
# 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](https://github.com/instadeepai/nucleotide-transformer)
- **Paper:** [The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics](https://www.biorxiv.org/content/10.1101/2023.01.11.523679v1) 

### Dataset Summary

The different datasets are collected from 4 different genomics papers: 
- [DeePromoter: Robust Promoter Predictor Using Deep Learning](https://www.frontiersin.org/articles/10.3389/fgene.2019.00286/full): 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](https://www.sciencedirect.com/science/article/abs/pii/S0301462222000643): 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](https://pubmed.ncbi.nlm.nih.gov/31881982): 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](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04471-3): 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](https://pubmed.ncbi.nlm.nih.gov/16901084/): 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             |
```




region:us
{}
TAGS
#region-us 


# 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_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





# 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_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
TAGS
#region-us 
 # 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_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
passage: TAGS
#region-us 
# 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