↓ Skip to main content

Comprehensive evaluation of AmpliSeq transcriptome, a novel targeted whole transcriptome RNA sequencing methodology for global gene expression analysis

Overview of attention for article published in BMC Genomics, December 2015
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 (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
twitter
13 X users

Citations

dimensions_citation
74 Dimensions

Readers on

mendeley
146 Mendeley
citeulike
2 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
Comprehensive evaluation of AmpliSeq transcriptome, a novel targeted whole transcriptome RNA sequencing methodology for global gene expression analysis
Published in
BMC Genomics, December 2015
DOI 10.1186/s12864-015-2270-1
Pubmed ID
Authors

Wenli Li, Amy Turner, Praful Aggarwal, Andrea Matter, Erin Storvick, Donna K. Arnett, Ulrich Broeckel

Abstract

Whole transcriptome sequencing (RNA-seq) represents a powerful approach for whole transcriptome gene expression analysis. However, RNA-seq carries a few limitations, e.g., the requirement of a significant amount of input RNA and complications led by non-specific mapping of short reads. The Ion AmpliSeq™ Transcriptome Human Gene Expression Kit (AmpliSeq) was recently introduced by Life Technologies as a whole-transcriptome, targeted gene quantification kit to overcome these limitations of RNA-seq. To assess the performance of this new methodology, we performed a comprehensive comparison of AmpliSeq with RNA-seq using two well-established next-generation sequencing platforms (Illumina HiSeq and Ion Torrent Proton). We analyzed standard reference RNA samples and RNA samples obtained from human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs). Using published data from two standard RNA reference samples, we observed a strong concordance of log2 fold change for all genes when comparing AmpliSeq to Illumina HiSeq (Pearson's r = 0.92) and Ion Torrent Proton (Pearson's r = 0.92). We used ROC, Matthew's correlation coefficient and RMSD to determine the overall performance characteristics. All three statistical methods demonstrate AmpliSeq as a highly accurate method for differential gene expression analysis. Additionally, for genes with high abundance, AmpliSeq outperforms the two RNA-seq methods. When analyzing four closely related hiPSC-CM lines, we show that both AmpliSeq and RNA-seq capture similar global gene expression patterns consistent with known sources of variations. Our study indicates that AmpliSeq excels in the limiting areas of RNA-seq for gene expression quantification analysis. Thus, AmpliSeq stands as a very sensitive and cost-effective approach for very large scale gene expression analysis and mRNA marker screening with high accuracy.

X Demographics

X Demographics

The data shown below were collected from the profiles of 13 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 146 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 1%
Italy 1 <1%
Chile 1 <1%
Japan 1 <1%
United States 1 <1%
Unknown 140 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 30 21%
Student > Ph. D. Student 21 14%
Student > Bachelor 18 12%
Student > Master 17 12%
Other 6 4%
Other 26 18%
Unknown 28 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 45 31%
Agricultural and Biological Sciences 36 25%
Medicine and Dentistry 14 10%
Immunology and Microbiology 4 3%
Engineering 4 3%
Other 9 6%
Unknown 34 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 22. 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 12 February 2019.
All research outputs
#1,473,627
of 22,835,198 outputs
Outputs from BMC Genomics
#328
of 10,655 outputs
Outputs of similar age
#27,175
of 390,452 outputs
Outputs of similar age from BMC Genomics
#15
of 326 outputs
Altmetric has tracked 22,835,198 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,655 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done particularly well, scoring higher than 96% 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 390,452 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 93% of its contemporaries.
We're also able to compare this research output to 326 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.