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X Demographics
Mendeley readers
Attention Score in Context
| Title |
SpoTyping: fast and accurate in silico Mycobacterium spoligotyping from sequence reads
|
|---|---|
| Published in |
Genome Medicine, February 2016
|
| DOI | 10.1186/s13073-016-0270-7 |
| Pubmed ID | |
| Authors |
Eryu Xia, Yik-Ying Teo, Rick Twee-Hee Ong |
| Abstract |
SpoTyping is a fast and accurate program for in silico spoligotyping of Mycobacterium tuberculosis isolates from next-generation sequencing reads. This novel method achieves high accuracy for reads of both uniform and varying lengths, and is about 20 to 40 times faster than SpolPred. SpoTyping also integrates the function of producing a report summarizing associated epidemiological data from a global database of all isolates having the same spoligotype. SpoTyping is freely available at: https://github.com/xiaeryu/SpoTyping-v2.0 . |
X Demographics
The data shown below were collected from the profiles of 6 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 Kingdom | 2 | 33% |
| Ireland | 1 | 17% |
| United States | 1 | 17% |
| Unknown | 2 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 50% |
| Scientists | 2 | 33% |
| Practitioners (doctors, other healthcare professionals) | 1 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 102 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | <1% |
| Unknown | 101 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 21 | 21% |
| Researcher | 17 | 17% |
| Student > Ph. D. Student | 10 | 10% |
| Student > Postgraduate | 9 | 9% |
| Student > Bachelor | 6 | 6% |
| Other | 14 | 14% |
| Unknown | 25 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 22 | 22% |
| Agricultural and Biological Sciences | 17 | 17% |
| Immunology and Microbiology | 7 | 7% |
| Medicine and Dentistry | 6 | 6% |
| Veterinary Science and Veterinary Medicine | 4 | 4% |
| Other | 18 | 18% |
| Unknown | 28 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. 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 16 January 2017.
All research outputs
#2,967,417
of 24,885,505 outputs
Outputs from Genome Medicine
#672
of 1,532 outputs
Outputs of similar age
#44,591
of 303,656 outputs
Outputs of similar age from Genome Medicine
#18
of 30 outputs
Altmetric has tracked 24,885,505 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,532 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.2. This one has gotten more attention than average, scoring higher than 56% 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 303,656 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.