ArticleGenome biology2020
MAUDE: inferring expression changes in sorting-based CRISPR screens.
Article in Genome biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed.
- Synthetic Regulatory Genomics.Annual review of genomics and human genetics · 2026Review
- The adaptive landscapes of three globaleLife · 2026Article
- Orobas: A computational approach for scoring and analysis of quantitative chemical-genetic interactions from CRISPR-Cas9 screens.STAR protocols · 2026Article
- Double-CRISPR Knockout Simulation (DKOsim): A Monte-Carlo randomization system to model cell growth behavior and infer the optimal library design for growth-based double knockout screens.PLoS computational biology · 2026Article
- Accurate variant effect estimation in FACS-based deep mutational scanning data with Lilace.Genome biology · 2026Article
- A pooled Cell Painting CRISPR screening platform enables de novo inference of gene function by self-supervised deep learning.Nature communications · 2025Article
- CRISPR-GATE: a one-stop repository and guide to computational resources for genome editing experimentation.Briefings in bioinformatics · 2025Review
- A community effort to optimize sequence-based deep learning models of gene regulation.Nature biotechnology · 2025Article
- Discovering nanoparticle corona ligands for liver macrophage capture.Nature nanotechnology · 2025Article
- Large-scale discovery of potent, compact and erythroid specific enhancers for gene therapy vectors.Nature communications · 2025Article
- A systematic search for RNA structural switches across the human transcriptome.Nature methods · 2024Article
- A model for accurate quantification of CRISPR effects in pooled FACS screens.bioRxiv : the preprint server for biology · 2024Article
- CRISPRi screens identify the lncRNA,Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Evaluation and optimization of sequence-based gene regulatory deep learning models.bioRxiv : the preprint server for biology · 2024Article
- Review
- Genome-wide CRISPR-Cas9 screen analyzed by SLIDER identifies network of repressor complexes that regulate TRIM24.iScience · 2023Article
- Mislocalization of pathogenic RBM20 variants in dilated cardiomyopathy is caused by loss-of-interaction with Transportin-3.Nature communications · 2023Article
- Bioinformatics approaches to analyzing CRISPR screen data: from dropout screens to single-cell CRISPR screens.Quantitative biology (Beijing, China) · 2022Article
- Identifying cell receptors for the nanoparticle protein corona using genome screens.Nature chemical biology · 2022Article
- Prioritization of autoimmune disease-associated genetic variants that perturb regulatory element activity in T cells.Nature genetics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Improved methods are needed to model CRISPR screen data for interrogation of genetic elements that alter reporter gene expression readout. We create MAUDE (Mean Alterations Using Discrete Expression) for quantifying the impact of guide RNAs on a target gene's expression in a pooled, sorting-based expression screen. MAUDE quantifies guide-level effects by modeling the distribution of cells across sorting expression bins. It then combines guides to estimate the statistical significance and effect size of targeted genetic elements. We demonstrate that MAUDE outperforms previous approaches and provide experimental design guidelines to best leverage MAUDE, which is available on https://github.com/Carldeboer/MAUDE.
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.