Evidence map›Paper›PMID 34940760›Full record

ReviewNon-coding RNA2021

CRISPR-Based Approaches for the High-Throughput Characterization of Long Non-Coding RNAs.

Joshua Hazan, Assaf Chanan Bester

Abstract readReview
In one paragraph

Review in Non-coding RNA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Long non-coding RNAs in exercise: the hidden regulators of adaptation.Pflugers Archiv : European journal of physiology · 2026
    Review
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  6. Article
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  9. The Current Situation and Development Prospect of Whole-Genome Screening.International journal of molecular sciences · 2024
    Review
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  11. Review
  12. 3D Chromatin Organization Involving MEIS1 Factor in theInternational journal of molecular sciences · 2022
    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

2 authors.

Joshua HazanFaculty of Biology, Technion, Technion City, Haifa 3200003, Israel.
Assaf Chanan BesterFaculty of Biology, Technion, Technion City, Haifa 3200003, Israel.ORCID 0000-0002-5490-2187

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Over the last decade, tens of thousands of new long non-coding RNAs (lncRNAs) have been identified in the human genome. Nevertheless, except for a handful of genes, the genetic characteristics and functions of most of these lncRNAs remain elusive; this is partially due to their relatively low expression, high tissue specificity, and low conservation across species. A major limitation for determining the function of lncRNAs was the lack of methodologies suitable for studying these genes. The recent development of CRISPR/Cas9 technology has opened unprecedented opportunities to uncover the genetic and functional characteristics of the non-coding genome via targeted and high-throughput approaches. Specific CRISPR/Cas9-based approaches were developed to target lncRNA loci. Some of these approaches involve modifying the sequence, but others were developed to study lncRNAs by inducing transcriptional and epigenetic changes. The discovery of other programable Cas proteins broaden our possibilities to target RNA molecules with greater precision and accuracy. These approaches allow for the knock-down and characterization of lncRNAs. Here, we review how various CRISPR-based strategies have been used to characterize lncRNAs with important functions in different biological contexts and how these approaches can be further utilized to improve our understanding of the non-coding genome.

Indexed as

Cas13Cas9CRISPRCRISPRaCRISPRienhancerlncRNAncRNA

Identifiers

PMID34940760
PMCPMC8704461

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.