Evidence map›Paper›PMID 27909684›Full record

ArticleGenomics data2017

Traditional

Sophie Tandonnet, Tatiana Teixeira Torres

Abstract read
In one paragraph

Article in Genomics data, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

  1. Article
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  5. The hidden RNA viruses inMicrobial genomics · 2024
    Article
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  7. Review
  8. Article
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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.

Sophie TandonnetDepartment of Genetics and Evolutionary Biology, Institute of Biosciences, University of São Paulo, São Paulo, SP, Brazil.
Tatiana Teixeira TorresDepartment of Genetics and Evolutionary Biology, Institute of Biosciences, University of São Paulo, São Paulo, SP, Brazil; Research Center on Biodiversity and Computing (BioComp-USP), Universidade de São Paulo (USP), São Paulo, SP, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

One limitation of the widely used RNA-seq method is that long transcripts are represented by more reads than shorter transcripts, resulting in a biased estimation of expression levels. The 3' RNA-seq method, which yields only one sequence per transcript, bypasses this limitation. Here, RNA was extracted from two samples, in which we expected to find differentially expressed genes. Each was processed by both traditional and 3' RNA-seq protocols. Both methods yielded similar differentially expressed genes and estimated expression levels in a comparable way, confirming they both represent valid tools for RNA-seq analysis. Notably, however, we identified more differentially expressed transcripts with the 3' RNA-seq method, suggesting a greater power to detect expression variation using this method. Hence, when little genomic information is available for the species studied, the standard RNA-seq presents a better cost-benefit compromise, whereas for model species, the 3' RNA-seq method might more accurately detect differential expression.

Indexed as

CalliphoridaeCochliomyia hominivoraxGene expression profilingLength biasTranscriptome

Identifiers

PMID27909684
PMCPMC5124356

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.