Literature DB >> 23393029

mRNA enrichment protocols determine the quantification characteristics of external RNA spike-in controls in RNA-Seq studies.

Tao Qing1, Ying Yu, Tingting Du, Leming Shi.   

Abstract

RNA-Seq promises to be used in clinical settings as a gene-expression profiling tool; however, questions about its variability and biases remain and need to be addressed. Thus, RNA controls with known concentrations and sequence identities originally developed by the External RNA Control Consortium (ERCC) for microarray and qPCR platforms have recently been proposed for RNA-Seq platforms, but only with a limited number of samples. In this study, we report our analysis of RNA-Seq data from 92 ERCC controls spiked in a diverse collection of 447 RNA samples from eight ongoing studies involving five species (human, rat, mouse, chicken, and Schistosoma japonicum) and two mRNA enrichment protocols, i.e., poly(A) and RiboZero. The entire collection of datasets consisted of 15650143175 short sequence reads, 131603796 (i.e., 0.84%) of which were mapped to the 92 ERCC references. The overall ERCC mapping ratio of 0.84% is close to the expected value of 1.0% when assuming a 2.0% mRNA fraction in total RNA, but showed a difference of 2.8-fold across studies and 4.3-fold among samples from the same study with one tissue type. This level of fluctuation may prevent the ERCC controls from being used for cross-sample normalization in RNA-Seq. Furthermore, we observed striking biases of quantification between poly(A) and RiboZero which are transcript-specific. For example, ERCC-00116 showed a 7.3-fold under-enrichment in poly(A) compared to RiboZero. Extra care is needed in integrative analysis of multiple datasets and technical artifacts of protocol differences should not be taken as true biological findings.

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Year:  2013        PMID: 23393029     DOI: 10.1007/s11427-013-4437-9

Source DB:  PubMed          Journal:  Sci China Life Sci        ISSN: 1674-7305            Impact factor:   6.038


  19 in total

1.  The Overlooked Fact: Fundamental Need for Spike-In Control for Virtually All Genome-Wide Analyses.

Authors:  Kaifu Chen; Zheng Hu; Zheng Xia; Dongyu Zhao; Wei Li; Jessica K Tyler
Journal:  Mol Cell Biol       Date:  2015-12-28       Impact factor: 4.272

2.  Normalization of RNA-seq data using factor analysis of control genes or samples.

Authors:  Davide Risso; John Ngai; Terence P Speed; Sandrine Dudoit
Journal:  Nat Biotechnol       Date:  2014-08-24       Impact factor: 54.908

Review 3.  Variation in transcriptome size: are we getting the message?

Authors:  Jeremy E Coate; Jeff J Doyle
Journal:  Chromosoma       Date:  2014-11-26       Impact factor: 4.316

4.  Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions.

Authors:  Ciaran Evans; Johanna Hardin; Daniel M Stoebel
Journal:  Brief Bioinform       Date:  2018-09-28       Impact factor: 11.622

5.  Molecular indexing enables quantitative targeted RNA sequencing and reveals poor efficiencies in standard library preparations.

Authors:  Glenn K Fu; Weihong Xu; Julie Wilhelmy; Michael N Mindrinos; Ronald W Davis; Wenzhong Xiao; Stephen P A Fodor
Journal:  Proc Natl Acad Sci U S A       Date:  2014-01-21       Impact factor: 11.205

6.  Normalization of RNA-sequencing data from samples with varying mRNA levels.

Authors:  Håvard Aanes; Cecilia Winata; Lars F Moen; Olga Østrup; Sinnakaruppan Mathavan; Philippe Collas; Torbjørn Rognes; Peter Aleström
Journal:  PLoS One       Date:  2014-02-25       Impact factor: 3.240

7.  Measuring Absolute RNA Copy Numbers at High Temporal Resolution Reveals Transcriptome Kinetics in Development.

Authors:  Nick D L Owens; Ira L Blitz; Maura A Lane; Ilya Patrushev; John D Overton; Michael J Gilchrist; Ken W Y Cho; Mustafa K Khokha
Journal:  Cell Rep       Date:  2016-01-07       Impact factor: 9.423

8.  Comprehensive RNA-Seq transcriptomic profiling across 11 organs, 4 ages, and 2 sexes of Fischer 344 rats.

Authors:  Ying Yu; Chen Zhao; Zhenqiang Su; Charles Wang; James C Fuscoe; Weida Tong; Leming Shi
Journal:  Sci Data       Date:  2014-06-24       Impact factor: 6.444

9.  Using mixtures of biological samples as process controls for RNA-sequencing experiments.

Authors:  Jerod Parsons; Sarah Munro; P Scott Pine; Jennifer McDaniel; Michele Mehaffey; Marc Salit
Journal:  BMC Genomics       Date:  2015-09-17       Impact factor: 3.969

10.  A rat RNA-Seq transcriptomic BodyMap across 11 organs and 4 developmental stages.

Authors:  Ying Yu; James C Fuscoe; Chen Zhao; Chao Guo; Meiwen Jia; Tao Qing; Desmond I Bannon; Lee Lancashire; Wenjun Bao; Tingting Du; Heng Luo; Zhenqiang Su; Wendell D Jones; Carrie L Moland; William S Branham; Feng Qian; Baitang Ning; Yan Li; Huixiao Hong; Lei Guo; Nan Mei; Tieliu Shi; Kevin Y Wang; Russell D Wolfinger; Yuri Nikolsky; Stephen J Walker; Penelope Duerksen-Hughes; Christopher E Mason; Weida Tong; Jean Thierry-Mieg; Danielle Thierry-Mieg; Leming Shi; Charles Wang
Journal:  Nat Commun       Date:  2014       Impact factor: 14.919

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