InstaDeepAI/nucleotide_transformer_downstream_tasks

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 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_all dataset will feature all the promoters and their negative counterparts, while the promoter_tata and promoter_no_tata respectively 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 enhancers and enhancers_types datasets.
• 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_all dataset.
• 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_acceptor features 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, H3K36me3 and H3K79me3

Dataset Structure

| Task                  | Number of train sequences | Number of test sequences | Number of labels | Sequence length |
| --------------------- | ------------------------- | ------------------------ | ---------------- | --------------- |
| promoter_all          | 53,276                    | 5,920                    | 2                | 300             |
| promoter_tata         | 5,509                     | 621                      | 2                | 300             |
| promoter_no_tata      | 47,767                    | 5,299                    | 2                | 300             |
| enhancers             | 14,968                    | 400                      | 2                | 200             |
| enhancers_types       | 14,968                    | 400                      | 3                | 200             |
| splice_sites_all      | 27,000                    | 3,000                    | 3                | 400             |
| splice_sites_acceptor | 19,961                    | 2,218                    | 2                | 600             |
| splice_sites_donor    | 19,775                    | 2,198                    | 2                | 600             |
| H3                    | 13,468                    | 1,497                    | 2                | 500             |
| H4                    | 13,140                    | 1,461                    | 2                | 500             |
| H3K9ac                | 25,003                    | 2,779                    | 2                | 500             |
| H3K14ac               | 29,743                    | 3,305                    | 2                | 500             |
| H4ac                  | 30,685                    | 3,410                    | 2                | 500             |
| H3K4me1               | 28,509                    | 3,168                    | 2                | 500             |
| H3K4me2               | 27,614                    | 3,069                    | 2                | 500             |
| H3K4me3               | 33,119                    | 3,680                    | 2                | 500             |
| H3K36me3              | 31,392                    | 3,488                    | 2                | 500             |
| H3K79me3              | 25,953                    | 2,884                    | 2                | 500             |