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". 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