Datasets:

GustavoHCruz
/

DNA_coding_regions

license: cc-by-4.0
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
dataset_info:
  features:
    - name: accession
      dtype: string
    - name: organism
      dtype: string
    - name: sequence
      dtype: string
    - name: introns
      list:
        - name: after
          dtype: string
        - name: before
          dtype: string
        - name: end
          dtype: int64
        - name: gene
          dtype: string
        - name: sequence
          dtype: string
        - name: start
          dtype: int64
    - name: exons
      list:
        - name: after
          dtype: string
        - name: before
          dtype: string
        - name: end
          dtype: int64
        - name: gene
          dtype: string
        - name: sequence
          dtype: string
        - name: start
          dtype: int64
    - name: proteins
      list:
        - name: end
          dtype: int64
        - name: gene
          dtype: string
        - name: sequence
          dtype: string
        - name: start
          dtype: int64
  splits:
    - name: train
      num_bytes: 11536678696
      num_examples: 1677609
  download_size: 5448417115
  dataset_size: 11536678696
task_categories:
  - text-classification
  - token-classification
  - translation
tags:
  - Exons
  - Introns
  - Proteins
  - DNA
pretty_name: DNA Coding Regions
size_categories:
  - 1M<n<10M

DNA Coding Regions Dataset

This is a curated collection of genomic sequences extracted directly from NCBI GenBank, designed to support research in introns and exons classification, DNA-to-protein translation, gene structure analysis, and biological sequence modeling with deep learning architectures.

Source and Extraction Pipeline

All records were extracted from GenBank using Biopython.
The dataset construction followed a reproducible data processing pipeline written in Python, which:

Downloads and parses GenBank records.
Extracts genomic DNA sequences, their associated exons, introns, and coding sequences (CDS).
Processes the strand orientation to produce normalized sequences.
Removes duplicate entries based on (sequence, organism) pairs.
Assembles each record into a structured JSONL format suitable for machine learning models.

The GenBank search query used for data collection was:

"genomic DNA"[Filter]
AND ("exon"[Feature Key] OR "intron"[Feature Key])
AND "CDS"[Feature Key]
AND ("3"[SLEN] : "16384"[SLEN])

You can find more information about the pipeline in the GitHub from the DNA Coding Regions repository.

Dataset Structure

Each entry in the dataset corresponds to a unique DNA sequence, identified by its GenBank accession.
The dataset is serialized in JSON Lines (.jsonl) format and can be loaded with the Hugging Face datasets library.

Example record

{
  "accession": "NC_045512.2",
  "organism": "Homo sapiens",
  "sequence": "ATTAAAGGTTTATACCTTCCCAGGTAACAAACCAACCAACTTTCGAT...",
  "introns": [
    {
      "sequence": "TTGTAGACCAGTGCAGTA...",
      "start": 1450,
      "end": 1783,
      "gene": "ORF1ab",
      "before": "ATGCCDG",
      "after": "TAACAFG"
    }
  ],
  "exons": [
    {
      "sequence": "ATGGACACAAGTCAGG...",
      "start": 1,
      "end": 1449,
      "gene": "ORF1ab",
      "before": null,
      "after": "GT"
    }
  ],
  "proteins": [
    {
      "sequence": "MESLVPGFNEKTHVQLSLPVLQVRDVLVRGFGDSVEEVL...",
      "start": 1,
      "end": 4405,
      "gene": "ORF1ab"
    }
  ]
}

Field Descriptions

Field	Type	Description
accession	`str`	GenBank accession number for the DNA sequence.
organism	`str`	Name of the organism from which the sequence was derived.
sequence	`str`	The full genomic DNA sequence (processed strand).
introns	`list`	List of intronic regions associated with this DNA sequence. Each item contains: `sequence`: only the nucleotide sequence of the intron `start`, `end`: coordinates relative to the DNA sequence `gene`: gene name when annotated `before`, `after`: short flanking sequences
exons	`list`	List of exonic regions associated with this DNA sequence. Same structure as `introns`.
proteins	`list`	List of coding sequences (CDS) translated to amino acid sequences, with: `sequence`: protein sequence `start`, `end`: coordinates in the DNA sequence `gene`: gene name

Applications

This dataset can be directly used for:

DNA to protein translation modeling
Exon and Introns classification
Splicing prediction
Genomic representation learning
Bioinformatics-focused LLM pretraining (DAPT)

Loading Example

from datasets import load_dataset

dataset = load_dataset("gu-dudi/DNA_coding_regions")

print(dataset)
print(dataset["train"][0])

Dataset Metadata

Source: NCBI GenBank
Processed with: Biopython, Pandas, tqdm
Maintainer: Gustavo Henrique Ferreira Cruz
License: Open for research and educational use
Format: JSON Lines (UTF-8)

Disclaimer on data completeness

Not all genomic entries in this dataset contain every type of annotation (exons, introns, and proteins).

While the GenBank records were filtered to include sequences annotated with "exon", "intron", and "CDS" feature keys, the underlying annotations in GenBank are not always deterministic or complete.

Some sequences may include only exons or introns without corresponding protein-coding regions, or vice versa.

This reflects the inherent variability and curation differences across submissions in the GenBank database.

Citation

If you use this dataset in your research, please cite:

@misc{gustavo_henrique_ferreira_cruz_2025,
  author       = {Gustavo Henrique Ferreira Cruz},
  title        = {DNA\_coding\_regions (Revision 16f4e3a)},
  year         = 2025,
  url          = {https://huggingface.co/datasets/GustavoHCruz/DNA_coding_regions},
  doi          = {10.57967/hf/7238},
  publisher    = {Hugging Face}
}

Version and Integrity

Version: 1.0
Total entries: 1,677,609
Deduplication: duplicates removed based on (sequence, organism) pair
Strand normalization: handled during extraction

Notes

Coordinates (start, end) are relative to the parent DNA sequence.
sequence inside each intron/exon corresponds only to that region (not the full DNA).
Protein sequences are already translated amino acid chains, not nucleotide fragments.

Developed as part of the Master’s research project on DNA sequence understanding and translation using deep learning models.