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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A simple data-adaptive probabilistic variant calling model
|
|---|---|
| Published in |
Algorithms for Molecular Biology, March 2015
|
| DOI | 10.1186/s13015-015-0037-5 |
| Pubmed ID | |
| Authors |
Steve Hoffmann, Peter F Stadler, Korbinian Strimmer |
| Abstract |
Several sources of noise obfuscate the identification of single nucleotide variation (SNV) in next generation sequencing data. For instance, errors may be introduced during library construction and sequencing steps. In addition, the reference genome and the algorithms used for the alignment of the reads are further critical factors determining the efficacy of variant calling methods. It is crucial to account for these factors in individual sequencing experiments. |
X Demographics
The data shown below were collected from the profiles of 19 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 | 2 | 11% |
| India | 2 | 11% |
| Mexico | 1 | 5% |
| China | 1 | 5% |
| Norway | 1 | 5% |
| France | 1 | 5% |
| Germany | 1 | 5% |
| Unknown | 10 | 53% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 12 | 63% |
| Scientists | 7 | 37% |
Mendeley readers
The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 5% |
| Canada | 1 | 5% |
| Unknown | 19 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 33% |
| Student > Ph. D. Student | 5 | 24% |
| Professor | 2 | 10% |
| Student > Bachelor | 2 | 10% |
| Other | 1 | 5% |
| Other | 2 | 10% |
| Unknown | 2 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 6 | 29% |
| Agricultural and Biological Sciences | 5 | 24% |
| Biochemistry, Genetics and Molecular Biology | 4 | 19% |
| Medicine and Dentistry | 2 | 10% |
| Unknown | 4 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 21. 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 April 2015.
All research outputs
#1,698,416
of 24,862,067 outputs
Outputs from Algorithms for Molecular Biology
#4
of 257 outputs
Outputs of similar age
#21,197
of 262,975 outputs
Outputs of similar age from Algorithms for Molecular Biology
#1
of 9 outputs
Altmetric has tracked 24,862,067 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 257 research outputs from this source. They receive a mean Attention Score of 3.3. This one has done particularly well, scoring higher than 98% of its peers.
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 262,975 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them