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FlashFry: a fast and flexible tool for large-scale CRISPR target design

Overview of attention for article published in BMC Biology, July 2018
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Title
FlashFry: a fast and flexible tool for large-scale CRISPR target design
Published in
BMC Biology, July 2018
DOI 10.1186/s12915-018-0545-0
Pubmed ID
Authors

Aaron McKenna, Jay Shendure

Abstract

Genome-wide knockout studies, noncoding deletion scans, and other large-scale studies require a simple and lightweight framework that can quickly discover and score thousands of candidate CRISPR guides targeting an arbitrary DNA sequence. While several CRISPR web applications exist, there is a need for a high-throughput tool to rapidly discover and process hundreds of thousands of CRISPR targets. Here, we introduce FlashFry, a fast and flexible command-line tool for characterizing large numbers of CRISPR target sequences. With FlashFry, users can specify an unconstrained number of mismatches to putative off-targets, richly annotate discovered sites, and tag potential guides with commonly used on-target and off-target scoring metrics. FlashFry runs at speeds comparable to commonly used genome-wide sequence aligners, and output is provided as an easy-to-manipulate text file. FlashFry is a fast and convenient command-line tool to discover and score CRISPR targets within large DNA sequences.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 128 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 128 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 21%
Researcher 21 16%
Student > Master 9 7%
Student > Bachelor 6 5%
Student > Doctoral Student 5 4%
Other 16 13%
Unknown 44 34%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 34 27%
Agricultural and Biological Sciences 22 17%
Computer Science 9 7%
Medicine and Dentistry 5 4%
Immunology and Microbiology 2 2%
Other 9 7%
Unknown 47 37%