Best CRISPR/Cas9 software tools
The development of CRISPR-Cas9 systems has revolutionized genome engineering in living organisms. This novel technology opens up a new era in genomics, along with a wide range of applications. Several bioinformatics tools have recently been developed for researchers designing CRISPR/Cas9 experiments, and analyzing and evaluating CRISPR/Cas9 genome editing.
A few weeks ago, we asked OMICtools members to choose their top 3 CRISPR/Cas9 favorite tools among those most used by the scientific community. Here are the results of your votes.
First position for CRISPR-GA, CROP-IT and CRISPRTarget tools
Three web applications came out equally on top – each voted as a number #1 tool by 45% of the users surveryed: CRISPR-GA (CRISPR Genome Analyzer), CROP-IT (CRISPR/Cas9 Off-target Prediction and Identification Tool) and CRISPRTarget.
CRISPR-GA (CRISPR Genome Analyzer)
The CRISPR-GA platform has become an essential tool for anyone wanting to assess the quality of their CRISPR/Cas9 experiment. It provides an easy (three mouse clicks), sensitive (detection limit 50.1%), and comprehensive analysis of gene editing results. The CRISPR-GA platform maps the reads, it estimates and locates insertions and deletions, computes the allele replacement efficiency, and then provides you with a report integrating all this information.
CRISPR-GA pipeline. (A) From experiment to report. Schematic pipeline of a gene editing assessment. (B) Output of CRISPR-GA estimating a range of information. Deletions, insertions, homologous recombination (HR) and corresponding efficiencies. Upper panels estimate the number of insertions and deletions and each corresponding size. Middle panels estimate the number of insertions and deletions, and their corresponding location within the genomic locus of interest. The bottom panel shows the number of deletions and HRs at each corresponding location, and outputs the HR and NHEJ (non-homologous end-joining) efficiency. (C) Experimental results assessed by CRISPR-GA from testing several mutants of cas9, gRNAs and a DNA template. HR and NHEJ values are shown. From Güell et al., 2014. Genome editing assessment using CRISPR Genome Analyzer (CRISPR-GA). Bioinformatics.
CROP-IT (CRISPR/Cas9 Off-Target Prediction and Identification Tool)
CROP-IT is a userfriendly web application where users can design optimal sgRNA guiding sequences and can search for potential off-target binding or cleavage sites. The CROP-IT tool integrates knowledge from experimentally identified Cas9 binding sites, cleavage sites as well as information on chromatin state (data from multiple studies and 125 cell types). CROP-IT scores predict off-target binding and cleavage Cas9 sites and outputs a list of the top sites.
Schematic of CROP-IT algorithm based on a computational model where each position of the guiding RNA sequence is differentially weighted based on experimental Cas9 binding and cleavage site information from multiple independent sources. Furthermore, it incorporates chromatin state information for the human genome by analyzing accessible chromatin regions from 125 human cell types. By integrating observed information from Cas9 DNA binding, CROP-IT performs significantly better than existing computational prediction tools. From Singh et al., 2015. Cas9-chromatin binding information enables more accurate CRISPR off-target prediction. Nucleic Acids Research.
CRISPRTarget is one of the first tools developed for predicting the targets of CRISPR RNA spacers. This web application interactively explores diverse databases. CRISPTarget provides the flexibility to search for matches in either or both orientations of the input, and to discover targets with protospacer adjacent motifs, as well as any adjacent pairing potential.
Graphical output of CRISPRTarget. Output of a search for targets of the Streptomyces thermophilus DGCC7710 CRISPR array. The direction of transcription is known, however both strands are shown in the diagram as if the direction of transcription was unknown. Two relatively low-scoring matches using these interactive settings are shown (rank 44–45). They have good spacer-protospacer base pairing but lack a WTTCTNN PAM. Match 45 is a match to a phage to which this strain is sensitive (Φ2972). Yellow indicates spacer/protospacer, blue shows flanking sequences, and mismatches between the crRNA and the target DNA protospacer are indicated in red. From Biswas et al., 2013. CRISPRTarget: bioinformatic prediction and analysis of crRNA targets. RNA Biology.
Second position for ZiFit
Second place went to ZiFiT (Zinc Finger Targeter v4.1), with 36% of the votes.
Originally developed to identify potential zinc finger nuclease (ZFN) sites in target sequences, ZiFiT also provides support for the identification of CRISPR/Cas target sites and reagents as well as a user-friendly guidance for construction of TALEN-encoding plasmids.
Third position for Crass and MAGeCK tools
Crass (CRISPR Assembler)
Crass identifies and reconstructs CRISPR loci and spacers from raw metagenomic data without the need for assembly or prior knowledge of CRISPR in the data set. The sensitivity, specificity and speed of Crass facilitates analysis of metagenomic data, phage-host interactions and co-evolution within microbial communities.
Comparison between different CRISPR loci visualization techniques. (A) Traditional approach to visualization where the spacers are shown as differently colored rectangles (the same color refers to the same spacer) anchored to the leader sequence (white triangle). (B) The same CRISPR loci reconstructed by Crass into a spacer graph. From Skennerton et al., 2013. Crass: identification and reconstruction of CRISPR from unassembled metagenomic data. Nucleic Acids Res.
MAGeCK (Model-based Analysis of Genome-wide CRISPR/Cas9 Knockout)
The MAGeCK algorithm was developed by Li et al. (Genome Biol. 2014) for prioritizing single-guide RNAs, genes and pathways in genome-scale CRISPR/Cas9 knockout screens. It identifies both positively and negatively selected genes simultaneously, and reports robust results across different experimental conditions. This computational method, with a low false discovery rate (FDR) and high sensitivity, brings new clues for answering biological questions and addressing therapeutic needs.
Follow this tutorial to see how the MAGeCK algorithm works.