Datasets:

davidcechak
/

Worm_DNA_v0_DNABert6tokenized

annotations_creators:
  - no-annotation
language_creators:
  - no-annotation
task_categories:
  - text-classification
tags:
  - genomics
  - dna
  - dnabert
  - bioinformatics
  - worm-dna
  - tokenized
source_datasets:
  - C. elegans (Caenorhabditis elegans) reference genome (unconfirmed)
language:
  - en
license: other
license_name: unspecified

`Worm_DNA_v0_DNABert6tokenized`

Dataset Description

The davidcechak/Worm_DNA_v0_DNABert6tokenized is a dataset containing DNA sequences from the model organism Caenorhabditis elegans (a nematode worm), preprocessed for use with large language models (LLMs) in genomics.

This dataset was tokenized into non-overlapping 6-mers (6-base pair sequences), which is a standard approach for applying transformer models like DNABert to genomic data. It is ready for direct use in comparative genomics tasks, such as building a classifier to distinguish between human and worm DNA when used in conjunction with a compatible human DNA dataset like simecek/Human_DNA_v0_DNABert6tokenized.

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 raw DNA sequences are likely derived from the reference genome of Caenorhabditis elegans.

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 worm DNA sequences, which can then be fine-tuned for more specific downstream tasks related to C. elegans genomics.

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. Users should exercise caution and attempt to verify licensing terms with the author, David Cechak, before commercial or public use.

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("davidcechak/Worm_DNA_v0_DNABert6tokenized")

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