Reproducibility of high-throughput mRNA and small RNA sequencing across laboratories View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2013-11

AUTHORS

Peter A C 't Hoen, Marc R Friedländer, Jonas Almlöf, Michael Sammeth, Irina Pulyakhina, Seyed Yahya Anvar, Jeroen F J Laros, Henk P J Buermans, Olof Karlberg, Mathias Brännvall, The GEUVADIS Consortium, Gert-Jan B van Ommen, Xavier Estivill, Roderic Guigó, Ann-Christine Syvänen, Ivo G Gut, Emmanouil T Dermitzakis, Stylianos E Antonarakis, Alvis Brazma, Paul Flicek, Stefan Schreiber, Philip Rosenstiel, Thomas Meitinger, Tim M Strom, Hans Lehrach, Ralf Sudbrak, Angel Carracedo, Maarten van Iterson, Jean Monlong, Esther Lizano, Gabrielle Bertier, Pedro G Ferreira, Paolo Ribeca, Thasso Griebel, Sergi Beltran, Marta Gut, Katja Kahlem, Tuuli Lappalainen, Thomas Giger, Halit Ongen, Ismael Padioleau, Helena Kilpinen, Mar Gonzàlez-Porta, Natalja Kurbatova, Andrew Tikhonov, Liliana Greger, Matthias Barann, Daniela Esser, Robert Häsler, Thomas Wieland, Thomas Schwarzmayr, Marc Sultan, Vyacheslav Amstislavskiy, Johan T den Dunnen

ABSTRACT

RNA sequencing is an increasingly popular technology for genome-wide analysis of transcript sequence and abundance. However, understanding of the sources of technical and interlaboratory variation is still limited. To address this, the GEUVADIS consortium sequenced mRNAs and small RNAs of lymphoblastoid cell lines of 465 individuals in seven sequencing centers, with a large number of replicates. The variation between laboratories appeared to be considerably smaller than the already limited biological variation. Laboratory effects were mainly seen in differences in insert size and GC content and could be adequately corrected for. In small-RNA sequencing, the microRNA (miRNA) content differed widely between samples owing to competitive sequencing of rRNA fragments. This did not affect relative quantification of miRNAs. We conclude that distributing RNA sequencing among different laboratories is feasible, given proper standardization and randomization procedures. We provide a set of quality measures and guidelines for assessing technical biases in RNA-seq data. More... »

PAGES

1015-1022

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/nbt.2702

    DOI

    http://dx.doi.org/10.1038/nbt.2702

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1010194085

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/24037425


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