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      • Record: found
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      DeepCRISPR: optimized CRISPR guide RNA design by deep learning

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          Abstract

          A major challenge for effective application of CRISPR systems is to accurately predict the single guide RNA (sgRNA) on-target knockout efficacy and off-target profile, which would facilitate the optimized design of sgRNAs with high sensitivity and specificity. Here we present DeepCRISPR, a comprehensive computational platform to unify sgRNA on-target and off-target site prediction into one framework with deep learning, surpassing available state-of-the-art in silico tools. In addition, DeepCRISPR fully automates the identification of sequence and epigenetic features that may affect sgRNA knockout efficacy in a data-driven manner. DeepCRISPR is available at http://www.deepcrispr.net/.

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          The online version of this article (10.1186/s13059-018-1459-4) contains supplementary material, which is available to authorized users.

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          Deep Learning in Neural Networks: An Overview

          (2014)
          In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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            Unraveling CRISPR-Cas9 genome engineering parameters via a library-on-library approach

            Raj Chari, Prashant Y. Mali, Mark Moosburner … (2015)
            We develop an in vivo library-on-library methodology to simultaneously assess single guide RNA (sgRNA) activity across ~1,400 genomic loci. Assaying across multiple human cell types, end-processing enzymes, and two Cas9 orthologs, we unravel underlying nucleotide sequence and epigenetic parameters. Our results enable improved design of reagents, shed light on mechanisms of genome targeting, and provide a generalizable framework to study nucleic acid-nucleic acid interactions and biochemistry in high throughput.
            Article 0 comments Cited 153 times – based on 0 reviews     Review now
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              Cas-Designer: a web-based tool for choice of CRISPR-Cas9 target sites.

              Jeongbin Park, Sangsu Bae, Jin-Soo Kim (2015)
              We present Cas-Designer, a user-friendly program to aid researchers in choosing appropriate target sites in a gene of interest for type II CRISPR/Cas-derived RNA-guided endonucleases, which are now widely used for biomedical research and biotechnology. Cas-Designer rapidly provides the list of all possible guide RNA sequences in a given input DNA sequence and their potential off-target sites including bulge-type sites in a genome of choice. In addition, the program assigns an out-of-frame score to each target site to help users choose appropriate sites for gene knockout. Cas-Designer shows the results in an interactive table and provides user-friendly filter functions.
              Article 0 comments Cited 133 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Deshuang Huang: dshuang@tongji.edu.cn
                Jia Wei: Jenny.Wei@astrazeneca.com
                Qi Liu: qiliu@tongji.edu.cn
                Journal
                Journal ID (nlm-ta): Genome Biol
                Journal ID (iso-abbrev): Genome Biol
                Title: Genome Biology
                Publisher: BioMed Central (London )
                ISSN (Print): 1474-7596
                ISSN (Electronic): 1474-760X
                Publication date (Electronic): 26 June 2018
                Publication date PMC-release: 26 June 2018
                Publication date Collection: 2018
                Volume: 19
                Electronic Location Identifier: 80
                Affiliations
                [1 ]ISNI 0000 0004 0527 0050, GRID grid.412538.9, Department of Endocrinology & Metabolism, , Shanghai Tenth People’s Hospital, Tongji University, ; Shanghai, 20009 China
                [2 ]ISNI 0000000123704535, GRID grid.24516.34, Bioinformatics Department, , School of Life Sciences and Technology, Tongji University, ; Shanghai, 20009 China
                [3 ]ISNI 0000000123704535, GRID grid.24516.34, Machine Learning & Systems Biology Lab, School of Electronics and Information Engineering, , Tongji University, ; Shanghai, 201804 China
                [4 ]R&D Information, Innovation Center China, AstraZeneca, 199 Liangjing Road, Shanghai, 201203 China
                [5 ]GRID grid.440637.2, School of Life Science and Technology, , ShanghaiTech University, ; Shanghai, China
                [6 ]ISNI 0000 0001 0348 3990, GRID grid.268099.c, State Key Laboratory Cultivation Base and Key Laboratory of Vision Science, Ministry of Health and Zhejiang Provincial Key Laboratory of Ophthalmology and Optometry, , School of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, ; Wenzhou, Zhejiang, 325027 China
                Article
                Publisher ID: 1459
                DOI: 10.1186/s13059-018-1459-4
                PMC ID: 6020378
                PubMed ID: 29945655
                SO-VID: 42f4396e-78f2-460f-8a24-ee2d63f70261
                Copyright statement: © The Author(s). 2018
                License:

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                Date received : 17 January 2018
                Date accepted : 28 May 2018
                Categories
                Subject: Method
                Custom metadata
                issue-copyright-statement © The Author(s) 2018

                ScienceOpen disciplines: Genetics
                Keywords: crispr system,gene knockout,deep learning,on-targets,off-targets
                Data availability:
                ScienceOpen disciplines: Genetics
                Keywords: crispr system, gene knockout, deep learning, on-targets, off-targets

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