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X Demographics
Mendeley readers
Attention Score in Context
| Title |
PSCC: Sensitive and Reliable Population-Scale Copy Number Variation Detection Method Based on Low Coverage Sequencing
|
|---|---|
| Published in |
PLOS ONE, January 2014
|
| DOI | 10.1371/journal.pone.0085096 |
| Pubmed ID | |
| Authors |
Xuchao Li, Shengpei Chen, Weiwei Xie, Ida Vogel, Kwong Wai Choy, Fang Chen, Rikke Christensen, Chunlei Zhang, Huijuan Ge, Haojun Jiang, Chang Yu, Fang Huang, Wei Wang, Hui Jiang, Xiuqing Zhang |
| Abstract |
Copy number variations (CNVs) represent an important type of genetic variation that deeply impact phenotypic polymorphisms and human diseases. The advent of high-throughput sequencing technologies provides an opportunity to revolutionize the discovery of CNVs and to explore their relationship with diseases. However, most of the existing methods depend on sequencing depth and show instability with low sequence coverage. In this study, using low coverage whole-genome sequencing (LCS) we have developed an effective population-scale CNV calling (PSCC) method. |
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% |
| Mexico | 1 | 33% |
| India | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Denmark | 1 | 2% |
| Unknown | 61 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 14 | 23% |
| Researcher | 12 | 19% |
| Student > Bachelor | 6 | 10% |
| Student > Master | 5 | 8% |
| Other | 4 | 6% |
| Other | 9 | 15% |
| Unknown | 12 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 23 | 37% |
| Biochemistry, Genetics and Molecular Biology | 16 | 26% |
| Computer Science | 5 | 8% |
| Engineering | 2 | 3% |
| Medicine and Dentistry | 2 | 3% |
| Other | 2 | 3% |
| Unknown | 12 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 31 October 2014.
All research outputs
#12,698,145
of 22,741,406 outputs
Outputs from PLOS ONE
#98,356
of 194,090 outputs
Outputs of similar age
#152,930
of 305,589 outputs
Outputs of similar age from PLOS ONE
#2,673
of 5,578 outputs
Altmetric has tracked 22,741,406 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 194,090 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.1. This one is in the 48th percentile – i.e., 48% 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 305,589 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5,578 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 51% of its contemporaries.