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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Reference-free SNP calling: improved accuracy by preventing incorrect calls from repetitive genomic regions
|
|---|---|
| Published in |
Biology Direct, June 2012
|
| DOI | 10.1186/1745-6150-7-17 |
| Pubmed ID | |
| Authors |
Jinzhuang Dou, Xiqiang Zhao, Xiaoteng Fu, Wenqian Jiao, Nannan Wang, Lingling Zhang, Xiaoli Hu, Shi Wang, Zhenmin Bao |
| Abstract |
Single nucleotide polymorphisms (SNPs) are the most abundant type of genetic variation in eukaryotic genomes and have recently become the marker of choice in a wide variety of ecological and evolutionary studies. The advent of next-generation sequencing (NGS) technologies has made it possible to efficiently genotype a large number of SNPs in the non-model organisms with no or limited genomic resources. Most NGS-based genotyping methods require a reference genome to perform accurate SNP calling. Little effort, however, has yet been devoted to developing or improving algorithms for accurate SNP calling in the absence of a reference genome. |
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 Kingdom | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 2% |
| Colombia | 1 | 1% |
| Germany | 1 | 1% |
| India | 1 | 1% |
| Brazil | 1 | 1% |
| Belgium | 1 | 1% |
| United Kingdom | 1 | 1% |
| Unknown | 83 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 27 | 30% |
| Student > Ph. D. Student | 20 | 22% |
| Student > Master | 18 | 20% |
| Student > Doctoral Student | 5 | 5% |
| Other | 5 | 5% |
| Other | 13 | 14% |
| Unknown | 3 | 3% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 60 | 66% |
| Biochemistry, Genetics and Molecular Biology | 6 | 7% |
| Environmental Science | 4 | 4% |
| Computer Science | 4 | 4% |
| Medicine and Dentistry | 3 | 3% |
| Other | 6 | 7% |
| Unknown | 8 | 9% |
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 18 June 2012.
All research outputs
#15,270,134
of 22,707,247 outputs
Outputs from Biology Direct
#369
of 487 outputs
Outputs of similar age
#106,337
of 166,813 outputs
Outputs of similar age from Biology Direct
#5
of 5 outputs
Altmetric has tracked 22,707,247 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 487 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one is in the 18th percentile – i.e., 18% 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 166,813 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one.