ReviewCell reports methods2024
Analyzing the functional effects of DNA variants with gene editing.
Review in Cell reports methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed.
- Large serine recombinase-mediated gene insertion for high-throughput screens: advantages, design principles, and applications.Nucleic acids research · 2026Review
- CRISPR-Cas9 and precision editing technologies linking functional genomics to clinical translation in genetic diseases.Clinical and translational medicine · 2026Review
- Prime Editing-Based Functional Characterization Supports a Likely Pathogenic Interpretation ofGenes · 2026Article
- Phenotype-Specific Recalibration of MAVE Data Enables Repurposing ofmedRxiv : the preprint server for health sciences · 2026Article
- BEstimate: a computational tool for the design and interpretation of CRISPR base editing experiments.Genome biology · 2026Article
- Programmable nanobody circuits for cell selection.bioRxiv : the preprint server for biology · 2026Article
- Prime Editing Driven Functional Genomics: Bridging Genotype to Phenotype in the Post-Genomic Era.International journal of molecular sciences · 2026Review
- Scaling perturbations: beyond genome-scale CRISPR screens.bioRxiv : the preprint server for biology · 2026Article
- ClinMAVE: a curated database for clinical application of data from multiplexed assays of variant effect.Nucleic acids research · 2026Article
- Breaking barriers: we need a multidisciplinary approach to tackle cancer drug resistance.BJC reports · 2025Review
- Base editing screens define the genetic landscape of cancer drug resistance mechanisms.Nature genetics · 2024Article
- Translation of genome-wide association study: from genomic signals to biological insights.Frontiers in genetics · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Continual advancements in genomics have led to an ever-widening disparity between the rate of discovery of genetic variants and our current understanding of their functions and potential roles in disease. Systematic methods for phenotyping DNA variants are required to effectively translate genomics data into improved outcomes for patients with genetic diseases. To make the biggest impact, these approaches must be scalable and accurate, faithfully reflect disease biology, and define complex disease mechanisms. We compare current methods to analyze the function of variants in their endogenous DNA context using genome editing strategies, such as saturation genome editing, base editing and prime editing. We discuss how these technologies can be linked to high-content readouts to gain deep mechanistic insights into variant effects. Finally, we highlight key challenges that need to be addressed to bridge the genotype to phenotype gap, and ultimately improve the diagnosis and treatment of genetic diseases.
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.