metadata
dataset_info:
features:
- name: full_sequence
dtype: string
- name: enhancer_sequence
dtype: string
- name: promoter
dtype: string
- name: discrete_label
dtype:
class_label:
names:
'0': 0
'1': 1
'2': 2
'3': 3
'4': 4
- name: activity
dtype: float32
splits:
- name: train
num_bytes: 3518883112
num_examples: 804592
- name: validation
num_bytes: 354865790
num_examples: 81140
- name: test
num_bytes: 360253942
num_examples: 82372
download_size: 611028266
dataset_size: 4234002844
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
Enhancer generation dataset for NTv3-generative model
This dataset contains the processed STARR-seq data from the DeepSTARR study. Here we processed it with promoter context for conditional sequence generation training for NTv3-generative. Each enhancer is paired and inserted into two promoter contexts (RpS12 and DSCP), allowing the study of promoter-specific enhancer activity.
Source Data
- Original Study: de Almeida et al., "DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers" (Nature Genetics, 2022)
- Organism: Drosophila melanogaster (fruit fly)
- Assay: STARR-seq (Self-Transcribing Active Regulatory Region sequencing)
Dataset Schema
| Field | Type | Description |
|---|---|---|
full_sequence |
string |
4096bp sequence with enhancer inserted into promoter backbone |
enhancer_sequence |
string |
Raw 249bp enhancer sequence |
promoter |
string |
Promoter type: "RpS12" (housekeeping) or "DSCP" (developmental) |
discrete_label |
int |
Discretized activity bin (0-4) |
activity |
float |
Original log2 enrichment value from STARR-seq |
Discrete Label Bins
Activity values are discretized using bin edges [-2.5, 0, 2.5, 5]:
| Label | Activity Range | Interpretation |
|---|---|---|
| 0 | activity < -2.5 | Very low / silencer |
| 1 | -2.5 <= activity < 0 | Low / inactive |
| 2 | 0 <= activity < 2.5 | Moderate |
| 3 | 2.5 <= activity < 5 | High |
| 4 | activity >= 5 | Very high |
Promoter Contexts
- RpS12: Housekeeping promoter (ribosomal protein S12), enhancer inserted at position 968
- DSCP: Developmental core promoter (Drosophila Synthetic Core Promoter), enhancer inserted at position 1018
Dataset Statistics
| Split | Samples | Description |
|---|---|---|
| train | ~804,592 | Training set (402,296 enhancers x 2 promoters) |
| validation | ~81,140 | Validation set (40,570 enhancers x 2 promoters) |
| test | ~82,372 | Test set (41,186 enhancers x 2 promoters) |
Usage
Loading the Dataset
from datasets import load_dataset
# Load all splits
dataset = load_dataset("InstaDeepAI/NTv3_enhancer_generation")
# Access specific splits
train_data = dataset["train"]
val_data = dataset["validation"]
test_data = dataset["test"]
Accessing Samples
# Get a single sample
sample = dataset["train"][0]
print(f"Promoter: {sample['promoter']}")
print(f"Activity: {sample['activity']:.2f}")
print(f"Discrete label: {sample['discrete_label']}")
print(f"Enhancer length: {len(sample['enhancer_sequence'])}")
print(f"Full sequence length: {len(sample['full_sequence'])}")
Filtering by Promoter
# Get only RpS12 (housekeeping) samples
rps12_data = dataset["train"].filter(lambda x: x["promoter"] == "RpS12")
# Get only DSCP (developmental) samples
dscp_data = dataset["train"].filter(lambda x: x["promoter"] == "DSCP")
Filtering by Activity Level
# Get high activity enhancers (discrete_label >= 3)
high_activity = dataset["train"].filter(lambda x: x["discrete_label"] >= 3)
# Get enhancers with specific activity range
moderate_to_high = dataset["train"].filter(lambda x: 0 <= x["activity"] < 5)
Streaming (for large-scale processing)
from datasets import load_dataset
# Stream without downloading entire dataset
dataset = load_dataset("InstaDeepAI/NTv3_enhancer_generation", streaming=True)
for sample in dataset["train"]:
# Process sample
pass