---
license: cc-by-4.0
task_categories:
- text-regression
language:
- en
tags:
- biology
- dna
- genomics
- promoter
- gene-expression
- carbon
---

# Random Promoter DREAM Challenge 2022

This dataset repackages the processed Random Promoter DREAM Challenge 2022
files from Zenodo record `10633252` for use with `datasets`.

The task is sequence-to-expression regression on synthetic yeast promoter
sequences. The canonical supervised config contains random promoter training
examples, validation examples, and labeled designed test promoters.

## Configs

- `supervised`: `train`, `validation`, and `test` splits with promoter
  sequences and measured activity.
- `challenge_test_sequences`: unlabeled test sequences for submission-style
  prediction workflows.
- `test_subset_membership`: normalized IDs from `test_subset_ids.tar.gz`.
- `public_leaderboard_ids`: normalized IDs from `public_leaderboard_ids.tar.gz`.

## Schema

`supervised`:

- `sequence`: DNA sequence.
- `activity`: measured promoter activity.
- `sequence_length`: sequence length in base pairs.
- `source_file`: source filename.
- `row_id`: zero-based row index within the source split.

ID metadata configs:

- `subset`: subset name inferred from the archive member.
- `item_id`: raw ID token from the source line.
- `row_id`: integer row ID when `item_id` is numeric; otherwise `-1`.
- `source_member`: archive member path.
- `line_number`: line number within the member.
- `raw_line`: unmodified stripped source line.

## Usage

```py
from datasets import load_dataset

ds = load_dataset("HuggingFaceBio/random-promoter-dream-2022", "supervised")
train = ds["train"]
validation = ds["validation"]
test = ds["test"]

subsets = load_dataset("HuggingFaceBio/random-promoter-dream-2022", "test_subset_membership", split="train")
```

## Source

Source: Random Promoter DREAM Challenge 2022, Zenodo DOI
`10.5281/zenodo.10633252`.

The source record is licensed CC BY 4.0. Cite the original DREAM Challenge data
and paper when using this dataset.

## Reproduction

This dataset repo includes `create_dataset.py`, the script used to download,
convert, and upload the configs.