Evidence map›Paper›PMID 36216516›Full record

SynthesisLife science alliance2023

Exploratory meta-analysis of hypoxic transcriptomes using a precise transcript reference sequence set.

Yoko Ono, Hidemasa Bono

Open access · goldAbstract readMeta-Analysis
In one paragraph

Synthesis in Life science alliance, 2023. 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
0.1field-weighted citation impact, top 59% 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, 1 citations in OpenAlex.

  1. Review
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 at 1 institution in 1 country.

Yoko OnoLaboratory of Genome Informatics, Graduate School of Integrated Sciences for Life, Hiroshima University, Higashihiroshima, Japan.ORCID 0000-0003-4616-4951
Hidemasa BonoLaboratory of Genome Informatics, Graduate School of Integrated Sciences for Life, Hiroshima University, Higashihiroshima, Japan bonohu@hiroshima-u.ac.jp.ORCID 0000-0003-4413-0651
Hiroshima University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gene expression studies are intrinsically biased, with many studies influenced by concomitant information such as gene-disease associations. This limitation can be overcome using a data-driven analysis approach without relying on ancillary information. The FANTOM CAGE-Associated Transcriptome project provides a comprehensive meta-assembly of the human transcriptome using coding and noncoding genes. Hypoxia strongly influences gene expression; in addition, noncoding RNA (ncRNA) metabolism is down-regulated in response to hypoxic stimuli. We evaluated the differential response of various transcripts to hypoxia by determining their hypoxia responsiveness scores. Enrichment analysis revealed that several genes associated with ncRNA metabolism, particularly those involved in ribosomal RNA processing, were down-regulated in response to hypoxia. Previously published information from the FANTOM CAGE-Associated Transcriptome project was suitable for meta-analysis of the transcriptome sequencing data from both coding and ncRNAs and to evaluate the hypoxia responsiveness of target transcripts and relationship between sense-antisense transcripts from the same locus. Our results may facilitate functional annotation of various transcripts including ncRNAs, allowing for both sense and antisense and coding and noncoding evaluations.

Indexed as

RNA, UntranslatedTranscriptomeHumansHypoxiaRNA, AntisenseRNA, RibosomalRNA, AntisenseRNA, RibosomalRNA, Untranslated

Identifiers

PMID36216516
PMCPMC9553900
OpenAlexW4304140831

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.