Zero-shot imputations across species are enabled through joint modeling of human and mouse epigenomics

Recent large-scale efforts to characterize functional activity in human have produced thousands of genome-wide experiments that quantify various forms of biochemistry, such as histone modifications, protein binding, transcription, and chromatin accessibility. Although these experiments represent a small fraction of the possible experiments that could be performed, they also make human more comprehensively characterized than any other species. We propose an extension to the imputation approach Avocado that enables the model to leverage genome alignments and the large number of human genomics data sets when making imputations in other species. We found that not only does this extension result in improved imputation of mouse functional experiments, but that the extended model is able to make accurate imputations for protein binding assays that have been performed in human but not in mouse. This ability to make "zero-shot" imputations greatly increases the utility of such imputation approaches and enables comprehensive imputations to be made for species even when experimental data are sparse. CCS CONCEPTS* Computing methodologies [->] Neural networks; Factorization methods; * Applied computing [->] Bioinformatics; Genomics. ACM Reference FormatJacob Schreiber, Deepthi Hegde, and William Noble. 2020. Zero-shot imputations across species are enabled through joint modeling of human and mouse epigenomics. In ACM-BCB 2020: 11th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, Sept 21-24, 2020, Virtual. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/1122445.1122456