Better data for better predictions: data curation improves deep learning for sgRNA/Cas9 prediction.

Tyler S Browne, David R Edgell, Gregory B Gloor

Abstract read

In one paragraph

Article in PeerJ, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it

0cells of the map it votes in

2citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

Machine-learning in optimization of CRISPR technology.Machine learning. Health · 2026
Review
Better data for better predictions: data curation improves deep learning for sgRNA/Cas9 prediction.PeerJ · 2026
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Tyler S BrowneDepartment of Biochemistry, Schulich School of Medicine & Dentistry, University of Western Ontario, London, Ontario, Canada.

David R EdgellDepartment of Biochemistry, Schulich School of Medicine & Dentistry, University of Western Ontario, London, Ontario, Canada.

Gregory B GloorDepartment of Biochemistry, Schulich School of Medicine & Dentistry, University of Western Ontario, London, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Cas9 enzyme along with a single guide RNA molecule is a modular tool for genetic engineering and has shown effectiveness as a species-specific antimicrobial. The ability to accurately predict on-target cleavage is critical as activity varies by target. Using the sgRNA nucleotide sequence and an activity score, predictive models have been developed with the best performance resulting from deep learning architectures. Prior work has emphasized robust and novel architectures to improve predictive performance. Here, we explore the impact of a data-centric approach through optimization of the input target site adjacent nucleotide sequence length and the use of data filtering for read counts in the control conditions to improve input data utility. Using the existing crisprHAL architecture, we develop crisprHAL Tev, a bacterial SpCas9 prediction model with performance that generalizes across related species and across data types. During this process, we also rebuilt two prior

Indexed as

CRISPR-Associated Protein 9CRISPR-Cas SystemsData CurationDeep LearningRNA, Guide, CRISPR-Cas SystemsEscherichia coliPrediction AlgorithmsPredictive Learning ModelsCRISPR-Associated Protein 9RNA, Guide, CRISPR-Cas SystemsBacterial CRISPR predictionCRISPR modelCRISPR predictionData cleaningHigh throughput sequencingMachine learningModel buildingSgRNA activity prediction

Identifiers

PMID41695711

DOI10.7717/peerj.20706

PMCPMC12903899

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

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.