Evidence map›Paper›PMID 31101892›Full record

ArticleScientific reports2019

Systematic analysis of TruSeq, SMARTer and SMARTer Ultra-Low RNA-seq kits for standard, low and ultra-low quantity samples.

Marie-Ange Palomares, Cyril Dalmasso, Eric Bonnet, Céline Derbois, Solène Brohard-Julien, Christophe Ambroise, Christophe Battail, Jean-François Deleuze, Robert Olaso

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2019. 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
1.5field-weighted citation impact, top 17% 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

26 citing papers in PubMed, 42 citations in OpenAlex.

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  15. Food science & nutrition · 2022
    Review
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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

9 authors at 2 institutions in 1 country.

Marie-Ange PalomaresCentre National de Recherche en Génomique Humaine (CNRGH), Institut de Biologie François Jacob, CEA, 91057, Evry, France.
Cyril DalmassoLaboratoire de Mathématiques et Modélisation ďÉvry (LaMME), Université ďEvry Val ďEssonne, 91000, Evry, France.
Eric BonnetCentre National de Recherche en Génomique Humaine (CNRGH), Institut de Biologie François Jacob, CEA, 91057, Evry, France. eric.bonnet@cng.fr.ORCID http://orcid.org/0000-0002-8468-2867
Céline DerboisCentre National de Recherche en Génomique Humaine (CNRGH), Institut de Biologie François Jacob, CEA, 91057, Evry, France.
Solène Brohard-JulienCentre National de Recherche en Génomique Humaine (CNRGH), Institut de Biologie François Jacob, CEA, 91057, Evry, France.
Christophe AmbroiseLaboratoire de Mathématiques et Modélisation ďÉvry (LaMME), Université ďEvry Val ďEssonne, 91000, Evry, France.
Christophe BattailCentre National de Recherche en Génomique Humaine (CNRGH), Institut de Biologie François Jacob, CEA, 91057, Evry, France.
Jean-François DeleuzeCentre National de Recherche en Génomique Humaine (CNRGH), Institut de Biologie François Jacob, CEA, 91057, Evry, France.
Robert OlasoCentre National de Recherche en Génomique Humaine (CNRGH), Institut de Biologie François Jacob, CEA, 91057, Evry, France.
Commissariat à l'Énergie Atomique et aux Énergies Alternatives · FRCentre National de la Recherche Scientifique · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-throughput RNA-sequencing has become the gold standard method for whole-transcriptome gene expression analysis, and is widely used in numerous applications to study cell and tissue transcriptomes. It is also being increasingly used in a number of clinical applications, including expression profiling for diagnostics and alternative transcript detection. However, despite its many advantages, RNA sequencing can be challenging in some situations, for instance in cases of low input amounts or degraded RNA samples. Several protocols have been proposed to overcome these challenges, and many are available as commercial kits. In this study, we systematically test three recent commercial technologies for RNA-seq library preparation (TruSeq, SMARTer and SMARTer Ultra-Low) on human biological reference materials, using standard (1 mg), low (100 ng and 10 ng) and ultra-low (<1 ng) input amounts, and for mRNA and total RNA, stranded and unstranded. The results are analyzed using read quality and alignment metrics, gene detection and differential gene expression metrics. Overall, we show that the TruSeq kit performs well with an input amount of 100 ng, while the SMARTer kit shows decreased performance for inputs of 100 and 10 ng, and the SMARTer Ultra-Low kit performs relatively well for input amounts <1 ng. All the results are discussed in detail, and we provide guidelines for biologists for the selection of an RNA-seq library preparation kit.

Indexed as

Exome SequencingGene Expression ProfilingHumansReagent Kits, DiagnosticRNA, MessengerRNA-SeqTranscriptomeReagent Kits, DiagnosticRNA, Messenger

Identifiers

PMID31101892
PMCPMC6525156
OpenAlexW2946676730

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.