Scholar iON
Academic Synthesis
The collected works on CRISPR genome editing technologies reveal significant advancements in optimizing guide RNA (gRNA) design through artificial intelligence (AI) and machine learning, enhancing both on-target activity and off-target safety. Abbaszadeh and Shahlai emphasize the integration of explainable AI (XAI) to demystify the predictive models driving CRISPR efficiency, while Bingham et al. introduce "Guide-Guard," a machine learning tool achieving 84% accuracy in off-target prediction. Concurrently, Teufel et al.'s CRISPR SWAPnDROP exemplifies innovations in large-scale interspecies gene transfer, broadening the application spectrum of CRISPR technologies beyond traditional boundaries. Collectively, these studies underscore a trend towards interdisciplinary approaches that leverage AI to surmount current limitations in CRISPR applications, promising enhanced specificity and novel therapeutic avenues.
CRISPR-based genome editing has revolutionized biotechnology, yet optimizing guide RNA (gRNA) design for efficiency and safety remains a critical challenge. Recent advances (2020--2025, updated to reflect current year if needed) demonstrate that artificial intelligence (AI), especially deep learning, can markedly improve the prediction of gRNA on-target activity and identify off-target risks. In parallel, emerging explainable AI (XAI) techniques are beginning to illuminate the black-box nature of these models, offering insights into sequence features and genomic contexts that drive Cas enzyme performance. Here we review how state-of-the-art machine learning models are enhancing gRNA design for CRISPR systems, highlight strategies for interpreting model predictions, and discuss new developments in off-target prediction and safety assessment. We emphasize breakthroughs from top-tier journals that underscore an interdisciplinary convergence of AI and genome editing to enable more efficient, specific, and clinically viable CRISPR applications.
The need for diverse chromosomal modifications in biotechnology, synthetic biology and basic research requires the development of new technologies. With CRISPR SWAPnDROP, we extend the limits of genome editing to large-scale in-vivo DNA transfer between bacterial species. Its modular platform approach facilitates species specific adaptation to confer genome editing in various species. In this study, we show the implementation of the CRISPR SWAPnDROP concept for the model organism Escherichia coli and the currently fastest growing and biotechnologically relevant organism Vibrio natriegens. We demonstrate the excision, transfer and integration of 151kb chromosomal DNA between E. coli strains and from E. coli to V. natriegens without size-limiting intermediate DNA extraction. With the transfer of the E. coli MG1655 wild type lac operon, we establish a functional lactose and galactose degradation pathway in V. natriegens to extend its biotechnological spectrum. We also transfer the E. coli DH5alpha lac operon and make V. natriegens capable of alpha-complementation - a step towards an ultra-fast cloning strain. Furthermore, CRISPR SWAPnDROP is designed to be the swiss army knife of genome engineering. Its spectrum of application comprises scarless, marker-free, iterative and parallel insertions and deletions, genome rearrangements, as well as gene transfer between strains and across species. The modular character facilitates DNA library applications and the recycling of standardized parts. Its novel multi-color scarless co-selection system significantly improves editing efficiency to 92% for single edits and 83% for quadruple edits and provides visual quality controls throughout the assembly and editing process.
Genome editing has been a transformative force in the life sciences and human medicine, offering unprecedented opportunities to dissect complex biological processes and treat the underlying causes of many genetic diseases. CRISPR-based technologies, with their remarkable efficiency and easy programmability, stand at the forefront of this revolution. In this Review, we discuss the current state of CRISPR gene editing technologies in both research and therapy, highlighting limitations that constrain them and the technological innovations that have been developed in recent years to address them. Additionally, we examine and summarize the current landscape of gene editing applications in the context of human health and therapeutics. Finally, we outline potential future developments that could shape gene editing technologies and their applications in the coming years.
With the introduction of cyber-physical genome sequencing and editing technologies, such as CRISPR, researchers can more easily access tools to investigate and create remedies for a variety of topics in genetics and health science (e.g. agriculture and medicine). As the field advances and grows, new concerns present themselves in the ability to predict the off-target behavior. In this work, we explore the underlying biological and chemical model from a data driven perspective. Additionally, we present a machine learning based solution named \textit{Guide-Guard} to predict the behavior of the system given a gRNA in the CRISPR gene-editing process with 84\% accuracy. This solution is able to be trained on multiple different genes at the same time while retaining accuracy.
We developed pgMAP, an analysis pipeline to map gRNA sequencing reads from dual-targeting CRISPR screens. pgMAP output includes a dual gRNA read counts table and quality control metrics including the proportion of correctly-paired reads and CRISPR library sequencing coverage across all time points and samples. pgMAP is implemented using Snakemake and is available open-source under the MIT license at https://github.com/fredhutch/pgmap_pipeline.
Targeted nucleases are powerful tools for mediating genome alteration with high precision. The RNA-guided Cas9 nuclease from the microbial clustered regularly interspaced short palindromic repeats (CRISPR) adaptive immune system can be used to facilitate efficient genome engineering in eukaryotic cells by simply specifying a 20-nt targeting sequence within its guide RNA. Here we describe a set of tools for Cas9-mediated genome editing via nonhomologous end joining (NHEJ) or homology-directed repair (HDR) in mammalian cells, as well as generation of modified cell lines for downstream functional studies. To minimize off-target cleavage, we further describe a double-nicking strategy using the Cas9 nickase mutant with paired guide RNAs. This protocol provides experimentally derived guidelines for the selection of target sites, evaluation of cleavage efficiency and analysis of off-target activity. Beginning with target design, gene modifications can be achieved within as little as 1β2 weeks, and modified clonal cell lines can be derived within 2β3 weeks.
Recent advances in genome engineering technologies based on the CRISPR-associated RNA-guided endonuclease Cas9 are enabling the systematic interrogation of mammalian genome function. Analogous to the search function in modern word processors, Cas9 can be guided to specific locations within complex genomes by a short RNA search string. Using this system, DNA sequences within the endogenous genome and their functional outputs are now easily edited or modulated in virtually any organism of choice. Cas9-mediated genetic perturbation is simple and scalable, empowering researchers to elucidate the functional organization of the genome at the systems level and establish causal linkages between genetic variations and biological phenotypes. In this Review, we describe the development and applications of Cas9 for a variety of research or translational applications while highlighting challenges as well as future directions. Derived from a remarkable microbial defense system, Cas9 is driving innovative applications from basic biology to biotechnology and medicine.
CRISPR-Cas9βbased genetic screens are a powerful new tool in biology. By simply altering the sequence of the single-guide RNA (sgRNA), one can reprogram Cas9 to target different sites in the genome with relative ease, but the on-target activity and off-target effects of individual sgRNAs can vary widely. Here, we use recently devised sgRNA design rules to create human and mouse genome-wide libraries, perform positive and negative selection screens and observe that the use of these rules produced improved results. Additionally, we profile the off-target activity of thousands of sgRNAs and develop a metric to predict off-target sites. We incorporate these findings from large-scale, empirical data to improve our computational design rules and create optimized sgRNA libraries that maximize on-target activity and minimize off-target effects to enable more effective and efficient genetic screens and genome engineering.