Abstract

BackgroundCRISPR/Cas (Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR associated nucleases) is a powerful component of the prokaryotic immune system that has been adapted for targeted genetic engineering in higher organisms. A key element of CRISPR/Cas is the “guide” RNA (gRNA) that is ~20 nucleotides (nts) in length and designed to be complementary to the intended target site. An integral requirement of the CRISPR/Cas system is that the target site be followed by a protospacer adjacent motif (PAM). Care needs to be exercised during gRNA design to avoid unintended (“off-target”) interactions.ResultsWe designed and implemented the Off-Spotter algorithm to assist with the design of optimal gRNAs. When presented with a candidate gRNA sequence and a PAM, Off-Spotter quickly and exhaustively identifies all genomic sites that satisfy the PAM constraint and are identical or nearly-identical to the provided gRNA. Off-Spotter achieves its extreme performance through purely algorithmic means and not through hardware accelerators such as graphical processing units (GPUs). Off-Spotter also allows the user to identify on-the-fly how many and which nucleotides of the gRNA comprise the “seed”. Off-Spotter’s output includes a histogram showing the number of potential off-targets as a function of the number of mismatches. The output also includes for each potential off-target the site’s genomic location, a human genome browser hyperlink to the corresponding location, genomic annotation in the vicinity of the off-target, GC content, etc.ConclusionOff-Spotter is very fast and flexible and can help in the design of optimal gRNAs by providing several PAM choices, a run-time definition of the seed and of the allowed number of mismatches, and a flexible output interface that allows sorting of the results, optional viewing/hiding of columns, etc. A key element of Off-Spotter is that it does not have a rigid definition of the seed: instead, the user can declare both the seed’s location and extent on-the-fly. We expect that this flexibility in combination with Off-Spotter’s speed and richly annotated output will enable experimenters to interactively and quickly explore different scenarios and gRNA possibilities.ReviewedThis article was reviewed by Dr Eugene Koonin and Dr Frank Eisenhaber.Electronic supplementary materialThe online version of this article (doi:10.1186/s13062-015-0035-z) contains supplementary material, which is available to authorized users.

Highlights

  • Many prokaryotes have evolved a natural defense mechanism against plasmids [1] and viruses [2,3] that is known as the CRISPR/Cas nuclease system

  • For each analyzed guide” RNA (gRNA), we report a histogram of the number of potential off-targets as a function of the mismatches that they have when compared to the gRNA sequence

  • We built the current implementation of Off-Spotter using the hg19 human genome assembly and allow the user to select among four different protospacer adjacent motif (PAM), define the maximum number of mismatches, decide whether to report annotations, and delineate the seed’s location and extent on-the-fly

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Summary

Results

We built the current implementation of Off-Spotter using the hg human genome assembly and allow the user to select among four different PAMs, define the maximum number of mismatches (up to 5), decide whether to report annotations, and delineate the seed’s location and extent on-the-fly. If one or more 20mers are entered, each 20-mer is processed in turn and the off-target results reported on separate tables, one table per 20-mer. The user can sort the reported potential off-targets separately for each 20-mer by chromosome id, strand id, number of mismatches, and actual off-target sequence. The user can download the generated results as tab-separated files either for select 20-mers or for all analyzed 20-mers together. Even pathological queries that necessitate the generation and reporting of hundreds of thousands of potential off-targets are completed in a few seconds. Additional file 1 presents a quantitative comparison of the various tools based on their run-time and the number of off-targets that they report. Additional file 2 provides a summary of the features that characterize the various tools

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