# Dataset Card for the Multi-species genome

## 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 Multi-species dataset was constructed by parsing the genomes available on [NCBI](https://www.ncbi.nlm.nih.gov/), before arbitrarily selecting only one species from each genus. Plant and virus genomes were not taken into account, as their regulatory elements differ from those of interest in the paper's tasks. The resulting collection of genomes was downsampled to a total of 850 species, in which several genomes that are heavily studied in the literature have been incorporated. The collection represents 174B nucleotides, resulting in roughly 29B tokens. The distribution of each genomics class in the dataset is displayed below:

```
| Class                | Number of species  | Number of nucleotides (B) |
| ---------------------| -------------------| --------------------------|
| Bacteria             | 667                | 17.1                      |
| Fungi                | 46                 | 2.3                       |
| Invertebrate         | 39                 | 20.8                      |
| Protozoa             | 10                 | 0.5                       |
| Mammalian Vertebrate | 31                 | 69.8                      |
| Other Vertebrate     | 57                 | 63.4                      |
```
### Supported Tasks and Leaderboards

This dataset has been used as a pre-training corpus for the Nucleotide Transformers models. Depending on the configuration used, each sequence is 6,200 or 12,200 base pase pairs long. If the dataset is iterated without being shuffled, the first 100 nucleotides of a sequence are the same as the last 100 base pairs of the previous sequence, and the last 100 nucleotides are the same as the first 100 base pairs of the next sequence. During training, this allows for randomly selecting a nucleotide between the first 200 nucleotides of the sequence and start the tokenization from this nucleotide. That way, all the chromosome is covered and the model sees different tokens for a given sequence at each epoch. 

### Languages

DNA

## Dataset Structure

[N/A]


### Data Instances

For each instance, there is a string representing the sequence, a string indicating the description of the sequence, two integers representing the index of the first and last nucleotide respectively and the link to the genome's fasta URL. An instance is shown below:
```python
{'sequence': 'AAACTACCACTGGCTAAATTTCGACCATCTGGGCTAATAGCAACTGACCGCACCCAATATTTATGTCCTTTAAGTGTGCGAATTAGCTTTCCTGTGCCTAAATTCCAAACTTTGAGAGTGTTGTCATCGCTACCACTCACCAAAATTTTCCCATTAGGACTAATTGTTAATGCTTGAATGGAGTCAGTATGTCCTGTTAATGTGTAGACTATTTTACCTGTTGCCAAATTCCAGGCTTTAATAGTTTGATCATCACTCCCGCTAACCAAAGTTTTGCCATTGGGACTGATAGCCACAGCATTAACTTTTTGCGAATGTCCACTCAGGGTTAGTATTTCTTTTCCTGTGGTCAGATTCCACATTTTAATTATGCGTTCCCCTTCGCCACTACTAGCAATTGTCTGCCCATCGGGACTAATGGCGACAGAGACAACAGATTTTGCCCCACCTTTGAGGGTGTTAGCTAAGGAAATATTTTTAACTGGAACATTGGGTGACTGACCAAAAACAACTTCACCCTGAGTAGGACTGTAATTTCCTGGCTTTAGTCTCGATAACAAACTGGTTTGAATTTGGTGATATTTTTGATACCAAGTATCACTAAAACCAAATAACAAAATGAAAGCAGCGCCTAAAACTAAACTTTTGACAAAAGCATATTTAAAGGAGAACTTTGCACTCGGTTGAGTTACGGTGAATTTTCCTGATGATTGTCCGGCGGCTGGTAAGGCGCGTGGGAGTGATGGAATCAAATCTTTAATCACTTCATCGGCTGACTGGTAGCGTTGACTTAAGTCTTTTTGCAACAGCTTCGTCATCACCCCTTCCAATTCTGGCGACAAAGGACTACGCAAATATTCCCGCCAACTGTTCGCCCAGCCATAGCCATGTTCCATCCACAATTGAAAAGGGGATGTTCCTGTTAAGAGATGAAAACAGGTAGCCCCCAAACTGAACAAATCACTAGCTGGGTAAGCTTTACCGTCTCTGATTTGTTCCAGTGGAGAATAACCATGCGAACCAATGGATGTACCATTTTTATTCTTGACTTTTTCGGTTAATTGCTTAGAAGAACCAAAATCAATCAAGCTAAGTCGCCCATCATAACGACAGCGAATTAAATTTTCTGGTTTAATGTCTCGGTGAATCACACCGCGATCGTGAATGAATTTGAGTACAGGCAGTAAATCAAGTAAAATTGCTTGAATTTCATTCGCTTTATAGACTTTGCGCTGTTGTAATTCTTTTAACAAGTTCTGCCCATTAATAAACTGTTGTACCAAATAAAGGCAGTTATCTTGTTCAAAGTAAGCAATCAGTGTAGGAATTTGCGGATGTTCGCCGAGTTCTTGCAGTCGCTTGGCTTCTTCTGCAAATAACTCCATTGCTTTTTTCTGCGACCAAGTTCCTTGAAATTTCGGTGCTAATTGCTTAATTACACACAGTTCATTGAGTTTATCGGTATCTTCAGATAAATAAGTTCTGCCAAATCCCCCCTCATCGGAAAGCACCCGAATCACTCGAAAGCGATTTCTTAATAGTGGCACCAAGGGGGTGCTACAAGTTTGGCATGACTGCTTTCCTTTGGGATTTAGGGGATTTGGACAATCGGGATTTAAGCAGCAGATCATTATCTGACAGGCGCAACTGCATAAAAATTTTTACTAAATTAACCCCGATATTTCCCTAGATGATGATTGACTCTCACGTATTGATGGTAGATCCCGCTGGTAGTGGGGAGTGGGGAATCAATTATATAGTCAATTTTGGTAAATGCTCATAAGTTTTCTTCAATGCAGGAAAACTACGAGAGTCATCAGCTGAATTTTATCGATTATAGCAGCAGGCAAAAGTAGCAGACAGGTTAAGAGTGTCATTAGTCAAGACAAATGACTCATGACTAATGACTCATGACTAATAACTAAGGCTTTTGGGTGGCGATCGCTAATTTTGCCCCCTGGACTTGTCTGACTTGATCCATCACTGCCACTACTTTACCGTGGGTGACTGTTGCATCAGCATTCACAATTACTAATGCTTCTTGGTTATCGCCTACCAAGGTACGCAATTGTCCGGCTAAACCGTCAACAGTGCTTGGTTGACGGTTAACACTTACTATTCCATCTTTATCTACTGTGACGGTAATTTTGGCTGGAACTTGCTGCTGTTTGGCTGTCGCCGCTTTGGGTAAGTTGACGGGTAAACCTTCTGAGCGAGTTAAAAATAACGTTGACATGATAAAAAATGTCAAAATCGCAAATATCACATCAATCATTGGCACGATGTTGATTTGCGGTGGTAAATCTGGCTCATCTTGTAGACGCATAGGTTCTGTCTCCTCGTTCAAAGCGGCGGCGATAGAGCAGTTCTAATTGTCCACCATATTCTTGTATTGCGGCAATCTGTCGTTGATATAACCCTCGAAAGGTATTAGCAAATAAAAGTATAAAAATAGCCACAATTAAACCTGAAGCTGTAGATACCAGCGCTTCACTAATACCTGCGGTAACTCCTGCGGTTTTTGTCCCGCCTACATCACCCAAGTTTAATGATGCAAAAGAAGCAATCAAACCTAATACAGTACCCAGTAGACCTAAAAGTGGTGCAAGACCAATAATTGTGTCAAACATATTTTGAAAACGTTTGAGAACTGGGATTTCGGCTTGCGCTTCACTTTCTAGTGCAAGCCGAAATTCTTCTGGGGTTGGTTCTTCTAATTGCAACGCCGCTAAAAAAATCCGTGTCATGGGCAAATCTGCATTCTTTTGCAATTTATCCAACGCGCCAACAACATTATCAAGGCGGTAAAGATTCAACACTTCTCTGACTATGCGGTTTTGCCGAGTATTGATGCGATACCAAAAGCGGACTCGCTCGATAATTAAAGCAATTCCCACCACACTAAACGCCAGCAGGGGCCACATGACTACGCCACCTGCTACAAACAACTCATACATGGGCAATATCTCTAGGAACTAAATGGACAACGTTACAGTTAGACTAGCAGTTTACGGTACTAAATGATATATCTTATCAATAAGGAGTAGACAAAATAAAAAGCTATGTCAAATTCGGTTGAGTTTTGATGACATAATTATTCATTCTTGTTCAAGGCTTGATTCGCTACAATCCTGATGATGAAAGTATTTGTGTAAGTATACAGTTGATGAAAGCTAACTCAGGAATTTTTTTCTTTATTGCTTGACTTTTGCGAGAGATGGTTTTGAACAGAGTAATTACTAATAAGAACTTGCAATAAATTTAAACAGAACAGTAGTTTGTAGCTTTGCTTGAGAAGCGATCGCCCGACGTTGAGAGTTAAAGTATATTTTGCGTACTAACTTACCCAACGCCCAAAAAATTACATCATTTGAATATCGTCAATTTGTACTCTTAATCATCTATGGCTAAACTATTTGACTCAATCACAGAAGAACTGCAAGAGTTTATTGCAGCCCAAAACCTTTTCTTTGTAGGAACCGCGCCTCTGAGTGCTACAGGTCACGTTAATTTATCTCCCAAAGGTCTCGATTGCTTGCGGATTTTATCACCCCACAAAGTCGCCTATCTCGATCTCACAGGTAGCGGTAACGAAACTTCAGCCCATCTGCAAGAAAATGGTCGCATTACCTTCATGTTTTGCGCCTTCACTGAACCAGCGCGCATCTTGCGACTTTACGGTCAAGGACACGTAATTTTACCTAGCTATCCTGATTGGGATTCTGTATATTCAGTGTTTCCGCCGCTACCAGGAACTCGTCAAATTATCGTAGCTGATATTGAGATTGTGCAAAGTTCCTGTGGTTTCGGCGTTCCTCTTTACGAATACCAAGGTCAACGCCAAACACTAGTAAATTGGGCTGCTAAAAAAGGCGAACAGGGAGTCCGAGAATATCAACAACAAAAAAACAGCATCAGCATTGATGGTTTACCGACACCATTAGGCCAATTATCTGACGGTTAAAGCGGCGTTTCATATATTTTTAGTTAATCTGAACCAAAAAATCTCAAATTTTTTGTCAATAGTCTCTAGTCCAAAGAAGCTTGATTTTTGACCATAGATTGTAGGCTTTTGACAAAAATAACCTTTATAGAGAAAATTTATCCTTGCTGACACTCTATAACTAAGTTTATAAAACATAGCGTCAAAAATCGATACATATCAGTTCTATTTTCTGCCTCTATTCCTAATTAAATTTGGTGTAAAGGAACTATTATGCGGTTTCCGTGTCTTGACGTAATGATTTGCAACGAATTATGATTCGAGTTTAGTCCGGATCAACCGAGACATCCTCGAAAATTGGTGCAAGTAAATTCAACTTTCGCTCTACATAATCACACGCATGAGATTACGCTTATTTCTGTTTAGCGTTGTCAGTATTGTCCTGCTTTCTTCTCCAGTAAGAGCATCTCGCTTAGAATCTTGGAGCTTTGACACCGCACAAAATCAACTGAATATTACTACTGTATCTGGTGTTAAACCAAGAGCATTTTTAATTCAAAATCCCACGCGGTTAGTTATCGATCTTCCTGGTACACAACTGAACACAAATACAGTTCGGAAAAACTTTGGTTCCACAGTACGTGAAATCCGTGTTGGTAAGGTTGACGATAACACAACAAGATTAGTAGTTGAATTAGCACCTGGATACACTGTAGACCCTAACAAGTTACTGCTGCAAGGTGATTCTTCCACTCATTGGATAGTGAAATTTCCATCGGTAGAACGGGTTCAAAATCCTGTTGATAATAATTTTTCTTTATCTAGTGAAGAGCAAATTCCGGTTTCTGTGAGTGATGTTTCTTTGTTTGCGGGAGTTGTACCGTTAGGTAAGGAAATACCACAATTGCGATCGCAGGTACAAGCCTTAGCTGCTCGTTATCGTTCCCTGGATGCAGGAATGTTCTTTTTAGATTTAGATACTGGTAACTATCTAGATTTAAATGGTGAGAAAGTCTTTCCTGCTGCTAGTACAATAAAGTTTCCCATTTTAGTAGCGTTATTTCAAGAAGTAGATGCAGGTAGAGTCAAACTGAATGAAACCTTAGTTATGCGGCGCGACTTAATAACTGGAGGTTCTGGAGAATTTCAATACAAGCGTGCAGGAAGTCGTTTTAGTCTGATAGAAACCGTGACTAAGATGATTACCATCAGCGACAACACAGCTACCAATATGGTAATTGACCGATTAGGTGGTAAAGCTAAGTTAAATCAGCGTTTTCGTGGTTGGGGTCTGCAAAACACCGTTGTGCGGAATTTACTCGGCGACTTTAAGGGAACGAATACAACTAGCGCCAAAGATTTAGTCAGGCTGTCTGCGTTGGTTGCAAAAAATCAATTATTGACTGATTCCAGCCGTAGCAAAGTTTTGGATATTATGCAGCGTGTTCACAACACCAAGTTATTACCTGCTGGTTTGGGTAAAGGTGCGGTAATTGCTCACAAAACCGGAACTCTAGGCATTGTACTAGGTGATGCCGGGATTATTCAAATGCCATCTGGTAAGCGCTACTTAGCCGGAATTTTTGTCAGAAGACCTTTTAATGATTTAAAAGCGCGAGATTTTATCAATCAAGTTTCTCGAATTGTTTACGGCTATTTAGACCAACCAAGAGTCGCCAGCAAGCCTTAATACTCCTGATGTAAAAAAGAAAAATTTTAATTGACGTAAGCCCCTGATATTCATTAATATCTAGGGGTTTTTGCATATCTATTTATAGCAGTGCTTAACGCACCCTATCTCTCAGTGCGTTACGGCTAATCCTTATTCTCTTAAACTAACAAATTCTTGCATAGCCGTAACACATTCTAATTCATATTGGCTTTGAAGGATATTGACTGTATTCCTGCCAAGTTGGCTACATATACCTAAGCCGCACTGCTAAATTATGAATGGGAAATAACTTGCGGGCTTGATAAACCAACTTTTACTACACTAAACATGCTAAAGCATTAACAACGGACGGATTTAGGTTAGTTGCTTATTTTGCTCACTCTTGTGAGAGATTGCTGCTGTTTTTATTGTAGCGATCGACATCAAACTTCTTTATCTCTAAAAGGACAAATATAACAGGAAGTCCTCATTGATTACTCCTATCCTCACCTCGTTCATCGCAAAATGTACGAGGGCTTTTTTTATTTGGCAGAATTTACCCCTATTACGCCAATGATAATTAAAGCTATCGAGAAAAGTTTGGTAAGAGACATTGATTCACGAAACCAAATTACCCCAATAGTAGCGATTACAGTTGTGCCTAAACCTGACCAAACAGCATACGCAATGCTGACTTCAATTTTTTTAAGAGCTAAAGTTAAAAAACTAAAACAAATTCCATAACAGATAAAAATTAAAACCGAGGGAATAGTTCTTGTAAACCCCTCAGACAATTTCATGGAAGTTGTACCAGCGACTTCAAATAAGATTGCTGCAATGAGATAAAGCCAACTATTTACCATGTTTATTGATTGATTATAAGGTGATGATGGGAATATGATTTTTCGACAAGCATAATGAGTCAAAATTCTATATTTAATCTATTAACTAATTCTGCTATTTTGACAACATTTATAGTTAGCTGATGAGATAGGCAAAAATCAAAATATTCATATTTCCGAATTAGTAAAGAAGTTGGTAATCTCTAAAGTTCAGTTTACCACACCAATATTATGGGGGTTTACCGTACTAATACTAAGGTTCGGAAATCATGATGTAATTGGTGATAAAAACCGAATTTACACTGTACTGGATTGTGAATACTATAAAAACAACGCAAATGATTTAAACCTAAATCAACTACACAAAATTAGAAATTAAACGAGGTGGAGACATGACATTAGTGCGTTGGAATCCTTGGCAAGAAATGAACACTCTCCAAAGACAAATCAACAATTTATTTGCAGACGAAATGCTCCCATCTACTTTACTTGAAAGAAGCCTTACAAAAGTTCCGGCGGCTGAATTACACGAATCTGAAGAAGCTATTCATCTCAAGCTAGAATTACCAGGAATTGAAGCCAAAGACCTAGATGTGCAAGTTACAGAAAAAGCTGTGTATATCAGCGGTGAACGGAAATCTGAAACTAAAACAGAAGGGAAAGGTGTAACCAAGAGTGAATTTCATTATGGGAAATTCCAACGTTTGATTCCTTTACCAACTCGCATTCAAAATACCAATGTTACTGCTGATTATAAAGATGGTATTTTGACTCTGACTTTGCCTAAAGCCGAAGAAGAAAAGAAAAAGGTTGTCAAGCTGAATCTTGAATCTATTGGCTAATATCAATTTTGGATTAGCGCTAAAATACCCGACTTCTTTAAGAAGTCGGGTATTTTGTTGTTCACTAATGATTTAAAATTGCTATAAGCTGCGATTTCTGCCTGTTGATTGTTGTCTGTCTACGGGAAAAACGTCAAAATCGAAAGTTGCAATTAGACGCTCATCAACGTATACCTGTATTTTATGCTTACCAGGAGGATCACCTGCGGCGATCGTCCAATAGTTTTCAATTACACCATCATTAGCTATAGTTTTGCGCCTCATTACCGACTCTGTACCGTCAGCGGAGACTGTGAAGTTTTCACCATCATCTGTAGCCCAAGTTTCTGGGGGTTTTGGTAAGCGTAGGACTTCTCGCCATGTAACTTCGCCTTGGTAGTCTTTGAGTTGAATTCGCCACCCATATTTACTACCTTCTTGTAGTGGGACTCTGAATGTGGGGATGAAGTTAACTTTACCTCTAGCATCGACTCTCGCTATGCCAAACTCAGCTTTGTCGATCGCTACCGACTTTTTAGTATTGTTTGCTTGAGAAATTGACCCTGATGATGCTATTTTTTCGTCGGAGATCGCTACTGTAGCATTGATTGGCTGAGACGCTACCAACCCGGAAACTAGCCAAGAAGAAGTTAGTACAACTATTGCAGTCCAAATTCTCATCAGCAAAATTTTTGGTCATTTACTAGTACTTATTCCCGCCTTCCCATTGGCTTCCGGGTACAGTCCCGATAAATAGCCAAGTTGGCAGAATAAAAGTTGCAGAATTAATAGTCAGTTTATAGTTAAATCGGCAACACCAGATCAAGCCACTCAAACTACTTTACTCTCGGGCCAGTTGCCAGAACTGCGAAAACTATCATCGCAGGTTTTCGGTGTAGGTGCTAAATATGCGTTTATTCTTAACTATTTTGTGTTCAATACGGAATTTTTAATATGTAAGCAATTGCTGACAGTCGGCTATTTGATCAATTGTCATTTCCTAGAGTTTCATCCCCTTGAGGGGAAGGAGTTTGGGAAATGTCAAAAACTGTCAAATGCTTAATGCAAAGATTAACAGTTGTGCCTAAGTGCGATCGCACTTAGGCATGACAAAGCATCAAAAATTAGCATTGGAGAACCGATATTTTCCTATTACCTGACTGCTATATATTGATAGTGAGGCGTTTTTGAGCAGCAAACAGCATGGCAGATATTCCAAATTCCATCGCATCATACCGTGCCTTAGCACTGCAAGTTACCTGTCATGCTGTGAATCAAGCGAGCGATCGCCACGCTGTCCAAGAAATCATTCATCATACTATCAACCGCCTGGCGCAACAAATCGCCGCCAGTATTGCTTTTATTGGTTTTGACTGTCGTTTAATTGTTTTACCAGAATATTTTCTGACAGGTTTCCCGATGGGTGAACCTTTGGCTGTTTGGGGAGAAAAGGCTTGTATAGAAATGCACGGTGCCGAGTATGAAGCCCTCAGTAAAATTGCTCAAAAACATCAGATATTTTTAGCTGGTAACGCCTACGAACTCGACCCCAATTTTCCTGGCTTATACTTTCAAACTTGCTTTGTGATTGACCCGGCTGGTGCTATTGTCTTGCGGTATCGGCGGCTAAATTCGTTATTTGCACCCACACCTCATGATGTTTGGGATAAATATCTTGATTGTTACGGCCTAGAAGGGGTGTTTCCTGTAGCGAAAACTGCAATTGGCAATTTAGCCGCTTTAGCTTCCGAAGAAATTTTGTATCCAGAAGTAGCGCGGTGTTTAGCAATGCGTGGTGCAGAAATTTTTCTGCATTCCACTTCTGAAATTTATAGCAAAAACCTCACACCTAAAGATGCGGCGAAAATTTCTCGCGCTGTGGAAAATATGGCTTACGTTGTGTCTGCGAATACCGCAGGTCTAGCTAATAGTTCTATACCCAGCGCTTCTGTTGATGGTGGCTCAAAAATAGTTGACTATCGCGGTATCGTATTAGCAGAAACAGGTGCAGGCGAAAGTATGGCAGCTTTTGCAGAGATAGATTTAACTGCTTTAAGACGCGATCGCCGTCGTCCAGGGTTAAATAATTTACTGTCTCGCCAGCGATTTGAACTCTACGCCCAAAGCTACAGCCAGTCACAATTTTATCCAGCAAACACTATGCTAAATCAAGAATGCGATCGCCAACACTTCATCCAAACACAGCAACAAACCATAGAACGTCTATCTCAGTTAGGAGTGATTTAAAAGTCTAAAGTCTGAAATTAGATTCTTTTGACCATTGACTATTGACAAATGACAAATGACAAAACCAATCGAAGTCCGTAACCCGCGAACGGGAAAATATGATTATGTAATTATCCCACCGCCGCCGAAACTGCTGGCGCAGCAATGTAACCGAGCGCGAAGGGCGCAAGTGCGTTGGCAAAAACTGGGCGTAGAAGGGAGAGTTGCAGCTTTAAAAGAATGGAAGCAAGCAGTTTTGGCTGGACGCGAAAAGCTCACAGATGCTTTGGTCAATGATACGGGTAGATTATCTATATCAGTGATGGAAATCGACTCATTCCTTTCTAGCATCGATCGCTGGTGTGGATTAGCGCCAGATTTATTACAAGATTCGGCCAAAAATACATCAATTCCGTTCATCGCCTTACAACAAACATCAACGCCTTACCCTGTAGTTGGGGTAATTAGTCCTTGGAATTTCCCTCTGTTGCTGTCTACGATAGATACCATTCCCGCACTGTTGGCGGGTTGTGCTGTAGTTGTCAAACCCAGTGAAATTGCACCGCGTTTCATCGCCCCACTGATAGCTGCAATTAATCAAGTACCCGCCTTGCGCGATGTTTTCAGTTTTGTGGAAGGTGCGGGAGAAACTGGCGCGGCTTTGATGGAGAATGTAGATTTAGTTTGTTTTACCGGTAGTGTCGCTACTGGACGCAAAGTTGCAGAAGTCGCCGCACAAAGATTTATCCCCGCTTTTTTGGAATTGGGCGGGAAAGATCCGGCGATCGTGTTGGAATCTGCCGATTTAGAATTAGCCACATCAGCGATTTTATGGGGTTCCGTCGTTAACACCGGACAGTCTTGTTTATCAATTGAGCGTATTTACGTTGCCGAATCTATCTTTGAAAAGTTTTATCATCAGTTAGTAGCCAAAGCACATCGCCTACAACTAGCCCATCCCACCATTGAAAGTGGCGAAATCGGCCCCATTATTGCTGAAAGACAAGCTGGCATAATTAACGAGCATATCTCCGATGCAGTGCAAAAAGGTGCAGTAATTCATTGTGGCGGTAAAGTTGAAGAGTTAGGCGGTGGTTGGTGGTGTCATCCCACAGTGCTGACTCATGTTAACCATACAATGAAAGTCATGACCGAAGAGACTTTTGGCCCGATCATGCCAATCATGCCTTTTGCCACAGTAGAGGAAGCTGTTAACTTAGCCAACGATTCAATTTATGGACTGAGTGCGGCGGTGTTTGCGGAAACCGAAACTGAAGCGTTAACAGTTGCCCAGCAAATAGATGCAGGTGCTATCAGTATTAATGATGCCGCCCTCACCGCCATTATGCACGAAGGTGAAAAAAACGCTTTCAAATTATCCGGTTTAGGCGGTTCACGTATGGGTGCAGCCGCCATCAAACGATTTTTGCGGAAAAAAGCGTTTTTGATTAAAACCAACTCAAATCAAGACCCTTGGTGGTTTGAGCCTAAAGTGTAGTGCAATCTTCTCTCAGCGACCTCTGCGTCTCTGTAGTTCGTTAAAAACCGTATTAGATTCTGTTTGTTGGGTTTCGCTGTCGCTTCACCCAACCTACTTTCCTTAAACCCCTACTACAGATTCATTCACAGTTTCACTAGCCGCAACACCATTAGTCAAAATCGCTTGCCGAGTTTTCAGGTTAAATTTATAACCATGTGGCAAAATATGCAGCTTCGCACCACAAATTGCCAAAGGTTCATCCCGGAGAATTGTATCTGCGTTGTTATATGTAGATTCAGACTCATCCACAATGGTGACTGAACCTTCACCAATAATTTCGATTTGGTCATCAGTCACGGCGATCGCTGTATTCTCATCAATCCCAAATCCTAACACCGCAGGTTCATGAATTAAAGCTGTAATTAAACGCCCTAAGCGTCCCCGTTGTAAGAAATGTTGGTCAATCACCACCCCTGGGAGAAAACCCATACCAGGCCCCATTTCCACAATTTCCATCCGTGGTGTACTTTGAGAATCACCCTCAACAATCATTTTATCGGGCATCACAGCCGCACCCGCACTAGTACCTGCAATTACTGCACCTTCAGCATAGCGTTGGTGAATAGCCGCATCGATTTCGGTATCCTTGAGGATACTAGTAATTCGCGCTTGGTCTCCTCCAGTAAAAAATATCCCAGTCGCCTTAGCAATAGCTTCTAAAGCCGTAGAAGACCTAGCATCTTCACGAGTTTCTGTATCAATAATGCGAACGTGTTCTGCACCTAGCCGTTCAAAAACTCTAATATAATTTTCCCCCACTTCTCTAGGCAGTTCTGTGGCGGCCGTCATAATTACAATATTGGCTTTTGTACCCCCAGCCCGACGGACAAATTCTCGCAGAATCACACAATCTCCTTCTTTATCTTCTGCGCCACCAATAATTACCAACTGGCGTTTATGTGCAGTTTCTGTCATAATGCCCCCCGGATAACCGGATTAGAATTTAATTTAGATTAATTTCAATAAAACATGACAATTATCACAATCAAATCATCCATTTGATAGATTAATTTTTAATGGCAAAAGTTAAATTATATATAACTTTATGTATATATAAACTCTTGCCAAATTTAGCATTTTTAATAATTGGTAATTCATTTAGCAGAATTACCAATTACTTATACAGTAATAATTTATGTATAACTCTTCTCAAGTAATAGCACTAAAATCTCATAGT',
 'description': 'NZ_AP018174.1 Anabaenopsis circularis NIES-21 DNA, nearly complete genome',
 'start_pos': 1824000,
 'end_pos': 1836200,
 'fasta_url': 'https://ftp.ncbi.nlm.nih.gov/genomes/refseq/bacteria/Anabaenopsis_circularis/latest_assembly_versions/GCF_002367975.1_ASM236797v1/GCF_002367975.1_ASM236797v1_genomic.fna.gz'}
```

### Data Fields

- `sequence`: a string containing a DNA sequence from the human reference genome
- `desciption`: a string indicating the Species of the sequence as well as the NCBI id.
- `start_pos`: an integer indicating the index of the sequence's first nucleotide 
- `end_pos`: an integer indicating the index of the sequence's last nucleotide 
- `fasta_url`: a string indicating the URL used to download the fasta from which the sequence was taken.

### Data Splits

The Multi-species dataset has 3 splits: train, validation, and test.                                              |

## Dataset Creation

[N/A]

### Curation Rationale

[N/A]


### Source Data

#### Initial Data Collection and Normalization

The data consists of sequences cut from the the whole genome sequences of the 850 species sampled that can be found in the `urls.csv` file of this dataset's repository.

#### Who are the source language producers?

[N/A]

### Annotations

The dataset does not contain any additional annotations.

#### Annotation process

[N/A]

#### Who are the annotators?

[N/A]

### Personal and Sensitive Information

[N/A]

## Considerations for Using the Data

### Social Impact of Dataset

[N/A]

### Discussion of Biases

[N/A]


### Other Known Limitations

[N/A]


## Additional Information

### Dataset Curators

[N/A]


### Licensing Information

[N/A]

### Citation Information

```bibtex
@article{dalla2023nucleotide,
  title={The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics},
  author={Dalla-Torre, Hugo and Gonzalez, Liam and Mendoza Revilla, Javier and Lopez Carranza, Nicolas and Henryk Grywaczewski, Adam and Oteri, Francesco and Dallago, Christian and Trop, Evan and Sirelkhatim, Hassan and Richard, Guillaume and others},
  journal={bioRxiv},
  pages={2023--01},
  year={2023},
  publisher={Cold Spring Harbor Laboratory}
}
```
DNA Genomics Nucleotide region:us
{"pretty_name": "Human Reference Genome", "tags": ["DNA", "Genomics", "Nucleotide"]}
TAGS
#DNA #Genomics #Nucleotide #region-us 

# Dataset Card for the Multi-species genome

## Dataset Description

- Repository: Nucleotide Transformer
- Paper: The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics

### Dataset Summary

The Multi-species dataset was constructed by parsing the genomes available on NCBI, before arbitrarily selecting only one species from each genus. Plant and virus genomes were not taken into account, as their regulatory elements differ from those of interest in the paper's tasks. The resulting collection of genomes was downsampled to a total of 850 species, in which several genomes that are heavily studied in the literature have been incorporated. The collection represents 174B nucleotides, resulting in roughly 29B tokens. The distribution of each genomics class in the dataset is displayed below:


### Supported Tasks and Leaderboards

This dataset has been used as a pre-training corpus for the Nucleotide Transformers models. Depending on the configuration used, each sequence is 6,200 or 12,200 base pase pairs long. If the dataset is iterated without being shuffled, the first 100 nucleotides of a sequence are the same as the last 100 base pairs of the previous sequence, and the last 100 nucleotides are the same as the first 100 base pairs of the next sequence. During training, this allows for randomly selecting a nucleotide between the first 200 nucleotides of the sequence and start the tokenization from this nucleotide. That way, all the chromosome is covered and the model sees different tokens for a given sequence at each epoch. 

### Languages

DNA

## Dataset Structure

[N/A]


### Data Instances

For each instance, there is a string representing the sequence, a string indicating the description of the sequence, two integers representing the index of the first and last nucleotide respectively and the link to the genome's fasta URL. An instance is shown below:


### Data Fields

- 'sequence': a string containing a DNA sequence from the human reference genome
- 'desciption': a string indicating the Species of the sequence as well as the NCBI id.
- 'start_pos': an integer indicating the index of the sequence's first nucleotide 
- 'end_pos': an integer indicating the index of the sequence's last nucleotide 
- 'fasta_url': a string indicating the URL used to download the fasta from which the sequence was taken.

### Data Splits

The Multi-species dataset has 3 splits: train, validation, and test.                                              |

## Dataset Creation

[N/A]

### Curation Rationale

[N/A]


### Source Data

#### Initial Data Collection and Normalization

The data consists of sequences cut from the the whole genome sequences of the 850 species sampled that can be found in the 'URL' file of this dataset's repository.

#### Who are the source language producers?

[N/A]

### Annotations

The dataset does not contain any additional annotations.

#### Annotation process

[N/A]

#### Who are the annotators?

[N/A]

### Personal and Sensitive Information

[N/A]

## Considerations for Using the Data

### Social Impact of Dataset

[N/A]

### Discussion of Biases

[N/A]


### Other Known Limitations

[N/A]


## Additional Information

### Dataset Curators

[N/A]


### Licensing Information

[N/A]




# Dataset Card for the Multi-species genome ## Dataset Description

- Repository: Nucleotide Transformer
- Paper: The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics ### Dataset Summary

The Multi-species dataset was constructed by parsing the genomes available on NCBI, before arbitrarily selecting only one species from each genus. Plant and virus genomes were not taken into account, as their regulatory elements differ from those of interest in the paper's tasks. The resulting collection of genomes was downsampled to a total of 850 species, in which several genomes that are heavily studied in the literature have been incorporated. The collection represents 174B nucleotides, resulting in roughly 29B tokens. The distribution of each genomics class in the dataset is displayed below: ### Supported Tasks and Leaderboards

This dataset has been used as a pre-training corpus for the Nucleotide Transformers models. Depending on the configuration used, each sequence is 6,200 or 12,200 base pase pairs long. If the dataset is iterated without being shuffled, the first 100 nucleotides of a sequence are the same as the last 100 base pairs of the previous sequence, and the last 100 nucleotides are the same as the first 100 base pairs of the next sequence. During training, this allows for randomly selecting a nucleotide between the first 200 nucleotides of the sequence and start the tokenization from this nucleotide. That way, all the chromosome is covered and the model sees different tokens for a given sequence at each epoch. ### Languages

DNA ## Dataset Structure

[N/A] ### Data Instances

For each instance, there is a string representing the sequence, a string indicating the description of the sequence, two integers representing the index of the first and last nucleotide respectively and the link to the genome's fasta URL. An instance is shown below: ### Data Fields

- 'sequence': a string containing a DNA sequence from the human reference genome
- 'desciption': a string indicating the Species of the sequence as well as the NCBI id.
- 'start_pos': an integer indicating the index of the sequence's first nucleotide 
- 'end_pos': an integer indicating the index of the sequence's last nucleotide 
- 'fasta_url': a string indicating the URL used to download the fasta from which the sequence was taken. ### Data Splits

The Multi-species dataset has 3 splits: train, validation, and test.                                              | ## Dataset Creation

[N/A] ### Curation Rationale

[N/A] ### Source Data #### Initial Data Collection and Normalization

The data consists of sequences cut from the the whole genome sequences of the 850 species sampled that can be found in the 'URL' file of this dataset's repository. #### Who are the source language producers?

[N/A] ### Annotations

The dataset does not contain any additional annotations. #### Annotation process

[N/A] #### Who are the annotators?

[N/A] ### Personal and Sensitive Information

[N/A] ## Considerations for Using the Data ### Social Impact of Dataset

[N/A] ### Discussion of Biases

[N/A] ### Other Known Limitations

[N/A] ## Additional Information ### Dataset Curators

[N/A] ### Licensing Information

[N/A]
TAGS
#DNA #Genomics #Nucleotide #region-us 
 # Dataset Card for the Multi-species genome ## Dataset Description

- Repository: Nucleotide Transformer
- Paper: The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics ### Dataset Summary

The Multi-species dataset was constructed by parsing the genomes available on NCBI, before arbitrarily selecting only one species from each genus. Plant and virus genomes were not taken into account, as their regulatory elements differ from those of interest in the paper's tasks. The resulting collection of genomes was downsampled to a total of 850 species, in which several genomes that are heavily studied in the literature have been incorporated. The collection represents 174B nucleotides, resulting in roughly 29B tokens. The distribution of each genomics class in the dataset is displayed below: ### Supported Tasks and Leaderboards

This dataset has been used as a pre-training corpus for the Nucleotide Transformers models. Depending on the configuration used, each sequence is 6,200 or 12,200 base pase pairs long. If the dataset is iterated without being shuffled, the first 100 nucleotides of a sequence are the same as the last 100 base pairs of the previous sequence, and the last 100 nucleotides are the same as the first 100 base pairs of the next sequence. During training, this allows for randomly selecting a nucleotide between the first 200 nucleotides of the sequence and start the tokenization from this nucleotide. That way, all the chromosome is covered and the model sees different tokens for a given sequence at each epoch. ### Languages

DNA ## Dataset Structure

[N/A] ### Data Instances

For each instance, there is a string representing the sequence, a string indicating the description of the sequence, two integers representing the index of the first and last nucleotide respectively and the link to the genome's fasta URL. An instance is shown below: ### Data Fields

- 'sequence': a string containing a DNA sequence from the human reference genome
- 'desciption': a string indicating the Species of the sequence as well as the NCBI id.
- 'start_pos': an integer indicating the index of the sequence's first nucleotide 
- 'end_pos': an integer indicating the index of the sequence's last nucleotide 
- 'fasta_url': a string indicating the URL used to download the fasta from which the sequence was taken. ### Data Splits

The Multi-species dataset has 3 splits: train, validation, and test.                                              | ## Dataset Creation

[N/A] ### Curation Rationale

[N/A] ### Source Data #### Initial Data Collection and Normalization

The data consists of sequences cut from the the whole genome sequences of the 850 species sampled that can be found in the 'URL' file of this dataset's repository. #### Who are the source language producers?

[N/A] ### Annotations

The dataset does not contain any additional annotations. #### Annotation process

[N/A] #### Who are the annotators?

[N/A] ### Personal and Sensitive Information

[N/A] ## Considerations for Using the Data ### Social Impact of Dataset

[N/A] ### Discussion of Biases

[N/A] ### Other Known Limitations

[N/A] ## Additional Information ### Dataset Curators

[N/A] ### Licensing Information

[N/A]
passage: TAGS
#DNA #Genomics #Nucleotide #region-us 
# Dataset Card for the Multi-species genome## Dataset Description

- Repository: Nucleotide Transformer
- Paper: The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics### Dataset Summary

The Multi-species dataset was constructed by parsing the genomes available on NCBI, before arbitrarily selecting only one species from each genus. Plant and virus genomes were not taken into account, as their regulatory elements differ from those of interest in the paper's tasks. The resulting collection of genomes was downsampled to a total of 850 species, in which several genomes that are heavily studied in the literature have been incorporated. The collection represents 174B nucleotides, resulting in roughly 29B tokens. The distribution of each genomics class in the dataset is displayed below:### Supported Tasks and Leaderboards

This dataset has been used as a pre-training corpus for the Nucleotide Transformers models. Depending on the configuration used, each sequence is 6,200 or 12,200 base pase pairs long. If the dataset is iterated without being shuffled, the first 100 nucleotides of a sequence are the same as the last 100 base pairs of the previous sequence, and the last 100 nucleotides are the same as the first 100 base pairs of the next sequence. During training, this allows for randomly selecting a nucleotide between the first 200 nucleotides of the sequence and start the tokenization from this nucleotide. That way, all the chromosome is covered and the model sees different tokens for a given sequence at each epoch.### Languages

DNA## Dataset Structure

[N/A]### Data Instances

For each instance, there is a string representing the sequence, a string indicating the description of the sequence, two integers representing the index of the first and last nucleotide respectively and the link to the genome's fasta URL. An instance is shown below: