↓ Skip to main content

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

Overview of attention for article published in Science China Life Sciences, February 2013
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

Mentioned by

twitter
6 X users

Citations

dimensions_citation
39 Dimensions

Readers on

mendeley
105 Mendeley
Title
mRNA enrichment protocols determine the quantification characteristics of external RNA spike-in controls in RNA-Seq studies
Published in
Science China Life Sciences, February 2013
DOI 10.1007/s11427-013-4437-9
Pubmed ID
Authors

Tao Qing, 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 6%
Netherlands 1 <1%
Australia 1 <1%
Germany 1 <1%
United Kingdom 1 <1%
Sweden 1 <1%
Spain 1 <1%
Denmark 1 <1%
Unknown 92 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 29 28%
Researcher 29 28%
Student > Postgraduate 7 7%
Student > Doctoral Student 6 6%
Student > Master 6 6%
Other 14 13%
Unknown 14 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 49 47%
Biochemistry, Genetics and Molecular Biology 24 23%
Computer Science 7 7%
Environmental Science 3 3%
Engineering 3 3%
Other 3 3%
Unknown 16 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 February 2013.
All research outputs
#7,091,676
of 22,696,971 outputs
Outputs from Science China Life Sciences
#258
of 996 outputs
Outputs of similar age
#80,961
of 284,073 outputs
Outputs of similar age from Science China Life Sciences
#3
of 11 outputs
Altmetric has tracked 22,696,971 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 996 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.4. This one has gotten more attention than average, scoring higher than 74% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 284,073 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.