Personalized genomes for DL models supporting data

Archive of models and data associated with our manuscript "Training deep learning models on personalized genomic sequences improves variant effect prediction".

Code for training and benchmarking LCL models is available at https://github.com/Danko-Lab/clipnet_ablation, whereas code for training and benchmarking K562 models is available at https://github.com/Danko-Lab/clipnet_k562/.

Model files & metadata:

n{i}_run{j}.tar

CLIPNET LCL models trained on i individuals

subsample_individuals_ids.tar

text files containing lists of the individuals used to train the above models.

reference_models.tar

CLIPNET LCL model trained on data from 67 PRO-cap libraries, but using hg38 sequences instead of personal genomes.

clipnet_k562_reference.tar

hg38-trained model described above transfer learned to K562.

Benchmark data:

across_loci_metrics.tar

benchmarks of LCL models at predicting transcription initiation at individual CREs within the genome

qtl_metrics.tar

benchmarks of LCL models at predicting differences in transcription initiation between individuals at initiation QTLs

k562_data.tar

benchmarks of the reference-trained K562 model and one transferred over from the personalized CLIPNET model on MPRA data from https://www.biorxiv.org/content/10.1101/2024.05.05.592437v1