Datasets:

simecek
/

Human_DNA_v0_DNABert6tokenized

annotations_creators:
  - no-annotation
language_creators:
  - no-annotation
task_categories:
  - text-classification
tags:
  - genomics
  - dna
  - dnabert
  - bioinformatics
  - human-dna
  - tokenized
source_datasets:
  - simecek/Human_DNA_v0
language:
  - en
license: other
license_name: unspecified

`Human_DNA_v0_DNABert6tokenized`

Dataset Description

The simecek/Human_DNA_v0_DNABert6tokenized is a processed version of the simecek/Human_DNA_v0 dataset. It consists of human DNA sequences that have been tokenized using a 6-mer approach, making it directly compatible with models like DNABert for classification and other downstream tasks.

This dataset can be used alongside the davidcechak/Worm_DNA_v0_DNABert6tokenized dataset for comparative genomic analysis or to build classifiers that can distinguish between human and worm DNA. This provides a valuable resource for cross-species machine learning tasks in bioinformatics.

Dataset Structure

The dataset is available in the parquet format and is split into training and testing subsets.

Data Fields

The dataset includes the following fields:

tokens: A list of integers representing the 6-mer token IDs.
text: The original DNA sequence string, consisting of the nucleotides A, T, C, and G.

Dataset Creation

Data Source

The base Human_DNA_v0 dataset likely consists of DNA sequences from the human reference genome.

Preprocessing and Tokenization

The raw sequences were processed using a 6-mer tokenization scheme:

Splitting: Original DNA sequences were split into non-overlapping 6-mer tokens.
Mapping: Each unique 6-mer was mapped to a unique integer ID to create a vocabulary.
Encoding: The tokenized sequences were then represented as a list of these integer IDs.

Intended Uses

The dataset can be used for:

Comparative Genomics: Comparing genomic features and training models to distinguish between species (e.g., human vs. worm).
Genomic Classification: Training and evaluating machine learning models on tasks like species identification from DNA sequences.
LLM Pre-training: Providing a corpus for pre-training large language models on human DNA sequences, which can then be fine-tuned for more specific downstream tasks.

Limitations and Ethical Considerations

Unspecified Origin: Without an official dataset card from the author, the precise origin and collection methodology of the sequences are unknown. This may impact reproducibility and potential biases.
Licensing: The license is currently unspecified. For any public or commercial use, it is necessary to verify the terms with the author, Petr Simecek, on Hugging Face.

How to Get the Dataset

You can easily load this dataset from the Hugging Face Hub using the datasets library:

from datasets import load_dataset

# Load the tokenized dataset
dataset = load_dataset("simecek/Human_DNA_v0_DNABert6tokenized")

# Access the training split
train_dataset = dataset["train"]