↓ Skip to main content

Comparison of CAGE and RNA-seq transcriptome profiling using clonally amplified and single-molecule next-generation sequencing

Overview of attention for article published in Genome Research, March 2014
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

Mentioned by

blogs
2 blogs
twitter
14 X users
patent
1 patent
wikipedia
1 Wikipedia page
googleplus
1 Google+ user

Citations

dimensions_citation
99 Dimensions

Readers on

mendeley
335 Mendeley
citeulike
7 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Comparison of CAGE and RNA-seq transcriptome profiling using clonally amplified and single-molecule next-generation sequencing
Published in
Genome Research, March 2014
DOI 10.1101/gr.156232.113
Pubmed ID
Authors

Hideya Kawaji, Marina Lizio, Masayoshi Itoh, Mutsumi Kanamori-Katayama, Ai Kaiho, Hiromi Nishiyori-Sueki, Jay W. Shin, Miki Kojima-Ishiyama, Mitsuoki Kawano, Mitsuyoshi Murata, Noriko Ninomiya-Fukuda, Sachi Ishikawa-Kato, Sayaka Nagao-Sato, Shohei Noma, Yoshihide Hayashizaki, Alistair R.R. Forrest, Piero Carninci, The FANTOM Consortium

Abstract

CAGE (cap analysis gene expression) and RNA-seq are two major technologies used to identify transcript abundances as well as structures. They measure expression by sequencing from either the 5' end of capped molecules (CAGE) or tags randomly distributed along the length of a transcript (RNA-seq). Library protocols for clonally amplified (Illumina, SOLiD, 454 Life Sciences [Roche], Ion Torrent), second-generation sequencing platforms typically employ PCR preamplification prior to clonal amplification, while third-generation, single-molecule sequencers can sequence unamplified libraries. Although these transcriptome profiling platforms have been demonstrated to be individually reproducible, no systematic comparison has been carried out between them. Here we compare CAGE, using both second- and third-generation sequencers, and RNA-seq, using a second-generation sequencer based on a panel of RNA mixtures from two human cell lines to examine power in the discrimination of biological states, detection of differentially expressed genes, linearity of measurements, and quantification reproducibility. We found that the quantified levels of gene expression are largely comparable across platforms and conclude that CAGE and RNA-seq are complementary technologies that can be used to improve incomplete gene models. We also found systematic bias in the second- and third-generation platforms, which is likely due to steps such as linker ligation, cleavage by restriction enzymes, and PCR amplification. This study provides a perspective on the performance of these platforms, which will be a baseline in the design of further experiments to tackle complex transcriptomes uncovered in a wide range of cell types.

X Demographics

X Demographics

The data shown below were collected from the profiles of 14 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 335 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 8 2%
United Kingdom 4 1%
Netherlands 2 <1%
Sweden 2 <1%
Denmark 2 <1%
Luxembourg 2 <1%
Japan 2 <1%
Spain 2 <1%
Italy 1 <1%
Other 8 2%
Unknown 302 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 90 27%
Researcher 88 26%
Student > Master 35 10%
Student > Doctoral Student 17 5%
Professor 16 5%
Other 61 18%
Unknown 28 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 164 49%
Biochemistry, Genetics and Molecular Biology 75 22%
Medicine and Dentistry 20 6%
Computer Science 19 6%
Immunology and Microbiology 4 1%
Other 15 4%
Unknown 38 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 27. 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 25 February 2020.
All research outputs
#1,457,204
of 25,706,302 outputs
Outputs from Genome Research
#619
of 4,445 outputs
Outputs of similar age
#14,115
of 238,784 outputs
Outputs of similar age from Genome Research
#21
of 47 outputs
Altmetric has tracked 25,706,302 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,445 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.2. This one has done well, scoring higher than 86% 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 238,784 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 47 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 55% of its contemporaries.