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An RNA-Seq Strategy to Detect the Complete Coding and Non-Coding Transcriptome Including Full-Length Imprinted Macro ncRNAs

Overview of attention for article published in PLOS ONE, November 2011
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Title
An RNA-Seq Strategy to Detect the Complete Coding and Non-Coding Transcriptome Including Full-Length Imprinted Macro ncRNAs
Published in
PLOS ONE, November 2011
DOI 10.1371/journal.pone.0027288
Pubmed ID
Authors

Ru Huang, Markus Jaritz, Philipp Guenzl, Irena Vlatkovic, Andreas Sommer, Ido M. Tamir, Hendrik Marks, Thorsten Klampfl, Robert Kralovics, Hendrik G. Stunnenberg, Denise P. Barlow, Florian M. Pauler

Abstract

Imprinted macro non-protein-coding (nc) RNAs are cis-repressor transcripts that silence multiple genes in at least three imprinted gene clusters in the mouse genome. Similar macro or long ncRNAs are abundant in the mammalian genome. Here we present the full coding and non-coding transcriptome of two mouse tissues: differentiated ES cells and fetal head using an optimized RNA-Seq strategy. The data produced is highly reproducible in different sequencing locations and is able to detect the full length of imprinted macro ncRNAs such as Airn and Kcnq1ot1, whose length ranges between 80-118 kb. Transcripts show a more uniform read coverage when RNA is fragmented with RNA hydrolysis compared with cDNA fragmentation by shearing. Irrespective of the fragmentation method, all coding and non-coding transcripts longer than 8 kb show a gradual loss of sequencing tags towards the 3' end. Comparisons to published RNA-Seq datasets show that the strategy presented here is more efficient in detecting known functional imprinted macro ncRNAs and also indicate that standardization of RNA preparation protocols would increase the comparability of the transcriptome between different RNA-Seq datasets.

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Mendeley readers

The data shown below were compiled from readership statistics for 268 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 12 4%
Germany 5 2%
Japan 2 <1%
Ireland 2 <1%
Switzerland 1 <1%
Italy 1 <1%
Uruguay 1 <1%
Austria 1 <1%
Australia 1 <1%
Other 9 3%
Unknown 233 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 81 30%
Student > Ph. D. Student 66 25%
Student > Master 31 12%
Student > Postgraduate 14 5%
Student > Doctoral Student 14 5%
Other 39 15%
Unknown 23 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 167 62%
Biochemistry, Genetics and Molecular Biology 37 14%
Medicine and Dentistry 16 6%
Immunology and Microbiology 6 2%
Computer Science 4 1%
Other 10 4%
Unknown 28 10%