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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Identification of pathogen genomic variants through an integrated pipeline
|
|---|---|
| Published in |
BMC Bioinformatics, March 2014
|
| DOI | 10.1186/1471-2105-15-63 |
| Pubmed ID | |
| Authors |
Micah J Manary, Suriya S Singhakul, Erika L Flannery, Selina ER Bopp, Victoria C Corey, Andrew Taylor Bright, Case W McNamara, John R Walker, Elizabeth A Winzeler |
| Abstract |
Whole-genome sequencing represents a powerful experimental tool for pathogen research. We present methods for the analysis of small eukaryotic genomes, including a streamlined system (called Platypus) for finding single nucleotide and copy number variants as well as recombination events. |
X Demographics
The data shown below were collected from the profiles of 12 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 | 17% |
| Norway | 2 | 17% |
| Germany | 2 | 17% |
| Mexico | 1 | 8% |
| United Kingdom | 1 | 8% |
| Thailand | 1 | 8% |
| Sweden | 1 | 8% |
| Unknown | 2 | 17% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 6 | 50% |
| Members of the public | 6 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 3% |
| United Kingdom | 3 | 3% |
| Australia | 2 | 2% |
| Kenya | 1 | 1% |
| Cuba | 1 | 1% |
| Netherlands | 1 | 1% |
| France | 1 | 1% |
| Sweden | 1 | 1% |
| Japan | 1 | 1% |
| Other | 1 | 1% |
| Unknown | 72 | 83% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 29 | 33% |
| Student > Ph. D. Student | 22 | 25% |
| Student > Bachelor | 9 | 10% |
| Student > Master | 7 | 8% |
| Professor > Associate Professor | 5 | 6% |
| Other | 8 | 9% |
| Unknown | 7 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 45 | 52% |
| Biochemistry, Genetics and Molecular Biology | 13 | 15% |
| Computer Science | 11 | 13% |
| Medicine and Dentistry | 5 | 6% |
| Engineering | 3 | 3% |
| Other | 3 | 3% |
| Unknown | 7 | 8% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 18 March 2014.
All research outputs
#4,214,594
of 23,864,690 outputs
Outputs from BMC Bioinformatics
#1,558
of 7,454 outputs
Outputs of similar age
#40,673
of 224,253 outputs
Outputs of similar age from BMC Bioinformatics
#24
of 101 outputs
Altmetric has tracked 23,864,690 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,454 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 79% 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 224,253 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 81% of its contemporaries.
We're also able to compare this research output to 101 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.