |
| H4K20me1 | 30,000 | 2,270 | 2 | 1,000 |