CRISPR-GATE: a one-stop repository and guide to computational resources for genome editing experimentation

Abstract

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)–CRISPR associated protein (CRISPR-Cas) has emerged and evolved as a revolutionary genome editing technology, transforming research across diverse biological disciplines. Over the past decade, this technology has unveiled numerous opportunities for precise genome manipulation. However, the processes of discovering Cas proteins, repurposing them as editing tools, selecting appropriate candidate tool from the CRISPR-toolbox, designing experiments, and analyzing data are often complex and require careful consideration. To support researchers at every stage of CRISPR experimentation, a wide array of web resources has been developed. In this article, we provide a comprehensive overview of standalone and web-based tools that assist in the identification of CRISPR-Cas systems and the design of guide RNAs (gRNAs). We also highlight tools for evaluating gRNA efficiency, predicting CRISPR-Cas9 mutation profiles, as well as tools for base editing and prime editing, and the analysis and visualization of experimental results. Additionally, we introduce CRISPR–Gateway for Accessing Tools and Resources (CRISPR-GATE), an all-inclusive web repository that consolidates publicly available tools for genome editing research. This repository offers a categorized and user-friendly interface, allowing researchers to quickly access relevant tools based on their specific needs. CRISPR-GATE aims to streamline the search for CRISPR resources, facilitating both education and accelerating innovation. The web repository can be accessed from https://crispr-gate.daasbioinfromaticsteam.in/.

Introduction

On account of its extraordinary efficiency, ease, directness, versatility, and ability to target both DNA and RNA, the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)–CRISPR associated protein (CRISPR-Cas) system has emerged as a promising genome and transcriptome manipulation technology and has swiftly superseded its forerunners in the past decade [1]. It finds applications in various fields of biology and beyond, such as gene therapy, crop improvement, and industrial biotechnology [2, 3].

Recent advancements have significantly expanded CRISPR-Cas functionalities. Dead Cas9 (dCas9) fused with effector proteins enables precise regulation of gene expression via CRISPR activation (CRISPRa) or interference (CRISPRi) [4, 5]. Cas-induced DNA breaks enable precise nucleotide replacement, insertion, or deletion via homology-directed repair (HDR) using a donor template. Fusion of nucleotide deaminases with nickase Cas9 (nCas9) or dCas9 facilitates base editing, enabling precise nucleotide changes without introducing double-strand breaks (DSBs) or requiring donor templates [6–8]. Prime editing allows versatile genomic modifications, including insertions/deletions, all types of base substitutions, and a combination of the above in the DNA [9]. Additionally, CRISPR technologies are extensively utilized for epigenetic studies [10], genetic screening [11], artificial chromatin loop formations [12], genome organization control systems [13], real-time chromatin and RNA imaging [14, 15], RNA editing [16], molecular diagnostics [17], gene drives [18], and much more.

The exploration of CRISPR-Cas systems in prokaryotic genomes initially relied on sequence similarity searches against known sequences, but later shifted toward signature-based methods that use algorithms to detect characteristic features. The combination of these approaches has enabled the effective discovery of CRISPR arrays, Cas genes, protospacer adjacent motif (PAM) sequences, and anti-CRISPR proteins (Acrs) [19–22]. The discovery of functional CRISPR-Cas systems in natural organisms has laid the foundation for their repurposing as genome editing tools.

Selecting and manually designing guide RNAs (gRNAs), evaluating different strategies, avoiding off-targets in complex genomes, predicting outcomes, and analyzing experimental data can be challenging for researchers new to the field. Fortunately, a wide range of computational tools has made it easier to perform CRISPR-Cas experiments in a more streamlined and user-friendly manner. Many of these tools incorporate diverse features to evaluate and optimize gRNAs, aiming to maximize efficiency and specificity in experimental setups [23, 24]. Moreover, gRNA design for base editing and prime editing is technically more complex, requiring dedicated tools for accurate modeling and interpretation. In the case of base editors, additional considerations such as bystander effects, influence of neighboring sequences, spacer position, editor-specific activity windows, and potential off-target effects are crucial for effective gRNA design [25, 26]. For prime editors, prime editing gRNA (pegRNA) design involves additional components, making the process more intricate and subject to various influencing factors [9, 27].

Although Cas-induced DNA double-strand breaks (DSBs) were long thought to produce random indels, recent studies have shown that these mutations can be predicted [28, 29]. The availability of prediction tools has made it easier to design more effective experiments [30–34]. The complexity of plant genomes, longer experimental timelines, and emerging applications like multiplexing and gene regulation underscore the critical need for CRISPR-Cas tools specifically optimized for plants.

The final hurdles often lie in data analysis and interpretation. To address this, both standalone and web-based tools have been developed to analyze mutation data using either Sanger sequencing and/or high-throughput sequencing datasets [35, 36]. More advanced tools have since emerged to support the analysis and visualization of outcomes from diverse experiments, including base editing, prime editing, and pooled CRISPR screens [37, 38].

Currently, studies with adequate details of the full spectrum of CRISPR-Cas tools, enlisting the majority of available tools, starting from the discovery of CRISPR-Cas systems to downstream analysis of editing experiments, are scarce. Researchers often need to browse through numerous resources to find the appropriate tools that suit their experimental needs. For instance, a conventional gRNA design tool cannot serve the purpose of base editing and prime editing experiments, highlighting the need for a unified resource. While several resources have compiled CRISPR-Cas tools, such as the CRISPR Software Matchmaker (https://tinyurl.com/e7nt8dxd), the Wikipedia page on CRISPR/Cas tools (https://en.wikipedia.org/wiki/CRISPR/Cas_tools), WeReview: CRISPR Tools [39], and the awesome-CRISPR GitHub repository (https://github.com/davidliwei/awesome-CRISPR), they often suffer from outdated content, limited scope, inconsistent maintenance, lack of depth, and advanced interactivity. Similarly, there have been extended reviews on computational tools and resources for CRISPR-Cas genome editing [40, 41]. However, none of them has produced an elaborate application-wise compilation with an integrated web resource.

In this review, we investigate and comprehensively discuss various CRISPR-Cas computational tools, including those used for the detection of CRISPR-Cas systems in prokaryotic genomes, gRNA design, evaluation of gRNA efficiency, prediction of CRISPR-Cas9 mutation profiles, base editing and prime editing techniques, and analysis and visualization of editing results (Fig. 1). We have placed greater emphasis on freely available tools over commercial ones. To address the challenges discussed earlier and to ensure easy access to CRISPR-Cas tools, we have tabulated and classified them based on the nature of experiments, expected outcomes, and popularity. The technical aspects of these tools, as they relate to specific tasks, are also reviewed in detail.

Figure 1.

Schematic pipeline for selecting different categories of bioinformatics tools for CRISPR-based experimentation.

We conducted a comparative analysis to help readers understand and make informed decisions based on the utilities of each tool. Each tool was systematically evaluated against a predefined set of main and sub-features. A three-tier scoring system was employed at the sub-feature level, where a score of 1 indicates full support, 0.5 denotes partial support or support with limitations, and 0 signifies no support. The data were then normalized to a consistent bounded scale (0–1) using the average score within the set for better visualisation and to enable fair comparison across tools. These results are visualized as heatmaps, providing a clear overview of each tool’s capabilities. Additionally, we developed a one-stop web resource, CRISPR-GATE, to help the scientific community identify suitable tools and minimizing time investment.

Discovery of novel CRISPR-Cas systems from prokaryotes

To repurpose CRISPR-Cas systems as genome editing tools, it is essential to thoroughly explore their fundamental components. In this section, we discuss the computational tools available for such initial exploration, including the identification of CRISPR arrays, Cas genes, PAM sequences, and other critical elements that constitute a functional system. Identifying the class, type, and subtype of CRISPR systems present in a genome of interest is crucial, as not all CRISPR systems possess desirable properties for genome editing applications. For example, Class 2 systems—specifically Type II and Type V—are particularly valuable due to the presence of a single-effector Cas nuclease, making them highly suitable for tool development. At a minimum, a computational tool should be capable of identifying CRISPR arrays and their associated Cas genes. More advanced tools offer system-wide classification by type or subtype and can identify critical components such as PAM sequences. Tools that determine the transcriptional orientation of arrays and support metagenomic datasets are also particularly useful for specific research contexts.

There are various tools and approaches for discovering CRISPR-Cas systems, either through sequence similarity searches or signature-based methods that employ machine learning algorithms. Bioinformatics tools can detect CRISPR arrays based on characteristics such as the presence of conserved repeats and spacers. Cas genes can be detected mainly by utilizing tools based on sequence similarity to known Cas genes. Eventually, a combination of these technologies will allow us to effectively uncover CRISPR-Cas systems in a genome or metagenome, as listed in Fig. 2.

Figure 2.

Tools for exploration of CRISPR-Cas systems and identifying anti-CRISPR proteins.

In the early days, sequence similarity-based matching tools such as BLAST [42], HMM [43], and PatScan [44] were used. However, those tools suffer from many drawbacks and demand manual curation of the outputs. Later, advanced tools with higher processing capabilities were developed like PILER-CR [45], CRISPR Recognition Tool (CRT) [46], CRISPRFinder [47], CRISPRDetect [48], CRISPRdigger [49], CRF [50], CRISPRidentify [51], and CRISPRclassify [52] for finding CRISPR repeats from both assembled and unassembled genomic sequences. PILER-CR is the first specialized tool for CRISPR detection, whereas CRF is the first tool to harness machine learning algorithms. CRISPRidentify produce very less to zero false positive rate, while CRISPRclassify employed a novel repeat-based method independent of Cas genes. CRISPRDirection [53], CRISPRstrand [54], and Potential Orientation and Cas Orientation [55] tools enable predicting an array’s transcriptional direction for exploring non-canonical functions and identifying leader regions. CRISPRDirection integrated into CRISPRCasFinder [20, 47] and CRISPRDetect, while CRISPRstrand has been integrated into the CRISPRmap [19]. CRISPRleader is a dedicated tool for the identification of CRISPR leader sequences [56].

Although many Cas proteins share structural similarities, significant variability exists even within the same subtype [57]. In the last decade, numerous computational tools have been developed for CRISPR-Cas system analysis—though most focus on CRISPR arrays rather than Cas genes [45, 48, 52]. Efforts to classify Cas genes based on the nature of effector proteins, sequences, and Cas operon architecture have resulted in a classification system with two classes, six types, and 33 subtypes [58, 59]. This has spurred the development of tools such as MacSyFinder [21], HMMCAS [60], CRISPRcasIdentifier [61], Casboundary [62], CASPredict [63], and CRISPRCasStack [64]. MacSyFinder and HMMCAS follow reference-guided approaches, whereas CRISPRcasIdentifier, Casboundary, CASPredict, and CRISPRCasStack employ machine learning techniques. HMMCAS, CASPredict, and CRISPRCasStack offer web tools, whereas MacSyFinder, CRISPRcasIdentifier, and Casboundary are standalone programs. An online resource of Cas genes was built through similarity search and genomic neighborhood analysis of metagenome datasets [65]. As a database, CasPDB serves as an informative and annotated resource [66]. Using CRISPR-Cas-Docker, optimal crRNA-Cas protein pair compatibility can be studied in silico before conducting expensive and time-consuming experiments [67]. Recently, CasPEDIA has come up with a comprehensive database of the Class 2 CRISPR-Cas proteins incorporating their enzymatic properties [68].

In order to support single-platform analysis, some tools and web servers have integrated CRISPR array and Cas gene identification. Such tools for exploration of CRISPR-Cas systems include CRISPRmap, CRISPRdisco [69], CRISPRCasFinder, CRISPRCasTyper [70], and CRISPRloci [71]. Both CRISPRmap and CRISPRloci tools are available under the Freiburg RNA tools platform [72]. The CRISPRmap tool detects CRISPR arrays using CRISPRFinder and CRT, and detects Cas genes using HMMER. CRISPRdisco makes use of MinCED [46] and BLAST tools for detecting CRISPR arrays and Cas genes, respectively. The CRISPRCasFinder web tool is accessible from the CRISPR-Cas++ platform [20], detects CRISPR arrays using an improved version of CRISPRFinder. The most recent tools, CRISPRCasTyper and CRISPRloci, use machine learning approaches based on signature genes. CRISPRCasTyper employs MinCED for detection of CRISPR arrays and HMMER for identification of Cas proteins. On the other hand, CRISPRloci has integrated several tools to support the discovery of CRISPR-Cas systems with detailed annotations. There are also web-based interactive databases equipped with graphical tools that facilitate the identification of CRISPR arrays and Cas genes, such as CRISPI [73], CRISPRBank [48], CRISPRone [74], CRISPRminer [75], CRISPRCasdb [76, 77], and CRISPRimmunity [78].

Identifying CRISPR-Cas systems from metagenome sequences

Discovery from complex environmental samples requires specialized tools optimized for fragmented data. These tools can identify CRISPR components from either assembled sequences or raw, unassembled sequencing reads. Though many of the available tools to detect CRISPR arrays, like CRT and CRISPRFinder, can be used for metagenomic assembly data, dedicated tools optimized for the complexity of such data were developed only later. The standalone metaCRT tool helps in the detection of CRISPRs from whole-metagenome assemblies [79]. However, Crass supports a raw read-based method for the identification of CRISPR arrays from metagenomes [80]. Likewise, metaCRISPR [81] and MetaCRAST [82] detect CRISPR arrays using unassembled raw metagenomic datasets. The functionalities for identification and annotation of CRISPRs from environmental datasets are also facilitated by tools like MinCED and CRISPRCasMeta [20].

Determining PAM

PAM is conserved and crucial for Cas effector to identify and cleave target sequence [83]. Prior to the availability of in silico analysis, the PAM sequences were determined via time-consuming experimental methods such as in vivo and in vitro selections [84, 85]. CASPERpam is a dedicated tool that makes it possible to identify and access the PAMs by analyzing CRISPR spacer sequences mined from genomic data within NCBI’s public repositories. The resulting PAM sequence database and groups are made available as a new feature in the CASPER software platform [86]. Spacer2PAM [87] and PAMpredict [88] also support the detection of functional PAMs. PAM prediction using PAMpredict is found to exhibit high accuracy [88].

Identifying Acrs

Acrs are inhibitors produced by bacteriophages and mobile genetic elements that counteract CRISPR-Cas immune systems [89]. Potential Acrs can act as natural off-switches for CRISPR-Cas systems, making them valuable tools for regulating genome editing. CRISPRminer and CRISPRimmunity provide database support for identifying self-targeting and anti-CRISPR proteins. Initially, a simple database was created to track Acr names and prevent nomenclature confusion [90]. Later, more advanced databases with detailed annotations were developed, such as AcrDB [91], AcrHub [92], and Anti-CRISPRdb [93, 94]. Among these, only AcrDB supports anti-CRISPR-associated proteins (Acas) exploration.

Several tools have been developed for Acr prediction, including AcRanker [22], AcrFinder [95], PaCRISPR [96], AcaFinder [97], DeepAcr [98], PreAcrs [99], and AcrNET [100]. Predictions have led to the discovery of many potential Acrs [22, 101]. Among these, AcrFinder, PaCRISPR, AcaFinder, and AcrNET are web-based tools. For Aca predictions, AcaFinder is the first exclusive tool, with integrated CRISPRCasTyper for additional assessments.

The development of tools for CRISPR-Cas system discovery reflects the evolution of bioinformatics programming paradigms. Early and foundational tools were implemented in Perl and Java, while modern integrated platforms are predominantly built using Python. Overall, a wide range of tools facilitate the discovery of CRISPR-Cas systems and their essential components, like CRISPR arrays, Cas proteins, Acr and Aca proteins, and PAM sequences. To help an end-user make an informed decision when choosing the best tool from the comprehensive options available, we have provided a feature-wise comparison heatmap in Fig. 3. This figure scores the major features of each tool based on its underlying capabilities, and the full details are available in Supplementary Table 1.

Figure 3.

Comparative account of tools to identify different components of CRISPR-Cas system and anti- CRISPR proteins. Scoring rule: 1, fully compatible; 0.5, partial support or support with limitations; 0, not compatible. Normalized scores, calculated as the average of sub-features, were used to enable a fair comparison across tools.

Guide RNA design for canonical CRISPR-Cas editing (knock-out or random genetic variation)

Once a suitable CRISPR-Cas9 system is identified and characterized, it could be utilized in genome editing applications. The gRNA provides information required for Cas9 to bind to and cleave a specific genetic locus. The success of CRISPR-Cas experiments depends largely on the efficiency (high on-target activity) and specificity (minimal off-target activity) of the gRNAs used. The central challenge in evaluating gRNA design tools is achieving an optimal balance between these two factors. Tools that employ learning-based approaches generally offer more accurate performance predictions. Furthermore, a tool’s utility is greatly enhanced by its versatility—such as support for multiple nuclease variants and advanced applications like transcriptional regulation or allele-specific targeting. Tools that incorporate the most advanced design rules—including considerations of GC content, secondary structure, tracrRNA sequence, and position-dependent nucleotide preferences—are preferable for achieving optimal outcomes. Several computational tools have employed various features for the evaluation and optimization of gRNAs to achieve maximum efficiency and specificity in experiments [23, 24]. Notable features include the position-dependent nucleotide preferences [102, 103], other sequence characteristics of the gRNA and target [104, 105], epigenetic features [106, 107], and structural properties [108, 109].

Previous studies have categorized gRNA design tools into three major types based on gRNA selection criteria: alignment-based, hypothesis-driven, and learning-based methods [23, 110, 111]. Alignment-based methods select gRNAs based solely on alignment criteria, while hypothesis-driven methods rely on empirically derived rule sets for gRNA selection and prediction. In contrast, learning-based tools use machine learning algorithms with experimental data to predict gRNA efficiency and specificity. Most of the tool development was carried out using the Python programming language, followed by Perl, Java, R and others.

Alignment-based and hypothesis-driven tools

Alignment-based tools generally perform a rapid search for potential gRNA target sites by detecting PAM sequences across the genome, without incorporating efficiency or specificity scoring metrics. In contrast, hypothesis-driven tools conduct a more elaborate off-target search by employing diverse sequence alignment algorithms such as BLAST, Bowtie [112], Jellyfish [113], or GGGenome [113], as well as specially developed algorithms tailored for genome-wide guide design [114, 115]. OffScan [116] uses an FM-index [117], while CRISPR-SE [118] employs an optimized brute force algorithm for efficient off-target identification. Different gRNA design platforms adopt a variety of on-target activity scoring schemes to balance predictive power and interpretability. Early tools often relied on empirically derived rule sets—Rule Set 1, based on experimental data from mammalian cells, consider sequence features that most strongly influence CRISPR-Cas9 cutting efficiency [102]. To enhance accuracy, Rule Set 2 integrated additional, larger datasets and introduced new features—position-independent nucleotide counts and the relative location of the gRNA target site within the gene [103], and is used by tools such as CRISPOR [119], and many others. The recently developed Rule Set 3 accounts for variations in the tracrRNA sequence that significantly affect gRNA activity and efficacy [120], which was further validated independently [121], and used to develop CRISPR-BEasy [122] and update CRISPOR. MIT and CFD scores are two widely used specificity scores [103, 123]. CHOPCHOP offers candidate primers, the display of restriction sites, and more, and is enriched with several functionalities with the latest update for conducting CRISPR-Cas experiments [112, 124, 125]. It now supports better visualization of results, targets more genomes, incorporates machine learning-derived evaluation scores, and allows for Cas13-based RNA editing. Some tools provide multiple scores for gRNA activity evaluations, such as CRISPOR and Guide Picker [126]. To help end-users make informed choices among the many available options, Fig. 4 presents a comparative heatmap scoring each tool’s key features based on their underlying functions, with Supplementary Table 2 offering a more detailed analysis.

Figure 4.

Feature-wise comparison of alignment-based and hypothesis-driven gRNA designing tools. Scoring rule: 1, fully compatible; 0.5, partial support or support with limitations; 0, not compatible. Normalized scores, calculated as the average of sub-features, were used to enable a fair comparison across tools.

The CRISPR-RGEN tools platform [127] provides two tools that facilitate gRNA designs, Cas-OFFinder [128] and Cas-Designer [129]. CT-Finder is found to support single and paired-gRNA designs for Cas9, nCas9, and RNA-guided FokI nucleases (RFNs) [130]. PhytoCRISP-Ex specializes in phytoplankton genome targeting [131]. The targetDesign tool from CRISPR-GE software toolkit, supports off-target evaluation and scoring with an associated offTarget program [132]. CRISPR-FOCUS has been optimized for CRISPR screening applications with batch mode functionality [133]. Interestingly, GuideMaker comes with support to facilitate gRNA designs for non-model organisms [134]. GuideScan2 overcomes the limitations of its previous version and provides a memory-efficient approach for scalable design of guide sequences [135, 136]. RNA/DNA bulges contribute to off-target effects [137], and is addressed by tools like Cas-Designer, CT-Finder, and CRISPRitz [138]. Recently, DANGER has employed a unique technique for on-target and off-target assessment without the need for a reference genome [139].

CRISPR-Cas13-based RNA editing experiments are supported by tools like CHOPCHOP, crisprVerse [140], and CASPER [141, 142]. The crisprVerse provides gRNA design for knockout, activation, and interference experiments. CASPER supports multitargeting and multispecies population analysis while facilitating the application of different CRISPR-Cas systems. Multitargeting functionality with support for multiple types of CRISPR-Cas systems is also provided by CRISPR MultiTargeter [143] and CRISPR-broad [144]. CRISPR MultiTargeter allows designing identical target sites in multiple genes, whereas CRISPR-broad facilitates experiments on large genomes.

Allele-specific gRNA design is crucial in precision applications because it enables CRISPR to selectively target mutant alleles, thereby minimizing off-target effects in dominantly inherited traits and similar contexts [145]. Only a few tools support allele-specific gRNA designs, which include CrisPam [146], SNP-CRISPR [147], AlleleAnalyzer [148], AsCRISPR [145], and AlPaCas [149]. AlPaCas is found to offer diverse functionalities and features, such as support for a broader range of Cas enzymes, structural analysis for Cas protein engineering, interactive visualizations, and greater customization.

Learning-based tools

The learning-based tools contribute to gRNA design by providing meaningful evaluations through the prediction of on-target and off-target activities, addressing the limitations of handcrafted rule-based scoring systems [111]. Learning-based tools can effectively comprehend large datasets and numerous features [150]. The common machine learning algorithms for gRNA design include regression [151, 152], classification [102, 153], and ensemble methods [154, 155], whereas deep learning tools mostly use artificial neural networks and convolutional neural networks (CNNs) for predictions [156–158].

The efficiency and specificity of tools employing machine learning algorithms depend on how well the model is trained. Recent tools that facilitate on-target prediction include Uni-deepSG [159], CIAO [160], CRISPR-Aidit [161, 162], and TIGER [163]. Tools specific for off-target activity prediction include CRISOT [164], CRISPR-HW [165], piCRISPR [166], CRISPR-M [167], and CRISPR-DIPOFF [168]. However, some tools have incorporated both on and off-target prediction modules, such as Azimuth 2.0 [103, 169], DeepCRISPR [170], DeepCpf1 [171], Cas12a_predictor [172], CRISep [173], DeepCas13 [174], CRISPR-AIdit, and TIGER. The off-target prediction tool Elevation has been integrated with Azimuth [169]. Likewise, CRISPRater can be accessed through the CCTop online platform [175].

Tools like sgRNA Scorer 2.0 [106, 176], CRISPick [103, 177], AttnToMismatch_CNN and AttnToCrispr_CNN [178], DeepGuide [179], and GuideHOM [180] support predictions for both Cas9 and Cas12a (formerly Cpf1) experiments. Attention-based modules are leveraged by AttnToMismatch_CNN and AttnToCrispr_CNN, CRISPR-ONT and CRISPR-OFFT [181], AttCRISPR [182], CRISPR-IP [183], CRISPR-HW, and CRISPR-M, whereas DeepCRISTL [184] and crisprHAL [185] make use of transfer learning. Similarly, CrisprDNT [186] and CRISPR-DIPOFF makes use of a transformer-based approach. The features utilized by tools for prediction also vary significantly; some tools use only sequence-based features, whereas other tools make use of epigenetic features as well [167, 170, 171]. Thermodynamic features were included in Azimuth, CRISPRpred [187], DeepHF [188], GNL-Scorer [189], CRISPRon and CRISPRoff [190], and BoostMEC [191]. Some tools have incorporated secondary structure features of gRNAs, such as WU-CRISPR [104], CRISTA [192], DeepHF, AttCRISPR, and DeepCRISTL. Interestingly, piCRISPR incorporates underutilized physically informed features.

Data augmentation techniques are employed by tools such as DeepCRISPR, CRISPRLearner [193], and DL-CRISPR [194]. CnnCrispr [195] utilizes the GloVe model [196], which addresses the limitations of the one-hot encoding approach for data embedding. Indels between target DNA and gRNA (RNA/DNA bulges) have been shown to contribute to off-target effects and CRISPR-Net can predict them [197]. Meaningful visualization of the results is supported by CNN-SVR [198], C-RNNCrispr [199], CRISPR-Net, CRISPR-ONT, DeepCRISTL, CRISPRedict [200], and CRISPR-M. DeepCpf1 and Cas12a_predictor support CRISPR-Cas12a systems, whereas DeepCas13 and TIGER support CRISPR-Cas13d systems. Machine learning models have enabled reliable off-target predictions for plant systems as well [201].

TnpB, an emerging next-generation genome-editing nuclease significantly smaller than conventional Cas9 and Cas12a, offers several advantages, including more efficient delivery [202, 203]. TnpB is guided by ωRNA and has distinct motif requirements in the target DNA. Recently, a deep learning model, TEEP (TnpB Editing Efficiency Predictor), has been developed to predict ωRNA activity [204]. Fig. 5 presents a feature-wise heatmap comparing learning-based tools to guide end-users in selecting the optimal option, with full details provided in Supplementary Table 3.

Figure 5.

Heatmap showing a comparative account of available learning-based gRNA designing tools. Scoring rule: 1, fully compatible; 0.5, partial support or support with limitations; 0, not compatible. Normalized scores, calculated as the average of sub-features, were used to enable a fair comparison across tools.

Prediction of Cas9-induced mutation outcome

Designing a gRNA to make a cut is just the first step toward achieving the desired edit. The next set of tools helps predict how the cell will repair the cut and what changes will result. It was a prevalent dogma that error-prone cell repair mechanisms (canonical NHEJ and MMEJ) lead to random mutations at Cas-induced DSBs. However, recent studies have invariably pointed out that such mutations are not random and are predictable [28]. Interestingly, it was shown that local sequence properties could be used to predict repair mechanisms and mutations [30, 205]. The stakes of such predictions and predictive models are very high for generating precise indels [206]. In plant systems, the study of specific DNA sequences at a target region has allowed scientists to predict the outcome of NHEJ-mediated single nucleotide indels and can introduce beneficial polymorphisms and allelic mutations [29, 207].

Many computational models have been developed to predict outcomes of non-templated DSB repair [28]. The development of models for predicting Cas9-induced mutation outcomes is intrinsically linked to the Python data science ecosystem, leveraging libraries for model training and deployment. The inDelphi is one of the earliest tools reported for the prediction of Cas9-induced indels and was experimentally validated [31]. Later, Lindel [32] and FORECasT [30] were developed for mutation prediction. Similarly, SPROUT, developed based on large experimental data, enables prediction of repair outcome [33]. The predictive accuracy of CROTON, a deep learning framework, was reported to be higher than that of SPROUT, FORECasT, and inDelphi [34]. It was also found that for the prediction of frameshift mutations, deep learning-based models such as Apindel and CROTON did not perform well [208]. FORECasT-repair extends FORECasT model to repair-deficient contexts [209]. Recently, the AIdit_DSB model was developed and demonstrated to outperform Lindel and FORECasT models for K652 cells [162]. Although a model trained exclusively on plant repair data is not available yet, Molla et al. [29] showed that Cas9-induced single nucleotide insertion can be efficiently predicted using inDelphi, FORECasT, and SPROUT. To select a tool for predicting Cas9-induced mutations, one must understand that DNA repair is not random but a predictable process influenced by local sequence context. The most valuable tools provide precise and quantitative predictions of both the class of indel and the frequency of each specific repair outcome. Advanced tools further enhance utility by forecasting the functional impact, such as frameshift mutations, and integrating cell-type-specific biological contexts. To assist users in selecting the most suitable tool, Fig. 6 presents a heatmap scoring the main features of all available tools based on their functionalities, with full details provided in Supplementary Table 4.

Figure 6.

Tools for predicting Cas9-induced mutation (indels) outcome. Scoring rule: 1, fully compatible; 0.5, partial support or support with limitations; 0, not compatible. Normalized scores, calculated as the average of sub-features, were used to enable a fair comparison across tools.

Tools for base editing and prime editing

The canonical CRISPR-Cas tools are highly efficient in generating random indels at target sites, which are useful in knocking out genes and creating random genetic variations at regulatory elements. Whereas, base editing and prime editing support precise alteration of genetic letters [210, 211]. This section covers specialized tools that can design complex gRNAs and predict outcomes for experiments aimed at correcting or rewriting specific genetic information. Computational tools for designing base and prime editing experiments are predominantly offered as web services to handle the complex design rules. These tools are built on diverse web frameworks, with back-end logic implemented in languages such as Perl and Python. To choose the optimal tool for base or prime editing experiments, a user must evaluate several critical design features.

Base editing

Base editors (BEs) are engineered by fusing a nucleotide deaminase to a nuclease-defective effector protein, mostly nCas9 or dCas9. The technique introduces precise, single nucleotide substitutions in a DNA or RNA strand without generating DSBs or indels. BE is directed to a genomic locus by a gRNA bound to the Cas enzyme, where the deaminase could edit its target base within a permissible activity window [206]. BEs mainly fall into two broad categories, DNA BEs and RNA BEs, and the DNA BEs could be further subdivided into cytosine BEs (CBEs), adenine BEs (ABEs), C-to-G BEs (CGBEs), dual-base editors, and organellar BEs [211]. The gRNA design for base editing is more complex owing to specific prerequisites for optimal activity window and suitably placed PAM and becomes more challenging when developing genome-wide gRNA libraries.

To support experimentations with BEs, user-friendly computational tools and web interfaces have been built by several groups [211]. BE-Designer is one of the earliest tools for gRNA design [26]. The beditor tool supports multiprocessing for the design of sizable gRNA libraries [25]. CRISPR-BEST is a dedicated base editing system for actinobacteria [212]. CRISPy-web 2.0 was developed to design gRNA for base editing experiments [114, 213]. BEable-GPS [214] and BE-FF [215] tools assist in choosing the most efficient BEs for correcting single nucleotide variations (SNVs). Tools that provide gRNA design assistance for plants include iSTOP [216], BE-Designer, PnB Designer [217], Betarget [218], CRISPR-BETS [219], and CRISPRbase [220]. Both Cas9- and Cas12a-based BEs are supported by BEtarget, which is accessible from the CRISPR-GE web toolkit. Tools specifically designed for BE-mediated gene knockout experiments with the induction of stop codons include iSTOP, gBIG [221], CRISPR-CBEI [222], and CRISPR-BETS. The EditABLE tool streamlines the identification of highly reliable CRISPR editors and gRNAs that are computationally validated, including base editing [223]. BEscreen [224] and CRISPR-BEasy [122] are two recently developed tools for the design of gRNA libraries for base editing screens.

The prediction of editing efficiency and outcome patterns for BEs have been made feasible with tools like DeepBaseEditor [225], BE-Hive [226], CGBE-Hive [227], CGBE-SMART [228], BE-DICT [229], Bedeepon [230], FORECasT-BE [231], Bedeepoff [232], and DeepBE [233]. DeepBaseEditor employs two models, namely DeepABE and DeepCBE. The CGBE-Hive was built by training the BE-Hive model. For ABEs and CBEs, BEdeepon enables the prediction of editing efficiency and outcome frequencies, and BEdeepoff offers off-target site prediction. Recently, DeepBE was coded to predict the editing efficiencies and outcomes of various BEs generated by different Cas9 variants.

For base editing, key criteria for selecting a tool include support for major editor types, the ability to predict both editing efficiency and product purity, definition of activity windows for each editor, options for managing bystander editing, and application-specific features such as designing guides for stop codon introduction or correction. The heatmap in Fig. 7 offers a visual guide for choosing the best tool from the comprehensive tools available, with further information provided in Supplementary Table 5.

Figure 7.

Available tools for base editing experiment designing. Scoring rule: 1, fully compatible; 0.5, partial support or support with limitations; 0, not compatible. Normalized scores, calculated as the average of sub-features, were used to enable a fair comparison across tools.

Prime editing

Even though BEs are capable of effectively introducing point mutations, they cannot generate accurate indels [210, 211, 234]. Prime editors (PEs), in contrast to BEs, allow for all 12 types of substitutions and indels, as well as their combinations, with favorable intended-editing to by-product indel ratios [9, 235–238]. The prime editing technique combines a prime editing guide RNA (pegRNA) with a nCas9 that has been fused to a reverse transcriptase [9]. Also, unlike BEs, PEs exhibit a PAM proximal activity window. A pegRNA contains three components: a spacer with scaffold, a primer binding site (PBS), and a reverse transcriptase template (RTT). An engineered pegRNA (epegRNA) addresses the limitations of pegRNA by incorporating a structured RNA motif at the 3′ terminus and has been proved to significantly improve editing efficiency [239]. The pegRNA designs, which must be unique for each edit, can be challenging, time-consuming, and tricky, and they have a significant impact on the effectiveness of prime editors [9, 27, 240]. The pegRNA’s extra components, the RTT and PBS sequence, significantly increase design complexity. PBS sequence with a melting temperature of 30°C and paired-pegRNA methods have been shown to further enhance efficiency [241].

Currently, many pegRNA design tools have been developed to accommodate various versions of PEs, most of which are web tools such as pegFinder [242], PlantPegDesigner [241], PrimeDesign [243], Primeedit [244], PE-Designer [245], Easy-Prime [246], PnB Designer [217], and EditABLE [223], whereas multicrispr [247] and PINE-CONE [248] need to be locally installed. Only a few tools provide dedicated pegRNA design support for plants. PlantPegDesigner is designed exclusively for plants, while PE-Designer and PnB Designer can also be utilized. PrimeDesign offers the provision for visualization of pegRNA secondary structure. For the design of epegRNAs, pegLIT identifies non-interfering nucleotide linkers between pegRNAs and 3′ motifs [239]. PETAL aids in the identification of all valid target sites around the sequence of interest [249]. The PRIME-Del was developed to facilitate the design of pegRNAs for achieving large deletions using the PRIME-Del method [250]. To design thousands of pegRNAs at once, the Prime Editing Guide Generator (PEGG), a free-to-use Python package, facilitates the screening of sensor libraries of genetic variants [251].

DeepPE is the first tool developed for the efficiency prediction of pegRNAs [252]. The Easy-Prime tool facilitates efficiency prediction for all small-sized edits. Later, DeepPrime emerged as an improvement upon the DeepPE model, offering enhanced accuracy [253]. DeepPrime and its variant, DeepPrime-FT, support predictions for various prime editing systems and cell types, while DeepPrime-Off offers predictions of off-target effects for PE systems [253]. The MinsePIE tool allows for the prediction of prime editing insertion efficiencies [254]. PRIDICT was developed for the prediction of pegRNA efficiency and unintended editing rates for short insertions and deletions and 1 bp replacements [255]. PRIDICT2.0 overcomes the shortcomings of its predecessor, while ePRIDICT enables pegRNA efficiency prediction based on chromatin contexts [256]. In prime editing, the central challenge lies in the complex design of the pegRNA. Therefore, the best tools are evaluated on their ability to optimize all three of its components, incorporate advanced versions of prime editors, and predict both editing efficiency and purity (Fig. 8 and Supplementary Table 6).

Figure 8.

Prime editing experiment designing tools. Scoring rule: 1, fully compatible; 0.5, partial support or support with limitations; 0, not compatible. Normalized scores, calculated as the average of sub-features, were used to enable a fair comparison across tools.

Tools exclusively for plant-based CRISPR-Cas experiments

CRISPR-Cas technology was first demonstrated in model organisms, particularly in bacteria and animals [257, 258], and soon extended to plants [259]. This section covers tools developed exclusively to overcome challenges unique to plant genome editing, such as handling multiple gene copies (polyploidy), ensuring the CRISPR machinery works efficiently in plant cells, and editing several genes at once.

CRISPR-P is noted to be the first plant-specific gRNA design tool [260]. The CRISPR-PLANT, which offers a database, supports model plants and other important agricultural crops [261]. The CRISPR Genome Analysis Tool (CGAT) provides off-target prediction and also ranks the potential target sites [262]. CRISPR-P 2.0 [263] is an updated version of the CRISPR-P, whereas CRISPR-PLANT v2 [264] is an updated version of CRISPR-PLANT, with improved off-target detection strategies. WheatCRISPR aids in specific gRNA design for editing the polyploid genome of wheat [265]. CRISPR-Local is well optimized for non-reference genomes and targeting multiple genes at once [266]. PhytoCRISP-Ex allows designing guide RNAs for algal genomes [131]. The gRNA sequence and the chromatin state of the target site are important factors governing CRISPR-Cas editing efficiency [267]. CRISPR-Cereal accounts for chromatin accessibility and single-nucleotide polymorphisms (SNPs) in the target region for the three major food crops—wheat, maize, and rice [268]. GB4.0 [269, 270], integrated into the GoldenBraid platform [271], provides tools to design and assemble CRISPR-Cas single guide and multiplex guide RNA constructs either for gene editing or gene regulation.

To select the most suitable tool for plant genome editing, researchers must consider features that address the unique challenges of plant biology. Specialized support for polyploid genomes and the ability to design guides that are specific to homoeologous genes are important. Advanced features include support for genomic target context annotation and genome-wide off-target identification. Finally, tool utility is enhanced by its support for multiplex gRNA design, which is critical for studying complex traits governed by multiple genes. A comparative user guide is given in Fig. 9 and Supplementary Table 7.

Figure 9.

Exclusive tools for plants to design canonical CRISPR-Cas experiments. PlantPegDesigner is not included here; it is listed under prime editing tools. Scoring rule: 1, fully compatible; 0.5, partial support or support with limitations; 0, not compatible. Normalized scores, calculated as the average of sub-features, were used to enable a fair comparison across tools.

Tools for downstream analysis and visualization

After an experiment is performed in the lab, it is crucial to verify the outcome, which is the purpose of this final section of tools. They differ from all preceding design tools by analyzing experimental sequencing data to quantify the efficiency and precision of the edits, thus validating the entire workflow. Mutations induced by CRISPR-Cas9 often result in uniform biallelic or heterozygous conditions in the first transgenic population of diploid organisms. However, in many cases, chimeric mutations occur frequently, where three or more allelic edits are present within a single individual [272, 273]. Identifying the mutated sequences of the targeted alleles is crucial. However, when using the Sanger method, the mutation sites produce overlapping sequencing peaks. Accurate identification through cloning the mutation-containing amplicons and sequencing multiple clones for each target site is labor-intensive, costly, and time-consuming. Issues like sequencing inconsistencies, variable sequence quality, ambiguous indel alignments, and deconvolution of mixed HDR-NHEJ outcomes further complicate the analysis [274]. This complexity is further exacerbated by the limitations of Sanger sequencing for evaluating a range of indels in large cell populations, as well as the analytical challenges posed by pooled experiments where different target sites are part of a single sequencing library. Therefore, simple and quick methods to accurately characterize and quantify the induced mutations become essential.

Many computational tools have been developed to address these challenges and simplify the analysis of CRISPR edits, as listed in Fig. 10. These tools are mostly equipped for the analysis of data pertaining to specific editing results, such as the NHEJ and HDR data, base editing and prime editing data, and pooled CRISPR screening data. To select the best tool for analyzing CRISPR editing outcomes, a user must first consider the type of experimental data generated, primarily Sanger sequencing for single clones or Next-Generation Sequencing (NGS) for pooled populations. The core analytical capabilities are then evaluated based on the tool’s ability to accurately quantify the frequency of different editing events, including NHEJ-mediated indels, HDR-templated repairs, and the specific substitutions generated by base or prime editing. Finally, the overall utility of a tool is determined by its support for high-throughput batch processing and its capacity to generate clear, informative visualizations, such as indel distribution plots and allele frequency charts.

Figure 10.

Tools for data analysis of different categories of editing experiments.

Tools for NHEJ and HDR data analysis

These tools are designed to quantify the outcomes of conventional CRISPR editing by analyzing sequencing data for indels and HDR events. They are essential for measuring the efficiency of standard gene knockout or knock-in experiments. CRISPR-GA [35] is the first computational tool with web support, whereas TIDE [36] offers a rapid computational method that identifies the predominant types of indels and quantifies the efficiency of genome editing. However, TIDE is limited in its applicability to templated genome editing via HDR; to address this, it was updated to TIDER [275]. From Sanger sequencing data, TIDER estimates templated point mutations and small indels and can also assist in quantifying non-templated indels.

CrispRVariants allows mutation analysis of CRISPR-Cas9 experimental data and is made available as a web tool that supports small-scale experiments, named CrispRVariantsLite [276]. BATCH-GE aids in the detection of Cas9-induced indel mutations and supports batch analysis [277]. CRISPR-DAV supports the detection and analysis of small and large indels [278]. DSDecode is used to decode Sanger sequencing chromatograms for genotyping targeted mutations, but it has limitations when analyzing chromatograms from edits obtained from PCR amplicons [279]. DSDecodeM, available under the CRISPR-GE software package and built over DSDecode, addresses these drawbacks and offers enhanced proficiency [132]. The Cas-Analyzer allows for genome editing assessment with support for paired nucleases [280]. The Hi-TOM allows for high-throughput identification of all types of mutations induced by CRISPR-Cas systems [281]. Also, CRIS.py [282] and CRISPRpic [283] facilitate downstream data analysis. DECODR is a versatile web tool capable of even analyzing indels of length ≥ 50 bases around every edit site from single guide or multi-guide CRISPR-Cas experiments [284].

CRISPAltRations is a cloud-hosted software tool available as the back-end tool of the rhAmpSeq™ CRISPR Analysis Tool provided by the IDT web platform [285]. It aids in the batch analysis of single and multiplex samples (up to 500 targets) at the same time. ICE tool provides quick and accurate analysis and is a simple-to-use software program available on the Synthego platform [286]. The edge of ICE is that it does not require uploading a full reference genome. However, the ICE tool may sometimes misrepresent templated editing data, as reported earlier [29]. Further, the CRISPRroots aids in off-target and on-target assessment of edits from RNA-seq data [287]. The CRISPR-GRANT offers a graphical user interface (GUI) for high-throughput assessment of indels from single or pooled amplicons and NGS data, and is widely accepted [288]. CrisprStitch is one of the latest tools for analyzing CRISPR-Cas editing that offers fast and efficient evaluation [289]. This serverless application allows researchers to analyze data directly in their web browser and supports the calculation of mutation frequency, editing efficiency, accuracy, and even visualization of the results.

Tools like TIDE, TIDER, DSDecodeM, DECODR, and ICE support Sanger sequencing data, while CRISPR-GA, BATCH-GE, CRISPR-DAV, Cas-Analyzer, Hi-TOM, CRIS.py, CRISPRpic, CRISPAltRations, CRISPRroots, CRISPR-GRANT, and CrisprStitch support high-throughput sequencing data for analysis and visualization. However, AGEseq [290], ampliconDIVider [291], and CrispRVariants support both types of datasets. The tools Cas-Analyzer, CRISPRMatch [292], DECODR, CRISPAltRations, and CrisprStitch offer support for both Cas9 and Cas12a nucleases. There are a number of tools available that support the analysis of HDR events along with batch analysis and visualization, including BATCH-GE, CRISPR-DAV, CRISPRMatch, CRISPRpic, CRISPAltRations, ICE, and CrisprStitch.

Tools for base editing and prime editing data analysis

Some tools are capable of extending their analytical competency beyond conventional CRISPR-Cas experimental data. This specialized group of tools analyzes precise nucleotide substitutions and complex edits generated by base and prime editors. They are necessary because the editing outcomes are different from the indels created by canonical CRISPR, requiring unique algorithms to assess substitution patterns and frequencies. The CRIS.py tool offers an assessment of multiple sequence modifications induced by base editing. Whereas the CRISPResso2 [37] suite, an updated and widely accepted version of CRISPResso [274], supports batch analysis of data from Cas9 knockout and knock-in, base editing, and prime editing experiments under a single platform that no other software packages facilitate. It allows for the estimation, visualization, and comparison of mutations from pooled amplicon experiments and NGS data sets. The ampliCan tool aids the analysis and visualization of indels, template-based repair, and base editing experiments [293]. AlleleProfileR supports NHEJ, HDR, and base editing data analysis [294]. Although CRISPResso2, ampliCan, and AlleleProfileR provide evaluations of substitution frequency, they were not developed exclusively for CRISPR-mediated substitution experiments. CRISPR-Sub is a dedicated tool built solely for the purpose of systematic assessment of substitution patterns and frequencies from the NGS datasets [295]. Hence, CRISPR-Sub could be useful in the study of base editing and prime editing experiments. At the same time, base editing outcome analysis is supported by specialized tools like BEEP [296], EditR [297], BE-Analyzer [26], and BEAT [298]. BEEP, BEAT, and EditR take Sanger sequencing data as input, whereas BE-Analyzer supports NGS data. BE-Analyzer allows for defining more parameters while performing the analysis when compared to other tools. Nevertheless, PE-Analyzer is the dedicated tool available for the assessment and visualization of insertions, deletions, and base conversions from prime editing results [245]. PE-Analyzer aids in the batch analysis of NGS data without the need for uploading data to the server. The tools CRISPR-Sub, BE-Analyzer, and PE-Analyzer are accessible from the CRISPR-RGEN tools platform.

Tools for pooled CRISPR screening data analysis

The emergence of CRISPR-Cas technology has brought a significant rise in pooled screening data [302]. Tools in this category are essential for the analysis of data from large-scale screens where thousands of genes are targeted simultaneously. They identify which gRNAs are enriched or depleted in the cell population, allowing researchers to link specific genes to cellular functions or phenotypes. Among the available tools, only PinAPL-Py [299], CRISPR-SURF [300], CRISPRcloud2 [301, 302], and VISPR-online [38] offer user-friendly web interfaces for analysis and visualization. The VISPR-online aids in the visualization of CRISPR screen outputs generated by MAGeCK [303], BAGEL [304], and JACKS [305], and thereby streamlines the data evaluation process. CRISPRO supports the analysis and visualization of saturating mutagenesis CRISPR screens from NGS data [306]. BAGEL2 is another popular tool with enhanced performance, specificity, sensitivity, and performance [304, 307]. MAGeCKFlute, an improvement over its earlier version, demonstrates better performance in downstream functional data analysis [303, 308, 309].

Most of the tools are optimized for coding regions with clear functional boundaries and thus are not capable of analyzing tiling screens in non-coding regions where the exact locations of functional sequences are unknown. However, this challenge is addressed by CRISPR-SURF and RELICS [310]. MAUDE also takes care of some parts of non-coding screen evaluation [311]. The ecosystem for downstream analysis of editing outcomes is characterized by two major platforms. The comprehensive pipelines such as CRISPResso2 are typically developed in Python, while tools focused on statistical analysis and visualization, like ampliCan, are often implemented as R packages within the Bioconductor framework. To empower end-users to make an informed choice from numerous options, we present a comparative heatmap in Supplementary File 8, and complete details are documented in Supplementary Table 9.

Web resource development

One of the key challenges in the CRISPR-Cas research landscape is the lack of a comprehensive, centralized online platform that systematically consolidates the wide array of computational tools used throughout the entire workflow—from exploring CRISPR-Cas systems and designing experiments to conducting downstream data analysis. This study addresses that gap by developing an online resource, CRISPR-GATE (Fig. 11), which is planned to be continually updated to meet the evolving needs of the research community. CRISPR-GATE is a meticulously curated repository that offers streamlined access to over 250 CRISPR-Cas tools that are designed by various groups to meet the specific needs of researchers in the field.

Figure 11.

Overview of the CRISPR-GATE web repository. Snapshot showing (a) home page of CRISPR-GATE web resource; (b–d) multiple tools available under different categories.

CRISPR-GATE provides a user-friendly interface, making it an invaluable resource for scientists working with genome editing technologies, from editing tool development to applications in basic biology, medicine, and agriculture. The front-end architecture is built using HTML, CSS, and JavaScript, while the back-end leverages PHP and a MySQL database. A standout feature of CRISPR-GATE is its intuitive browsing and filtering functionality, allowing users to quickly locate relevant tools based on specific categories. This efficiency not only saves researchers’ significant time and effort but also enables them to focus on their experimental goals without the burden of extensive searches. CRISPR-GATE categorizes tools into eight primary areas of application, each accompanied by subcategory lists and filter options for enhanced navigation. Rather than merely listing available tools, CRISPR-GATE goes further by providing detailed insights into each tool’s key features and limitations. Additionally, users can sort tools by several criteria, such as popularity, which is based on the total citation counts as indexed by Google Scholar. All this critical information empowers researchers to make informed decisions about the most suitable and relevant tools for their specific projects. The CRISPR-GATE web resource, its contents, and all associated data are made available under the ICAR Data Use License. The full terms of the license can be viewed on the resource’s website.

Conclusion and future direction

The rapid development of reliable and user-friendly tools has fueled the realization of the potential of CRISPR-Cas technology in transforming the domain of genome manipulation in diverse organisms. Currently, the majority of biologists use CRISPR-Cas tools to add desired dimensions and prove their hypothesis in a short span of time. The contributions of computer programming experts and bioinformaticians have made CRISPR-Cas technologies more easily accessible, overcoming earlier limitations associated with genome editing technologies like ZFNs and TALENs. The simplicity of targeting genomic areas has enabled its exponential adoption across labs and institutions. Web resources have further accelerated the adoption by reducing complexity and computational demands.

Most of the tools available for the detection of CRISPR-Cas systems do not output unanimous results and development of more accurate tools is still underway. The detection of novel Cas proteins and the delineation of the functions of auxiliary Cas proteins provide an attractive direction for future research. Although specific tools already available for the identification of CRISPR-Cas systems from metagenomics data, they still struggle to cover the environmental sample heterogeneity, highlighting the need for robust web databases and tools to manage large dataset and associated computational burden. Additionally, developing new tools to mine microbial genomes for emerging genome editor nucleases like TnpB, IscB will facilitate enriching genome editing reagents.

Improvement of gRNA efficiency and specificity remains an active research area, and the use of advanced machine learning algorithms that can handle and analyze complex datasets could help in making better decisions. Deep learning holds promise for high-impact predictions once sufficiently large datasets become available. Incorporation of features such as folding pattern analysis and target association with nucleosome could enhance gRNA design. Plant-specific, learning-based tools for predicting Cas9-induced mutations are lacking, and validated web tools for NHEJ outcome prediction are in high demand. Moreover, underfunded labs would benefit from tools that can accurately quantify edit types and percentages from simple Sanger chromatograms.

Downstream analysis tools could perform better through machine learning integration, improved sequencing methodologies, and enhanced noise filtration techniques. More web tools are needed to support designing for diverse naturally available and engineered nucleases. Tools for designing BE and PE experiments encompassing diverse genomes are the need of the time. Nevertheless, researchers often struggle to find the most suitable tool for their experiments, even when multiple options are available. The CRISPR-GATE web resource streamlines this process and would speed up and democratize various applications of CRISPR-Cas technology across multiple disciplines.

Key Points

• CRISPR-GATE is a comprehensive, user-friendly online repository that brings together computational resources for each step of CRISPR-Cas genome editing, from identifying Cas proteins to designing gRNAs and analyzing results.

• This article systematically classifies available computational tools (standalone and web-based), helping researchers quickly select the best tools tailored to their experimental needs.

• Detailed coverage is given to computational tools for multiple CRISPR applications, including traditional gene knockout, base and prime editing, and precise mutation outcome prediction and downstream analysis.

• The article highlights the recent integration of machine learning and deep learning techniques, significantly improving predictions of gRNA efficiency and reducing off-target risks.

• CRISPR-GATE aims to streamline research by saving time, reducing complexity, and promoting innovation in genome editing by offering easy access to categorized and regularly updated computational resources.

Author contributions

Kutubuddin A. Molla, Mir Asif Iquebal, and Sarika Jaiswa conceptualized,designed and supervised the study, and provided resources. Asif Ali Vadakkethil, Sonali Panda, Aranya Mitra, and Manaswini Dash did the data curation and analysis. Asif Ali Vadakkethil and Ulavappa. B. Angadi were involved in web resource and software development. Asif Ali Vadakkethil, Sonali Panda, Kutubuddin A. Molla, Aranya Mitra, Mir Asif Iquebal, and Sarika Jaiswal wrote the first draft of the manuscript. Kutubuddin A. Molla, Mir Asif Iquebal, Sarika Jaiswa, Dinesh Kumar, and Mirza J. Baig administered the project, reviewed and edited the manuscript. All authors contributed to the article and approved the submitted version.

Conflict of interest

None declared.

Funding

Indian Council of Agricultural Research (ICAR), New Delhi.

Data availability

Data presented in the study are included as tables in the Supplementary material. All supplementary materials are available via Zenodo at https://doi.org/10.5281/zenodo.15254840.