Dataset Information

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.

SUBMITTER: Zaslavskiy M

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

REPOSITORIES: biostudies-literature

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Publications

Efficient design of meganucleases using a machine learning approach.

Zaslavskiy Mikhail M Bertonati Claudia C Duchateau Philippe P Duclert Aymeric A Silva George H GH

BMC bioinformatics 20140617

<h4>Background</h4>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.<h4>Results</h4>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 wit ...[more]

PMID: 24934562

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Dataset Information

Efficient design of meganucleases using a machine learning approach.

Background

Results

Conclusions

Publications

Efficient design of meganucleases using a machine learning approach.

Similar Datasets

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