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