---
license: cc-by-nc-4.0
tags:
- genomics
- ESKAPE pathogens
- bioinformatics
- ProkBERT
dataset_info:
  features:
  - name: contig_id
    dtype: string
  - name: segment_id
    dtype: string
  - name: strand
    dtype: string
  - name: seq_start
    dtype: int64
  - name: seq_end
    dtype: int64
  - name: segment_start
    dtype: int64
  - name: segment_end
    dtype: int64
  - name: label
    dtype: string
  - name: segment_length
    dtype: int64
  - name: Nsegment
    dtype: int64
  - name: segment
    dtype: string
  splits:
  - name: ESKAPE
    num_bytes: 19414538
    num_examples: 55653
  download_size: 7614923
  dataset_size: 19414538
configs:
- config_name: default
  data_files:
  - split: ESKAPE
    path: data/ESKAPE-*
---

# Dataset Card for ESKAPE Genomic Features Dataset

## Dataset Description

This dataset includes genomic segments from ESKAPE pathogens, characterized by various genomic features such as coding sequences (CDS), intergenic regions, ncRNA, and pseudogenes. It was analyzed to understand the representations captured by models like ProkBERT-mini, ProkBERT-mini-c, and ProkBERT-mini-long.

### Data Fields

- `contig_id`: Identifier of the contig.
- `segment_id`: Unique identifier for each genomic segment.
- `strand`: DNA strand of the segment (`+` or `-`).
- `seq_start`: Starting position of the segment in the contig.
- `seq_end`: Ending position of the segment in the contig.
- `segment_start`: Starting position of the segment in the sequence.
- `segment_end`: Ending position of the segment in the sequence.
- `label`: Genomic feature category (e.g., CDS, intergenic).
- `segment_length`: Length of the genomic segment.
- `Nsegment`: Length of the genomic segment.
- `segment`: Genomic sequence of the segment.

### UMAP Embeddings and Silhouette Scores

The dataset was used to assess the zero-shot capabilities of the ProkBERT models in predicting genomic features. UMAP technique was employed to reduce dimensionality and derive embeddings, which were then evaluated using silhouette scores. The embeddings and scores reveal the models' proficiency in differentiating between genomic features and capturing the genomic structure of ESKAPE pathogens.

## Dataset Creation

The dataset is compiled from the RefSeq database and other sources, focusing on ESKAPE pathogens. The genomic features were sampled randomly, followed by contigous segmentation. The segment length is 256, shorter fragments were discarded. 


## Overview of ESKAPE Pathogens

ESKAPE pathogens are a group of bacteria that pose a significant threat to public health due to their high levels of antibiotic resistance. The acronym ESKAPE represents six genera of bacteria:

- **Enterococcus faecium**
- **Staphylococcus aureus**
- **Klebsiella pneumoniae**
- **Acinetobacter baumannii**
- **Pseudomonas aeruginosa**
- **Enterobacter species**

These pathogens are known for "escaping" the effects of antibiotics and are responsible for a large proportion of nosocomial infections (hospital-acquired infections). They are particularly concerning in healthcare settings because they can lead to severe infections that are increasingly difficult to treat due to their resistance to multiple antibiotics.


## Considerations for Using the Data

This dataset is relevant for genomic research and bioinformatics, particularly for understanding the genomic structure of ESKAPE pathogens and their representation in embedding spaces.

## Contact Information

For inquiries or feedback regarding this dataset, please contact:

- Balázs Ligeti
- Email: obalasz@gmail.com


### Dataset Curators

This dataset was curated by Balázs Ligeti from the Neural Bioinformatics Research Group, Faculty of Information Technology and Bionics, Pázmány Péter Catholic University (PPCU-FITB).


### Citation Information


If you use the code or data in this package, please cite:

```bibtex
@Article{ProkBERT2024,
  author  = {Ligeti, Balázs and Szepesi-Nagy, István and Bodnár, Babett and Ligeti-Nagy, Noémi and Juhász, János},
  journal = {Frontiers in Microbiology},
  title   = {{ProkBERT} family: genomic language models for microbiome applications},
  year    = {2024},
  volume  = {14},
  URL={https://www.frontiersin.org/articles/10.3389/fmicb.2023.1331233},       
	DOI={10.3389/fmicb.2023.1331233},      
	ISSN={1664-302X}
}
```