Evidence map›Paper›PMID 42911498›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2027

Cas13d-Mediated Knockdown of Long Noncoding RNAs.

Lumen Chao, Zexu Li, Zihan Li, Xiaolong Cheng, Teng Fei, Wei Li

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2027. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Lumen Chao *Center for Precision Medicine and Genomics Research, Children's National Hospital, Washington, DC, 20010, USA.
Zexu Li *Department of Anatomy, Engineering Research Center for Organ Intelligent Biological Manufacturing of Chongqing, Key Lab for Biomechanics and Tissue Engineering of Chongqing, Third Military Medical University, Chongqing, 400038, China.
Zihan Li *Key Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Northeastern University, Shenyang, 110819, China.
Xiaolong ChengCenter for Precision Medicine and Genomics Research, Children's National Hospital, Washington, DC, 20010, USA.
Teng FeiKey Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Northeastern University, Shenyang, 110819, China. feiteng@mail.neu.edu.cn.
Wei LiUniversity of Maryland-Institute for Health Computing, North Bethesda, MD, 20852, USA. wli2@som.umaryland.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

CRISPR-Cas13 systems represent a powerful and programmable platform for RNA-targeted applications, including RNA knockdown. Long noncoding RNA (lncRNA), which lacks an open reading frame and is thereby often refractory to Cas9-mediated indel production, can be successfully knocked down by Cas13 systems. DeepCas13, a deep learning-based model, was developed to predict Cas13d on-target activity for RNA interference with high accuracy. Here, we describe detailed protocols about sgRNA design and experimental procedures for the lentiviral vector-based CRISPR-Cas13 system to knock down individual lncRNAs. Our protocol can be naturally extended to knockdown other types of coding and noncoding RNAs, including messenger RNA (mRNA) circular RNA (circRNA).

Indexed as

CRISPR-Cas SystemsGene Knockdown TechniquesRNA, Long NoncodingGenetic VectorsHEK293 CellsHumansLentivirusRNA, Guide, CRISPR-Cas SystemsRNA InterferenceRNA, Guide, CRISPR-Cas SystemsRNA, Long NoncodingCas13DeepCas13lncRNARNA knockdownsgRNA

Identifiers

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.