CRISPR/CAS9 Target Prediction with Deep Learning

The CRISPR/CAS9 system is a powerful tool for regulating damaged genome sequences. Nucleases that are damaged in their sequence are called miRNAs (micro RNAs). The miRNAs targeted by multiple promoter sgRNA (single guide RNA) are cut or regulated from RNA by the CRISPR/CAS9 method. The sgRNAs targeted to the wrong miRNAs may cause unwanted genome distortions. In order to minimize these genome distortions, sgRNA target estimation was performed for CRISPR/CAS9 with deep learning in this study. In this article, convolutional neural networks (Convolutional Neural Networks- CNN) and multilayer perceptron (Multi Layer Perceptron-MLP) algorithms are used. A performance comparison of the CRISPR/CAS9 system for both algorithms was performed.

[1]  Qinghua Zhang,et al.  CRISPR-Local: a local single-guide RNA (sgRNA) design tool for non-reference plant genomes , 2018, Bioinform..

[2]  Melike Günay,et al.  Digital Data Forgetting: A Machine Learning Approach , 2018, 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT).

[3]  Ka-Chun Wong,et al.  Off-target predictions in CRISPR-Cas9 gene editing using deep learning , 2018, Bioinform..

[4]  Albert Pla,et al.  miRAW: A deep learning-based approach to predict microRNA targets by analyzing whole microRNA transcripts , 2018, PLoS Comput. Biol..

[5]  R. Lin,et al.  MicroRNA-focused CRISPR-Cas9 library screen reveals fitness-associated miRNAs , 2018, RNA.

[6]  Guohui Chuai,et al.  DeepCRISPR: optimized CRISPR guide RNA design by deep learning , 2018, Genome Biology.

[7]  Houxiang Zhu,et al.  CRISPR-DT: designing gRNAs for the CRISPR-Cpf1 system with improved target efficiency and specificity , 2018, bioRxiv.

[8]  Jennifer Listgarten,et al.  Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs , 2018, Nature Biomedical Engineering.

[9]  G. Aquino-Jarquin.  Emerging Role of CRISPR/Cas9 Technology for MicroRNAs Editing in Cancer Research. , 2017, Cancer research.

[10]  Itay Mayrose,et al.  A machine learning approach for predicting CRISPR-Cas9 cleavage efficiencies and patterns underlying its mechanism of action , 2017, PLoS Comput. Biol..

[11]  Hirohide Saito,et al.  Cell-type-specific genome editing with a microRNA-responsive CRISPR–Cas9 switch , 2017, Nucleic acids research.

[12]  Fan Yang,et al.  Identification of Key Genes Affecting Results of Hyperthermia in Osteosarcoma Based on Integrative ChIP-Seq/TargetScan Analysis , 2017, Medical science monitor : international medical journal of experimental and clinical research.

[13]  Jon Cohen.  The Birth of CRISPR Inc. , 2017, Science.

[14]  Jan Winter,et al.  GenomeCRISPR - a database for high-throughput CRISPR/Cas9 screens , 2016, Nucleic Acids Res..

[15]  J. Joly,et al.  Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR , 2016, Genome Biology.

[16]  Yaguang Xi,et al.  CRISPR/cas9, a novel genomic tool to knock down microRNA in vitro and in vivo , 2016, Scientific Reports.

[17]  Daniel R. Zerbino,et al.  Ensembl 2016 , 2015, Nucleic Acids Res..

[18]  Xiao-Hui Zhang,et al.  Off-target Effects in CRISPR/Cas9-mediated Genome Engineering , 2015, Molecular therapy. Nucleic acids.

[19]  Tyra G. Wolfsberg,et al.  CRISPRz: a database of zebrafish validated sgRNAs , 2015, Nucleic Acids Res..

[20]  Matthew E. Ritchie,et al.  limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.

[21]  Enrique Blanco,et al.  ENCODE (Encyclopedia of DNA Elements) , 2014 .

[22]  B. Wiedenheft,et al.  A CRISPR method for genome engineering , 2014, F1000prime reports.

[23]  Peggy Hall,et al.  The NHGRI GWAS Catalog, a curated resource of SNP-trait associations , 2013, Nucleic Acids Res..

[24]  Norbert Gretz,et al.  miRWalk - Database: Prediction of possible miRNA binding sites by "walking" the genes of three genomes , 2011, J. Biomed. Informatics.

[25]  Tolga Can,et al.  Using network context as a filter for miRNA target prediction , 2011, Biosyst..

[26]  D. Bartel.  MicroRNAs: Target Recognition and Regulatory Functions , 2009, Cell.

[27]  Stijn van Dongen,et al.  miRBase: tools for microRNA genomics , 2007, Nucleic Acids Res..

[28]  A. Hatzigeorgiou,et al.  TarBase: A comprehensive database of experimentally supported animal microRNA targets. , 2005, RNA.

[29]  Paul T. Groth,et al.  The ENCODE (ENCyclopedia Of DNA Elements) Project , 2004, Science.

[30]  Thomas L. Madden,et al.  Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. , 1997, Nucleic acids research.

[31]  A. Kassens.  Targets , 2019, Intemperate Spirits.

[32]  Paul Kersey,et al.  Ensembl Plants: Integrating Tools for Visualizing, Mining, and Analyzing Plant Genomics Data. , 2016, Methods in molecular biology.

[33]  Mehmet Tugrul Tekbulut.  MicroRNA target prediction by constraint programming , 2006 .

[34]  Anton J. Enright,et al.  MicroRNA Targets in Drosophila , 2003, Genome Biology.