Evidence map›Paper›PMID 25870415›Full record

ArticleNucleic acids research2015

Improving small RNA-seq by using a synthetic spike-in set for size-range quality control together with a set for data normalization.

Mauro D Locati, Inez Terpstra, Wim C de Leeuw, Mateusz Kuzak, Han Rauwerda, Wim A Ensink, Selina van Leeuwen, Ulrike Nehrdich, Herman P Spaink, Martijs J Jonker and 2 more

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
2.4field-weighted citation impact, top 11% 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

22 citing papers in PubMed, 41 citations in OpenAlex.

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  17. Tissue dual RNA-seq allows fast discovery of infection-specific functions and riboregulators shaping host-pathogen transcriptomes.Proceedings of the National Academy of Sciences of the United States of America · 2017
    Article
  18. Review
  19. Cajal body function in genome organization and transcriptome diversity.BioEssays : news and reviews in molecular, cellular and developmental biology · 2016
    Review
  20. Review
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

12 authors at 3 institutions in 1 country.

Mauro D LocatiRNA Biology & Applied Bioinformatics research group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam 1090 GE, The Netherlands.
Inez TerpstraRNA Biology & Applied Bioinformatics research group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam 1090 GE, The Netherlands.
Wim C de LeeuwRNA Biology & Applied Bioinformatics research group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam 1090 GE, The Netherlands Netherlands eScience Center, Amsterdam 1098 XG, The Netherlands.
Mateusz KuzakRNA Biology & Applied Bioinformatics research group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam 1090 GE, The Netherlands Netherlands eScience Center, Amsterdam 1098 XG, The Netherlands.
Han RauwerdaRNA Biology & Applied Bioinformatics research group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam 1090 GE, The Netherlands Netherlands eScience Center, Amsterdam 1098 XG, The Netherlands.
Wim A EnsinkRNA Biology & Applied Bioinformatics research group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam 1090 GE, The Netherlands.
Selina van LeeuwenRNA Biology & Applied Bioinformatics research group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam 1090 GE, The Netherlands.
Ulrike NehrdichDepartment of Molecular Cell Biology, Institute of Biology, Leiden University, Gorlaeus Laboratories - Cell Observatorium, Leiden 2333 CE, The Netherlands.
Herman P SpainkDepartment of Molecular Cell Biology, Institute of Biology, Leiden University, Gorlaeus Laboratories - Cell Observatorium, Leiden 2333 CE, The Netherlands.
Martijs J JonkerRNA Biology & Applied Bioinformatics research group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam 1090 GE, The Netherlands.
Timo M BreitRNA Biology & Applied Bioinformatics research group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam 1090 GE, The Netherlands t.m.breit@uva.nl.
Rob J DekkerRNA Biology & Applied Bioinformatics research group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam 1090 GE, The Netherlands.
University of Amsterdam · NLNetherlands eScience Center · NLLeiden University · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is an increasing interest in complementing RNA-seq experiments with small-RNA (sRNA) expression data to obtain a comprehensive view of a transcriptome. Currently, two main experimental challenges concerning sRNA-seq exist: how to check the size distribution of isolated sRNAs, given the sensitive size-selection steps in the protocol; and how to normalize data between samples, given the low complexity of sRNA types. We here present two separate sets of synthetic RNA spike-ins for monitoring size-selection and for performing data normalization in sRNA-seq. The size-range quality control (SRQC) spike-in set, consisting of 11 oligoribonucleotides (10-70 nucleotides), was tested by intentionally altering the size-selection protocol and verified via several comparative experiments. We demonstrate that the SRQC set is useful to reproducibly track down biases in the size-selection in sRNA-seq. The external reference for data-normalization (ERDN) spike-in set, consisting of 19 oligoribonucleotides, was developed for sample-to-sample normalization in differential-expression analysis of sRNA-seq data. Testing and applying the ERDN set showed that it can reproducibly detect differential expression over a dynamic range of 2(18). Hence, biological variation in sRNA composition and content between samples is preserved while technical variation is effectively minimized. Together, both spike-in sets can significantly improve the technical reproducibility of sRNA-seq.

Indexed as

AnimalsGene Expression ProfilingQuality ControlReference StandardsRNA, Small UntranslatedSequence Analysis, RNAZebrafishRNA, Small Untranslated

Identifiers

PMID25870415
PMCPMC4538800
OpenAlexW2149156028

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.