---
license: apache-2.0
tags:
- biology
- bacteria
- genomics
- phenotypes
- DNA
pretty_name: Dataset for predicting phenotypic traits from whole bacterial genomes (DNA)
size_categories:
- 10K<n<100K
---
# Dataset for phenotypic traits prediction from whole bacterial genomes (DNA)

A dataset of 24,462 bacterial genomes across 15,477 species with diverse phenotypic traits as labels.

The genome protein sequences have been extracted from [GenBank](https://www.ncbi.nlm.nih.gov/genbank/). Each row contains the whole bacterial genome DNA, with the DNA from different contigs separated by a space.

The phenotypic traits have been extracted from a number of sources [1, 2, 3] and include a diversity of categorical phenotypes. We exclude phenotypic traits with a low nr of samples, giving us `139` uniqe phenotypic traits. If the same or similar label appeared in two different sources,
we kept it as separate labels as the label collection setup may differ for the labels.

## Labels
We provide labels in the `Files and versions` (`labels.csv`).

## Usage
We recommend loading the dataset in a streaming mode to prevent memory errors.
```python
from datasets import load_dataset


ds = load_dataset("macwiatrak/bacbench-phenotypic-traits-dna", split="train", streaming=True)
```

### Fetch the labels for the genome
```python
import pandas as pd

from datasets import load_dataset


ds = load_dataset("macwiatrak/bacbench-phenotypic-traits-dna", split="train", streaming=True)
item = next(iter(ds))

# read labels (available in repo root)
labels_df = pd.read_csv("<input-dir>/labels.csv").set_index("genome_name")

# fetch labels
labels = labels_df.loc[item["genome_name"]]
# drop labels without a value for the genome (NaN)
labels = labels.dropna()
```

## Split

Due to low number of samples for many phenotypic traits and the variability between genomes, which may skew the results when using a single split, we recommend training and evaluating 
the model across numerous splits. 
Specifically, for each phenotypic traits we recommend:
1. Splitting the data into `60 / 20 / 20` (%) train, validation and test partitions respectively
2. Training the model on the train set and monitoring the results on the validation set using macro `AUROC`.
3. Using the best performing model on validation to evaluate the model on the test set.
4. Repeat the (1-3) steps 5 times and report the average result across the runs.

See [github repository](https://github.com/macwiatrak/Bacbench) for details on how to embed the dataset with DNA and protein language models as well as code to predict antibiotic
resistance from sequence. For coding sequence representation of the genome see the [phenotypic-traits-protein-sequences](https://huggingface.co/datasets/macwiatrak/bacbench-phenotypic-traits-protein-sequences)
dataset.

## References

[1] Madin, Joshua S., et al. "A synthesis of bacterial and archaeal phenotypic trait data." Scientific data 7.1 (2020): 170.

[2] Weimann, Aaron, et al. "From genomes to phenotypes: Traitar, the microbial trait analyzer." MSystems 1.6 (2016): 10-1128.

[3] Brbić, Maria, et al. "The landscape of microbial phenotypic traits and associated genes." Nucleic acids research (2016): gkw964.

---
dataset_info:
  features:
  - name: genome_name
    dtype: string
  - name: contig_name
    sequence: string
  - name: dna_sequence
    dtype: string
  - name: taxid
    dtype: string
  splits:
  - name: train
    num_bytes: 104142909218
    num_examples: 24462
  download_size: 48217470302
  dataset_size: 104142909218
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---