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Mendeley readers
Attention Score in Context
Title |
RNA-seq and microarray complement each other in transcriptome profiling
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Published in |
BMC Genomics, November 2012
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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
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.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 23% |
France | 2 | 15% |
Germany | 2 | 15% |
Netherlands | 1 | 8% |
Unknown | 5 | 38% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 9 | 69% |
Members of the public | 4 | 31% |
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
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.