Shen, MaxZhao, KevinLiu, David2022-02-112021-10-11Shen, Max, Kevin Zhao, David Liu. "Reconstruction of evolving gene variants and fitness from short sequencing reads." Nature Chemical Biology 17, no. 11 (2021): 1188-1198. DOI: 10.1038/s41589-021-00876-61552-44501552-4469https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37370767Directed evolution can generate proteins with tailor-made activities. However, full-length genotypes, their frequencies and fitnesses are difficult to measure for evolving gene-length biomolecules using most high-throughput DNA sequencing methods, as short read lengths can lose mutation linkages in haplotypes. Here we present Evoracle, a machine learning method that accurately reconstructs full-length genotypes (R2 = 0.94) and fitness using short-read data from directed evolution experiments, with substantial improvements over related methods. We validate Evoracle on phage-assisted continuous evolution (PACE) and phage-assisted non-continuous evolution (PANCE) of adenine base editors and OrthoRep evolution of drug-resistant enzymes. Evoracle retains strong performance (R2 = 0.86) on data with complete linkage loss between neighboring nucleotides and large measurement noise, such as pooled Sanger sequencing data (~US$10 per timepoint), and broadens the accessibility of training machine learning models on gene variant fitnesses. Evoracle can also identify high-fitness variants, including low-frequency ‘rising stars’, well before they are identifiable from consensus mutationsen-USCell BiologyMolecular BiologyJournal Article2022-02-1110.1038/s41589-021-00876-6