Evidence map›Paper›PMID 36051880›Full record

ReviewComputational and structural biotechnology journal2022

Exploring long non-coding RNA networks from single cell omics data.

Xue Zhao, Yangming Lan, Dijun Chen

Open access · goldAbstract readReview
In one paragraph

Review in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.4field-weighted citation impact, top 9% 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

13 citing papers in PubMed, 26 citations in OpenAlex.

  1. Article
  2. Review
  3. Long Non-Coding RNA Function in Smooth Muscle Cell Plasticity and Atherosclerosis.Arteriosclerosis, thrombosis, and vascular biology · 2025
    Review
  4. Article
  5. Review
  6. Article
  7. Review
  8. Article
  9. Review
  10. Article
  11. Article
  12. Review
  13. 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

3 authors at 1 institution in 1 country.

Xue ZhaoState Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China.
Yangming LanState Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China.
Dijun ChenState Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China.
Nanjing University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Single-cell omics technologies provide an unprecedented opportunity to decipher molecular mechanisms underlying various biological processes in a cellular heterogeneity manner. The emergence of such techniques promotes the exploration of lncRNAs, which are known to be tissue- and cell-specific noncoding transcripts involving the regulation of multiple important cellular processes. In this review, we introduce the advancement of lncRNA studies which benefit from single-cell omics data analysis. We discuss the expression heterogeneity of lncRNAs, their cell-type specificity and associated gene regulatory networks (GRNs) from a single-cell perspective. We also summarized the state-of-the-art single-cell omics resources and tools for the construction of single-cell GRNs (scGRNs) that could be potentially used for lncRNA functional study. Finally, we highlight the challenges and prospective for scGRN exploration in lncRNA biology.

Indexed as

Gene regulatory networks (GRNs)Long non-coding RNAs (lncRNAs)Multi-omcsSingle cell sequencing

Identifiers

PMID36051880
PMCPMC9403499
OpenAlexW4289745780

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.