title: Efficient design of meganucleases using a machine learning approach.
abstract: BACKGROUND
  Meganucleases are important tools for genome engineering, providing an efficient way to generate DNA double-strand breaks at specific loci of interest. Numerous experimental efforts, ranging from in vivo selection to in silico modeling, have been made to re-engineer meganucleases to target relevant DNA sequences.
  
  
  RESULTS
  Here we present a novel in silico method for designing custom meganucleases that is based on the use of a machine learning approach. We compared it with existing in silico physical models and high-throughput experimental screening. The machine learning model was used to successfully predict active meganucleases for 53 new DNA targets.
  
  
  CONCLUSIONS
  This new method shows competitive performance compared with state-of-the-art in silico physical models, with up to a fourfold increase in terms of the design success rate. Compared to experimental high-throughput screening methods, it reduces the number of screening experiments needed by a factor of more than 100 without affecting final performance.
authors.forename: Mikhail
authors.initials: M
authors.lastname: Zaslavskiy
authors.forename: Claudia
authors.initials: C
authors.lastname: Bertonati
authors.affiliation: Research and Development department, Cellectis, 8 rue de la Croix Jarry, Paris 75013, France. philippe.duchateau@cellectis.com.
authors.forename: Philippe
authors.initials: P
authors.lastname: Duchateau
authors.forename: Aymeric
authors.initials: A
authors.lastname: Duclert
authors.forename: George H
authors.initials: GH
authors.lastname: Silva
mesh_terms: D001185:Artificial Intelligence; D003198:Computer Simulation; D004247:DNA / Q000737:chemistry / Q000235:genetics*; D057166:High-Throughput Screening Assays / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*
chemical_list: D004247:DNA