Evidence map›Paper›PMID 35980578›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2022

CRISPR-Based Transcriptional Activation in Drosophila.

Yuting Han, Xinyi Lu, Yutong Li, Yuhao Qiu, Xizhi Dong, Xiaochen Li, Xu Si, Qingfei Liu, Jian-Quan Ni

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2022. 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
1.0field-weighted citation impact, top 25% of its field
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, 2 citations in OpenAlex.

  1. Article
  2. 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

9 authors at 1 institution in 1 country.

Yuting HanGene Regulatory Lab, School of Medicine, Tsinghua University, Beijing, China.
Xinyi LuGene Regulatory Lab, School of Medicine, Tsinghua University, Beijing, China.
Yutong LiGene Regulatory Lab, School of Medicine, Tsinghua University, Beijing, China.
Yuhao QiuGene Regulatory Lab, School of Medicine, Tsinghua University, Beijing, China.
Xizhi DongGene Regulatory Lab, School of Medicine, Tsinghua University, Beijing, China.
Xiaochen LiGene Regulatory Lab, School of Medicine, Tsinghua University, Beijing, China.
Xu SiGene Regulatory Lab, School of Medicine, Tsinghua University, Beijing, China.
Qingfei LiuGene Regulatory Lab, School of Medicine, Tsinghua University, Beijing, China.
Jian-Quan NiGene Regulatory Lab, School of Medicine, Tsinghua University, Beijing, China. nijq@mail.tsinghua.edu.cn.
Tsinghua University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Overexpression is one of the classical approaches to study pleiotropic functions of genes of interest. To achieve overexpression, we often increase the transcription by introducing genes on exogenous vectors or by using the CRISPR/dCas9-based transcriptional activation system. To date, the most efficient CRISPR/dCas9-based transcriptional activator is the Synergistic Activation Mediator (SAM) system whereby three different transcriptional activation domains are directly fused to dCas9 and MS2 phage Coat Protein (MCP), respectively, and the system in Drosophila is named flySAM. Here we describe the effective and convenient transcriptional activation system, flySAM, starting from vector construction, microinjection, and transgenic fly selection to the phenotypic analysis.

Indexed as

CRISPR-Cas SystemsDrosophilaAnimalsAnimals, Genetically ModifiedTranscriptional ActivationTranscription FactorsTranscription FactorsCRISPR/dCas9DrosophilaflySAMOverexpressionTranscriptional activation

Identifiers

PMID35980578
OpenAlexW4295753284

What OpenQuestion holds

Textmetadata
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.