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Very fast CRISPR on demand.

ABSTRACT: CRISPR-Cas systems provide versatile tools for programmable genome editing. Here, we developed a caged RNA strategy that allows Cas9 to bind DNA but not cleave until light-induced activation. This approach, referred to as very fast CRISPR (vfCRISPR), creates double-strand breaks (DSBs) at the submicrometer and second scales. Synchronized cleavage improved kinetic analysis of DNA repair, revealing that cells respond to Cas9-induced DSBs within minutes and can retain MRE11 after DNA ligation. Phosphorylation of H2AX after DNA damage propagated more than 100 kilobases per minute, reaching up to 30 megabases. Using single-cell fluorescence imaging, we characterized multiple cycles of 53BP1 repair foci formation and dissolution, with the first cycle taking longer than subsequent cycles and its duration modulated by inhibition of repair. Imaging-guided subcellular Cas9 activation further facilitated genomic manipulation with single-allele resolution. vfCRISPR enables DNA-repair studies at high resolution in space, time, and genomic coordinates.

SUBMITTER: Liu Y

PROVIDER: S-EPMC7608738 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Very fast CRISPR on demand.

Liu Yang Y Zou Roger S RS He Shuaixin S Nihongaki Yuta Y Li Xiaoguang X Razavi Shiva S Wu Bin B Ha Taekjip T

Science (New York, N.Y.) 20200601 6496

CRISPR-Cas systems provide versatile tools for programmable genome editing. Here, we developed a caged RNA strategy that allows Cas9 to bind DNA but not cleave until light-induced activation. This approach, referred to as very fast CRISPR (vfCRISPR), creates double-strand breaks (DSBs) at the submicrometer and second scales. Synchronized cleavage improved kinetic analysis of DNA repair, revealing that cells respond to Cas9-induced DSBs within minutes and can retain MRE11 after DNA ligation. Phos ...[more]

PMID: 32527834

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Project description:BackgroundAutocoding (or automatic concept indexing) occurs when a software program extracts terms contained within text and maps them to a standard list of concepts contained in a nomenclature. The purpose of autocoding is to provide a way of organizing large documents by the concepts represented in the text. Because textual data accumulates rapidly in biomedical institutions, the computational methods used to autocode text must be very fast. The purpose of this paper is to describe the doublet method, a new algorithm for very fast autocoding.MethodsAn autocoder was written that transforms plain-text into intercalated word doublets (e.g. "The ciliary body produces aqueous humor" becomes "The ciliary, ciliary body, body produces, produces aqueous, aqueous humor"). Each doublet is checked against an index of doublets extracted from a standard nomenclature. Matching doublets are assigned a numeric code specific for each doublet found in the nomenclature. Text doublets that do not match the index of doublets extracted from the nomenclature are not part of valid nomenclature terms. Runs of matching doublets from text are concatenated and matched against nomenclature terms (also represented as runs of doublets).ResultsThe doublet autocoder was compared for speed and performance against a previously published phrase autocoder. Both autocoders are Perl scripts, and both autocoders used an identical text (a 170+ Megabyte collection of abstracts collected through a PubMed search) and the same nomenclature (neocl.xml, containing over 102,271 unique names of neoplasms). In side-by-side comparison on the same computer, the doublet method autocoder was 8.4 times faster than the phrase autocoder (211 seconds versus 1,776 seconds). The doublet method codes 0.8 Megabytes of text per second on a desktop computer with a 1.6 GHz processor. In addition, the doublet autocoder successfully matched terms that were missed by the phrase autocoder, while the phrase autocoder found no terms that were missed by the doublet autocoder.ConclusionsThe doublet method of autocoding is a novel algorithm for rapid text autocoding. The method will work with any nomenclature and will parse any ascii plain-text. An implementation of the algorithm in Perl is provided with this article. The algorithm, the Perl implementation, the neoplasm nomenclature, and Perl itself, are all open source materials.

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Very fast CRISPR on demand.

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Very fast CRISPR on demand.

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