Evidence map›Paper›PMID 36250792›Full record

ArticleBioinformatics (Oxford, England)2022

CEDA: integrating gene expression data with CRISPR-pooled screen data identifies essential genes with higher expression.

Yue Zhao, Lianbo Yu, Xue Wu, Haoran Li, Kevin R Coombes, Kin Fai Au, Lijun Cheng, Lang Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

8 authors.

Yue ZhaoDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.ORCID 0000-0001-6029-7123
Lianbo YuDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
Xue WuDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
Haoran LiDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
Kevin R CoombesDepartment of Population Health Sciences, Georgia Cancer Center at Augusta University, Augusta, GA 30912, USA.
Kin Fai AuDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.ORCID 0000-0002-9222-4241
Lijun ChengDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
Lang LiDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.ORCID 0000-0002-0746-1809

Funding

Translational Therapeutics Research Program (TT)P30CA016058 · NCI · OHIO STATE UNIVERSITY · PI Jennifer A. Woyach · 1985 to 2026
$132.3M
Quantitative and function analysis platform for repetitive genes and gene isoforms in pluripotency regulation and differentiationsR01HG011469 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI AU, KIN FAI · 2021 to 2024
$2.6M
Bioinformatics platform for Hybrid-Seq transcriptome data analysisR01HG008759 · NHGRI · UNIVERSITY OF IOWA · PI AU, KIN FAI · 2016 to 2020
$1.9M
Experimental and bioinformatics platform for epigenome analysis using nanopore sequencingR01GM136886 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI AU, KIN FAI · 2021 to 2024
$1.7M
NCI NIH HHS P30 CA016058NHGRI NIH HHS R01 HG008759NHGRI NIH HHS R01 HG011469NIGMS NIH HHS R01 GM136886NIH HHS
6 · The paper itself

Abstract

motivationClustered regularly interspaced short palindromic repeats (CRISPR)-based genetic perturbation screen is a powerful tool to probe gene function. However, experimental noises, especially for the lowly expressed genes, need to be accounted for to maintain proper control of false positive rate.

methodsWe develop a statistical method, named CRISPR screen with Expression Data Analysis (CEDA), to integrate gene expression profiles and CRISPR screen data for identifying essential genes. CEDA stratifies genes based on expression level and adopts a three-component mixture model for the log-fold change of single-guide RNAs (sgRNAs). Empirical Bayesian prior and expectation-maximization algorithm are used for parameter estimation and false discovery rate inference.

resultsTaking advantage of gene expression data, CEDA identifies essential genes with higher expression. Compared to existing methods, CEDA shows comparable reliability but higher sensitivity in detecting essential genes with moderate sgRNA fold change. Therefore, using the same CRISPR data, CEDA generates an additional hit gene list. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

Clustered Regularly Interspaced Short Palindromic RepeatsGenes, EssentialBayes TheoremCRISPR-Cas SystemsGene ExpressionReproducibility of ResultsRNA, Small UntranslatedRNA, Small Untranslated

Identifiers

PMID36250792
PMCPMC9710553

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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.