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Mendeley readers
Attention Score in Context
| Title |
Incorporating predicted functions of nonsynonymous variants into gene-based analysis of exome sequencing data: a comparative study
|
|---|---|
| Published in |
BMC Proceedings, November 2011
|
| DOI | 10.1186/1753-6561-5-s9-s20 |
| Pubmed ID | |
| Authors |
Peng Wei, Xiaoming Liu, Yun-Xin Fu |
| Abstract |
Next-generation sequencing has opened up new avenues for the genetic study of complex traits. However, because of the small number of observations for any given rare allele and high sequencing error, it is a challenge to identify functional rare variants associated with the phenotype of interest. Recent research shows that grouping variants by gene and incorporating computationally predicted functions of variants may provide higher statistical power. On the other hand, many algorithms are available for predicting the damaging effects of nonsynonymous variants. Here, we use the simulated mini-exome data of Genetic Analysis Workshop 17 to study and compare the effects of incorporating the functional predictions of single-nucleotide polymorphisms using two popular algorithms, SIFT and PolyPhen-2, into a gene-based association test. We also propose a simple mixture model that can effectively combine test results based on different functional prediction algorithms. |
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 % |
|---|---|---|
| United States | 2 | 5% |
| United Kingdom | 1 | 2% |
| Netherlands | 1 | 2% |
| Unknown | 38 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 29% |
| Student > Master | 10 | 24% |
| Student > Ph. D. Student | 7 | 17% |
| Student > Bachelor | 3 | 7% |
| Student > Postgraduate | 3 | 7% |
| Other | 4 | 10% |
| Unknown | 3 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 18 | 43% |
| Biochemistry, Genetics and Molecular Biology | 9 | 21% |
| Medicine and Dentistry | 4 | 10% |
| Mathematics | 2 | 5% |
| Social Sciences | 2 | 5% |
| Other | 5 | 12% |
| Unknown | 2 | 5% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 30 November 2014.
All research outputs
#20,245,139
of 22,772,779 outputs
Outputs from BMC Proceedings
#318
of 374 outputs
Outputs of similar age
#218,846
of 240,459 outputs
Outputs of similar age from BMC Proceedings
#32
of 44 outputs
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So far Altmetric has tracked 374 research outputs from this source. They receive a mean Attention Score of 4.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 44 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.