InstaDeepAI/NTv3_enhancer_generation

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