ArticleBMC bioinformatics2014
Efficient design of meganucleases using a machine learning approach.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Review
- CRISPR-Cas9 in basic and translational aspects of cancer therapy.BioImpacts : BI · 2024Review
- 'Off-the-shelf' allogeneic CAR T cells: development and challenges.Nature reviews. Drug discovery · 2020Review
- Genome-Editing Technologies: Concept, Pros, and Cons of Various Genome-Editing Techniques and Bioethical Concerns for Clinical Application.Molecular therapy. Nucleic acids · 2019Review
- Application of Genome Editing Techniques in Immunology.Archivum immunologiae et therapiae experimentalis · 2018Review
- Synthetic biology in cell-based cancer immunotherapy.Trends in biotechnology · 2015Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMeganucleases 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.
resultsHere 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.
conclusionsThis 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.
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Registered trials
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