Noud HM Klaassen

Abstract Promoters are the core regulatory elements of all genes. Their activity ensures the correct transcription level of each individual gene, which is essential for cellular homeostasis and responses to a wide range of signals. One of the major challenges in genomics is to build computational models that accurately predict genome-wide gene expression from the sequences of regulatory elements1.

Abstract Transcription of genes is regulated by DNA elements such as promoters and enhancers, the activity of which are in turn controlled by many transcription factors. Owing to the highly complex combinatorial logic involved, it has been difficult to construct computational models that predict gene activity from DNA sequence.

Abstract One of the major challenges in genomics is to build computational models that accurately predict genome-wide gene expression from the sequences of regulatory elements. At the heart of gene regulation are promoters, yet their regulatory logic is still incompletely understood. Here, we report PARM, a cell-type specific deep learning model trained on specially designed massively parallel reporter assays that query human promoter sequences.