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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Fast detection of de novo copy number variants from SNP arrays for case-parent trios
|
|---|---|
| Published in |
BMC Bioinformatics, December 2012
|
| DOI | 10.1186/1471-2105-13-330 |
| Pubmed ID | |
| Authors |
Robert B Scharpf, Terri H Beaty, Holger Schwender, Samuel G Younkin, Alan F Scott, Ingo Ruczinski |
| Abstract |
In studies of case-parent trios, we define copy number variants (CNVs) in the offspring that differ from the parental copy numbers as de novo and of interest for their potential functional role in disease. Among the leading array-based methods for discovery of de novo CNVs in case-parent trios is the joint hidden Markov model (HMM) implemented in the PennCNV software. However, the computational demands of the joint HMM are substantial and the extent to which false positive identifications occur in case-parent trios has not been well described. We evaluate these issues in a study of oral cleft case-parent trios. |
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 % |
|---|---|---|
| Germany | 1 | 33% |
| Norway | 1 | 33% |
| Japan | 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 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 42 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 26% |
| Researcher | 6 | 14% |
| Student > Master | 5 | 12% |
| Student > Bachelor | 4 | 10% |
| Student > Postgraduate | 3 | 7% |
| Other | 8 | 19% |
| Unknown | 5 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 15 | 36% |
| Biochemistry, Genetics and Molecular Biology | 7 | 17% |
| Medicine and Dentistry | 7 | 17% |
| Social Sciences | 3 | 7% |
| Computer Science | 2 | 5% |
| Other | 3 | 7% |
| Unknown | 5 | 12% |
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 13 December 2012.
All research outputs
#14,740,534
of 22,689,790 outputs
Outputs from BMC Bioinformatics
#5,033
of 7,252 outputs
Outputs of similar age
#174,021
of 278,733 outputs
Outputs of similar age from BMC Bioinformatics
#87
of 138 outputs
Altmetric has tracked 22,689,790 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 7,252 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 278,733 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 138 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.