Evidence map›Paper›PMID 26473061›Full record

ArticleRoyal Society open science2015

Concordance between RNA-sequencing data and DNA microarray data in transcriptome analysis of proliferative and quiescent fibroblasts.

Brett Trost, Catherine A Moir, Zoe E Gillespie, Anthony Kusalik, Jennifer A Mitchell, Christopher H Eskiw

Abstract read
In one paragraph

Article in Royal Society open science, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
–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

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  10. Advantages of RNA-seq compared to RNA microarrays for transcriptome profiling of anterior cruciate ligament tears.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2018
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  11. Orthogonal Comparison of Molecular Signatures of Kidney Transplants With Subclinical and Clinical Acute Rejection: Equivalent Performance Is Agnostic to Both Technology and Platform.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2017
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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

6 authors.

Brett TrostDepartment of Computer Science , University of Saskatchewan , Saskatoon Canada S7N 5C9.
Catherine A MoirDepartment of Life Sciences , Brunel University , Uxbridge UB8 3PH, UK ; Nuclear Dynamics Programme , Babraham Institute , Cambridge CB22 3AT, UK.
Zoe E GillespieDepartment of Food and Bioproduct Sciences , University of Saskatchewan , Saskatoon Canada S7N 5A8.
Anthony KusalikDepartment of Computer Science , University of Saskatchewan , Saskatoon Canada S7N 5C9.
Jennifer A MitchellDepartment of Cell and Systems Biology , University of Toronto , Toronto, Canada M5S 3G5 ; Centre for the Analysis of Genome Evolution and Function , University of Toronto , Toronto Canada M5S 3G5.
Christopher H EskiwDepartment of Life Sciences , Brunel University , Uxbridge UB8 3PH, UK ; Department of Food and Bioproduct Sciences , University of Saskatchewan , Saskatoon Canada S7N 5A8.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

DNA microarrays and RNA sequencing (RNA-seq) are major technologies for performing high-throughput analysis of transcript abundance. Recently, concerns have been raised regarding the concordance of data derived from the two techniques. Using cDNA libraries derived from normal human foreskin fibroblasts, we measured changes in transcript abundance as cells transitioned from proliferative growth to quiescence using both DNA microarrays and RNA-seq. The internal reproducibility of the RNA-seq data was greater than that of the microarray data. Correlations between the RNA-seq data and the individual microarrays were low, but correlations between the RNA-seq values and the geometric mean of the microarray values were moderate. The two technologies had good agreement when considering probes with the largest (both positive and negative) fold change (FC) values. An independent technique, quantitative reverse-transcription PCR (qRT-PCR), was used to measure the FC of 76 genes between proliferative and quiescent samples, and a higher correlation was observed between the qRT-PCR data and the RNA-seq data than between the qRT-PCR data and the microarray data.

Indexed as

fibroblastsgene expressionmicroarraysRNA-seqtranscriptome analysis

Identifiers

PMID26473061
PMCPMC4593695

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.