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Mendeley readers
Attention Score in Context
| Title |
Efficient linkage mapping using exome capture and extreme QTL in schistosome parasites
|
|---|---|
| Published in |
BMC Genomics, July 2014
|
| DOI | 10.1186/1471-2164-15-617 |
| Pubmed ID | |
| Authors |
Frédéric D Chevalier, Claudia LL Valentim, Philip T LoVerde, Timothy JC Anderson |
| Abstract |
Identification of parasite genes that underlie traits such as drug resistance and host specificity is challenging using classical linkage mapping approaches. Extreme QTL (X-QTL) methods, originally developed by rodent malaria and yeast researchers, promise to increase the power and simplify logistics of linkage mapping in experimental crosses of schistosomes (or other helminth parasites), because many 1000s of progeny can be analysed, phenotyping is not required, and progeny pools rather than individuals are genotyped. We explored the utility of this method for mapping a drug resistance gene in the human parasitic fluke Schistosoma mansoni. |
X Demographics
The data shown below were collected from the profiles of 2 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 | 1 | 50% |
| United Kingdom | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 2% |
| Burkina Faso | 1 | 2% |
| United States | 1 | 2% |
| Uruguay | 1 | 2% |
| Unknown | 40 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 10 | 23% |
| Student > Ph. D. Student | 9 | 20% |
| Professor > Associate Professor | 4 | 9% |
| Student > Bachelor | 3 | 7% |
| Student > Doctoral Student | 3 | 7% |
| Other | 10 | 23% |
| Unknown | 5 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 23 | 52% |
| Biochemistry, Genetics and Molecular Biology | 7 | 16% |
| Medicine and Dentistry | 3 | 7% |
| Environmental Science | 2 | 5% |
| Unspecified | 1 | 2% |
| Other | 2 | 5% |
| Unknown | 6 | 14% |
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 05 February 2016.
All research outputs
#13,917,225
of 22,758,963 outputs
Outputs from BMC Genomics
#5,338
of 10,637 outputs
Outputs of similar age
#114,730
of 228,570 outputs
Outputs of similar age from BMC Genomics
#88
of 205 outputs
Altmetric has tracked 22,758,963 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,637 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 46th percentile – i.e., 46% 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 228,570 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 205 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.