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RNA-seq and microarray complement each other in transcriptome profiling

Overview of attention for article published in BMC Genomics, November 2012
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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 (91st percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

blogs
1 blog
twitter
13 X users

Citations

dimensions_citation
128 Dimensions

Readers on

mendeley
307 Mendeley
citeulike
6 CiteULike
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Title
RNA-seq and microarray complement each other in transcriptome profiling
Published in
BMC Genomics, November 2012
DOI 10.1186/1471-2164-13-629
Pubmed ID
Authors

Sunitha Kogenaru, Qing Yan, Yinping Guo, Nian Wang

Abstract

RNA-seq and microarray are the two popular methods employed for genome-wide transcriptome profiling. Current comparison studies have shown that transcriptome quantified by these two methods correlated well. However, none of them have addressed if they complement each other, considering the strengths and the limitations inherent with them. The pivotal requirement to address this question is the knowledge of a well known data set. In this regard, HrpX regulome from pathogenic bacteria serves as an ideal choice as the target genes of HrpX transcription factor are well studied due to their central role in pathogenicity.

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 307 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 10 3%
Brazil 5 2%
United Kingdom 3 <1%
Canada 2 <1%
Spain 2 <1%
India 2 <1%
Italy 1 <1%
South Africa 1 <1%
France 1 <1%
Other 11 4%
Unknown 269 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 78 25%
Student > Ph. D. Student 72 23%
Student > Master 45 15%
Student > Bachelor 27 9%
Student > Doctoral Student 16 5%
Other 50 16%
Unknown 19 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 172 56%
Biochemistry, Genetics and Molecular Biology 52 17%
Computer Science 16 5%
Medicine and Dentistry 16 5%
Immunology and Microbiology 4 1%
Other 21 7%
Unknown 26 8%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 28 November 2012.
All research outputs
#2,180,766
of 22,685,926 outputs
Outputs from BMC Genomics
#639
of 10,616 outputs
Outputs of similar age
#15,587
of 178,791 outputs
Outputs of similar age from BMC Genomics
#10
of 146 outputs
Altmetric has tracked 22,685,926 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,616 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done particularly well, scoring higher than 93% 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 178,791 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 91% of its contemporaries.
We're also able to compare this research output to 146 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 93% of its contemporaries.