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Mendeley readers
Attention Score in Context
Title |
Viral quasispecies inference from 454 pyrosequencing
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Published in |
BMC Bioinformatics, December 2013
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DOI | 10.1186/1471-2105-14-355 |
Pubmed ID | |
Authors |
Wan-Ting Poh, Eryu Xia, Kwanrutai Chin-inmanu, Lai-Ping Wong, Anthony Youzhi Cheng, Prida Malasit, Prapat Suriyaphol, Yik-Ying Teo, Rick Twee-Hee Ong |
Abstract |
Many potentially life-threatening infectious viruses are highly mutable in nature. Characterizing the fittest variants within a quasispecies from infected patients is expected to allow unprecedented opportunities to investigate the relationship between quasispecies diversity and disease epidemiology. The advent of next-generation sequencing technologies has allowed the study of virus diversity with high-throughput sequencing, although these methods come with higher rates of errors which can artificially increase diversity. |
X Demographics
The data shown below were collected from the profiles of 3 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 | 2 | 67% |
Norway | 1 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 67% |
Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 5% |
United Kingdom | 1 | 2% |
India | 1 | 2% |
Unknown | 52 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 22 | 39% |
Student > Ph. D. Student | 7 | 12% |
Student > Bachelor | 6 | 11% |
Professor > Associate Professor | 5 | 9% |
Student > Master | 5 | 9% |
Other | 9 | 16% |
Unknown | 3 | 5% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 24 | 42% |
Biochemistry, Genetics and Molecular Biology | 7 | 12% |
Medicine and Dentistry | 7 | 12% |
Computer Science | 6 | 11% |
Immunology and Microbiology | 2 | 4% |
Other | 5 | 9% |
Unknown | 6 | 11% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 10 December 2013.
All research outputs
#14,767,396
of 22,733,113 outputs
Outputs from BMC Bioinformatics
#5,037
of 7,266 outputs
Outputs of similar age
#185,564
of 306,767 outputs
Outputs of similar age from BMC Bioinformatics
#62
of 107 outputs
Altmetric has tracked 22,733,113 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,266 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 26th percentile – i.e., 26% of its peers scored the same or lower than it.
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 306,767 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 107 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.