Evidence map›Paper›PMID 41667269›Full record

ArticleGenome research2026

Analysis of coding gene expression from small RNA sequencing.

Aygun Azadova, Anthonia Ekperuoh, Greg N Brooke, Antonio Marco

Abstract read
In one paragraph

Article in Genome research, 2026. 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

4 authors.

Aygun Azadova *School of Life Sciences, University of Essex, Colchester CO4 3SQ, United Kingdom.
Anthonia Ekperuoh *School of Life Sciences, University of Essex, Colchester CO4 3SQ, United Kingdom.
Greg N BrookeSchool of Life Sciences, University of Essex, Colchester CO4 3SQ, United Kingdom amarco@essex.ac.uk gbrooke@essex.ac.uk.
Antonio MarcoSchool of Life Sciences, University of Essex, Colchester CO4 3SQ, United Kingdom amarco@essex.ac.uk gbrooke@essex.ac.uk.ORCID 0000-0002-8603-7503

Funding

BBSRC BB/X511171/1 BB/W020033/1 BB/X018997/1
6 · The paper itself

Abstract

The popularity of microRNA expression analyses is reflected by the existence of thousands of sRNA-seq studies in which matched total RNA-seq data are often unavailable. The lack of paired sequencing experiments limits the analysis of microRNA-gene regulatory networks. Here, we explore whether protein-coding gene expression can be quantified directly from transcript fragments present in sRNA-seq experiments. We analyze studies containing matched total RNA and small RNA from four human tissues and recover transcript fragments from the sRNA-seq data sets. We find that the expression levels of protein-coding gene transcripts derived from sRNA-seq data sets are comparable to those from total RNA-seq experiments (

Indexed as

Gene Expression ProfilingMicroRNAsRNA, MessengerSequence Analysis, RNAAnimalsHumansMicroRNAsRNA, Messenger

Identifiers

PMID41667269
PMCPMC12951955

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.