The Kipoi repository accelerates community exchange and reuse of predictive models for genomics

To the Editor — Advances in machine learning, coupled with rapidly growing genome sequencing and molecular profiling datasets, are catalyzing progress in genomics 1 . In particular, predictive machine learning models, which are mathematical functions trained to map input data to output values, have found widespread usage. Prominent examples include calling variants from whole-genome sequencing data 2 , 3 , estimating CRISPR guide activity 4 , 5 and predicting molecular phenotypes, including transcription factor binding, chromatin accessibility and splicing efficiency, from DNA sequence 1 , 6 , 7 , 8 , 9 , 10 , 11 . Once trained, these models can be probed in silico to infer quantitative relationships between diverse genomic data modalities, enabling several key applications such as the interpretation of functional genetic variants and rational design of synthetic genes.