Sequential regulatory activity prediction with long-range convolutional neural networks

Functional genomics approaches to better model genotype-phenotype relationships have important applications toward understanding genomic function and improving human health. In particular, methods to predict transcription factor (TF) binding and chromatin attributes from DNA sequence show promise for determining mechanisms for the plethora of noncoding variants statistically associated with disease in human populations. However, TF binding and chromatin are primarily interesting insofar as they affect gene expression. Thus, such modeling frameworks would likely prove more valuable if they could predict gene expression from DNA sequence. In large mammalian genomes, gene expression depends on very large regions of sequence with complex rules that have been established as high-level principles, but only rarely described in detail for individual loci. Here, I will suggest solutions to these challenges and describe an initial machine learning system to predict transcription across large genomes from DNA sequence using deep convolutional neural networks.Non UBCUnreviewedAuthor affiliation: Calico LabsPostdoctora