Datasets:

HuggingFaceBio
/

malinois-mpra-regression

pretty_name: Malinois/Gosai MPRA Regression
task_categories:
  - tabular-regression
tags:
  - biology
  - genomics
  - dna
  - mpra
  - carbon
size_categories:
  - 100K<n<1M

Malinois/Gosai MPRA Regression

This dataset preprocesses the Gosai et al. 2024 supplementary MPRA table used by the Malinois benchmark for supervised DNA-to-activity regression. Each row contains a DNA sequence and three cell-type-specific activity targets: K562_log2FC, HepG2_log2FC, and SKNSH_log2FC.

No new license is asserted by this preprocessing. Users should follow the terms of the source publication and supplementary data.

Source

Publication: Gosai et al., Machine-guided design of cell-type-targeting cis-regulatory elements, Nature 2024.
Source table: 41586_2024_8070_MOESM4_ESM.txt.
Source URL: https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-024-08070-z/MediaObjects/41586_2024_8070_MOESM4_ESM.txt.

Splits

Chromosome splits match the Carbon fine-tuning experiments and the public Malinois setup we used:

Split	Chromosomes	Rows	Rows with all SE <= 1.0
train	all except validation/test chromosomes	668,946	627,661
validation	19, 21, X	62,406	58,811
test	7, 13	66,712	62,582

Total rows after filtering finite targets/standard errors and nonempty sequences: 798,064.

Columns

id: original row identifier from the source table.
split: train, validation, or test.
chromosome: normalized chromosome label.
data_project, oligo, variant_class: source metadata.
sequence: uppercase DNA sequence.
reverse_complement: reverse complement of sequence.
forward_rc_concat: <dna>sequence</dna><dna>reverse_complement</dna>, matching the best Carbon fine-tuning recipe.
K562_log2FC, HepG2_log2FC, SKNSH_log2FC: raw regression targets.
K562_lfcSE, HepG2_lfcSE, SKNSH_lfcSE: target standard errors.
*_train_zscore: target standardized using train-split mean/std.
all_se_le_1: true when all three SE columns are <= 1.0; this was the main reported validation/test metric filter.
any_log2fc_gt_0_5: true when any target is greater than 0.5; this was used for optional high-activity training upsampling.

Train z-score statistics:

Target	Mean	Std
K562_log2FC	0.49943020	1.17725282
HepG2_log2FC	0.46267671	1.05124023
SKNSH_log2FC	0.41405871	1.16609108

Usage

from datasets import load_dataset

ds = load_dataset("HuggingFaceBio/malinois-mpra-regression")
train = ds["train"]
validation_metric = ds["validation"].filter(lambda row: row["all_se_le_1"])

example = train[0]
sequence = example["forward_rc_concat"]
labels = [
    example["K562_log2FC_train_zscore"],
    example["HepG2_log2FC_train_zscore"],
    example["SKNSH_log2FC_train_zscore"],
]

To recreate the dataset:

python create_dataset.py --repo-id HuggingFaceBio/malinois-mpra-regression --push