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Becker, M. ; Noll-Puchta, H.* ; Amend, D. ; Nolte, F.* ; Fuchs, C. ; Jeremias, I. ; Braun, C.J.*

CLUE: a bioinformatic and wet-lab pipeline for multiplexed cloning of custom sgRNA libraries.

Nucleic Acids Res. 48:e78 (2020)
Publ. Version/Full Text Research data DOI
Open Access Gold
Creative Commons Lizenzvertrag
The systematic perturbation of genomes using CRISPR/Cas9 deciphers gene function at an unprecedented rate, depth and ease. Commercially available sgRNA libraries typically contain tens of thousands of pre-defined constructs, resulting in a complexity challenging to handle. In contrast, custom sgRNA libraries comprise gene sets of self-defined content and size, facilitating experiments under complex conditions such as in vivo systems. To streamline and upscale cloning of custom libraries, we present CLUE, a bioinformatic and wet-lab pipeline for the multiplexed generation of pooled sgRNA libraries. CLUE starts from lists of genes or pasted sequences provided by the user and designs a single synthetic oligonucleotide pool containing various libraries. At the core of the approach, a barcoding strategy for unique primer binding sites allows amplifying different user-defined libraries from one single oligonucleotide pool. We prove the approach to be straightforward, versatile and specific, yielding uniform sgRNA distributions in all resulting libraries, virtually devoid of cross-contaminations. For in silico library multiplexing and design, we established an easy-to-use online platform at www.crispr-clue.de. All in all, CLUE represents a resource-saving approach to produce numerous high quality custom sgRNA libraries in parallel, which will foster their broad use across molecular biosciences.
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Publication type Article: Journal article
Document type Scientific Article
ISSN (print) / ISBN 0305-1048
e-ISSN 1362-4962
Quellenangaben Volume: 48, Issue: 13, Pages: , Article Number: e78 Supplement: ,
Publisher Oxford University Press
Reviewing status
Institute(s) Research Unit Apoptosis in Hematopoietic Stem Cells (AHS)
Institute of Computational Biology (ICB)