ArticleNature communications2023
A generalizable Cas9/sgRNA prediction model using machine transfer learning with small high-quality datasets.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR.Journal of translational medicine · 2025Pooled it
- Machine-learning in optimization of CRISPR technology.Machine learning. Health · 2026Review
- Predation in microbial communities: gradients of nutritive killing.Nature reviews. Microbiology · 2026Review
- Harnessing Deep Learning Models for Guide RNA Optimization and Off-Target Prediction in CRISPR Systems.Biotechnology journal · 2026Review
- PHYCUT: Scalable Multiplex CRISPR/Cas9 Editing for Genome Engineering in the DiatomACS synthetic biology · 2026Article
- Machine learning reveals sequence and methylation determinants of SaCas9-PAM interactions in bacteria.Nucleic acids research · 2026Article
- The Chimeric Nuclease SpRYc Exhibits Highly Variable Performance Across Biological Systems.International journal of molecular sciences · 2026Article
- Better data for better predictions: data curation improves deep learning for sgRNA/Cas9 prediction.PeerJ · 2026Article
- Similarity-based transfer learning with deep learning networks for accurate CRISPR-Cas9 off-target prediction.PLoS computational biology · 2025Article
- EASY-edit: a toolbox for high-throughput single-step custom genetic editing in bacteria.Nucleic acids research · 2025Article
- Leveraging scenario differences for cross-task generalization in water plant transfer machine learning models.Environmental science and ecotechnology · 2025Article
- CRISPR-GATE: a one-stop repository and guide to computational resources for genome editing experimentation.Briefings in bioinformatics · 2025Review
- Machine Learning Empowering Microbial Cell Factory: A Comprehensive Review.Applied biochemistry and biotechnology · 2025Review
- Revolutionizing CRISPR technology with artificial intelligence.Experimental & molecular medicine · 2025Review
- Learning to utilize internal protein 3D nanoenvironment descriptors in predicting CRISPR-Cas9 off-target activity.NAR genomics and bioinformatics · 2025Article
- Multiplex CRISPR-Cas Genome Editing: Next-Generation Microbial Strain Engineering.Journal of agricultural and food chemistry · 2024Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The CRISPR/Cas9 nuclease from Streptococcus pyogenes (SpCas9) can be used with single guide RNAs (sgRNAs) as a sequence-specific antimicrobial agent and as a genome-engineering tool. However, current bacterial sgRNA activity models struggle with accurate predictions and do not generalize well, possibly because the underlying datasets used to train the models do not accurately measure SpCas9/sgRNA activity and cannot distinguish on-target cleavage from toxicity. Here, we solve this problem by using a two-plasmid positive selection system to generate high-quality data that more accurately reports on SpCas9/sgRNA cleavage and that separates activity from toxicity. We develop a machine learning architecture (crisprHAL) that can be trained on existing datasets, that shows marked improvements in sgRNA activity prediction accuracy when transfer learning is used with small amounts of high-quality data, and that can generalize predictions to different bacteria. The crisprHAL model recapitulates known SpCas9/sgRNA-target DNA interactions and provides a pathway to a generalizable sgRNA bacterial activity prediction tool that will enable accurate antimicrobial and genome engineering applications.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.