Commit
·
d6aeb72
1
Parent(s):
1eb5300
Update README.md
Browse files
README.md
CHANGED
|
@@ -5,7 +5,9 @@
|
|
| 5 |
---
|
| 6 |
|
| 7 |
# Dataset Card for Dataset Name
|
| 8 |
-
The
|
|
|
|
|
|
|
| 9 |
## Dataset Description
|
| 10 |
|
| 11 |
- **Repository:** [Nucleotide Transformer](https://github.com/instadeepai/nucleotide-transformer)
|
|
@@ -13,18 +15,35 @@ The nucleotide_transformer_downstream_tasks_public dataset features 2 of the 18
|
|
| 13 |
|
| 14 |
### Dataset Summary
|
| 15 |
|
| 16 |
-
|
| 17 |
-
- [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.
|
| 18 |
- [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
|
| 19 |
-
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
|
|
|
|
|
|
|
| 20 |
|
| 21 |
## Dataset Structure
|
| 22 |
-
|
| 23 |
```
|
| 24 |
-
| Task
|
| 25 |
-
|
|
| 26 |
-
| promoter_all
|
| 27 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 28 |
```
|
| 29 |
|
| 30 |
|
|
|
|
| 5 |
---
|
| 6 |
|
| 7 |
# Dataset Card for Dataset Name
|
| 8 |
+
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.
|
| 9 |
+
|
| 10 |
+
|
| 11 |
## Dataset Description
|
| 12 |
|
| 13 |
- **Repository:** [Nucleotide Transformer](https://github.com/instadeepai/nucleotide-transformer)
|
|
|
|
| 15 |
|
| 16 |
### Dataset Summary
|
| 17 |
|
| 18 |
+
The different datasets are collected from 4 different genomics papers:
|
| 19 |
+
- [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.
|
| 20 |
- [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
|
| 21 |
+
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.
|
| 22 |
+
- [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. The datasets `splice_sites_acceptors` and `splice_sites_donors` are the same dataset where the donors and acceptors splice sites have been removed respectively.
|
| 23 |
+
- [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`
|
| 24 |
|
| 25 |
## Dataset Structure
|
|
|
|
| 26 |
```
|
| 27 |
+
| Task | Number of train sequences | Number of test sequences | Number of labels |
|
| 28 |
+
| --------------------- | ------------------------- | ------------------------ | ---------------- |
|
| 29 |
+
| promoter_all | 53,276 | 5,920 | 2 |
|
| 30 |
+
| promoter_tata | 5,509 | 621 | 2 |
|
| 31 |
+
| promoter_no_tata | 47,767 | 5,299 | 2 |
|
| 32 |
+
| enhancers | 14,968 | 400 | 2 |
|
| 33 |
+
| enhancers_types | 14,968 | 400 | 3 |
|
| 34 |
+
| splice_sites_all | 27,000 | 3,000 | 3 |
|
| 35 |
+
| splice_sites_acceptor | 19,961 | 2,218 | 2 |
|
| 36 |
+
| splice_sites_donor | 19,775 | 2,198 | 2 |
|
| 37 |
+
| H3 | 13,468 | 1,497 | 2 |
|
| 38 |
+
| H4 | 13,140 | 1,461 | 2 |
|
| 39 |
+
| H3K9ac | 25,003 | 2,779 | 2 |
|
| 40 |
+
| H3K14ac | 29,743 | 3,305 | 2 |
|
| 41 |
+
| H4ac | 30,685 | 3,410 | 2 |
|
| 42 |
+
| H3K4me1 | 28,509 | 3,168 | 2 |
|
| 43 |
+
| H3K4me2 | 27,614 | 3,069 | 2 |
|
| 44 |
+
| H3K4me3 | 33,119 | 3,680 | 2 |
|
| 45 |
+
| H3K36me3 | 31,392 | 3,488 | 2 |
|
| 46 |
+
| H3K79me3 | 25,953 | 2,884 | 2 |
|
| 47 |
```
|
| 48 |
|
| 49 |
|