Evidence map›Paper›PMID 36587048›Full record

ArticleScientific reports2022

A pathway analysis-based algorithm for calculating the participation degree of ncRNA in transcriptome.

Xinyi Gu, Shen Wang, Bo Jin, Zhidan Qi, Jin Deng, Chen Huang, Xiaofeng Yin

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 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
0.3field-weighted citation impact, top 49% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  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

7 authors at 2 institutions in 1 country.

Xinyi GuDepartment of Orthopedics and Traumatology, Peking University People's Hospital, Beijing, 100044, China.
Shen WangDepartment of Orthopedics and Traumatology, Peking University People's Hospital, Beijing, 100044, China.
Bo JinDepartment of Orthopedics and Traumatology, Peking University People's Hospital, Beijing, 100044, China.
Zhidan QiDepartment of Orthopedics and Traumatology, Peking University People's Hospital, Beijing, 100044, China.
Jin DengDepartment of Orthopedics and Traumatology, Peking University People's Hospital, Beijing, 100044, China.
Chen HuangDepartment of Orthopedics and Traumatology, Peking University People's Hospital, Beijing, 100044, China.
Xiaofeng YinDepartment of Orthopedics and Traumatology, Peking University People's Hospital, Beijing, 100044, China. xiaofengyin@bjmu.edu.cn.
Peking University · CNPeking University People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

After sequencing, it is common to screen ncRNA according to expression differences. But this may lose a lot of valuable information and there is currently no indicator to characterize the regulatory function and participation degree of ncRNA on transcriptome. Based on existing pathway enrichment methods, we developed a new algorithm to calculating the participation degree of ncRNA in transcriptome (PDNT). Here we analyzed multiple data sets, and differentially expressed genes (DEGs) were used for pathway enrichment analysis. The PDNT algorithm was used to calculate the Contribution value (C value) of each ncRNA based on its target genes and the pathways they participates in. The results showed that compared with ncRNAs screened by log2 fold change (FC) and p-value, those screened by C value regulated more DEGs in IPA canonical pathways, and their target DEGs were more concentrated in the core region of the protein-protein interaction (PPI) network. The ranking of disease critical ncRNAs increased integrally after sorting with C value. Collectively, we found that the PDNT algorithm provides a measure from another view compared with the log2FC and p-value and it may provide more clues to effectively evaluate ncRNA.

Indexed as

Protein Interaction MapsTranscriptomeAlgorithmsCell MovementComputational BiologyGene Expression ProfilingGene Regulatory NetworksRNA, UntranslatedRNA, Untranslated

Identifiers

PMID36587048
PMCPMC9805457
OpenAlexW4313396987

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.