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Deep-Sequencing Protocols Influence the Results Obtained in Small-RNA Sequencing

Overview of attention for article published in PLOS ONE, February 2012
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Title
Deep-Sequencing Protocols Influence the Results Obtained in Small-RNA Sequencing
Published in
PLOS ONE, February 2012
DOI 10.1371/journal.pone.0032724
Pubmed ID
Authors

Joern Toedling, Nicolas Servant, Constance Ciaudo, Laurent Farinelli, Olivier Voinnet, Edith Heard, Emmanuel Barillot

Abstract

Second-generation sequencing is a powerful method for identifying and quantifying small-RNA components of cells. However, little attention has been paid to the effects of the choice of sequencing platform and library preparation protocol on the results obtained. We present a thorough comparison of small-RNA sequencing libraries generated from the same embryonic stem cell lines, using different sequencing platforms, which represent the three major second-generation sequencing technologies, and protocols. We have analysed and compared the expression of microRNAs, as well as populations of small RNAs derived from repetitive elements. Despite the fact that different libraries display a good correlation between sequencing platforms, qualitative and quantitative variations in the results were found, depending on the protocol used. Thus, when comparing libraries from different biological samples, it is strongly recommended to use the same sequencing platform and protocol in order to ensure the biological relevance of the comparisons.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 140 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 3%
Germany 3 2%
France 2 1%
Netherlands 1 <1%
Norway 1 <1%
Cuba 1 <1%
Chile 1 <1%
Czechia 1 <1%
Brazil 1 <1%
Other 2 1%
Unknown 123 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 47 34%
Student > Ph. D. Student 39 28%
Student > Master 13 9%
Other 8 6%
Student > Doctoral Student 6 4%
Other 18 13%
Unknown 9 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 92 66%
Biochemistry, Genetics and Molecular Biology 15 11%
Medicine and Dentistry 5 4%
Computer Science 4 3%
Engineering 3 2%
Other 8 6%
Unknown 13 9%