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

SUBMITTER: Zaslavskiy M 

PROVIDER: S-EPMC4065607 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature