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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Copy Number Variation detection from 1000 Genomes project exon capture sequencing data
|
|---|---|
| Published in |
BMC Bioinformatics, November 2012
|
| DOI | 10.1186/1471-2105-13-305 |
| Pubmed ID | |
| Authors |
Jiantao Wu, Krzysztof R Grzeda, Chip Stewart, Fabian Grubert, Alexander E Urban, Michael P Snyder, Gabor T Marth |
| Abstract |
DNA capture technologies combined with high-throughput sequencing now enable cost-effective, deep-coverage, targeted sequencing of complete exomes. This is well suited for SNP discovery and genotyping. However there has been little attention devoted to Copy Number Variation (CNV) detection from exome capture datasets despite the potentially high impact of CNVs in exonic regions on protein function. |
X Demographics
The data shown below were collected from the profiles of 10 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 | 20% |
| France | 1 | 10% |
| Montenegro | 1 | 10% |
| Spain | 1 | 10% |
| United Kingdom | 1 | 10% |
| Germany | 1 | 10% |
| Sweden | 1 | 10% |
| India | 1 | 10% |
| Unknown | 1 | 10% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 50% |
| Scientists | 5 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 134 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 3% |
| United Kingdom | 3 | 2% |
| Brazil | 2 | 1% |
| France | 1 | <1% |
| Sweden | 1 | <1% |
| Korea, Republic of | 1 | <1% |
| Spain | 1 | <1% |
| Germany | 1 | <1% |
| Unknown | 120 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 48 | 36% |
| Student > Ph. D. Student | 31 | 23% |
| Student > Master | 11 | 8% |
| Professor > Associate Professor | 9 | 7% |
| Other | 7 | 5% |
| Other | 19 | 14% |
| Unknown | 9 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 67 | 50% |
| Biochemistry, Genetics and Molecular Biology | 32 | 24% |
| Computer Science | 9 | 7% |
| Medicine and Dentistry | 6 | 4% |
| Engineering | 3 | 2% |
| Other | 5 | 4% |
| Unknown | 12 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 02 September 2021.
All research outputs
#4,219,542
of 25,502,817 outputs
Outputs from BMC Bioinformatics
#1,421
of 7,712 outputs
Outputs of similar age
#39,490
of 284,865 outputs
Outputs of similar age from BMC Bioinformatics
#20
of 107 outputs
Altmetric has tracked 25,502,817 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,712 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 81% 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 284,865 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 86% of its contemporaries.
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 has done well, scoring higher than 82% of its contemporaries.