PromoterAI

This repository contains the source code for PromoterAI, a deep learning model for predicting the impact of promoter variants on gene expression.

Precomputed PromoterAI scores for all human promoter single nucleotide variants are freely available for academic and non-commercial research use upon acceptance of the academic license agreement. Scores range from –1 to 1, with negative values indicating under-expression and positive values indicating over-expression.

Overview

The scripts included in this repository serve as a reference for the PromoterAI training and inference pipeline. Full execution requires additional resources and model weights that are currently not part of this repository.

To begin, download the appropriate reference genome (.fa) and regulatory profile (.bigWig) tracks. Organize the .bigWig file paths and their corresponding transformations into a .tsv file, where each row represents a prediction target, with the following columns:

• fwd: path to the forward-strand .bigWig file
• rev: path to the reverse-strand .bigWig file
• xform: transformation applied to the prediction target

fwd	rev	xform
data/bigwig/ENCFF245ZZX.bigWig	data/bigwig/ENCFF245ZZX.bigWig	lambda x: np.arcsinh(np.nan_to_num(x))
data/bigwig/ENCFF279QDX.bigWig	data/bigwig/ENCFF279QDX.bigWig	lambda x: np.arcsinh(np.nan_to_num(x))
data/bigwig/ENCFF480GFU.bigWig	data/bigwig/ENCFF480GFU.bigWig	lambda x: np.arcsinh(np.nan_to_num(x))
data/bigwig/ENCFF815ONV.bigWig	data/bigwig/ENCFF815ONV.bigWig	lambda x: np.arcsinh(np.nan_to_num(x))

In addition, create a .tsv file listing the genomic positions of interest, with the following columns: chrom, pos, strand.

chrom	pos	strand
chr1	11868	1
chr1	12009	1
chr1	29569	-1
chr1	17435	-1

Once these files are prepared, set up the promoterai conda environment (environment.yml) and run preprocess.sh with the paths to the genome .fa file, the profile and position .tsv files, and an output folder for writing the generated TFRecord files. Next, run train.sh, specifying the TFRecord folder(s) and an output folder for saving the trained model. After training, run finetune.sh using the trained model as input. The fine-tuned model will be saved in a new folder with _finetune appended to the original model folder name.

To score variants, organize them in a .tsv file with the following columns: chrom, pos, ref, alt, strand.

chrom	pos	ref	alt	strand
chr16	84145214	G	T	1
chr16	84145333	G	C	1
chr2	55232249	T	G	-1
chr2	55232374	C	T	-1

Run score.sh with the path to the genome .fa file, the model folder, and the variant .tsv file. Scores will be added as a new column labeled score, with the output file name constructed by appending the model folder’s basename to the variant file name.

Contact

Kishore Jaganathan: kjaganathan@illumina.com
Gherman Novakovsky: gnovakovsky@illumina.com
Kyle Farh: kfarh@illumina.com