Evidence map›Paper›PMID 28545146›Full record

ArticlePLoS computational biology2017

Transcriptomics technologies.

Rohan Lowe, Neil Shirley, Mark Bleackley, Stephen Dolan, Thomas Shafee

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 437 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
437citing papers in PubMed, 5 pooled it
28.3field-weighted citation impact, top 1% 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

437 citing papers in PubMed, 5 syntheses or guidelines pooled it, 1,112 citations in OpenAlex.

  1. Brain Proteomic Responses to Glucocorticoids and Their Relationship With Transcriptome: A Systematic Meta-Analysis.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026
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  10. Current Applications and Future Prospects of High-ThroughputAnimals : an open access journal from MDPI · 2026
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  19. Molecular heterogeneity in human stroke - What can we learn from the peripheral blood transcriptome?Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026
    Review
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377 more citing papers are in PubMed but not listed here.

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

5 authors at 3 institutions in 2 countries.

Rohan LoweLa Trobe Institute for Molecular Science, La Trobe University, Melbourne, Australia.ORCID 0000-0003-0653-9704
Neil ShirleyARC Centre of Excellence in Plant Cell Walls, University of Adelaide, Adelaide, Australia.
Mark BleackleyLa Trobe Institute for Molecular Science, La Trobe University, Melbourne, Australia.
Stephen DolanDepartment of Biochemistry, University of Cambridge, Cambridge, United Kingdom.ORCID 0000-0002-7391-2137
Thomas ShafeeLa Trobe Institute for Molecular Science, La Trobe University, Melbourne, Australia.ORCID 0000-0002-2298-7593
La Trobe University · AUThe University of Adelaide · AUUniversity of Cambridge · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Transcriptomics technologies are the techniques used to study an organism's transcriptome, the sum of all of its RNA transcripts. The information content of an organism is recorded in the DNA of its genome and expressed through transcription. Here, mRNA serves as a transient intermediary molecule in the information network, whilst noncoding RNAs perform additional diverse functions. A transcriptome captures a snapshot in time of the total transcripts present in a cell. The first attempts to study the whole transcriptome began in the early 1990s, and technological advances since the late 1990s have made transcriptomics a widespread discipline. Transcriptomics has been defined by repeated technological innovations that transform the field. There are two key contemporary techniques in the field: microarrays, which quantify a set of predetermined sequences, and RNA sequencing (RNA-Seq), which uses high-throughput sequencing to capture all sequences. Measuring the expression of an organism's genes in different tissues, conditions, or time points gives information on how genes are regulated and reveals details of an organism's biology. It can also help to infer the functions of previously unannotated genes. Transcriptomic analysis has enabled the study of how gene expression changes in different organisms and has been instrumental in the understanding of human disease. An analysis of gene expression in its entirety allows detection of broad coordinated trends which cannot be discerned by more targeted assays.

Indexed as

AnimalsCluster AnalysisGene Expression ProfilingHigh-Throughput Nucleotide SequencingHumansImage Processing, Computer-AssistedMiceOligonucleotide Array Sequence AnalysisOrgan SpecificityRatsRNASequence Analysis, RNARNA

Identifiers

PMID28545146
PMCPMC5436640
OpenAlexW3191661929

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.